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NLP-Notebooks-Newspaper-Collections | nlp notebooks for newspaper collections a collection of notebooks for natural language processing the following notebooks are aimed particularly at digital humanities scholars who use newspapers as a source the focus lies on topic specific collection building a field that is becoming increasingly interesting with better article separation very specific research problems are addressed such as building up collections with ambiguous keywords or working with certain genres in order to best meet the needs of digital humanities scholars nlp methods are adapted in new ways the output is human readable and the processed newspaper articles can be exported in the form of the original file in addition the notebooks allow the users to control the single steps and to choose what is best for their collection while the newseye demonstrator offers the possibility to create datasets quickly and effectively these notebooks offer possibilities to work on these collections according to specific questions 1 text classification for topic specific newspaper collections text classification for topic specific newspaper collections 2 group similar newspaper articles group similar newspaper articles 3 discover a newspaper collection with diachronic ngram clouds discover a newspaper collection with diachronic ngram clouds 4 discourse in spanish flu coverage discourse in spanish flu coverage 5 topic modeling and uses of the term telegraph in the context of journalism topic modeling and uses of the term telegraph in the context of journalism text classification for topic specific newspaper collections a href https github com newseye nlp notebooks newspaper collections blob master text classification of newspaper clippings notebook ipynb target blank go to notebook a text classification is the process of categorizing text into pre defined groups by using natural language processing nlp text classifiers can automatically analyze text and then assign a set of given categories based on the research question this automated classification of text into predefined categories is an important method for managing and processing a large number of newspaper clippings this also applies to subcorpora for a specific research topic e g migration the aim of this notebook is to train a model using your previously manually created training test corpus and to use this model to get an overview of the category distribution throughout your collection see figure below another goal is to export your categorized data for further analysis this makes it possible to examine for example the advertisement about a specific topic this notebook was used with a collection for the case study on emigration and shows how a model can be trained to classify topic specific collections for the training testing corpus a collection with the keywords auswander ausgewanderte emigrant emigrierte emigration kolonist and ansiedler all different german words for emigrants or emigration have been created in addition information on the pre defined gropus news ads culture were added using numbers between one and ten for classification topic modelling lda was chosen because it showed the best performance in classification after experiments with word embeddings or lda and word embeddings combined lda provides a way to group documents by topic and perform similarity searches and improve precision thanks to sklearn it is relatively easy to test different classifiers for a given topic classification task logistic regression was chosen as binary classifier output graph using an unseen collection on the topic of emigration sample of 1631 newspaper clippings collection on the topic of emigration images cat png group similar newspaper articles a href https github com newseye nlp notebooks newspaper collections blob master news article similarity notebook ipynb target blank go to notebook a many researchers have the problem that their data sets contain articles that are irrelevant to their research question for example if the goal is to find newspaper articles or news items on return migration researchers have to deal with some ambiguous search terms the german words heimkehr returning home or r ckkehr returning back lead to many articles that are relevant to the research question but also to articles that are not relevant e g return from a mountain tour work etc by using topic models and document similarity measurements this notebook allows me to exclude these articles without combining the word heimkehr with other search terms furthermore the same code can also be used to remove or prefer a certain genre e g advertising sports news etc to give another example if i want to create a collection of articles about the disease cancer one of the important german words for cancer is krebs but krebs in german is also a common surname an animal crab or a sign of the zodiac the main purpose of this notebook is to take into account the context of articles in order to automatically refine a search query this means that even ambiguous words can be used for the search without having to combine them with other words making the search less influenced by the researcher s prior knowledge and avoiding a too narrow tunnel vision so how is this working given a manually annotated collection of articles containing relevant as well as non relevant articles this program will get the topic distribution of each document using lda gensim library these topic distributions serve as a comparison for other unseen articles in order to automatically distinguish between relevant and non relevant articles the annotations are used for evaluation and counting the relevance probability for an unseen article for the comparison the jensen shannon distance method is used to measure the similarity between the topic distribution of an unseen article and the topic distribution of the training corpus therefore the topic distribution of each new article will be compared to the topic distribution of the articles in the trained corpus then for each unseen article the 10 most similar articles from the training corpus are being extracted these articles carry the information about the manually assigned relevancy if 60 precent of the automatically found similar articles were annotated as relevant the new article will be marked as relevant otherwise it will be marked as irrelevant using two different datasets one about cancer and one about return migration the average score of correct selected articles is between 80 and 90 percent images nk png discover a newspaper collection with diachronic ngram clouds a href https github com newseye nlp notebooks newspaper collections blob master n gram clouds notebook ipynb target blank go to notebook a ngrams are connected sequences of n items from a given text or speech sample this means that words are not considered as individual units but in relation to each other for scholars in the humanities ngrams can be helpful to get an overview of their collection or to identify discourse markers discourse a group of related texts belonging to a common system of formation ngrams can never be a research result per se which is true for any output of nlp methods but they can help to find important patterns in a particular collection ngrams images wc png if ngrams are used to identify discourse markers it can be useful to create diachrinic ngrams to explore the change of rextual patterns this notebook therefore shows how diachronic ngrams can be build and visualized for cultural heritage material visualizations should make it possible to open up and experience the collections in new ways but they should always be linked to the original documents the graphic representation and the original material cannot be perceived as two different elements they are rather interwoven and interact with each other therefore this notebook allows to browse the original texts within the notebook thus the results of the ngram clouds can be researched in the context of the original text discourse in spanish flu coverage a href https mybinder org v2 gh soberbichler discourse in spanish flu coverage notebook head target blank go to notebook a this notebook was created with the aim of using it in workshops and for teaching purposes the notebook was launched via binder which allows one to turn a jupyter notebook into a interactive notebook making the code immediately reproducible by anyone without prior installations this is especially helpful for digital humanities courses because it allows students to get started with jupyter notebooks the notebook enables the creation of diachronic ngrams and topic visualizations based on exports from the newseye demonstrator tm images tm png topic modeling and uses of the term telegraph in the context of journalism a href https gitlab phaidra org bekesij9 newseye blob master workflow ipynb target blank go to notebook a this notebook was created by j nos b k si and martin gasteiner with the aim to investigate the usage of the term telepraph in austrian historical newspapers the data analysed in this notebook was exported from the newseye platform which is based on the content management system blacklight the data used here is based on a general search on the basis of the search engine solr for articles on the word telegraph in the following time periods 1864 1874 1895 1901 1911 1922 the search was carried out in the following german language newspapers namely neue freie presse innsbrucker nachrichten arbeiter zeitung and illustrierte kronen zeitung the resulting data package was exported as json and processed with regard to topic modelling the notebooks shows how topic models can be trained visualized and how diachronic topic models can be created tm images martin png | lda topic-modeling shannon nlp-notebooks digital-humanities newspaper-collections newspaper-clippings text-classification similarity | ai |
awesome-federated-learning | awesome federated learning awesome https awesome re badge svg https awesome re a list of resources releated to federated learning and privacy in machine learning related awesome lists tushar semwal awesome federated computing https github com tushar semwal awesome federated computing papers introduction survey towards efficient synchronous federated training a survey on system optimization strategies https ieeexplore ieee org document 9780218 the internet of federated things ioft https ieeexplore ieee org document 9611259 advances and open problems in federated learning https arxiv org pdf 1912 04977 pdf federated machine learning concept and applications https arxiv org pdf 1902 04885 federated learning systems vision hype and reality for data privacy and protection https arxiv org abs 1907 09693 demystifying parallel and distributed deep learning an in depth concurrency analysis https arxiv org abs 1802 09941 edgeai a visionfor deep learning in iot era https arxiv org abs 1910 10356 machine learning systems for highly distributed and rapidly growing data https arxiv org abs 1910 08663 no peek a survey of private distributed deep learning https arxiv org pdf 1812 03288 federated learning in mobile edge networks a comprehensive survey https arxiv org abs 1909 11875 privacy and security federated learning with formal differential privacy guarantees https ai googleblog com 2022 02 federated learning with formal html applying differential privacy to large scale image classification https ai googleblog com 2022 02 applying differential privacy to large html towards causal federated learning for enhanced robustness and privacy https arxiv org pdf 2104 06557 pdf iclr dpml 2021 fedaux leveraging unlabeled auxiliary data in federated learning https arxiv org abs 2102 02514 openfl an open source framework for federated learning https arxiv org abs 2105 06413 a bayesian federated learning framework with multivariate gaussian product https arxiv org abs 2102 01936 communication efficient learning of deep networks from decentralized data https arxiv org pdf 1602 05629 pdf practical secure aggregation for federated learning on user held data https arxiv org abs 1611 04482 practical secure aggregation for privacy preserving machine learning https storage googleapis com pub tools public publication data pdf ae87385258d90b9e48377ed49d83d467b45d5776 pdf a hybrid approach to privacy preserving federated learning https arxiv org abs 1812 03224 analyzing federated learning through an adversarial lens https arxiv org pdf 1811 12470 how to backdoor federated learning https arxiv org abs 1807 00459 comprehensive privacy analysis of deep learning stand alone and federated learning under passive and active white box inference attack https arxiv org abs 1812 00910 beyond inferring class representatives user level privacy leakage from federated learning https arxiv org pdf 1812 00535 exploiting unintended feature leakage in collaborative learning https arxiv org abs 1805 04049 analyzing federated learning through an adversarial lens https arxiv org abs 1811 12470 deep models under the gan information leakage from collaborative deep learning https arxiv org abs 1702 07464 protection against reconstruction and its applications in private federated learning https arxiv org pdf 1812 00984 boosting privately privacy preserving federated extreme boosting for mobile crowdsensing https arxiv org abs 1907 10218 differentially private data generative models https arxiv org pdf 1812 02274 differentially private federated learning a client level perspective https arxiv org abs 1712 07557 privacy preserving collaborative deep learning with unreliable participants https arxiv org abs 1812 10113 scalable private learning with pate https arxiv org abs 1802 08908 reducing leakage in distributed deep learning for sensitive health data https www media mit edu publications reducing leakage in distributed deep learning for sensitive health data accepted to iclr 2019 workshop on ai for social good 2019 deep leakage from gradients http papers nips cc paper 9617 deep leakage from gradients pdf gradient leaks understanding and controlling deanonymization in federated learning https arxiv org abs 1805 05838 system and application pisces efficient federated learning via guided asynchronous training https dl acm org doi abs 10 1145 3542929 3563463 record and reward federated learning contributions with blockchain https mblocklab com recordandreward pdf flower a friendly federated learning framework https arxiv org pdf 2007 14390 pdf learning private neural language modeling with attentive aggregation https arxiv org pdf 1812 07108 dynamic sampling and selective masking for communication efficient federated learning https arxiv org abs 2003 09603 decentralized knowledge acquisition for mobile internet applications https link springer com article 10 1007 s11280 019 00775 w a generic framework for privacy preserving deep learning https arxiv org pdf 1811 04017 pdf federated learning of n gram language models https arxiv org pdf 1910 03432 pdf towards federated learning at scale system design https arxiv org pdf 1902 01046 pdf federated learning for keyword spotting https arxiv org abs 1810 05512 federated learning in distributed medical databases meta analysis of large scale subcortical brain data https arxiv org abs 1810 08553 federated collaborative filtering for privacy preserving personalized recommendation system https arxiv org pdf 1901 09888 confederated machine learning on horizontally and vertically separated medical data for large scale health system intelligence https arxiv org abs 1910 02109 privacy preserving deep learning computation for geo distributed medical big data platform http www cs ucf edu mohaisen doc dsn19b pdf institutionally distributed deep learning networks https arxiv org abs 1709 05929 multi institutional deep learning modeling without sharing patient data a feasibility study on brain tumor segmentation https arxiv org abs 1810 04304 split learning for health distributed deep learning without sharing raw patient data https www media mit edu publications split learning for health distributed deep learning without sharing raw patient data continuous delivery for machine learning https martinfowler com articles cd4ml html evolvingintelligentsystemswithoutbias ease ml ci ease ml meter towards data management for statistical generialization http ease ml visionair using federated learning to estimate air quality using the tensorflow api for java https blog tensorflow org 2020 02 visionair using federated learning to estimate airquality tensorflow api java html federated optimization in heterogeneous networks https arxiv org abs 1812 06127 un org fedprof optimizing federated learning with dynamic data profiling https arxiv org abs 2102 01733 fedbn federated learning on non iid features via local batch normalization https arxiv org abs 2102 07623 a scalable approach for partially local federated learning https ai googleblog com 2021 12 a scalable approach for partially local html m 1 federated visual classification with real world data distribution https arxiv org abs 2003 08082 measuring the effects of non identical data distribution for federated visual classification https arxiv org abs 1909 06335 leaf a benchmark for federated settings https arxiv org abs 1812 01097 on the convergence of fedavg on non iid data https arxiv org abs 1907 02189 privacy preserving federated brain tumour segmentation https arxiv org pdf 1910 00962 pdf expertmatcher automating ml model selection for users in resource constrained countries https www media mit edu publications expertmatcher detailed comparison of communication efficiency of split learning and federated learning https www media mit edu publications detailed comparison of communication efficiency of split learning and federated learning 1 split learning distributed and collaborative learning https aiforsocialgood github io iclr2019 accepted track1 pdfs 31 aisg iclr2019 pdf asynchronous federated optimization https arxiv org pdf 1903 03934 robust and communication efficient federated learning from non iid data https arxiv org pdf 1903 02891 one shot federated learning https arxiv org pdf 1902 11175 high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions https arxiv org pdf 1902 08999 agnostic federated learning https arxiv org pdf 1902 00146 c2 a0 peer to peer federated learning on graphs https arxiv org pdf 1901 11173 secureboost a lossless federated learning framework https arxiv org pdf 1901 08755 federated reinforcement learning https arxiv org pdf 1901 08277 lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems https arxiv org pdf 1901 06455 federated learning via over the air computation https arxiv org pdf 1812 11750 broadband analog aggregation for low latency federated edge learning extended version https arxiv org pdf 1812 11494 multi objective evolutionary federated learning https arxiv org pdf 1812 07478 efficient training management for mobile crowd machine learning a deep reinforcement learning approach https arxiv org pdf 1812 03633 a hybrid approach to privacy preserving federated learning https arxiv org pdf 1812 03224 applied federated learning improving google keyboard query suggestions https arxiv org pdf 1812 02903 differentially private data generative models https arxiv org pdf 1812 02274 protection against reconstruction and its applications in private federated learning https arxiv org pdf 1812 00984 split learning for health distributed deep learning without sharing raw patient data https arxiv org pdf 1812 00564 beyond inferring class representatives user level privacy leakage from federated learning https arxiv org pdf 1812 00535 loadaboost loss based adaboost federated machine learning on medical data https arxiv org pdf 1811 12629 communication efficient on device machine learning federated distillation and augmentation under non iid private data https arxiv org pdf 1811 11479 biscotti a ledger for private and secure peer to peer machine learning https arxiv org pdf 1811 09904 dancing in the dark private multi party machine learning in an untrusted setting https arxiv org pdf 1811 09712 federated learning approach for mobile packet classification https arxiv org abs 1907 13113 collaborative learning on the edges a case study on connected vehicles https www usenix org conference hotedge19 presentation lu federated learning for time series forecasting using hybrid model http www diva portal se smash get diva2 1334629 fulltext01 pdf federated learning challenges methods and future directions https arxiv org pdf 1908 07873 pdf federated learning with matched averaging https openreview net forum id bkluqlsfds code openfl an open source framework for federated learning https github com intel openfl flower https flower dev pysyft https github com openmined pysyft tensorflow federated https www tensorflow org federated crypten https github com facebookresearch crypten fate https fate fedai org dvc https dvc org leaf https leaf cmu edu federated inaturalist landmarkds https github com google research google research tree master federated vision datasets fedml a research library and benchmark for federated machine learning https github com fedml ai fedml xaynet open source framework for federated learning in rust https xaynet webflow io envisedge https github com nimbleedge envisedge use cases mit csail harvard medical tsinghua university s academy of arts and design https arxiv org ftp arxiv papers 1903 1903 09296 pdf https venturebeat com 2019 03 25 federated learning technique predicts hospital stay and patient mortality microsoft research university of chinese academy of sciences beijing china https arxiv org pdf 1907 09173 pdf boston university massachusetts general hospital https www ncbi nlm nih gov pubmed 29500022 google https ai googleblog com 2017 04 federated learning collaborative html https www statnews com 2019 09 10 google mayo clinic partnership patient data tencent webank https www digfingroup com webank clustar nvidia king s college london american college of radiology mgh and bwh center for clinical data science and ucla health etc https venturebeat com 2019 10 13 nvidia uses federated learning to create medical imaging ai https blogs nvidia com blog 2019 12 01 clara federated learning company integrate ai https integrate ai integratefl a saas platform for federated learning https integrate ai integratefl adap https adap com en snips https snips ai https www theverge com 2019 11 21 20975607 sonos buys snips ai voice assistant privacy privacy ai https privacy ai openmined https www openmined org arkhn https arkhn org en scaleout https scaleoutsystems com melloddy https www melloddy eu datafleets https www datafleets com xayn ag https www xayn com nimbleedge https www nimbleedge ai | federated-learning medical-data privacy deep-learning | ai |
Data-Engineering-and-Data-Analysis | data engineering and data analysis import the csvs into a sql database and answer questions about the data data engineering and data analysis | server |
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PlasticNet | plastic net trash detection an ibm space tech team project plasticnet is an ibm tech for good open source project developed by the ibm space tech team to build a repository of ai object detection models to classify types brands of plastics trash on beaches trash in the ocean and more we can scale this effort with the global community of developers participating and contributing towards this noble effort with long term goals to help with ocean cleanup and positively impact climate change for more information on how to get started check out the plasticnet wiki https github com ibm plasticnet wiki goals the goals for our project are listed below as the following real time detection of different types of trash plastic in particular in the ocean utilizing transfer learning on different machine learning object detection architectures in the future we would also like to be able to improve our model to be able to recognize logos brands on trash in order to detect and identify which company different types of ocean beach trash come from to build a fully functional plasticnet machine learning pipeline that can be easily used to train and test object detection models based from architectures such as yolov4 faster rcnn ssd resnet efficient det tensorflow etc all accessible inside a command line client to provide a set of pretrained plasticnet models that can be utilized for future development and improvement via transfer learning implement our models to work on real time satellite and camera footage basic project structure and technologies used feat diagram img img plasticnetprojectarchitecturediagram png the plasticnet command line program combines yolov4 and tensorflow object detection api technologies into a single easily usable machine learning pipeline cli collaborators can use the plasticnet cli to prepare models for training via transfer learning from the provided pre trained plasticnet models train custom detection models built upon pre trained plasticnet models export the trained models and finally test the trained models the cli was created so these steps can all be done with a few simple commands https github com ibm plasticnet wiki utilizing the plasticnet command line client initially trained via transfer learning from pre trained yolo weights https github com mattokc35 darknet pre trained models and pre trained tensorflow models from the tensorflow detection model zoo https github com tensorflow models blob master research object detection g3doc tf2 detection zoo md our official plasticnet model zoo https github com ibm plasticnet blob main modelzoo md can be used by collaborators for the further improvement development of new plasticnet object detection models for labeling images we utilized ibm s cloud annotations https github com ibm plasticnet wiki creating your own dataset for custom training demo of object detection yolov4 9 class v3 plasticnet demo best yolo model with face masks and fishing nets included plasticnet demo https img youtube com vi eqrklsfv8cy 0 jpg https youtu be eqrklsfv8cy yolo plasticnet demo tensorflow efficientdet d1 9 class demo plasticnet demo https img youtube com vi jwky3ooc7rw 0 jpg https youtu be jwky3ooc7rw tensorflow efficientdet d1 plasticnet demo get started to get started with plasticnet you first must clone the repository using the following command sh git clone https github com ibm plasticnet git for more detailed instructions on how to get started check out the plasticnet wiki https github com ibm plasticnet wiki once the repository is cloned you can run the following command in the python evironment of your choice note it is recommended that this is done in a new python environment to avoid any issues between package dependencies the setup script currently only supports macos but windows and linux support will be added soon sh cd plasticnet python setup py once the setup script has finished running you should restart your terminal session and run the following command sh plasticnet this will open the plasticnet terminal so you can easily download our models from our model zoo test the models on videos webcam and images or train on top of an existing model a list of all commands can be found by typing help and more detailed instructions about aguments for any command can be found with help command name to exit the command line type quit using yolo models if you intend to train yolo models you may have to make some changes to the makefile depending on your system the makefile is located in darknet and you will want to update these parameters to whatver your system has 1 meaning enabled make gpu 1 cudnn 0 cudnn half 0 opencv 1 avx 0 openmp 0 libso 0 it is highly recommended you install cudnn opencv for use with darknet as it will expedite the training process you can additionally update the yolo obj cfg file located in darknet cfg with any parameters you choose specifically the number of iterations classes and filters here s a guide for setting these values https github com mattokc35 darknet how to train to detect your custom objects after you have updated this makefile and or the configuration file run make clean and make to be able to start darknet training with the plasticnet cli test results see our spreadsheet documentating our test results from different trained models https docs google com spreadsheets d 1mcfc2hqjohrp2 g723d8 xd19luuukd frroiy5ksoq edit usp sharing resources forked yolov4 plasticnet darknet repository https github com mattokc35 darknet labeling images with ibm cloud annotations https cloud annotations ai darknet yolo https pjreddie com darknet yolo restoring integrity to the oceans rio https www oceansintegrity com pacific whale foundation https www pacificwhale org tensorflow object detection api https github com tensorflow models tree master research object detection yolo with tensorflow https github com theaiguyscode tensorflow yolov4 tflite ocean plastic statistics more than 1 million seabirds and 100 000 marine animals die from plastic pollution every year 100 of baby sea turtles have plastic in their stomachs there is now 5 25 trillion macro and micro pieces of plastic in our ocean 46 000 pieces in every square mile of ocean weighing up to 269 000 tonnes every day around 8 million pieces of plastic makes their way into our oceans the great pacific garbage patch is around 1 6 million square kilometers bigger than texas the world produces 381 million tonnes in plastic waste yearly this is set to double by 2034 50 of this is single use plastic only 9 has ever been recycled over 2 million tonnes of plastic packaging are used in the uk each year 88 of the sea s surface is polluted by plastic waste between 8 to 14 million tonnes enters our ocean every year britain contributes an estimated 1 7 million tonnes of plastic annually the us contributes 38 million tonnes of plastic every year plastic packaging is the biggest culprit resulting in 80 million tonnes of waste yearly from the us alone on uk beaches there are 5000 pieces of plastic 150 plastic bottles for each mile more than 1 million plastic bags end up in the trash every minute the world uses over 500 billion plastic bags a year that s 150 for each person on earth 8 3 billion plastic straws pollute the world s beaches but only 1 of straws end up as waste in the ocean by 2020 the number of plastics in the sea will be higher than the number of fish 1 in 3 fish caught for human consumption contains plastic plastic microbeads are estimated to be one million times more toxic than the seawater around it products containing microbeads can release 100 000 tiny beads with just one squeeze source condor ferries shocking ocean plastic statistics https www condorferries co uk plastic in the ocean statistics | ai |
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VisualTransformers | pytorch visual transformers token based image representation and processing for computer vision a pytorch implementation of the following paper visual transformers token based image representation and processing for computer vision visual transformers find the original paper here https arxiv org abs 2006 03677 p align center img src overview png width 600 title vision transformer p this pytorch implementation is based on this repo https github com tahmid0007 visiontransformer the default dataset used here is cifar10 which can be easily changed to imagenet or anything else you might need to install einops | ai |
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CEDS-Data-Warehouse-Parquet | ceds data warehouse parquet logo res data warehouse parquet logo full png ceds data warehouse parquet ceds data warehouse parquet dw parquet welcome to the ceds open source community the common education data standards ceds data warehouse parquet dw parquet standard is designed for data engineering and data science needs in the cloud the dw parquet models mirror the sql based ceds data warehouse parquet files are designed for rapid and distributed reporting across multiple technology stacks data processing and bi tools and are cloud vendor agnostic this standard is ideal for stakeholders implementing reporting structures in a data lake environment submitting a use case use cases may be submitted through the issues https github com cedstandards ceds data warehouse parquet issues tab by clicking on new issue and then get started which is located next to ceds integrated data store and data warehouse use case contributing please read contributing md contributing md for details on the process for submitting pull requests versioning the ceds open source community uses a customized version of explicit versioning to keep the various ceds open source projects in alignment with the ceds elements the concept of disruptive releases has been replaced with alignment releases these releases ensure that the data models are in sync with the official ceds community approved list of ceds elements for the versions available see the tags on this repository here is an example of how explicit versioning will occur assuming an official release of ceds has just occurred data warehouse dw version 7 0 0 0 data warehouse parquet dw parquet version 7 0 0 0 integrated data store ids version 7 0 0 0 ceds elements 7 0 0 0 use case brand new elements are introduced to ceds because they are new they represent no breaking change to any of the other ceds elements ceds elements version 7 0 1 0 these new elements however result in a new understanding of how data is integrated in the ids resulting in a restructuring of the ids which is not backwards compatible ids version 7 1 0 0 these elements are added to the dw structure and result in backwards compatibility but a defect is identified in doing so that needs to be corrected dw version 7 0 1 1 these elements are also added to the dw parquet structure and result in backwards compatibility but a defect is identified in doing so that needs to be corrected the ceds dw and ceds dw parquet standards are updated simultaneously to maintain cross compatibility dw parquet version 7 0 1 1 throughout the remainder of the year many changes occur in each of the different repositories the changes prior to the annual release of ceds results in the following dw version 7 27 1 6 dw parquet version 7 27 1 6 ids version 7 3 2 0 ceds elements 7 2 18 2 the annual official ceds release occurs typically around january february of the calendar year all resources are brought into version alignment note no changes occurred to the resource the annual release simply restarts aligns the versioning dw version 8 0 0 0 dw parquet version 8 0 0 0 ids version 8 0 0 0 ceds elements version 8 0 0 0 note the ceds collaborative exchange contains resources contributed by stakeholders these resources should contain the compatible version they were created under the versions are not changed for a resource unless a stakeholder updates them to function under a newer version license this project is licensed under the apache 2 0 license license see the license file for details | data-warehouse data-standard data-standards data-warehouses parquet ceds | cloud |
android-takeaway | android takeaway mobile application development course assignments | front_end |
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interlock | introduction interlock https github com usbarmory interlock copyright c withsecure corporation the interlock application is a file encryption front end developed for but not limited to usage with the usb armory https github com usbarmory usbarmory the primary interface consists of a web based file manager for an encrypted partition running on the device hosting the json application server e g usb armory the file manager allows uploading downloading of files to from the encrypted partition as well as symmetric asymmetric cryptographic operations on the individual files interlock screenshot https github com usbarmory interlock wiki images interlock png a command line mode is available to execute selected operations locally without the web interface authors andrea barisani andrea barisani withsecure com daniele bianco daniele bianco withsecure com documentation the main documentation is included in the present file https github com usbarmory interlock blob master readme md additional information can be found on the project wiki https github com usbarmory interlock wiki binary releases pre compiled binary releases for arm targets are available here https github com usbarmory interlock releases architecture the package provides a web application client side and its counterpart json application server implementing the protocol specified in the api document a command line mode is available to execute selected operations locally without the web interface this is primarily intended to aid encryption decryption operation with key derivation using hsm support on embedded firmwares the json application server is written in golang the client html javascript application is statically served by the application server implementing the presentation layer the interaction between the static html javascript and the json application server is entirely documented in the api document the web application authentication is directly tied to linux unified key setup luks disk encryption setup on the server side a successful login unlocks the specified encrypted volume while logging out locks it back design goals clear separation between presentation and server layer to ease auditability and integration minimum amount of external dependencies currently no code outside of go standard and supplementary libraries is required for the basic server binary authentication process directly tied to luks partition locking unlocking support for additional symmetric asymmetric encryption on individual files directories minimize exposure of sensitive data to the client with support for disposable authentication passwords server side operations key generation encryption decryption archive creation extraction and locking of private keys minimal footprint single statically linked binary to ease integration execution on the usb armory platform ciphers encrypted volumes luks encrypted partitions asymmetric ciphers openpgp using golang org x crypto openpgp symmetric ciphers aes 256 ofb w pbkdf2 password derivation sha256 4096 rounds and hmac sha256 security tokens time based one time password algorithm totp rfc6238 https datatracker ietf org doc html rfc6238 implementation google authenticator hardware security modules the hsm support allows symmetric ciphering using device specific secret keys allowing to uniquely tie derived keys to the specific hardware unit being used an hsm specific aes ofb based symmetric cipher is exposed with keys derived from the user password as well as device specific secret additionally the luks password for accessing encrypted volumes can filtered through the hsm to make it device specific finally the tls certificates can also be stored encrypted for a specific device supported drivers nxp security controller sccv2 nxp cryptographic acceleration and assurance module caam nxp data co processor dcp key storage a pre defined directory stored on the encrypted filesystem is assigned to public and private key storage see the configuration section for related settings the keys can be uploaded using the file manager imported as free text or generated server side the key storage directory structure is the following key path cipher name private public key identifier key format once uploaded in their respective directory private keys can only be deleted or overwritten they cannot be downloaded moved or copied the keys for otp ciphers e g totp implementing google authenticator generate a valid otp code for the current time when the key information is queried key info action on the right click menu requirements operation the use of interlock is coupled with the presence of at least one luks encrypted partition its initial creation is left to the user an example setup using cryptsetup and lvm2 follows the example uses a microsd partition to illustrate typical usb armory setup the partition mmcblk0p2 is assumed to have been previously created with fdisk using the desired size and the linux lvm type 8e pvcreate dev mmcblk0p2 initialize physical volume vgcreate lvmvolume dev mmcblk0p2 create volume group lvcreate l 20g n encryptedfs lvmvolume create logical volume of 20 gb cryptsetup y cipher aes xts plain64 set up encrypted partition key size 256 hash sha1 luksformat with default cryptsetup dev lvmvolume encryptedfs settings cryptsetup luksopen dev lvmvolume encryptedfs interlockfs mkfs ext4 dev mapper interlockfs create ext4 filesystem cryptsetup luksclose interlockfs the login procedure of interlock prompts for an encrypted volume name e g encryptedfs in the previous example and one valid password for luksopen a successful login unlocks the encrypted partition a successful logout locks it back once logged in users can change add remove luks passwords within interlock any login password can be disposed of using a dedicated flag during login this deletes the password from its luks key slot right after encrypted partition unlocking warning removing the last remaining password makes the luks encrypted container permanently inaccessible this is a feature not a bug the following sudo configuration meant to be included in etc sudoers illustrates the permission requirements for the user running the interlock server the example assumes username interlock with home directory home interlock and volume group set to its default lvmvolume interlock all root nopasswd bin date s sbin poweroff bin mount dev mapper interlockfs home interlock interlock mnt bin umount home interlock interlock mnt bin chown interlock home interlock interlock mnt sbin cryptsetup luksopen dev lvmvolume interlockfs sbin cryptsetup luksopen dev lvmvolume sbin cryptsetup luksclose dev mapper interlockfs sbin cryptsetup luksclose dev mapper sbin cryptsetup lukschangekey dev lvmvolume sbin cryptsetup lukschangekey dev lvmvolume sbin cryptsetup luksremovekey dev lvmvolume sbin cryptsetup luksremovekey dev lvmvolume sbin cryptsetup luksaddkey dev lvmvolume sbin cryptsetup luksaddkey dev lvmvolume compiling the interlock app requires a working go 1 4 2 environment to be compiled or cross compiled under linux it is not supported by or designed for other oses at this time git clone https github com usbarmory interlock cd interlock make this compiles the interlock binary that can be executed with options illustrated in the next section alternatively you can automatically download compile and install the package under your gopath as follows go install github com usbarmory interlock latest options h options help b 0 0 0 0 4430 binding address port pair c interlock conf configuration file path o operation unlock volume lock derive data d false debug mode t false test mode warning disables authentication the operation flag allows selected actions to be performed locally without a web interface the following operations are supported unlock volume unlock luks volume to mapping interlockfs prompts password once uses hsm key derivation when configured lock lock the luks volume mapped to interlockfs derive data hsm key derivation from data e g diversifier specified in hex format e g derive 12ef derive hsm key derivation from password prompted twice interactively configuration debug enable debugging logs set time use the client browser time to set server time at login useful on non routed usb armory devices unable to set the clock on their own bind address ip address port pair tls on use tls cert and tls key paths as https tls keypair gen generate a new tls keypair and save it to tls cert and tls key paths when pointing to non existent files otherwise behaves like on useful for testing and tofu trust on first use schemes off disable https tls cert https server tls certificate tls key https server tls key tls client ca optional ca for https client authentication client certificate requires tls web client authentication x509v3 extended key usage extension to be correctly validated hsm model options enable model hsm support with options multiple options can be combined in a comma separated list e g mxc scc2 luks tls cipher off disable hsm support available modules mxc scc2 nxp security controller sccv2 requires kernel driver mxc scc2 https github com usbarmory mxc scc2 caam keyblob nxp cryptographic acceleration and assurance module caam note stores encrypted derived keys in luks kb which must be accompanied to the luks partition itself when creating data backups requires kernel driver caam keyblob https github com usbarmory caam keyblob mxs dcp nxp data co processor dcp requires kernel driver mxs dcp https github com usbarmory mxs dcp available options luks use hsm secret key to aes encrypt luks passwords and make them device specific before use luks login and password operations add change remove fallback in case of failure to plain ones in order to allow change of credentials on pre hsm deployments tls use hsm secret key to aes 256 ofb encrypt the https server tls key tls key automatically convert existing plaintext keys cipher expose aes 256 ofb derived symmetric cipher with password key derivation through hsm encryption to make it device specific key path path for public private key storage on the encrypted filesystem volume group volume group name ciphers array of cipher names to enable supported values are openpgp aes 256 ofb totp the following example illustrates the configuration file format plain json and its default values debug false set time false bind address 0 0 0 0 4430 tls on tls cert certs cert pem tls key certs key pem tls client ca hsm off key path keys volume group lvmvolume ciphers openpgp aes 256 ofb totp at startup the interlock server dumps the applied configuration in its file format logging the application generates debug audit notification and error logs debugging logs are only generated when debug is set to true in the configuration file or command line switch in debug mode all logs are printed on standard output and never saved audit and error logs are shown live in a dedicated area on the web client application logs and saved on the root directory of the encrypted partition in the interlock log file notifications are shown live in a dedicated area on the web client current activity they are only kept in memory in a circular buffer and never stored on disk any non debug log generated outside an unauthenticated session is issued through standard syslog facility license interlock https github com usbarmory interlock copyright c withsecure corporation this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation under version 3 of the license this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details see accompanying license file for full details | front_end |
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MachineLearning_notes | machine and deep learning resources mit license https img shields io apm l atomic design ui svg https github com tterb atomic design ui blob master licenses pr s welcome https img shields io badge prs welcome brightgreen svg style flat http makeapullrequest com machine and deep learning and data analysis resources please contribute and get in touch contributing md see mdmisc notes https github com mdozmorov mdmisc notes for other programming and genomics related notes table of content start doctoc generated toc please keep comment here to allow auto update don t edit this section instead re run doctoc to update cheatsheets cheatsheets awesome deep learning awesome deep learning keras tensorflow keras tensorflow pytorch pytorch jax jax graph neural networks graph neural networks transformers transformers dl books dl books dl courses tutorials dl courses tutorials dl videos dl videos dl papers dl papers dl papers genomics dl papers genomics dl tools dl tools auto ml auto ml dl models dl models dl projects dl projects chatgpt llms chatgpt llms dl misc dl misc awesome machine learning awesome machine learning ml books ml books ml courses tutorials ml courses tutorials ml videos ml videos ml papers ml papers ml tools ml tools ml misc ml misc end doctoc generated toc please keep comment here to allow auto update cheatsheets artificial intelligence https github com niraj lunavat artificial intelligence awesome ai learning with 100 ai cheat sheets free online books top courses best videos and lectures papers tutorials 99 researchers premium websites 121 datasets conferences frameworks tools over 200 of the best machine learning nlp and python tutorials 2018 edition https medium com machine learning in practice over 200 of the best machine learning nlp and python tutorials 2018 edition dd8cf53cb7dc source https twitter com iamtrask status 1318464483883470849 s 20 101 machine learning algorithms for data science with cheat sheets https blog 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com terryum awesome deep learning papers the most cited deep learning papers awesome computer vision https github com jbhuang0604 awesome computer vision a curated list of awesome computer vision resources ai and deeprl https github com smc77 deeprl source code links and other learning materials related to artificial intelligence especially focused on deep reinforcement learning the neural network zoo https www asimovinstitute org neural network zoo infographics of different neural network architectures explanation of each references to the original papers over 150 of the best machine learning nlp and python tutorials https medium com machine learning in practice over 150 of the best machine learning nlp and python tutorials ive found ffce2939bd78 hn tweet by andrew trask https twitter com iamtrask status 1289658159972270080 s 20 keras tensorflow deep learning with r https www manning com books deep learning with r by fran ois chollet the creator of keras with j j allaire the founder 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latest breakthroughs in ai in 2022 by release date with a clear video explanation link to a more in depth article and code deepmind research https github com deepmind deepmind research implementations and illustrative code to accompany deepmind publications jupyter notebooks and data list of projects lee benjamin d anthony gitter casey s greene sebastian raschka finlay maguire alexander j titus michael d kessler et al ten quick tips for deep learning in biology https arxiv org abs 2105 14372 arxiv 29 may 2021 1 use appropriate method 2 establish baseline 3 train reproducibly 4 know your data 5 select sensible architecture 6 optimize hyperparameters 7 mitigate overfitting 8 maximize interpretability 9 avoid over interpretation 10 prioritize research ethics summary in figure 1 references latest version https benjamin lee github io deep rules sebastian ruder https ruder io an overview of gradient descent optimization algorithms http arxiv org abs 1609 04747 june 15 2017 gradient descent optimization algorithm review by definitions intuitive progression of algorithm improvements gradient descent variants batch stochastic mini batch gradient descent algorithme momentum nesterov accelerated gradient adagrad adadelta rmsprop adam adamax nadam visualizattion parallel implementations eugeneyan applied ml https github com eugeneyan applied ml papers tech blogs by companies sharing their work on data science machine learning in production 2020 a year full of amazing ai papers a review https github com louisfb01 best ai paper 2020 a curated list of the latest breakthroughs in ai by release date with a clear video explanation link to a more in depth article and code awesome deepbio https github com gokceneraslan awesome deepbio a curated list of awesome deep learning publications in the field of computational biology deep learning papers reading roadmap https github com floodsung deep learning papers reading roadmap deep learning papers reading roadmap for anyone who are eager 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yangkky machine learning for proteins list of papers about machine learning for proteins key papers in deep reinforcement learning https spinningup openai com en latest spinningup keypapers html twitter https twitter com strnr status 1110172093155627008 s 03 lecun bengio and hinton deep learning https www nature com articles nature14539 classical deep learning review areas of application historical development principles of supervised learning stochastic gradient descent figure 1 illustration of forward and backpropagation with equations convolutional neural networks for image recognition and in other areas language processing recurrent neural networks lstms vincent et al extracting and composing robust features with denoising autoencoders https www cs toronto edu larocheh publications icml 2008 denoising autoencoders pdf denoising autoencoder paper statistical formulations schmidhuber deep learning in neural networks https www sciencedirect com science article abs pii s0893608014002135 deep overview of deep learning history year by year description of types of dl approaches algorithmic backpropagation improvements problems and solutions dl papers genomics genomicsnotebook https github com microsoft genomicsnotebook genomics data analysis with jupyter notebooks on azure avsec iga vikram agarwal daniel visentin joseph r ledsam agnieszka grabska barwinska kyle r taylor yannis assael john jumper pushmeet kohli and david r kelley effective gene expression prediction from sequence by integrating long range interactions https doi org 10 1038 s41592 021 01252 x nature methods october 2021 enformer https github com deepmind deepmind research tree master enformer a deep learning model transformers to predict epigenetic and gene expression profiles 128bp resolution from human and mouse cell types using only the dna sequence as input incorporating information from up to 100 kb on either side of the target locus 200kb of input dna sequence transformers allow to increase the receptive field up to 100kb in contrast to 20kb for basenji2 or expecto significant increase in performance predictions using cage data improves variant effect prediction on eqtl data excellent transformer description greener joe g shaun m kandathil lewis moffat and david t jones a guide to machine learning for biologists https doi org 10 1038 s41580 021 00407 0 nature reviews molecular cell biology september 13 2021 introduction to machine deep learning focusing on biology applications general terms supervised unsupervised learning loss function parameters and hyperparameters training validation testing overfitting bias variance tradeoff classification regression clustering dimensionality reduction neural networks cnn lstm rnn transformers autoencoders network training data leakage evaluation of machine learning reports references on each topic deep review opportunities and obstacles for deep learning in biology and medicine https greenelab github io deep review a collaboratively written review paper on deep learning genomics and precision medicine led by casey greene and many others deep learning genomics primer https github com abidlabs deep learning genomics primer this tutorial is a supplement to the manuscript a primer on deep learning in genomics https www nature com articles s41588 018 0295 5 nature genetics 2018 by james zou mikael huss abubakar abid pejman mohammadi ali torkamani amalio telentil box 1 and 2 concepts and definitions box 3 online resources cloud platforms gpu services software libraries educational resources more python tutorial on detecting dna motifs eraslan et al deep learning https www nature com articles s41576 019 0122 6 deep learning in genomics review big data description evolution of machine learning into deep learning with the help of gpus supervised learning four major classes of neural networks fully connected convolutional recurrent and graph convolutional two unsupervised learning techniques autoencoders and generative adversarial networks gans from basic logistic regression to each network architecture illustrated on figures theory descriptions examples of applications in genomics transfer learning model zoos interpretation feature importance angermueller et al deep learning for computational biology https doi org 10 15252 msb 20156651 review on machine learning epi genomics examples supervised vs unsupervised learning deep neural networks box 1 network basics box 2 convolutional nn tools caffe theano torch7 tensorflow data preparation model training and optimization min lee and yoon deep learning in bioinformatics https academic oup com bib article 18 5 851 2562808 deep neural networks in bioinformatics overview of deep learning development programming libraries basic structure of neural networks convolutional nns recurrent nns table 4 omics applications biomedical imaging biomedical signal processing references code examples jupyter notebooks of eight bioinformatics deep learning applications https github com lykaust15 deep learning examples zou et al a primer on deep learning in genomics https www nature com articles s41588 018 0295 5 deep learning in genomics overview feed forward convolutional recurrent and a python tutorial on detecting dna motifs box 1 and 2 concepts and definitions box 3 online resources cloud platforms gpu services software libraries educational resources more github repo https github com abidlabs deep learning genomics primer and colab notebook https colab research google com drive 17e4h5aaoioh5dito7mzg4hpl6z 0fywr with interactive tutorial to build a convolutional neural network to discover dna binding motifs p rez enciso and zingaretti a guide for using deep learning for complex trait genomic prediction genes 2019 https doi org 10 3390 genes10070553 deep learning for predicting phenotypes from genomics data deep learning basics definitions sakellaropoulos theodore konstantinos vougas sonali narang filippos koinis athanassios kotsinas alexander polyzos tyler j moss et al a deep learning framework for predicting response to therapy in cancer cell reports december 2019 https doi org 10 1016 j celrep 2019 11 017 drug response prediction from gene expression data deep neural network dnn h2o ai framework compared with elastic net random forest trained on highly variable by mad gene expression in 1001 cell lines and 251 drugs pharmacogenomic dataset gdsc cclp to predict ic50 hyper parameter optimization using 5 fold cross validation and minimizing mean square error batch correction between the datasets tested on unseen patient cohorts occams md anderson tcga multiple myeloma consortium to predict ic50 and test low medium high ic50 groups for survival differences rds files data https genome med nyu edu public tsirigoslab deep drug response r code https genome med nyu edu public tsirigoslab deep drug response m jannesari m habibzadeh h aboulkheyr p khosravi o elemento m totonchi and i hajirasouliha breast cancer histopathological image classification a deep learning approach in 2018 ieee international conference on bioinformatics and biomedicine bibm 2018 https doi org 10 1109 bibm 2018 8621307 breast cancer image classification data from stanford tissue microarray database tmad https tma im cgi bin home pl and breast cancer histopathological database breakhis https web inf ufpr br vri databases breast cancer histopathological database breakhis 6k images different variants of resnet and inception architectures data augmentation resizing rotation cropping flipping training details classification into malignant and benign or into subtypes can handle images at different magnifications resnet performs better github repository https github com machinelearning4work deepbreastcancer includes crawler to get images dl tools interactive tools https github com machine learning tokyo interactive tools interactive tools for machine learning deep learning and math play with deep neural network in browser ivy https github com unifyai ivy the unified machine learning framework supporting jax tensorflow pytorch mxnet and numpy python module documentation https lets unify ai ivy index html keras https github com keras team keras deep learning for humans http keras io http keras io mxnet gluon style transfer https github com stacyyang mxnet gluon style transfer neural artistic style transfer using mxnet pytorch and torch implementations available openai com https openai com gpt 3 access without the wait api access to gpt 3 opencv https opencv org open source computer vision library github https github com opencv opencv opencv python https github com opencv opencv python cpu only opencv packages for python documentation https docs opencv org 4 x index html video https youtu be z846tkgl9 u 3h opencv crash course pathology learning https github com millett pathology learning using traditional machine learning and deep learning methods to predict stuff from tcga pathology slides ruta https github com fdavidcl ruta unsupervised deep architechtures in r autoencoders requires keras and tensorflow book https ruta software tensor2tensor https github com tensorflow tensor2tensor library of deep learning models and datasets designed to make deep learning more accessible and accelerate ml research janggu https github com bimsbbioinfo janggu deep learning interface to genomic data fasta bam bigwig bed gff numpy like bioseq and cover objects accessable by keras includes model evaluation and interpretation features pypi https pypi org project janggu docs https janggu readthedocs io en latest janggu deep learning for genomics https www biorxiv org content 10 1101 700450v2 maui https github com bimsbbioinfo maui multi omics autoencoder integration latent factors from different data types stacked variational autoencoders and their clustering testing for association with survival tested vs latent factors extracted using multifactor analysis mfa and icluster on tcga colorectal cancer rna seq snps cnvs evaluation of colorectal cancer subtypes and cell lines using deep learning https www life science alliance org content 2 6 e201900517 ludwig http ludwig ai is a toolbox built on top of tensorflow that allows to train and test deep learning models without the need to write code github https github com uber ludwig mask rcnn https github com matterport mask rcnn mask r cnn for object detection and instance segmentation on keras and tensorflow pennai https github com epistasislab pennai ai driven data science entry level machine learning interface for non experts a system for accessible artificial intelligence https arxiv org abs 1705 00594 auto ml clearml https github com allegroai clearml auto magical suite of tools python to streamline your ml workflow experiment manager mlops and data management documentation https clear ml docs latest docs youtube channel https www youtube com c clearml videos tpot http epistasislab github io tpot a python automated machine learning tool that optimizes machine learning pipelines using genetic programming simplified interface to many machine learning algorithms scaling tree based automated machine learning to biomedical big data with a feature set selector https doi org 10 1093 bioinformatics btz470 dl models pytorch model zoos https pytorch org docs stable torchvision models html keras model zoos https keras io applications tensorflow model zoos https github com tensorflow models kipoi https kipoi org a model zoo for genomics examples of transfer learning predicting pathogenic variants tfbss avsec et al the kipoi repository accelerates community exchange and reuse of predictive models for genomics https www nature com articles s41587 019 0140 0 github repo https github com kipoi kipoi caffe model zoo http caffe berkeleyvision org model zoo html bert https github com google research bert bidirectional encoder representations from transformers for natural language processing tasks model architecture implemented 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github com mlr org mlr3 including videos ml misc the algorithms r https github com thealgorithms r github repo with code examples of main machine learning algorithms algorithms in ipython notebooks https github com rasbt algorithms in ipython notebooks a repository with ipython notebooks of algorithms implemented in python https github com rasbt algorithms in ipython notebooks awesome decision tree papers https github com benedekrozemberczki awesome decision tree papers a collection of research papers on decision classification and regression trees with implementations understanding the bias variance tradeoff http scott fortmann roe com docs biasvariance html bias variance total error classic figures and explanation by scott fortmann roe lares https github com laresbernardo lares r library for analytics and machine learning ml techniques https github com shiring ml techniques r code for performing typical ml tasks and techniques e g naive bayes random forest by shirin glander ml from scratch https github com eriklindernoren ml from scratch bare bones python implementations of some of the fundamental machine learning models and algorithms gradient boosting essentials in r using xgboost http www sthda com english articles 35 statistical machine learning essentials 139 gradient boosting essentials in r using xgboost mlpb https github com ben519 mlpb machine learning problem bible problems and solutions in r xgboost svm neural networks and other methods best xgboost settings a second xgboost version xgboost best with the best parameter settings that i obtained in on of my publications https arxiv org abs 1802 09596 these are nrounds 500 eta 0 0518715 subsample 0 8734055 booster gbtree max depth 11 min child weight 1 750185 colsample bytree 0 7126651 colsample bylevel 0 6375492 from is catboost the best gradient boosting r package https www r bloggers com is catboost the best gradient boosting r package post on r bloggers com material in russian scientific graphics in python https github com whitehorn scientific graphics in python matplotlib for scientific graphics 3 parts 13 chapters by pavel shabanov ml course hse https github com esokolov ml course hse machine learning course at the computer sciences department high schoool of economy multiple years videos mlcourse open https github com yorko mlcourse open opendatascience machine learning course both in english and russian python based ml course with video lectures video https www youtube com playlist list plvly 7ijcmjdgcctqfzj5j8ovb y0gjcl dl cshse spring2018 https github com aosokin dl cshse spring2018 deep learning anton osokin higher school of economics computer sciences department russian course material and video lectures https www youtube com playlist list plzy5g rvmfayekccgo3 it6hzwpzl3ld ordinary differential equations https ode mathbook info calculus https calculus mathbook info tweet https twitter com ilyaschurov status 1432811362904944644 s 20 mathprofi ru http mathprofi ru mirror http mathprofi net | machine-learning deep-learning | ai |
os-design-assign-2 | os design assign 2 assignment 2 syst44288 operating systems design and systems programming about using this for your assignment submission if you re going to download this assignment and submit it for class you should ensure that you at the very least understand how this works you d be doing yourself a disservice if you just copy paste and click submit part 1 an echo server echoes back whatever it receives from a client for example if a client sends the server the string hello there the server will respond with hello there write an echo server using the java networking api described in section 3 6 1 this server will wait for a client connection using the accept method when a client connection is received the server will loop performing the following steps read data from the socket into a buffer write the contents of the buffer back to the client the server will break out of the loop only when it has determined that the client has closed the connection the date server shown in figure 3 21 uses the java io bufferedreader class bufferedreader extends the java io reader class which is used for reading character streams however the echo server cannot guarantee that it will read characters from clients it may receive binary data as well the class java io inputstream deals with data at the byte level rather than the character level thus your echo server must use an object that extends java io inputstream the read method in the java io inputstream class returns 1 when the client has closed its end of the socket connection apply appropriate error handling see the general notes section below input sanitizing while it is always recommended to sanitize input the fact that this program will compile and execute any code it is sent over the internet basically makes it impossible to secure properly just do basic sanitizing and don t worry too much part 2 design a file copying program in the c programming language named filecopy using ordinary pipes this program will be passed two parameters the name of the file to be copied and the name of the copied file the program will then create an ordinary pipe and write the contents of the file to be copied to the pipe the child process will read this file from the pipe and write it to the destination file for example if we invoke the program as follows bash filecopy input txt copy txt the file input txt will be written to the pipe the child process will read the contents of this file and write it to the destination file copy txt write this program using unix pipes | c networking buffers java | os |
lamorel | language models for reinforcement learning lamorel lamorel is a python library designed for people eager to use large language models llms in interactive environments e g rl setups news 2023 07 12 an example examples ppo lora finetuning showing how to use lora https arxiv org abs 2106 09685 through the peft https github com huggingface peft library for lightweight finetuning has been added why lamorel what is the difference between lamorel and rlhf libs first of all lamorel was initially designed to easily use llms in interactive environments for this reason it is not specialised in rl however one can easily implement an rl loop as provided in examples examples a key component of rlhf is that generating a sequence of tokens given a prompt is considered as a full and terminated episode steps are therefore the generation of each token in the generated sequence this method is for instance useful to finetune llms to generate token sequences that maximize a reward function e g learned from interactions with humans however this setup is not suited for interacting with an external environment that requires multiple interactions e g multiple text generations to end an episode this is why we advise users that lie in the rlhf setup and do not want to reimplement the rl loop by themselves to use such libs that already come with rl implementations e g rl4lms https github com allenai rl4lms trl https github com lvwerra trl on the opposite if you want to use your llm in an interactive environment where acting in the environment leads your agent to a new state and possibly a new prompt for your llm that is still part of the same episode lamorel may help you lamorel s key features 1 abstracts the use of llms e g tonekization batches into simple calls python lm server generate contexts this is an examples prompt continue it with lm server score contexts this is an examples prompt continue it with candidates a sentence another sentence 2 provides a method to compute the probability of token sequences e g action commands given a prompt 3 is made for scaling up your experiments by deploying multiple instances of the llm and dispatching the computation thanks to a simple configuration file yaml distributed setup args n rl processes 1 n llm processes 1 4 provides access to open sourced llms from the hugging face s hub https huggingface co models along with model parallelism https huggingface co docs transformers v4 15 0 parallelism to use multiple gpus for an llm instance yaml llm args model type seq2seq model path t5 small pretrained true minibatch size 4 parallelism use gpu true model parallelism size 2 5 allows one to give their own pytorch modules to compute custom operations e g to add new heads on top of the llm 6 allows one to train the llm or part of it thanks to a data parallelism https pytorch org tutorials intermediate ddp tutorial html setup where the user provides its own update method distributed and scalable lamorel relies on a client server s architecture where your rl script acts as the client sending requests to the llm server s in order to match the computation requirements of rl lamorel can deploy multiple llm servers and dispatch the requests on them without any modification in your code distributed scoring docs images distributed architecture gif installation 1 cd lamorel 2 pip install examples we provide a set of examples examples that use lamorel in interactive environments saycan examples saycan a saycan https arxiv org abs 2204 01691 implementation that controls a robot hand in a simulated pickplace environment ppo finetuning examples ppo finetuning a lightweight implementation of the ppo approach introduced in grounding large language models in interactive environments with online reinforcement learning https arxiv org abs 2302 02662 to finetune an llm policy in babyai text https github com flowersteam grounding llms with online rl tree main babyai text ppo lora finetuning examples ppo lora finetuning a lightweight implementation of the ppo approach introduced in grounding large language models in interactive environments with online reinforcement learning https arxiv org abs 2302 02662 but use lora https arxiv org abs 2106 09685 through the peft https github com huggingface peft library for lightweight finetuning in babyai text https github com flowersteam grounding llms with online rl tree main babyai text how to use lamorel lamorel is built of three main components a python api to interact with llms a configuration file to set the llm servers a launcher deploying the multiple llm servers and launching your rl script instantiating the server in your rl script lamorel leverages hydra https hydra cc for its configuration file because of this you need to add the hydra decorator on top of your main function then you must instantiate the caller class from lamorel which will create the object allowing you to interact with the llm servers do not forget to initialize lamorel once imported with lamorel init to initialize the communication with the servers python import hydra from lamorel import caller lamorel init lamorel init hydra main config path config config name config def main config args lm server caller config args lamorel args do whatever you want with your llm lm server close if name main main do not forget to close the connection with servers at the end of your script using the caller once instantiated you can use the different methods of the caller object to send requests to your llms scoring first we provide the score method to compute the probability of a sequence of tokens a candidate given a prompt context lamorel allows to provide multiple candidates for a single context but also to batch this computation for multiple contexts along with their associated candidates using this one can use a classic vectorized rl setup where at each step multiple environments running in parallel return their current state and expect an action python lm server score contexts this is an examples prompt continue it with candidates a sentence another sentence generation lamorel also provides a method for text generation similarly to the score method one can give multiple prompts contexts our generate method can use any keyword argument from transformers api https huggingface co docs transformers main classes text generation in addition of the generated texts it also returns the probability of each generated sequence python lm server generate contexts this is an examples prompt continue it with lm server generate contexts this is an examples prompt continue it with temperature 0 1 max length 25 custom modules while lamorel provides two main uses of llms i e scoring and generating we also allow users to provide their own methods to perform custom operations using llms we expect these custom methods to be pytorch modules https pytorch org docs stable generated torch nn module html additional modules docs images additional modules gif in order to define such a custom operation users must extend our basemodulefunction class for this you must extend two main methods initialize self initialize your custom operations here forward self forward outputs minibatch tokenized contexts kwargs perform your operations here and return the results lamorel will give your custom module to all llm servers and ensure your variables e g weights are the same on each server see the example below where we implement a multi layer perceptron mlp on top of our llm python from lamorel import basemodulefunction class twolayersmlpmodulefn basemodulefunction def init self model type n outputs super init self model type model type self n outputs n outputs def initialize self use this method to initialize your module operations self llm config gives the configuration of the llm e g useful to know the size of representations self device gives you access to the main device e g gpu the llm is using llm hidden size self llm config to dict self llm config attribute map hidden size self mlp torch nn sequential torch nn linear llm hidden size 128 torch nn linear 128 128 torch nn linear 128 self n outputs to self device def forward self forward outputs minibatch tokenized contexts kwargs perform your operations here forward outputs gives access the output of the computations performed by the llm e g representations of each layer minibatch gives access to the input data i e a prompt and multiple candidates given to the llm tokenized context gives access to the prompt used get the last layer s representation from the token right after the prompt if self model type causal adapt to the transformers api differing between encoder decoder and decoder only models model head forward outputs hidden states 0 0 len tokenized contexts input ids 1 else model head forward outputs encoder last hidden state 0 len tokenized contexts input ids 1 give representation to our mlp output self mlp model head return output once implemented you can give your custom module s to the lamorel caller along with a key it will then be possible to use your module either by calling it directly using the custom module fns or by using it in addition of scoring python lm server caller config args lamorel args custom module functions mlp head twolayersmlpmodulefn config args lamorel args llm args model type 2 direct call lm server custom module fns module function keys mlp head contexts this is an examples prompt continue it with scoring lm server score additional module function keys mlp head contexts this is an examples prompt continue it with candidates a sentence another sentence updaters we have seen so far how to use an llm along with possibly custom modules for inference however lamorel also provides tools to update e g train these operations with a baseupdater class which can be extended and perform any update operation our class gives access to the whole computation graph self llm module it can for instance be used to perform operations with gradient by calling self llm module mlp head score with the leaf operations wanted i e scoring and or custom modules or to select weights to train the llm itself self llm module llm model custom modules self llm module module module functions the whole graph self llm module python from lamorel import baseupdater class testupdater baseupdater def perform update self contexts candidates current batch ids kwargs if not hasattr self loss fn self loss fn torch nn crossentropyloss if not hasattr self optimizer you can train 1 only the llm self optimizer torch optim adam self llm module llm model parameters 2 only some custom modules self optimizer torch optim adam self llm module module module functions mlp head parameters 3 everything self optimizer torch optim adam self llm module parameters use the computational graph with gradient 1 only the llm s scoring module output self llm module score contexts contexts require grad true 2 only some custom modules output self llm module mlp head contexts contexts require grad true 3 both output self llm module score mlp head contexts contexts require grad true stack outputs to batch loss computation stacked output torch stack o mlp head for o in output to cpu compute loss with the labels corresponding to the current batch loss self loss fn stacked output kwargs labels current batch ids compute gradients and update graph loss backward self optimizer step self optimizer zero grad return anything you want using a dictionary return loss loss once defined users must give their custom updater to the caller whenever needed users can call their updater with data i e contexts and candidates along with any additional keyword argument e g labels because multiple llm servers can be deployed we also dispatch the updater s computation when one calls the updater with data contexts and candidates are dispatched over the multiple servers where each runs the updater because lamorel does not know a priori what additional keyword arguments are these are copied and sent to each llm as users may need to know how to associate these arguments to the data handled by the current server we provide the current batch ids variable giving the indexes of contexts and by extension candidates that are given to the current llm lamorel is in charge of gathering the gradients of all servers such that self optimizer step produces the same on each server python lm server caller config args lamorel args custom module functions mlp head twolayersmlpmodulefn config args lamorel args llm args model type 2 custom updater testupdater result lm server update contexts this is an examples prompt continue it with candidates a sentence labels torch tensor 0 1 dtype torch float32 losses r loss for r in result one loss returned per llm training docs images training gif initializers additionally one may also provide an initializer object applying any modification on the model e g freezing some weights before it is given to the distributed architecture that synchronizes all llms python from lamorel import basemodelinitializer class custominitializer basemodelinitializer def initialize model self model do whatevever your want here for instance freeze all the llm s weights llm module model modules llm model for param in llm module parameters param requires grad false lm server caller config args lamorel args custom model initializer custominitializer setting up the configuration the configuration of the llm and the client server s architecture is done using a yaml configuration file following hydra https hydra cc s api here is what this file should contain yaml lamorel args arguments for lamorel log level debug debug info error level of logs returned by lamorel allow subgraph use whith gradient false distributed setup args use this section to specify how many llm servers must be deployed n rl processes 1 n llm processes 1 llm servers accelerate args lamorel leverages hugging face accelerate for the distributed setup config file accelerate default config yaml keep this machine rank each machine should launch the script and provide its rank id see section below num machines 2 number of machines used in the experiment llm args specify which llm to use and how model type seq2seq seq2seq causal encoder decoder or decoder only model model path t5 small name if downloaded from the hugging face s hub or absolute path to the model pretrained true true false set this to false if you want to keep the llm s architecture but re initialize its wegihts pre encode inputs true whether encoding contexts should be optimized when using encoder decoder models minibatch size 4 batch size per forward passes adapt this number to your gpu memory parallelism model parallelism use gpu true true false set this to false if you want your llm s to use cpu model parallelism size 1 number of gpus user per llm server synchronize gpus after scoring false only useful for specific gpu optimizations empty cuda cache after scoring false only useful for specific gpu optimizations rl script args arguments for lamorel path absolute path to your rl script provide any additional arguments for your rl script here examples of configuration files are provided in section below launch lamorel comes with a launcher that handles how to launch multiple processes on each machine to launch your experiment on a machine you must use the following command python m lamorel launcher launch config path path config name name rl script args path path lamorel args accelerate args machine rank rank additional arguments to override config warning use absolute paths launch examples several examples of configurations can be found in examples examples single machine and no gpu config local cpu config yaml examples configs local cpu config yaml launch command s shell python m lamorel launcher launch config path absolute path to project examples configs config name local cpu config rl script args path absolute path to project examples example script py single machine and gpu s config local gpu config yaml examples configs local gpu config yaml launch command s rl script shell python m lamorel launcher launch config path absolute path to project examples configs config name local gpu config rl script args path absolute path to project examples example script py lamorel args accelerate args machine rank 0 llm server shell python m lamorel launcher launch config path absolute path to project examples configs config name local gpu config rl script args path absolute path to project examples example script py lamorel args accelerate args machine rank 1 if you do not want your llm process to use all your gpus for instance if you plan to launch multiple llm servers set an appropriate value to model parallelism size in the config slurm cluster we here provide an example with a slurm cluster where our experiment deploys 6 llm servers on 3 machines as the schema below shows the first machine hosts 2 llm servers and the rl script which also has access to gpus the two other machines both host only 2 llm servers multi nodes docs images multi nodes png config multi node slurm cluster config yaml examples configs multi node slurm cluster config yaml launch command s shell sbatch examples slurm job slurm technical details and contributions lamorel relies on pytorch distributed for communications we chose to use the gloo backend to allow both cpu and gpu platforms lamorel launches the rl script n rl processes n llm processes times every time it reaches the lm server caller line lamorel checks whether the current process should be part of the client or the llm servers if it is a client the script returns the caller object that the user can use to send requests and continues otherwise it creates a server lamorel src lamorel server server py that loads the llm and starts listening for the communication between the client and servers we create a process group between the client and one of the servers which is considered as the master this master server listens to requests and dispatches calls using the dispatcher lamorel src lamorel server dispatcher py to all the servers using another process group only shared by llm servers each llm server performs the asked operations on its received data and sends the results to the master llm server the latter gathers results and sends them back to the rl script lamorel is still in its very early phase and we are happy to accept any external contribution to it please follow the contributing md contributing md file | ai |
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word_embedding_theano | alt text https travis ci org wenjiesha word embedding svg branch master continuous test status word embedding theano implementation of paper natural language processing almost from scratch http arxiv org pdf 1103 0398v1 pdf status there are still quite a few important items to finish but it seems like learning the embedding input atis data contains 46635 sentences with 572 words output word embedding for each word how to run shell python train py you might need to install at least theano 0 7 and numpy to run the program todo 1 cpickle the embedding at the end of each epoch 2 use validation set to avoid over fitting 3 hyper param tuning 4 gpu perhaps cos it is really slow right now 5 normalizing the embedding useful links a nice blog about python internal https jakevdp github io blog 2014 05 09 why python is slow | ai |
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python-toolz | python toolz v0 1 9 zguillez https zguillez io guillermo de la iglesia python helpers getting started install pip install upgrade python toolz usage from python toolz import helper as ztools ztools log test db ztools database conn os environ db host database os environ db name user os environ db user password os environ db pass data db sql select id name from template order by id asc limit 0 5 db close print data 0 data db dict select id name from template order by id asc limit 0 5 id name db close print data 0 name contributing and issues contributors are welcome please fork and send pull requests if you have any ideas on how to make this project better then please submit an issue or email mailto guillermo delaiglesia email me license 2023 zguillez io original code licensed under mit https en wikipedia org wiki mit license open source projects used within this project retain their original licenses changelog v0 1 0 february 11 2023 initial commit | developer-tools helper python | server |
rimble-ui | rimble ui rimble design system x27 s react component library npm https img shields io npm v rimble ui svg https www npmjs com package rimble ui javascript style guide https img shields io badge code style standard brightgreen svg https standardjs com travis build status https travis ci com consensys rimble ui svg branch master https travis ci com consensys rimble ui join the community on spectrum https withspectrum github io badge badge svg https spectrum chat rimble rimble is a project from consensys design aiming to provide adaptable components and design standards for decentralized applications dapps our goal is simply to make it easier to build dapps with outstanding user experience if you re interested we have written a bit more about our rationale and approach to building rimble https blog prototypr io this is rimble d0f1ad26b8b6 rimble is in initial development and should not be considered stable today we have made the project public in a very early stage of work in order to gather feedback from the community of designers and developers building dapps we are actively working on adding new components to rimble and will be sharing more information on the roadmap very soon install bash npm install save rimble ui styled components usage jsx import react component from react import button from rimble ui class example extends component render return button size medium click me button change log 0 14 0 new feature export es6 modules for tree shaking 0 13 1 bug fix added default props for heading component 0 13 0 new feature added crypto icons under the icon component bug fix fixed heading component as prop not working fixed text component as prop not working fixed select component arrow icon placement 0 12 0 new feature updated all components to styled system v5 0 11 1 bug fix fixed icon background color on toastmessage component 381 fixed display prop not rendering correctly for icon component 380 fixed disabled styling for slider component 414 0 11 0 new feature added basestyles component bug fix updated text component to pass as prop correctly updated heading component to pass as prop correctly updated select component to adjust width updated field to inherit color updated radio and checkbox components to inherit text and icon colors correctly fixed pre commit deprecation warning 372 fixed warnings when building library 376 enhancement adjusted ethaddress component padding addeded default font sizes for h1 h6 elements styled input type color for better visibility updated default props for input textarea card removed copycolor from theme js added text and background colors to theme js 0 10 0 fixed select arrow display bug added missing props to qr component added title and aria label attributes to ethaddress inputs accessibility changed default module from cjs to umd upgraded to storybook app and updated stories reorganized component testing app cra 0 9 8 updated vulnerable dependencies fixed onchange event not firing for file inputs added new ethaddress component 0 9 7 fixed onchange events not firing for file inputs 0 9 6 fixed default theme in rimble themeprovider 0 9 5 fixed select component accepts more options children fixed outdated dependencies vulnerability fixed publicaddress width property being passed to it s parent field component 0 9 4 added ref forwarding for all components fixed input validation icon padding fixed tooltip positioning fixed broken copy button on publicaddress component 0 9 3 revert to last stable version 0 9 2 fixed slider bar in firefox added proptype definitions for all components 0 9 1 fixed tooltip breaking server side rendering 0 9 0 refactored box component to add overflow prop refactored heading component to remove default margins refactored text component to remove default margins bug fix for anchor elements inside flash component removed selected props from pill component 0 8 0 refactored button with text and outline as compounds of button refactored metamaskbutton and uportbutton to use button as base added more colors to theme for success warning danger info bug fix for ref property on input component bug fix for ref property on button component 0 7 1 removed background color on image component 0 7 0 flash component custom labels for publicaddress component bug fixes 0 6 0 tables bug fixes 0 5 0 better form validation uport connect button upgraded to storybook 5 bug fixes 0 4 0 toast messages and toast message provider qr code modal pills expanded test coverage 0 3 0 styling cleanup for lots of components bug fixes 0 2 0 publicaddress component metamask buttons and more button variants basic layout components cards loaders 0 1 0 theming buttons and other simple components blockies license mit consensys https github com consensys | design-systems react react-components javascript web3 ethereum ui ui-components styled-components | os |
vui-ad-hoc-alexa-recognizer | build status https travis ci org rationalanimal vui ad hoc alexa recognizer svg branch master https travis ci org rationalanimal vui ad hoc alexa recognizer npm downloads http img shields io npm dm vui ad hoc alexa recognizer svg style flat label npm 20downloads https npm stat com charts html package vui ad hoc alexa recognizer open open source software https img shields io badge open oss e2 9c 94 brightgreen svg http open oss com release https img shields io github release rationalanimal vui ad hoc alexa recognizer svg label last 20release a https www npmjs com package vui ad hoc alexa recognizer average time to resolve an issue http isitmaintained com badge resolution rationalanimal vui ad hoc alexa recognizer svg http isitmaintained com project rationalanimal vui ad hoc alexa recognizer average time to resolve an issue percentage of issues still open http isitmaintained com badge open rationalanimal vui ad hoc alexa recognizer svg http isitmaintained com project rationalanimal vui ad hoc alexa recognizer percentage of issues still open patreon https img shields io badge back on patreon red svg https www patreon com rationalanimal vui ad hoc alexa recognizer provides natural language understanding processing capability to enable easy implementation of chat bots and voice services high performance run time in only 2 lines of code require to include it and the call to process the text these can run anywhere node js is running backend browser mobile apps etc with or without internet connection has a rich set of built in intents and extensible slots equivalent to alexa s custom slots both list based and regular expression based synonyms slot flags parametrized flags transformation functions soundex matching wild card matching option lists text equivalents sets mix in post processing sentiment analysis unlimited sets of recognizers to build large segmented apps domains with state specific processing builtin and custom chainable responders sub domains trusted and non trusted etc what s in the name you may be wondering why such an odd name glad you asked here is the explanation 1 vui stands for voice user interface because this module allows building skills apps that have voice user interface 2 ad hoc because this module creates a pre configured run time for specific set s of intents requiring no further configuration at run time 3 alexa because this module started out by using alexa skill configuration files it has expanded well beyond that but can still be used as before so if you already have an alexa skill using this module should be very easy and fast and even if you don t it s still easy and fast just a little longer to configure also you can use this module simply to ease your alexa coding you can configure everything using vui ad hoc alexa recognizer saving yourself time and effort by not having to manually enter all the variations then use the included alexify utility to output alexa compatible files 4 recognizer because that s what it does recognizes and processes utterances to identify intents extract slot values and optionally provide responses and update the app state repository this module as well as related vui modules can be found here https github com rationalanimal tutorials examples documentation i have finally gotten to a point where the major features are at a good spot and i can spare some time for tutorials examples and documentation to that end i have set up a web site on github pages and included the first set of tutorials with more on the way here is the website https rationalanimal github io vui ad hoc alexa recognizer recognizer tutorials the first set of tutorials deals with the lower level i e recognizer functionality hello world and using hello world these two tutorials together comprise the usual hello world example one shows how to configure generate and test a recognizer json file the other tutorial shows how to write your own code to use the generated file you have options option lists that is this tutorial shows how to avoid having to manually enter and maintain a large number of related utterances great intrinsic value this one shows how to get information back from the user via built in slot types let s talk here the user is shown how to build an actual chat bot a small app that takes input from the user parses it stores some values in the state for future use and responds to the user based on the parsed information and state count on it introduces the use of what is probably the singularly most useful built in slot type number he spoke bespoke deals with how to define the simplest type of a custom slot list based custom slot known intentions deals with another built in feature shows how to use configure and turn off built in intents it s all the same to me demonstrates custom slots that use synonyms to simplify the code that has to deal with multiple values that map to a smaller subset wild card thing explains how to get values from the user that are not part of the accepted set i e using wildcard matches express yourself regularly explains how to use regular expressions based custom slots to get user input that matches a particular pattern rather than specific list of values that sounds like you explains how to use soundex matching to process words that sound similar this helps with commonly substituted words in chat bots and words that sound the same but aren t spelled the same way in voice services six of one half a dozen of the other covers text equivalents sets and their use text equivalents feature lets you define words and phrases that are equivalent to each other so that you need only to specify a simple utterance and have vui ad hoc alexa recognizer match on any variation of that utterance that may result from using text equivalents this allows solving many different issues from typos to homophones to special vocabularies etc would you like some fries with that introduces the concept of mix in aka add on processing shows how you can use mix ins to cleanly separate matching and business logic change matching through configuration and mix in application add logging and more domain tutorials finally here is the first domain tutorial hello domain this is a very simple introductory example of a domain that doesn t use the state nor any of the advanced features but does respond to every handled intent without the need for any conversation specific code articles in addition to tutorials i will from time to time publish articles related to either chatbots voice services in general or vui ad hoc alexa recognizer in particular or some of both they are located on the same website as the tutorials here is the first of these better way of building better chatbots at https rationalanimal github io vui ad hoc alexa recognizer articles betterwayofbuildingbetterchatbots note on licenses the code in this project is distributed under the mit license some data files found in the builtinslottypes directory e g colors json use values taken from wikipedia and thus they are licensed under creative commons attribution sharealike license such files have appropriate attribution and license information within them if you don t wish to deal with these licenses simply delete such file s from the builtinslottypes directory you will need to provide your own versions of these files note that only some of those files have a different license you don t need to delete the entire directory to remove them simply search in builtinslottypes directory for license and or attribution also afinn related json data files in the builtinmixins directory are a modification of the original files see attribution within the files distributed under apache 2 0 license installation shell npm install vui ad hoc alexa recognizer save summary this module provides the ability to match user s text input possibly from speech to text source to an intent with slot values as well as the ability to configure return values and state updates right within it there are two levels of functionality a lower level allowing a match of a string against a single recognizer returning an intent match with parsed out slot values and a higher level domain functionality that allows configuring an entire app returning not just a match but results and even updating the current state recognizer or lower level functionality npm module that provides vui voice user interface special ad hoc recognizer designed to parse raw text from the user and map it to intents this could be useful in many cases if you already have an alexa skill and would like to convert it to google assistant or some other service this module makes it really easy many skills can be converted in less than an hour also you can use this to quickly create a skill or an app even if you don t already have an alexa skill you will simply need to create the required intents utterances and optionally custom slot value files equivalent of which you d have to do anyway it uses the same two files intents and utterances that are used to configure alexa skills it also supports the beta alexa configuration but i don t recommend using it yet as this allows easy middleware implementation that can be placed between google assistant or other service and the alexa backend if you have custom slots and you want to use exact or soundex matches on those slots then you would also need file s listing these values supports almost all alexa features built in intents all major built in slot types most minor ones as well as extra features such as the ability to do wildcard or soundex matches transforming the values before sending them to be processed text equivalents matching such as misspellings equivalent words or phrases homophones and more etc additional configuration can be added through config file you can also use it without any backend service whatsoever simply use it with your javascript code same way you would use any other npm module it will provide complete utterance parsing and slot values mapping simply use simple branching code e g switch statement using the intent to complete processing keep in mind that many text parsing tasks can be trivially configured as intents utterances even if you have no intention of building a chat bot or a voice service for example if you wanted to parse spelled out numbers or even combinations of spelled out numbers and numerals you can easily setup to do it like this utterances text numberintent numberslot intents json intents intent numberintent slots name numberslot type amazon number now you can call the match function pass it any combination of spelled out and numerals and it will return a match on numberintent with the value of the numberslot being set to the parsed number shell node matcher js 51 thousand 2 hundred sixty 3 json name numberintent slots numberslot name numberslot value 51263 note that the number slot is coded to be able to accept both normal numbers and numbers that people spell out digit by digit or groups of digits such as zip codes or phone numbers so one two three four five will parse as 12345 etc this does mean that occasionally there may come up a way to parse the same expression in more than one way and the attempt is made to make the most likely match similarly you can parse dates etc even if that s the only thing you want to do e g you have an app where the user can type in a date simply use vui ad hoc alexa recognizer to parse it and return a date dates will match not only the exact date specification but strings such as today etc domain higher level functionality domains are a higher level of parsing than recognizers domains do use recognizer parsing but add the follow abilities define a list of recognizers to be used define application state based conditions for using a particular match e g only test against a particular recognizer if you are in a specific state allow returning of results in addition to simply matching on an intent e g if the user says how are you doing not only will it match on a greeting intent but also will return good and you allow updating of the application state right within the matching code rather than having to write the extra code to do it e g if the user says my name is bob then some portion of the state will be set to bob by the domain handling code allow nesting of the domains this is particularly useful as whole types of interactions can be encapsulated as domains and then reused it also allows breaking large apps into smaller chunks i e domains usage recognizer or lower level functionality it has two pieces of functionality run it offline to generate a recognizer json file that will be used in matching parsing the text add two lines of code to your app skill to use it to match the raw text at run time using the generated recognizer json file imagine you already have an alexa skill and you would like to port it to cortana or google assistant or even if you don t but want to create a chat bot service from scratch here are examples of files that you will have for your alexa skill or will need to create if you don t have any yet these are not complete files you can find the complete sample files in the test directory shell cat test utterances txt testintent test testintent test me testintent test please testintent test pretty please testintent test pleeeeeeease minionintent one of the minions is minionslot minionintent minionslot stateintent stateslot stateintent new england includes stateslot as one of its states blahintent here is my number blahslot use it wisely and here is another one blehslot don t squander it blahintent here is blahslot and blehslot anotherintent first is someslot and then there is someotherslot shell cat test intents json json intents intent testintent slots intent blahintent slots name blahslot type amazon number name blehslot type amazon number intent anotherintent slots name someslot type some name someotherslot type someother intent minionintent slots name minionslot type minions intent stateintent slots name stateslot type amazon us state and also here is an example of a custom slot type file shell cat test minions txt bob steve stewart beta interactionmodel js file support currently amazon is beta testing a new gui tools for editing skills interaction models this produces a single json file that includes all the information for the skill intents utterances custom slot types and some new bits prompts dialogs confirmations the support for this is being added to various parts of vui ad hoc alexa recognizer including the generator and alexifyer and is functional however until amazon finishes the beta testing this will not be finalized and may change note at this time i do not recommend using interaction model files while i have finished the support i have not tested it fully generate recognizer json file the first step is to generate a run time file recognizer json this file has all the information that is needed to parse user text later to create it run the generator e g shell node generator js intents test intents json utterances test utterances txt config test config json the example intents json utterances txt and config json files are included in the test directory this will produce a recognizer json in the current directory additionally there is beta support for the beta interaction model builder by amazon to use it specify interactionmodel parameter instead of intents and utterances shell node generator js interactionmodel test interactionmodel json config test config json note at this time i do not recommend using interaction model files while i have finished the support i have not tested it fully note that you can use the extra features in the interactionmodel json file just as you could with intents json and utterances txt e g options lists slot flags transcend specific slot types simply use alexifyutterances js see later to prepare interactionmodel json for import into alexa developer console for usage simply run the generator without any arguments shell node generator js and the generator command will list the needed arguments e g shell usage node users ilya alexaprojects vui ad hoc alexa recognizer vui ad hoc alexa recognizer generator js sourcebase basesourcedirectory that is the base for the other file references on the command line or in the config file this will be used for both build and run time source base unless overridden by other command line arguments buildtimesourcebase buildtimebasesourcedirectory that is the base for the other file references on the command line or in the config file at build time will override sourcebase value for build time directory if both are supplied runtimesourcebase runtimebasesourcedirectory that is the base for the other file references e g in the config file at run time will override sourcebase value for run time directory if both are supplied vuibase basevuidirectory that is the location of vui ad hoc alexa recognizer this will be used for both build and run time vui base unless overridden by other command line arguments defaults to node modules vui ad hoc alexa recognizer buildtimevuibase buildtimebasevuidirectory that is the location of vui ad hoc alexa recornizer executable files at build time will override vuibase value for build time directory if both are supplied runtimevuibase runtimebasevuidirectory that is the location of vui ad hoc alexa recognizer executable files at run time will override vuibase value for run time directory if both are supplied runtimeexebase runtimebaseexedirectory that is the location of javascript executable files at run time config configfilename specify configuration file name optional if not specified default values are used intents intentsfilename specify intents file name required there is no point in using this without specifying this file utterances utterancesfilename specify utterances file name optional this is optional only in the sense that it can be omitted but in practice it is required there only time you would invoke this function without an utterance file argument is if your skill generates only build in intents which would make it rather useless optimizations single stage optional single stage means no pre matches using wildcards depending on the recognizer this may be slower or faster suppressrecognizerdisplay does not send recognizer json to console note here that you should already have the intents json and utterances txt files as these files are used to configure the alexa skill also you can specify how to parse built in intents in the config json for example json builtinintents name amazon repeatintent enabled false will turn off parsing of the amazon repeatintent you can also specify additional utterances for built in intents either directly in the config file or in an external file json builtinintents name amazon stopintent enabled true extendedutterances enough already quit now extendedutterancesfilename test stopintentextendedutterances txt similarly you can affect built in slot types using config json builtinslots name amazon us first name extendedvalues prince abubu extendedvaluesfilename test usfirstnameextendedvalues txt this will add prince abubu and whatever names are found in test usfirstnameextendedvalues txt file to the list of first names recognized by the amazon us first name slot parse user text the second step is to use recognizer json file at run time to parse the user text and produce the output json that can be used to set the intent portion of the request json you only need 2 lines of code to be added to your app to use it js let recognizer require vui ad hoc alexa recognizer let parsedresult recognizer recognizer matchtext some text to match to intent if this is not working check to make sure you have generated your recognizer json first and that it s located in the same directory where your code is note that there are additional arguments to matchtext you can specify sorting order excluded intents and a different recognizer file you can also use it from the command line to test your configuration and to see the matches for an example of how to use it assuming you cloned the code from github and ran npm test to have it configured with the test samples try shell node matcher js bob which will produce json name minionintent slots minionslot name minionslot value bob or you could specify a particular recognizer file to use e g shell node matcher js bob recognizer json or try shell node matcher js here is four hundred eighty eight million three hundred fifty two thousand five hundred twelve and also six oh three five five five one two one two which will produce json name blahintent slots blahslot name blahslot value 488352512 blehslot name blehslot value 6035551212 shell node matcher js thirty five fifty one which will produce json name fourdigitintent slots fooslot name fooslot value 3551 shell node matcher js sure which will produce json name amazon yesintent slots shell node matcher js new england includes new hampshire as one of its states which will produce json name stateintent slots stateslot name stateslot value new hampshire shell node matcher js my first name is jim which will produce json name firstnameintent slots firstnameslot name firstnameslot value jim shell node matcher js december thirty first nineteen ninety nine which will produce json name dateintent slots dateslot name dateslot value 1999 12 31 shell node matcher js lets do it on tuesday which will produce json name dayofweekintent slots dayofweekslot name dayofweekslot value tuesday please note that matcher js is just a convenience and also serves as an example you will not be using it at run time most likely though some might find the use for it if you are porting an existing alexa skill to for example cortana you will probably deploy your code that uses the parser to some middleware layer like this alexa alexa middleware cortana skill aws lambda aws lambda skill where in the middleware aws lambda exposed via api gateway you will be able to see the raw user text from cortana passed as the message field in the request then call it same way matcher js does get the resulting json and update the intent in the request from none to the resulting json the backend lambda can then process it further if you are using vui ad hoc alexa recognizer for new development you have two main options use it just for nlp to map utterances to intents which you will then use for branching code use higher level domain functionality to define much of your app skill matched custom slot values there are differences between what kind of values different services may send alexa appears to respect capitalization of the custom slot values or it sends lower case versions while cortana capitalizes the first letter under some circumstances but not always and also adds a period at the end of utterances or even other punctuation signs i ve seen entering a zip code changed from 12345 to is 12345 to keep cortana as well as other misbehaving services behaving consistently the returned matches use the capitalization of the custom slot values supplied in the config file rather than what cortana will send thus if your custom slot value in the config file is petunia then petunia will be returned even if cortana will send you petunia slot flags in some cases you would like to match on a particular slot differently from the standard algorithm for example if you are trying to get the user s first name you may way to match on anything the user says so that unusual names are matched in this case you can modify your utterances file to include special flags e g firstnameintent my first name is firstnameslot include wildcard match exclude values match these flags will be used in parsing here are the different currently available flags 1 include values match exclude values match to include exclude custom slot values in the matching pattern 2 include wildcard match exclude wildcard match to include exclude a wildcard in the matching pattern 3 soundex match to use soundex for matching will match on values that sound like desired values but are not necessarily spelled the same 4 include synonyms match exclude synonyms match to include exclude synonym values in the matching pattern this is only relevant for custom slot types that actually use synonyms 5 exclude year only dates this flag is only applied to the amazon date type slot and turns off parsing of a single number as a year this is useful when there are otherwise identical utterances that may match on a number or on a date if the year only match is allowed then there is no way to differentiate between the two 6 exclude non states this flag is only applied to the amazon us state type slot and turns off parsing of us territories and d c 7 state this is a parametrized flag see below currently it only applies to the amazon airport slot type and it restricts the matches to the specified states 8 country this is a parametrized flag see below currently it only applies to the amazon airline and amazon airport slot types and it restricts the matches to the specified countries 9 continent type these are parametrized flags see below currently they only apply to the amazon airline slot type and they restrict the matches to the specified types and continents 10 sport league these are parametrized flags see below currently they only apply to the amazon sportsteam slot type and they restrict the matches to the specified sports and leagues 11 include prior names exclude prior names currently these only apply to the amazon sportsteam and amazon corporation slot type and they include exclude the prior team or corporation names in the search default is exclude prior names if you don t specify any of these then include values match and exclude wildcard match will be used as the default also if you include by mistake both include and exclude for the same flag the default value is silently going to be used if you are concerned you can look at the generated recognizer json to see how the flags were parsed also note that soundex match will automatically imply exclude values match and exclude wildcard match flags however soundex match is only available for the custom slot types at this time it would typically not be useful at this time with only these sets of flags to specify include for both or exclude for both wildcard and value matches unless you are specify soundex match if you are going to include wildcard then there is no reason to include values as well it will only slow down the parsing if you exclude both then it will be the same as if you had removed that slot from the utterance completely for this reason parsing ignores these combinations if you specify include wildcard match then only the wild card will be used if you specify both exclude values match and exclude wildcard match then only exclude wildcard match is used also note that you have to be very careful when using wildcards for example imagine this utterance instead of the above example firstnameintent firstnameslot include wildcard match exclude values match this will match on anything the user says so don t use wildcards in naked utterances ones that use nothing but slots unless you are absolutely sure that that is what you want this is why these flags exist at the utterance level rather than intent level also you should probably not specify wildcard matches on slots of many of the built in intents such as date or number this will likely not end well and it doesn t make sense for this reason parsing ignores these flags at this time on most of these slot types parsing will also ignore soundex match on non custom slot types though this may be added in the future for some built in types in the future there will be other flags added possibly specific to particular built in slot types e g i may add a flag to return only the female first names from the amazon us first name type slot or numeric values within a certain range from the amazon number type slot parameterized flags some flags can take parameters for example country continent and type flags are used to specify countries continents and types to use in the match for example if your utterances file contains these lines shell airlineintent airlineslot country canada is a canadian airline airlineintent airlineslot continent north america is a north american airline then only canadian airlines will match the first one and only north american airlines will match the second one custom list based slot types with synonyms you can create a very simple custom slot type based on a list of simple values loaded either from config json or a separate text file but you can also load values which are objects these objects must themselves contain a value field that replaces the simple field in addition these objects can also contain a field synonyms which must be an array of strings for example here is a custom slot type defined in a config json json name kitchenstuff values spoon value pan synonyms skillet this will match on spoon pan and skillet furthermore and this is the real value of the synonyms when matching on the skillet the actual returned value will be pan otherwise you could have simply added more values instead of using synonyms couple of important points you can mix strings and objects within config json if you want to specify json objects in a separate file then you must use a file with a json extension and it must contain valid json whatever you specify in a file that does not have json extension will be loaded as plain strings one per line even if it contains valid json synonyms and soundex custom slot type that has synonyms will work with soundex flag just like one without synonyms custom slot types based on regular expressions in addition to the normal custom type slots one based on a list of values you can also define a custom slot type based on a regular expression this might be useful if you are looking for some value that has a particular format for example a serial number for a product might have a specific format and you may be looking for it in user input it would be impractical to specify all the serial numbers even if you had the up to date list instead you can define a custom slot that will match the regular expression for the serial number and return it e g given config json text name customregexp customregexpstring abc123 xyz789 and otherwise standard intents and utterances files when shell node matcher js here is xyz789 if you see it will produce json name customregexpintent slots customregexpslot name customregexpslot value xyz789 you can also load the reg ex for a custom slot from a file this can be useful for sharing the same reg ex between many different recognizers to do this use customregexpfile member instead of customregexpstring text name customregexp customregexpfile customregexpfile txt options list instead of creating multiple similar utterance lines like you would do with alexa utterances you can specify variations with options lists text dateintent i want to wish to like to would like to can meet you with you dateslot is equivalent to these text dateintent i want to meet you dateslot dateintent i want to meet with you dateslot dateintent i wish to meet you dateslot dateintent i wish to meet with you dateslot dateintent i like to meet you dateslot dateintent i like to meet with you dateslot dateintent i would like to meet you dateslot dateintent i would like to meet with you dateslot dateintent i can meet you dateslot dateintent i can meet with you dateslot note that you can specify an options list that s optional by omitting one value text dateintent i want to wish to like to would like to can meet you with you dateslot will match on for instance text i want to meet tomorrow text equivalents similarly to the options list text equivalents allow variations in the text to be matched unlike the options list you don t have to manually add all of the possibilities instead simply enclose the text that should match using equivalents and this module will do it for you e g text hiintent hi what time is it is equivalent to these text hiintent hi what time is it hiintent hello what time is it hiintent hey what time is it hiintent how are you what time is it hiintent good morning what time is it hiintent good day what time is it hiintent good night what time is it hiintent hi there what time is it etc at this time the following implementation is in place it uses two data sets a very small default data set and a common misspellings data set it will match both single word substitutions and phrase substitutions this will soon be expanded to include an independent npm module containing additional values so that they can be updated independently of this module as well as the ability to add your own modules to support special domain equivalents for example you can add slang data set or medical jargon data set so an example similar to the above that substitutes both the phrases and the individual words and uses multiple data sets e g correcting for typos skipping optional words like please etc could be text hitimeintent how are you can you tell me please what is the acceptable time to come to work is equivalent to these text hitimeintent how are you can you tell me please what is the acceptable time to come to work hitimeintent how are you doing can you tell me please what is the acceptable time to come to work hitimeintent how are you can you tell me what is the acceptable time to come to work hitimeintent how are you doing can you tell me what is the acceptable time to come to work hitimeintent how are you can you tell me please what is the acceptible time to come to work hitimeintent how are you doing can you tell me please what is the acceptible time to come to work hitimeintent how are you can you tell me what is the acceptible time to come to work hitimeintent how are you doing can you tell me what is the acceptible time to come to work hitimeintent hi can you tell me please what is the acceptable time to come to work hitimeintent hello can you tell me please what is the acceptable time to come to work hitimeintent good morning can you tell me please what is the acceptable time to come to work hitimeintent good day can you tell me please what is the acceptable time to come to work hitimeintent good evening can you tell me please what is the acceptable time to come to work hitimeintent good night can you tell me please what is the acceptable time to come to work hitimeintent whats up can you tell me please what is the acceptable time to come to work hitimeintent hey can you tell me please what is the acceptable time to come to work hitimeintent hi can you tell me what is the acceptable time to come to work hitimeintent hello can you tell me what is the acceptable time to come to work hitimeintent good morning can you tell me what is the acceptable time to come to work hitimeintent good day can you tell me what is the acceptable time to come to work hitimeintent good evening can you tell me what is the acceptable time to come to work hitimeintent good night can you tell me what is the acceptable time to come to work hitimeintent whats up can you tell me what is the acceptable time to come to work hitimeintent hey can you tell me what is the acceptable time to come to work hitimeintent hi can you tell me please what is the acceptible time to come to work hitimeintent hello can you tell me please what is the acceptible time to come to work hitimeintent good morning can you tell me please what is the acceptible time to come to work hitimeintent good day can you tell me please what is the acceptible time to come to work hitimeintent good evening can you tell me please what is the acceptible time to come to work hitimeintent good night can you tell me please what is the acceptible time to come to work hitimeintent whats up can you tell me please what is the acceptible time to come to work hitimeintent hey can you tell me please what is the acceptible time to come to work hitimeintent hi can you tell me what is the acceptible time to come to work hitimeintent hello can you tell me what is the acceptible time to come to work hitimeintent good morning can you tell me what is the acceptible time to come to work hitimeintent good day can you tell me what is the acceptible time to come to work hitimeintent good evening can you tell me what is the acceptible time to come to work hitimeintent good night can you tell me what is the acceptible time to come to work hitimeintent whats up can you tell me what is the acceptible time to come to work hitimeintent hey can you tell me what is the acceptible time to come to work note that the matching algorithm is pretty efficient and does not actually try to match on these utterances but instead uses a single regular expression removing flags cleaning up the utterance file there is also a utility available to clean up utterance files for use with alexa this may be needed if you want to use a single file as your utterances file for both alexa and porting projects since the slot flags don t exist in alexa they need to be stripped from the utterance file for that use alexifyutterances js utility shell node alexifyutterances js utterances test utterances txt intents test intents json output testutterances txt noconfig result was saved to testutterances txt you can now import testutterances txt into the alexa developer console note that not only will alexifyutterances js remove flags it will also unfold options lists into multiple utterances as well as unfold any text equivalents so that you can use them with alexa this feature would be useful even if you only want to use this module to reduce the tedium of entering multiple lines into alexa and don t even intent to create your own chat bot or convert your alexa skill there is also support for the beta amazon interaction model editor you can edit the files it generates to add features supported by this module e g you can options lists or slot flags or even transcend native slot types then run it through the alexifyutterances js and the result will be importable back into alexa console shell node alexifyutterances js interactionmodel test interactionmodel json output alexifiedmodel json noconfig result was saved to alexifiedmodel json nominal support for some built in list slots many of the list slots e g amazon actor have very large value lists these are often not needed in a typical vui skill thus a compromise support is provided for them they are there and can be used but they only have a few values if you actually do have a need for them you have two options 1 you can provide your own expansion list of values in the config json file 2 you can use wildcard slot matching to match on any value the user can provide transform functions custom transform functions you can transform matched values before returning them you do this by specifying transform functions in the config file here are examples for the built in and custom slot types json customslottypes name some values apple star fruit pear orange transformsrcfilename test transformsome js builtinslots name amazon us state transformsrcfilename test transformusstate js name amazon month transformsrcfilename test transformfirstwordtitlecase js js you then put into the specified transformsrcfilename file the source code for the function to do the transformation then when you type shell node matcher js january is the best month you will get note the capitalized first letter of the month json name monthintent slots monthslot name monthslot value january see the test directory for more examples there are many reasons you may want to do this transforming states into postal code or fixing issues with speech recognition etc for example a particular service may not understand some spoken phrases well one that i ve ran into is the word deductible is understood to be the duck tibble this will never match well you could add this to your list of acceptable values this will only solve half a problem once you match it and send it to your alexa backend it will choke on this so you can add a transform function to map the duck tibble to deductible before sending it off to alexa backend when you write a custom transform function be aware that it has this signature javascript function sometransformfunction value intentname slotname slottype do something and return transformed value and that it returns a transformed value or undefined if the input value is undefined or null you can use the other arguments to change how your function may transform the matched value for example you may specify a particular transform function of a slot type but you may check within your function that the slot name equals a particular slot name and change the transformation built in transform functions in addition to being able to write your own custom transform functions you can also use some built in ones the current list is text addanglebrackets surrounds the matched value with addcurlybrackets surrounds the matched value with addparentheses surrounds the matched value with addsquarebrackets surrounds the matched value with codetostate converts passed in us state postal code to the corresponding state name does not convert territories formatasusphonenumber1 formats transcend us phone number matched value as 111 111 1111 formatasusphonenumber2 formats transcend us phone number matched value as 111 111 1111 formatasusphonenumber3 formats transcend us phone number matched value as 111 111 1111 formatasusphonenumber4 formats transcend us phone number matched value as 111 111 1111 removedigits removes all digits from the matched value removedollar removes all occurrences of s from the matched value removenondigits removes all non digits from the matched value removenonalphanumericcharacters removes all non alphanumeric characters from the matched value removenonwordcharacters same as removenonalphanumericcharacters but allows underscore removes anything that s not a number letter or underscore from the matched value removeperiod removes all occurrences of from the matched value removepoundsign removes all occurrences of s from the matched value removewhitespaces removes all continuous sequences of any white space characters from the matched value replacewhitespaceswithspace replaces all continuous sequences of any white space characters in the matched value with a single space statetocode converts passed in us state name to the corresponding postal code does not convert territories tolowercase converts the matched value to lower case touppercase converts the matched value to upper case you can also see the currently available ones in the builtintransforms directory to use them specify transformbuiltinname member instead of the transformsrcfilename json name meaningless values foo bar transformbuiltinname touppercase chaining transform functions both custom and built in transform functions can be chained simply by specifying an array instead of a single value in the configuration file for example json name meaningless values foo bar transformbuiltinname touppercase addparentheses addsquarebrackets will apply all the specified transforms mix ins sometimes you may want to do some additional processing of the result before returning it it could be almost anything for example add logging to all matches compute sentiment score and add it to the result adjust update replace matched slot values and many other possible examples mix in or add on processing allows you to do that and you can do it mostly through configuration some coding may be required built in mix ins currently there are only about seven built in mix ins here is the list with a short description for each adddefaultslots can be used to inject slot s with hard coded values changeintent can be used to change the matched intent to another one charactercount counts the characters in the matched utterance and attaches this count to the result countregexp counts the occurence of the specified reg exp and attaches this count to the result noop a simple logging mix in does not modify the result in any way simply logs it to console removeslots removes all matched slots from the result wordcount counts the words in the matched utterance and attaches this count to the result imagine that you update you config json file to add the mixins section like this text mixins bundles bundlename loggingmixin mixincode mixinbuiltinname noop arguments log true appliesto bundlename loggingmixin intentmatchregexstring what this does is defines a mix in bundle i e bundle of the code noop and argument and give it a name loggingmixin then it specifies that this bundle applies to every intent i e appliesto field has a pairing of this bundle with the intentmatchregexstring which matches on every intent as a result the noop mix in will run after every match and log the results you can modify which intents it applies to by chaining the matching reg exp the code that will actually be run is noop js located in the builtinmixins directory javascript use strict module exports function standardargs customargs eslint disable line no unused vars let intentname let utterance let priorresult if typeof standardargs undefined intentname standardargs intentname utterance standardargs utterance priorresult standardargs priorresult if typeof customargs undefined customargs log true console log noop built in mix in called if typeof standardargs undefined console log noop standardargs json stringify standardargs else console log noop standardargs undefined if typeof customargs undefined console log noop customargs json stringify customargs else console log noop customargs undefined note the signature two arguments are passed in both are objects the first one is passed to your mix in by vui ad hoc alexa recognizer automatically it contains intent name utterance that matched and the result to be returned to the user the second one contains the arguments specified in the config json log true passed to this function on your behalf by vui ad hoc alexa recognizer you might be wondering why some of these exist after all why have code that removes parts of the result produced by the matching process here is a simple for instance you are encountering issues with some intent s you don t want to delete the code nor do you want to change skill definitions you just want to temporarily disable these intents so you may remove all the slot values then change the intent name to something like removedintent which will handle any such cases and will respond to the user with i am sorry i didn t get that essentially disabling the intents or you may have decided that you want to experiment with changing the conversation flow and remap an intent to a closely related but different one custom mix ins in addition to the built in functionality you can define your own code to run for some intents imagine you have an intent on which you want to do some post processing for example you may have an intent that collects some numerical input from the user you might ask the user how many television sets do you have and you may define multiple utterances to recognize some contain just the number some might be a full sentence containing a number i have 2 television sets but the user might say something like i have a television set or i have a couple of television sets now these two last utterances do not contain an explicit number but they do implicitly specify the count you could construct several intents numberoftvsetintent onetvsetintent twotvsetsintent and then map corresponding utterances to their intents and the handler code would know about the implied counts in the 1 and 2 tv sets intents however that requires a complication of the code and potentially mixing parsing and business logic together wouldn t it be nice if we simply could somehow extract the counts 1 and 2 respectively and add them to the result as slot values so that the business logic would simply use them well that s what a custom mix in would let you do text mixins bundles bundlename tvcountmixin mixincode mixinsrcfilename injecttvcountslotvalue js arguments appliesto bundlename tvcountmixin intentmatchregexstring tvcountintent now after a successful match on tvcountintent injecttvcountslotvalue js will run and add the corresponding slot and value to the result what would this code look like something like this javascript use strict module exports function standardargs customargs eslint disable line no unused vars let intentname let utterance let priorresult if typeof standardargs undefined intentname standardargs intentname utterance standardargs utterance priorresult standardargs priorresult if typeof priorresult undefined priorresult null typeof priorresult slots undefined priorresult slots null typeof priorresult slots countslot undefined if utterance endswith a television set priorresult slots countslot name countslot value 1 else if utterance endswith a couple of television sets priorresult slots countslot name countslot value 2 note the signature just as with the built in mix ins two arguments are passed in both are objects with multiple potentially fields the first one is passed to your mix in by vui ad hoc alexa recognizer automatically it contains intent name utterance that matched and the result to be returned to the user the second one contains the arguments specified in the config json nothing in this case here this code checks to see if the result already has a countslot value if not it will attempt to determine whether it s 1 or 2 by looking at the utterance and updating the result with injected countslot applying mix ins when there is no match sometimes you may want to apply a particular mix in not when there is an intent match but when there isn t one a typical common example is replacing all non matches with a default intent e g unknownintent you can easily do this by specifying unmatched true in your config json json bundlename setintentmixin unmatched true you can even combine both matched intent and unmatched specifications json bundlename loggingmixin intentmatchregexstring unmatched true the above will execute on every match attempt whether it successfully matches or not sentiment analysis afinn you can use afinn built in mix in for sentiment analysis currently it supports afinn96 and afinn111 data sets also there is an afinn96 misspelled words data set that includes misspelled versions of the words in afinn96 more data sets are on the way some of them will be alternative base sets afinn165 others are additional data sets that can be added to the base set such as scored misspelled words or emoji data set this mix in takes a single argument and returns a single score for example given this config snippet json bundlename afinnsentimentbundle mixincode mixinbuiltinname afinn arguments ratingdatasetfiles afinn96 json afinn96misspelled json might attach this sentiment afinn score to the result json name afinnintent slots sentiment afinn score 3 precompiled sentiment data sets when you specify the data sets for sentiment analysis you should be aware that it may have some performance implications for your convenience you can specify the data set s individually as many as you d like as part of the ratingdatasetfiles array however this would then mean that the sentiment analysis code would have to do extra work at run time namely merge the data sets sort remove duplicates etc you can eliminate these steps if you use precompiled data sets these include one or more data sets already merged sorted etc you can specify only one such file since the intent is to create a single precompiled file that does not need to be processed further so instead of the ratingdatasetfiles array please use precomputeddataset field json bundlename afinnsentimentbundle mixincode mixinbuiltinname afinn arguments precomputeddataset afinn96withmisspelledwords precompiled json currently there is only one precomputed data set afinn96withmisspelledwords precompiled json but you can make a set yourself it you need it making your own precompiled sets if you want to create a custom precompiled set you can use provided afinndatasetcombiner js utility to do so you can run it without arguments to get the usage info but it s really simple shell node afinndatasetcombined js i input file1 input file2 o output file e g shell node afinndatasetcombiner js i builtinmixins afinn96 json builtinmixins afinn96misspelled json builtinmixins afinnemoticon 8 json o builtinmixins afinn96withmisspelledwordsandemoticons precompiled json dollar values if a service like cortana passes a dollar value e g 1000 it will be mapped to 1000 dollars as would be expected by an alexa skill note that if you want to test it with matcher js you have to either escape the character or enclose the whole string in rather than to avoid command line handling of shell node matcher js the first price is 1000 and the second price is 525000 which will produce json name priceintent slots priceoneslot name priceoneslot value 1000 pricetwoslot name pricetwoslot value 525000 note that this is identical to shell node matcher js the first price is 1000 dollars and the second price is 525000 dollars which will produce json name priceintent slots priceoneslot name priceoneslot value 1000 pricetwoslot name pricetwoslot value 525000 trailing punctuation trailing periods exclamation signs and question marks are ignored during parsing commas in numeric slots any commas within numeric input e g 20 000 are ignored during parsing optimizations note now that multi stage matching has been enabled the performance should be a lot better for many previously slow scenarios however you can still make it faster by arranging for the parsing order and excluding some intents from parsing in some interesting cases multi stage matching is actually slower sometimes by a large factor than single stage matching to accommodate such cases you can generate recognizer files without multi stage matching to do so simply add optimizations single stage to the generator command line shell node generator js intents test intents json utterances test utterances txt config test config json optimizations single stage intent parsing order you can pass to the matching call the name s of the intents that you want to try to match first currently it only supports custom intents but that s not a problem since built in intents are very fast then this call will likely execute much faster since most of the time you know what the next likely answers i e utterances are going to be you can provide them to the matching call for example the following call will try to match countryintent first javascript let result recognizer recognizer matchtext have you been to france countryintent intent exclusion in addition to the intent parsing order you can also pass a list of intents to be excluded from the matching process this is useful if you have intents that have very large sets of custom values and you are pretty sure you don t want to parse then in a particular place in your skill i e if you are in a flow that does not include some intents then you should be able to exclude them from parsing the following call will try to match countryintent first and will not even try to match firstnameintent javascript let result recognizer recognizer matchtext have you been to france countryintent firstnameintent alternate recognizer files in addition to the intent parsing order and intent exclusion lists you can pass an alternate recognizer file to use in the matching call note that the normal behavior is to assume a recognizer named recognizer json explicitly specifying the recognizer simply overrides the default javascript let result recognizer recognizer matchtext have you been to france countryintent firstnameintent alternativerecognizer this can be used both for performance optimization as well as for breaking up large skills apps into smaller chucks typically by functionality though now that domain functionality has been added you should probably use domains for modularizing your app unless there is a reason not to let s say you have a large skill that has several logical flows in it for example you can have a travel skill that lets you book a hotel and or a car check for special events reserve a table at a restaurant etc each of these may have its own logic its flow so you may define a separate set of intents and utterances and custom slot values for each then use a session variable to keep track of the current flow for each flow you can generate its own recognizer file then at run time use the recognizer for the flow that the user is in this has multiple advantages performance will increase the skill app can be developed separately by different teams each having its own skill app portion that they are working on and they will update only the recognizer for their portion options list using options lists instead of multiple similar utterances may improve performance testing with a simple one slot date example and an utterance that unfolds to about 3800 utterances reduces the time from 330 ms to 74 ms on a desktop computer considering that if you run it on aws lambda which is much slower than a typical higher end desktop computer you may be shaving off seconds off of your time which for voice interactions is quite important soundex support soundex support has been added at the utterance level for custom slot types you can now specify a soundex match flag for a custom slot type and soundex match will be used this allows matching of expressions that are not exact matches but approximate matches shell cat utterances txt where utterances txt includes this line minionintent another minion is minionslot soundex match then shell node matcher js another minion is steward will return json name minionintent slots minionslot name minionslot value stewart note that stewart matched on steward domain higher level functionality domainrunner js more documentation for domains is coming meanwhile you can test your domain files using domainrunner js utility to see the usage simply run it shell node domainrunner js will return text usage node domainrunner js domain path to a domain state path to state json outputstate true false builtinaccessor basic readonly to exit type exit if you specify the path to the domain file and to the state json object then you will see a prompt once you run it shell please type user text if you do you will see the results being returned the domainrunner js will continue running and accepting user input and possible updating the state object until you kill the process or type exit domain configuration you can create a domain rather easily for the simplest setup all you need is an existing recognizer json file and you are in business imagine you already have a recognizer file lets call it myrecognizer json that s located in your current directory you would also need a state json file for now you can simply create a file named mystate json in the current directory that contains an empty object then you can define a domain json like this json description simplest domain recognizers key mine path myrecognizer json states matchcriteria default matchspecs recognizer mine you could now test it with domain runner shell node domainrunner js you will see a prompt assuming you have a corresponding dateintent defined and you type in tomorrow you will see something like this shell please type user text tomorrow your text was tomorrow domain response match name dateintent slots dateslot name dateslot value 2017 08 27 please type user text returning hard coded result this is nice it works and it shows how easy it is to set up a domain howeveer there is really nothing new here yet how about specifying actual results we can do it quite easily by adding just a few more values to the domain file json description simplest domain recognizers key mine path myrecognizer json states matchcriteria default matchspecs recognizer mine responder result directvalue text thank you notice that we ve added the result field this specifies what the returned results will be in this case they will consist of just one object that has a single field named text with the value thank you run the domain runner again and see the results shell please type user text tomorrow your text was tomorrow domain response match name dateintent slots dateslot name dateslot value 2017 08 27 result text thank you you can see that there is now a result field in the domain s response that has the value we ve specified returning one of several hard coded results at random if you look closely at the domain file you ll see that we are specifying just one value you can specify many values with one of them being chosen at random like this json description simplest domain recognizers key mine path myrecognizer json states matchcriteria default matchspecs recognizer mine responder result directvalues pickmethod random values text thanks a bunch text danke text thank you if you run domain runner again you will see one of the three messages displaying at random every time you type tomorrow shell please type user text tomorrow your text was tomorrow domain response match name dateintent slots dateslot name dateslot value 2017 08 27 result text danke returning one of several hard coded results at random without repeating this is better but still very simple what if you wanted the replied to not repeat at least until all of them were used up you can do that too by simply changing the value of the pickmethod field from random to randomdonotrepeat and adding a repeatselector field json description simplest domain recognizers key mine path myrecognizer json states matchcriteria default matchspecs recognizer mine responder result directvalues pickmethod randomdonotrepeat repeatselector squirrelledawayalreadyused values text thanks a bunch text danke text thank you now if you run domain runner again you will see that the values don t repeat at least until you use up all 3 then the cycle starts again how is this done quite simple to see it run the domain runner with the outputstate true option and you should see something like this shell please type user text tomorrow your text was tomorrow domain response match name dateintent slots dateslot name dateslot value 2017 08 27 result text thank you state object squirrelledawayalreadyused text thank you please type user text notice that the state object which was empty to begin with mystate json now has a field squirrelledawayalreadyused it s an array and it contains the values of the outputs that have already been used every time a particular output is provided this field is updated to include it so it will not be used again until all the values have been used up the squirrelledawayalreadyused field name comes from the domain configuration you can specify anything you want as the name the reason by the way for specify the field name is to avoid collisions and overwriting some portions of the state that you didn t mean to overwrite this way you can pick a name for the field to keep track of the used values combining multiple results you can specify multiple results to be returned when you do you can specify how to combine them to do so specify responders plural rather than responder field for example json description simplest domain recognizers key mine path myrecognizer json states matchcriteria default matchspecs recognizer mine responders result combinerule setto directvalues pickmethod randomdonotrepeat repeatselector squirrelledawayalreadyused values text thanks a bunch text danke text thank you result combinerule mergeappend directvalues pickmethod randomdonotrepeat repeatselector squirrelledawayalreadyused2 values text second text 1 ssml speak thanks a bunch speak videos http someotherurl com text second text 2 ssml speak thanks a bunch with a card speak videos http somethirdurl com card title card title here we have two responders the first one is just as before except that it has a combinerule field with setto value this tells the code to reset the result to the output of this responder the second one has a combine rule set to mergeappend this will attempt to merge and or append the second result with the first one text please type user text tomorrow your text was tomorrow domain response match name dateintent slots dateslot name dateslot value 2017 08 27 result text danke second text 2 ssml speak thanks a bunch with a card speak videos http somethirdurl com card title card title state object squirrelledawayalreadyused text danke squirrelledawayalreadyused2 text second text 2 ssml speak thanks a bunch with a card speak videos http somethirdurl com card title card title as you can see not only have the two results been merged different fields from both have been added to the final result but also where there are the same fields present in both they have been appended see the text fields when combining different outputs the following happens text fields are concatenated with a space in between ssml fields contents are concatenated and surrounded with a single set of speak tags e g speak one speak concatenated with speak two speak will result in speak one two speak videos arrays are combined separate card elements are added to an array merge and replace results in addition to merging appending you can also use mergereplace method of combining when you do that non conflicting fields from the results will be added to the final output however when the fields are conflicting e g two text fields instead of being appended they will be replaced by the later result so if you were to change the previous domain file to this json description simplest domain recognizers key mine path myrecognizer json states matchcriteria default matchspecs recognizer mine responders result combinerule setto directvalues pickmethod randomdonotrepeat repeatselector squirrelledawayalreadyused values text thanks a bunch text danke text thank you result combinerule mergereplace directvalues pickmethod randomdonotrepeat repeatselector squirrelledawayalreadyused2 values text second text 1 ssml speak thanks a bunch speak videos http someotherurl com text second text 2 ssml speak thanks a bunch with a card speak videos http somethirdurl com card title card title and re run domain runner you would get this output text please type user text tomorrow your text was tomorrow domain response match name dateintent slots dateslot name dateslot value 2017 08 27 result text second text 2 ssml speak thanks a bunch with a card speak videos http somethirdurl com card title card title state object squirrelledawayalreadyused text thank you squirrelledawayalreadyused2 text second text 2 ssml speak thanks a bunch with a card speak videos http somethirdurl com card title card title note that the text value comes from the second responder only setto and ignore combine rules you ve already seen the setto combine rule it s used in the first responder it s designed not to merge two results but rather to set the result to the second one if its combine rule is setto ignore combine rule is the opposite the result if any produced by the responder with this combine rule is ignored completely this exists to support the responders that are there primarily to update the state rather than produce the output sometimes you may also want to use it to temporarily disable the output from a responder without actually deleting it custom responders you can create completely custom responders by providing function body source right within the domain file for example json result functionsource return text thanks when this function is created at run time it will be created with these 3 arguments match stateaccessor selectorarray and the corresponding values will be passed in if you need to go ahead and use them in your function body custom responders modules while it s all well and good to add a function body right within the domain json file it s a little problematic the source code must be escape to be part of the json file if you make a mistake in escaping it the function will not work additionally you can t unit test it by itself so for longer more complicated functions it s better to put them into a separate module and require the module you can do that using functionmodule field json result combinerule setto functionmodule test greetingdomain customresponderfunction js where the contents of test greetingdomain customresponderfunction js are javascript use strict let responderfunction function match stateaccessor selectorarray let intent match name if intent greetingintent stateaccessor mergereplacestate selectorarray customfunctionmodulewasrun true return text hi from the custom function module module exports responderfunction now if you re run the domain runner you will get this text please type user text hi there your text was hi there domain response match name greetingintent slots result text hi from the custom function module hello ssml speak hello speak state object greetingdomain customfunctionmodulewasrun true greetingalreadyused text hello ssml speak hello speak note that the function correctly ran the result includes the text from it combined with the text from other responders state object was correctly adjusted as well setting state object directly so now you have seen how simply returning a particular value can update the state randomdonotrepeat pick method but that is part of the default built in behavior you can also directly update the state to do that simply add an updatestate field to your responder e g json result combinerule ignore directvalue text ignore this text updatestate updaterule mergereplace updateselector someuselessvalue some other useless value directvalue update result of replacemerge updaterule first notice that in the above example while we do return a result field the combinerule is ignore so this responder will not contribute to the returned result the updatestate field contains a couple of fields that should look somewhat familiar now updaterule is similar to the combinerule of the result field but applies to how the state object is to be updated by this responder in this example we are specifying that the value of directvalue field is to be merged replacing existing values where conflicting with the current state object you can also use setto here to replace without merging but what about the updateselector well here is where you specify which part of the state object is to be updated in this example if we run domain runner again with the option to show state we ll see this text state object squirrelledawayalreadyused text thanks a bunch squirrelledawayalreadyused2 text second text 2 ssml speak thanks a bunch with a card speak videos http somethirdurl com card title card title someuselessvalue some other useless value update result of replacemerge updaterule so the other updates to the state object still take place and then the direct update of the state object happens right where the selector specified it note currently only directvalue field is supported but other ways including user custom functions will be added shortly important note on selectors selectors are used in multiple places in domains this is the first use that you ve seen but the concept is the same elsewhere also in this particular example the selector is used logically i e the way you d expect that s because behind the scenes domainrunner js is using a built in accessor collection of functions that actually access the state object there are two built in accessors at this time the other one is a read only accessor so if you used that one none of the changes to the state object would take place more built in accessors will be coming in the future but you can also write your own if you do that you can use the selector in different ways for instance if your state object is saved in a nosql database the selector may be a key that s used to look up portions of the state it s up to you how you implement it this of the state as not being manipulated directly rather manipulated through accessors so you could create a react compatible accessor that will treat the state as read only but will issue updates to the state via a separate mechanism the domain code doesn t care and the accessors are designed to be pluggable built in accessors there are two built in accessors basic and readonly they work the same way but the read only accessor does not update the state note that you can also select a different built in accessor when using domainrunner js by specifying an extra argument shell builtinaccessor basic readonly on the command line non default single value state match criteria so far you ve only seen default match criteria meaning you ve only seen a domain using a single recognizer without any regard to anything else here is the relevant snippet from the domain file text states matchcriteria default however you can specify that a particular recognizer should only be used under certain conditions for example if the state object contains field startedenrollment and its value is status yes then use a different recognizer if you update your domain file to include that e g text recognizers key mine path myrecognizer json key greeting path test greetingdomain greetingrecognizer json states matchcriteria type state selector startedenrollment match true value status yes matchspecs recognizer greeting responder result directvalue text hello to you too and re run domain runner without changing the state you will get text please type user text hi there your text was hi there domain response undefined state object but if you now edit the state object to include startedenrollment status yes then you ll get text please type user text hi there your text was hi there domain response match name greetingintent slots result text hello to you too state object startedenrollment status yes so this showed how you can specify which recognizer s to use based on some criteria note that default criteria will always match non default single value negative match criteria you can also set the match to false indicating that this match criteria will succeed if the state does not match the provided value text states matchcriteria type state selector startedenrollment match false value status yes matchspecs recognizer greeting responder result directvalue text hello to you too this should be used carefully the non matching criteria may should be designed to avoid frequent always matches an example of proper match might be a global setting indicating whether the user has completed some step and if not then proceeding down that user interaction path non default multi valued match criteria in addition to the single value match criteria you can also specify an array of matching values the only difference is that instead of text match true value status yes you would specify something like text match true values status yes status tbd this will match if the status is either yes or tbd non default multi valued negative match criteria just as for single values you can set match to be false to indicate that the match will succeed if the state does not match any of the provided values testing for null in the match criteria sometimes you just need to test whether a value is a null or not here is a simple and concise way of checking whether a value is null text matchcriteria type state selector some selector isnull true similarly here is how you would check if the value is not null text matchcriteria type state selector some selector isnull false testing for undefined in the match criteria testing for undefined is another common test that you may want to perform this would typically be done when you want to test whether some state value has been set the specification is similar to testing for null text matchcriteria type state selector some selector isundefined true similarly here is how you would check if the value is not undefined text matchcriteria type state selector some selector isundefined false testing for a numeric value being greater than a reference value in the match criteria testing for a numeric value being greater than a reference value is also fairly common here is how you could do it text matchcriteria type state selector some threshold greaterthan 5 note that unlike isnull or isundefined there is no way to specify false i e negative condition this can instead be done using lessthanorequal test lessthanorequal is not yet implemented testing for a numeric value being greater than or equal to a reference value in the match criteria similar to testing for a numeric value being greater than a reference value you can test for a numberic value being greater than or equal to a reference value text matchcriteria type state selector some threshold greaterthanorequal 4 similar to greaterthan there is no way to specify false i e negative condition this can instead be done using lessthan lessthan is not yet implemented testing for a numeric value being less than a reference value in the match criteria you can also test for a numeric value being less than a reference value text matchcriteria type state selector some threshold lessthan 10 testing for a numeric value being less than or equal to a reference value in the match criteria to test for a numeric value being less than or equal to a reference value text matchcriteria type state selector some threshold lessthanorequal 10 testing for a string value consisting only of alpha characters in the match criteria to test for a string value consisting entirely of alpha characters a through z case insensitive text matchcriteria type state selector some value isalpha true note that specifying isalpha false does not test for whether the entire string is composed of non alpha characters instead it s testing for whether any of the characters are not alpha text matchcriteria type state selector some value isalpha false testing for a string value consisting only of numeric characters in the match criteria to test for a string value consisting entirely of numeric characters text matchcriteria type state selector some value isnumeric true again specifying isnumeric false tests for whether any of the characters are not alpha text matchcriteria type state selector some value isnumeric false testing for a string value consisting only of alpha numeric characters in the match criteria to test for a string value consisting entirely of alpha numeric characters text matchcriteria type state selector some value isalphanumeric true again specifying isalphanumeric false tests for whether any of the characters are not alpha numeric text matchcriteria type state selector some value isalphanumeric false testing for a string value consisting only of white space characters in the match criteria to test for a string value consisting entirely of white space characters text matchcriteria type state selector some value iswhitespace true as before specifying iswhitespace false tests for whether any of the characters are not white space text matchcriteria type state selector some value iswhitespace false testing for a string value consisting only of upper case in the match criteria to test for a string value consisting entirely of upper case characters text matchcriteria type state selector some value isuppercase true again specifying isuppercase false tests for whether any of the characters are not upper case text matchcriteria type state selector some value isuppercase false testing for a string value consisting only of lower case in the match criteria to test for a string value consisting entirely of lower case characters text matchcriteria type state selector some value islowercase true same as with other isxxx specifying islowercase false tests for whether any of the characters are not lower case text matchcriteria type state selector some value islowercase false testing for a string value containing a substring in the match criteria to test for a string value containing a substring text matchcriteria type state selector some value containssubstring true substring somesubstring specifying containssubstring false tests to ensure that the specified substring is not contained text matchcriteria type state selector some value containssubstring false substring somesubstring match criteria within responders so far you have seen match criteria used only to specify whether a particular recognizer is to be used this gives a lot of flexibility however if limited to just this case this arrangement makes conditional use of responders difficult that s why you can also use the match criteria within a responder as well json matchcriteria type state selector conditionalrespondervalue isundefined false matchspecs recognizer conditionalrespondertest responders matchcriteria type state selector conditionalrespondervalue useresponder1 isundefined false result directvalue text conditional responder 1 matchcriteria type state selector conditionalrespondervalue useresponder3 isundefined false result combinerule mergeappend directvalue text conditional responder 3 matchcriteria type state selector conditionalrespondervalue useresponder2 isundefined false result combinerule mergeappend directvalue text conditional responder 2 in the example above match criteria is used both ways to restrict when to use the recognizer as well as to restrict when to apply a particular responder here if the state has a defined conditionalrespondervalue then the recognizer will be applied however each of the 3 responders also have their own match criteria if a particular responder s match criteria is not met then that responder will not contribute to the response for example if the state object is json conditionalrespondervalue useresponder1 true useresponder2 true and we match on the utterance only 2 out of 3 responders will contribute to the result json match name conditionalrespondertestingintent slots result text conditional responder 1 conditional responder 2 using conditional responders conditional responders are a very powerful tool you can create a different output depending on the current state for example imagine if your user says something like i would like to get two tickets for today s 7 30 showing of deadpool 27 if your user is already authenticated and the credit card info is on file you can respond with your etickets have been ordered please see your email however if the user has not yet authenticated you can respond with please log in first so we can help you note that you could still do this without conditional responders but you would have to create multiple recognizers to accomplish that and it would unnecessarily complicate the code slot test match criteria if you are using match criteria with responders then you can also test slot values this does not make sense when using match criteria as a recognizer use condition that s because you don t have the intent and thus slots parsed yet when you are deciding whether to use the recognizer the recognizer is the one doing the parsing but with responders you do have parsed slot values what follows is a description of the various slot tests available with match criteria non default single value slot match criteria use this when you want to compare the slot value to a single predefined value e g json matchcriteria default matchspecs recognizer conditionalrespondertest responders matchcriteria type slot slot numberslot value 5 match true result directvalue text you said 5 in this case if the slot value parsed out of the utterance for the numberslot equals 5 then the domain will add you said 5 to the result s text else nothing will be added subdomains if domains simply added results and state awareness and manipulation they would already be a pretty big improvement over just using recognizers directly however domains can use other domains this is particularly powerful because it allows one to modularize an application breaking it up into individual reusable modules this reduces complexity allows code reuse and has other benefits additionally separate domains can be defined by other people possibly outside your group your company organization etc as long as they are written correctly they can be used by other people teams using domain withing domain aka sub domains is easy and similar to using recognizers first you must add the sub domain to the list of domains text description simplest domain recognizers key mine path myrecognizer json domains key greeting path test greetingdomain greetingdomain json trusted read true write true selector greetingdomain states a few things to note here just as with including recognizers you must provide a key with a value this is an arbitrary value determined by you it s needed so that this domain can be referenced elsewhere by this key second there is a trusted field this field specifies whether this subdomain is trusted to read and or write to the parent s i e this domain s state in this exmaple we have a fully trusted sub domain such a subdomain has an optional selector field its value if provided is used to select a portion of the state object that this subdomain will be able to see thus in this example the selector is greetingdomain so any modifications will be done to the state object greetingdomain field as if it s the entire state if you have closely cooperating modules that need to know each other s state then the selector field would probably either be absent or be the same for these modules of course the super domain the one that s using a subdomain can alway see the subdomains portion of the state once you ve added the sub domain to the list of domains you can now use it this part is actually simpler than the set up for recogizers that s because the sub domain already describes the results and the state update thus all you have to do is specify when to use it you simply add it to the list of responders for the specific match criteria and you are all set text states matchcriteria default matchspecs domain greeting recognizer mine now whent you re run the domain runner you ll get something similar to this depending on which greeting get randomly chosen text please type user text hi there your text was hi there domain response match name greetingintent slots result text hello ssml speak hello speak state object greetingdomain greetingalreadyused text hello ssml speak hello speak you can include sub domains within sub domains the only restriction is that you can t do circular subdomains don t include b as a side domain of a if a is already a subdomain of b trusted vs non trusted sub domains in the initial example above you ve seen a fully trusted domain what if you are using a third party domain that you aren t sure about furthermore such a sub domain may not even need to have access to your state so why provide it you can simply accommodate this by specifying it as a non trusted sub domain text description simplest domain recognizers key mine path myrecognizer json domains key greeting path test greetingdomain greetingdomain json trusted read false write false states now even if there is no selector field in the trusted field the sub domain will be separated from the main part of the state and even custom responders won t be able to see outside of that sub portion if you were to re run the domain runner you would get text please type user text hi there your text was hi there newresult text hi from the custom function module newresult text nice to meet you ssml speak nice to meet you speak domain response match name greetingintent slots result text hi from the custom function module nice to meet you ssml speak nice to meet you speak state object untrusted customfunctionmodulewasrun true greetingalreadyused text nice to meet you ssml speak nice to meet you speak note that the state of the sub domain is now relegated to the untrusted subfield and only the contents of that subfield will be visible to anything in that sub domain sometimes you may want to be able to specify the name of this sand boxing field you can do it like this text description simplest domain recognizers key mine path myrecognizer json domains key greeting path test greetingdomain greetingdomain json trusted read false write false selector greetingdomain sandboxkeys separateddomain states now if you re run the domain runner yet again you will see text your text was hi there newresult text hi from the custom function module newresult text hello ssml speak hello speak domain response match name greetingintent slots result text hi from the custom function module hello ssml speak hello speak state object separateddomain greetingdomain customfunctionmodulewasrun true greetingalreadyused text hello ssml speak hello speak note that the entire sub domain s portion of the state has now been placed into the field specified by the sandboxkeys field and also that the selector field is still used if you specified it missing trusted specification you can also skip specifying trusted information completely when you do it s the same as specifying an untrusted domain with the sandboxkeys being set to untrusted selector value will be used if present for example json key greeting path test greetingdomain greetingdomain json selector greetingdomain is equivalent to json key greeting path test greetingdomain greetingdomain json trusted read false write false selector greetingdomain sandboxkeys untrusted hybrid trusted sub domains currently you can only specify completely trusted read and write or completely untrusted sub domains i am in the process of adding partially trusted sub domains e g can read but not write or can write but not read the parent domain s state this will function differently from the obvious expectation for example the domain that is trusted to read the value will still be able to write to it but it won t write to the parent s state rather to its own portion and vice versa the domain that is write trusted will be able to write to the parent s state but will be able to read only the values it has previously written non alexa support you don t have to generate just the alexa intents slot types this module can now generate other platform intents though the only one supported at this time is transcend which is what the other vui xxx projects are using as their native built in type support for microsoft xxx and possibly others will be added if when there is a need demand for it in order to specify an output type simply configure it in your config file json platform output transcend by default if you don t specify anything the output will be amazon for compatibility reasons transcend features supported currently there are two transcend built in slot type supported transcend us phone number and transcend us president transcend us phone number will match on seven digit number expression that s structured the way people tend to pronounce phone numbers so either listing it out as numbers or using one two digit word equivalents e g fifteen or thirty five additionally this slot type will accept parenthesis around the area code dash between exchange and user number or dots instead e g text 123 456 7890 123 456 7890 123 456 7890 one twenty three four fifty six seventy eight ninety alexa features supported currently you can parse 1 all alexa built in intents 2 utterances without slots 3 utterances with custom slots 4 utterances with all the numbers date time duration built in slot types amazon number amazon four digit number amazon date amazon time amazon duration 5 utterances with these list built in slot types amazon us state amazon us first name amazon airline all us canadian mexican airlines amazon airport usa canada mexico australia new zealand uk germany italy and austria airports amazon color amazon corporation amazon country amazon dayofweek amazon month amazon room amazon socialmediaplatform amazon sportsteam includes nfl cfl nba mlb nhl and mls teams 6 utterances with these list built in slot types with nominal support see nominal support section amazon actor amazon administrativearea amazon artist amazon athlete amazon author amazon book amazon bookseries amazon broadcastchannel amazon civicstructure amazon comic amazon dessert amazon director amazon educationalorganization amazon festival amazon fictionalcharacter amazon foodestablishment amazon game amazon landform amazon landmarksorhistoricalbuildings amazon localbusiness amazon localbusinesstype amazon medicalorganization amazon movie amazon movieseries amazon movietheater amazon musicalbum amazon musicgroup amazon musician amazon musicrecording amazon musicvenue amazon musicvideo amazon organization amazon person amazon professional amazon residence amazon screeningevent amazon service amazon softwareapplication amazon softwaregame amazon sportsevent amazon tvepisode amazon tvseason amazon tvseries amazon videogame more amazon built in slot types are coming shortly | ai |
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chartership2 | readme chartership 2 is a database and tracker for engineering competencies unlike other tools it is not specific to any one field of engineering or even to engineering altogether use it how you want | devise ruby-on-rails bootstrap-4 | server |
iot-android | android gson master school xt300 motor xt300 android 1 http www phodal com | server |
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DevOps-cloud-engineer | devops cloud engineer for all devops cloud engineering projects | cloud |
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memery | memery use human language to search your image folders what is memery meme about having too many memes images e2goemyweaakclz jpeg the problem you have a huge folder of images memes screenshots datasets product photos inspo albums anything you know that somewhere in that folder is the exact image you want but you can t remember the filename or what day you saved it there s nothing you can do but scroll through the folder skimming hundreds of thumbnails hoping you don t accidentally miss it and that you ll recognize it when you do see it humans do this amazingly well but even with computers local image search is still a manual effort you re still sorting through folders of images like an archivist of old now there s memery the memery package provides natural language search over local images you can use it to search for things like a line drawing of a woman facing to the left and get reasonably good results you can do this over thousands of images it s not optimized for performance yet but search times scale well under o n you can view the images in a browser gui or pipe them through command line tools you can use memery or its modules in jupyter notebooks including gui functions under the hood memery makes use of clip the contrastive language image pretraining transformer https github com openai clip released by openai in 2021 clip trains a vision transformer and a language transformer to find the same latent space for images and their captions this makes it perfect for the purpose of natural language image search clip is a giant achievement and memery stands on its shoulders outline usage install locally use gui use cli use the library development contributing who works on this project quickstart windows run the windows install bat file run the windows run bat file installation with python 3 9 or greater from github recommended pip install git https github com deepfates memery git or git clone https github com deepfates memery git cd memery poetry install from pypi pip install memery pip install git https github com openai clip git currently memery defaults to gpu installation this will probably be switched in a future version for now if you want to run cpu only run the following command after installing memery pip install torch 1 7 1 cpu torchvision 0 8 2 cpu torchaudio 0 7 2 f https download pytorch org whl torch stable html someday memery will be packaged in an easy to use format but since this is a python project it is hard to predict when that day will be if you want to help develop memery you ll need to clone the repo see below usage what s your use case i have images and want to search them with a gui app use the browser gui i have a program workflow and want to use image search as one part of it use as a python module use from command line or shell scripts i want to improve on and or contribute to memery development start by cloning the repo use gui currently memery has a rough browser based gui to launch it run the following in a command line memery serve or set up a desktop shortcut that points to the above command optionally you can pass a directory to open on startup like so memery serve home user pictures memes relative directories will also work cd pictures memery serve memes the default directory passed will be images which is memery s example meme directory memery will open in a browser window the interface is pretty straightforward but it has some quirks screenshot of memery gui displaying wholesome memes images streamlit screenshot png the sidebar on the left controls the location and query for the search the directory box requires a full directory path unfortunately streamlit does not yet have a folder picker component the path is relative to your current working directory when you run memery serve the search will run once you enter a text or image query if you enter both text and image queries memery will search for the combination beneath these widgets is the output area for temporary messages displayed with each search mostly this can be ignored the right hand panel displays the images and associated options major errors will appear here as giant stack traces sometimes changing variables in the other widgets will fix these errors live if you get a large error here it s helpful to take a screenshot and share it with us in github issues use cli the memery command line matches the core functionality of memery use the recall command to search for images passing the path and optionally passing the n flag to control how many images are returned default 10 use the t flag to pass a text query and the i flag to pass an image query or both memery recall path to image folder t text query i path to image jpg n 20 you can encode and index all the images with the build command optionally specifying the number of workers to build the dataset with default 0 memery build path to image folder workers 4 clear out the encodings and index using the purge command memery purge path to image folder use as a library the core functionality of memery is wrapped into the memery class from memery core import memery memery memery the function currently called query flow accepts a folder name and a query and returns a ranked list of image files you can query with text or a filepath to an image or both ranked memery query flow images dad joke print ranked 5 converting query searching 82 images done in 4 014755964279175 seconds images memes wholesome meme 68 jpg images memes wholesome meme 74 jpg images memes wholesome meme 88 jpg images memes wholesome meme 78 jpg images memes wholesome meme 23 jpg here s the first result from that list images memes wholesome meme 68 jpg so that s how to use memery let s look at how you can help make it better development pull the repo clone this repository from github git clone https github com deepfates memery git install dependencies and memery enter the memery folder and install requirements cd memery poetry install and finally install your local editable copy of memery with pip install e contributing memery is open source and you can contribute see contributing md contributing md for guidelines on how you can help who works on this project memery was first written by max anton brewer aka deepfates in the summer of 2021 some commits are listed from robotface io but that was just me using the wrong account when i first started many ui and back end improvements were added by wkrettek in 2022 i wrote this to solve my own needs and learn notebook based development i hope it helps other people too if you can help me make it better please do i welcome any contribution guidance or criticism the ideal way to get support is to open an issue on github however the fastest way to get a response from me is probably to direct message me on twitter twitter com deepfates | natural-language computer-vision local-search image-search python | ai |
blockchain-notes | blockchain notes as my notes about blockchain and different blockchain implementations e g litecoin ethereum bitcoin grew too large on gist found github more productive contents blockchain blockchain md consensus algorithm protocol consensus algorithm md cryptocurrency cryptocurrency md litecoin blockchain litecoin md bitcoin blockchain bitcoin md wallet wallet md segregated witness segwit segwit md lightning network lightning network md initial coin offering ico ico md coin mining mining md soft and hard forks soft hard forks md bitcoin cash bcc hard fork bcc md bitcoin gold btg hard fork btg md proof of stake pos proof of stake md proof of work pow proof of work md ethereum blockchain ethereum md solidity ethereum s programming language ethereum solidity md iota iota md blockchain magazines magazines md alternative blockchain application platforms to ethereum alternative blockchain platforms md cryptocurrency exchanges exchanges md further reading further reading md | blockchain litecoin ethereum bitcoin iota cryptocurrency | blockchain |
hotel | hotel at a glance individual stage 3 https github com ada developers academy pedagogy blob master classroom rule of three md stage 3 project due before class date here introduction our company has been contracted to build a booking system for a small hotel this system will be used by employees who manage bookings and booking data and will not be available to the general public learning goals reinforce and practice all of the ruby and programming concepts we ve covered in class so far design a system using object oriented principles create and instantiate classes with attributes create class and instance methods within our classes write pseudocode and create tests to drive the creation of our code objective for our hotel booking system we should make a module full of business logic that tracks which rooms are reserved and at what dates this should be organized into appropriate classes and methods we should use tests to verify that this system works as intended we should not build a cli for this project project expectations this project is both a culmination of our intro to ruby unit and our first stage 3 project this means the requirements are more open ended and ambiguous than previous projects you have worked on this is intentional you will be expected to make decisions on how to structure your classes and methods ask questions when you need clarification understand that the way you implement something may be different than the way your neighbor implements it it is expected that you will not be able to complete all requirements the three waves are organized by difficulty and relevance to the learning goals and should be tackled in order hints we have included some optional design scaffolding https github com adagold hotel blob design scaffolding design scaffolding notes md for this project to help you get started if you don t know where to start or to provide inspiration if you re a little stuck any student should feel free to use this scaffolding in whatever way is most helpful to them however we recommend that you spend at least 1 full day thinking about design before reaching for this scaffolding to get practice thinking about this type of problem independently getting started we will use the same project structure we used for the previous project library code such as classes should be in files in the lib folder and tests should be in files in the test folder 1 fork this repository in github 1 clone the repository to your computer verify setup with your first commit follow the steps below first to verify that your setup is working if you cannot get guard to run a failing test to run or a test to pass at the beginning please ask for help before continuing the project 1 open a terminal and run the guard command from within the project s root directory where the guardfile is 1 create a test to check the instantiation of one of your object types red 1 create the class for the object tested in the step above green 1 use git add commit and push commands to push your initial code to github process requirements you should use the following process as much as possible 1 write pseudocode 1 write test s 1 write code 1 refactor 1 commit your git commit history should provide a clear description of how your code developed letting the reader know what changed when and why making frequent small commits is essential wave 0 design requirements before starting work on the functional requirements create a first and second draft of your project s design first draft instructor led in class design activity complete your first draft of the hotel design by following the instructor led in class design activity second draft details summary after completing the in class design activity click here for the second draft instructions summary we have our own first draft design for this project we d like you all to look at our first draft compare it with your own and adjust your own first draft if you d like a description of our code exists here https github com adagold hotel blob design scaffolding design scaffolding notes md our code exists here https github com adagold hotel tree design scaffolding use the two links above to explore the files and answer these questions what classes exist why what are they named what do they represent and what state and behavior do they have what tests exist what parts of this design inspires you and you want to steal what parts of this design are you unsure about and need to consider again later what parts of this design do you think you can do without details br spend no more than 1 hour answering those questions and adjusting your project s first draft design after one hour get started don t forget that a useful skill for the programmer is the ability to get started and adjust in small ways often also remember that you can always refactor later to improve your design functional requirements wave one tracking reservations in this wave write the functionality for the system to track valid reservations so that a user of the hotel system can make and find valid bookings for their hotel remember your job is to only build the classes that store information and handle business logic and the tests to verify they re behaving as expected building a user interface is not part of this project user stories as a user of the hotel system i can access the list of all of the rooms in the hotel i access the list of reservations for a specified room and a given date range i can access the list of reservations for a specific date so that i can track reservations by date i can get the total cost for a given reservation i want exception raised when an invalid date range is provided so that i can t make a reservation for an invalid date range details the hotel has 20 rooms and they are numbered 1 through 20 every room is identical and a room always costs 200 night the last day of a reservation is the checkout day so the guest should not be charged for that night when reserving a room the user provides only the start and end dates the library should determine which room to use for the reservation for this wave any room can be reserved at any time and you don t need to check whether reservations conflict with each other this will come in wave 2 hints these functionalities do not all need to be implemented in the same class you might want to investigate ruby s date gem https ruby doc org stdlib libdoc date rdoc date html details summary a hint on dates summary when programming it s generally helpful to convert or validate your data as soon as possible if you do this when you first read receive data that means that the rest of your code can assume that you have data in the desired form this is a job for driver code in this case we are not writing driver code that means that your code should deal entirely in ruby date objects your tests should create date objects and your library code should assume that it s receiving date objects to start when making tests you will want to use something like date new 1993 2 24 to create a date representing february 24 1993 or date today for today instead of trying to parse a string or storing and re parsing strings internally details wave two room availability user stories as a user of the hotel system i can view a list of rooms that are not reserved for a given date range so that i can see all available rooms for those days i can make a reservation of a room for a given date range and that room will not be part of any other reservation overlapping that date range i want an exception raised if i try to reserve a room during a date range when all rooms are reserved so that i cannot make two reservations for the same room that overlap by date details a reservation is allowed start on the same day that another reservation for the same room ends wave three hotel blocks if you are not familiar with what a block of hotel rooms here is a brief description a hotel block is a group of rooms set aside for a specific group of customers for a set period of time hotel blocks are commonly created for large events like weddings or conventions they contain a number of rooms and a specific set of days these rooms are set aside and are made available for reservation by certain customers at a discounted rate these rooms are not available to be reserved by the general public user stories as a user of the hotel system i can create a hotel block if i give a date range collection of rooms and a discounted room rate i want an exception raised if i try to create a hotel block and at least one of the rooms is unavailable for the given date range given a specific date and that a room is set aside in a hotel block for that specific date i cannot reserve that specific room for that specific date because it is unavailable given a specific date and that a room is set aside in a hotel block for that specific date i cannot create another hotel block that includes that specific room for that specific date because it is unavailable i can check whether a given block has any rooms available i can reserve a specific room from a hotel block i can only reserve that room from a hotel block for the full duration of the block i can see a reservation made from a hotel block from the list of reservations for that date see wave 1 requirements details a block can contain a maximum of 5 rooms when a room is reserved from a block of rooms the reservation dates will always match the date range of the block all of the availability checking logic from wave 2 should now respect room blocks as well as individual reservations non functional requirements testing requirements utilize the test helper file run tests automatically whenever files are added or changed using the guard command the final project submission should have 95 code coverage using simplecov optional enhancements you should not be working on these or even thinking about them until you have fully completed wave 3 details add functionality that allows for setting different rates for different rooms read and write csv files for each piece of data that your system is storing create a cli to interact with your booking system put all of this code in a file main rb that is separate from your lib code working on this optional enhancement should not break the other requirements of this project details before submission refactors txt requirement usually by the end of a project we can look back on what we made with a clearer understanding of what we actually needed in industry this is a great time to do a refactor of some sort for this project however you re off the hook for the moment we will be revisiting our hotel project submissions later on on the course and you may want to make some changes at that point in order to keep track of your current thoughts about design refactoring as part of project submission please log down design thoughts with the following process 1 create a new file in the project called refactors txt 1 in that file make a short list of the changes that you could make such as naming conventions etc these notes will be used by you in a few weeks so make sure that they are detailed enough that someone else could understand your thinking and follow your directions commit and push this file to your project repo 1 do not make any further changes to your code at this time what we re looking for you can find what instructors will be looking for in the feedback feedback md markdown document | os |
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Cloud-data-engineering | cloud data engineering cloud data engineering projects | cloud |
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Blog | blog blog getting started these instructions will get you a copy of the project up and running on your local machine for development and testing purposes prerequisites what things you need to install the software and how to install them node v12 x x postgresql 11 x environment variable used in application variable description example server port set this variable to run your app on this port 3002 db connection string this variable is used to connect with database dialect username password host port database name installing a step by step series that will tell you how to get a development env running cd server npm ci installing via using docker compose a step by step series to run application via using docker compose command use the following link to setup docker compose on different operating system click here to check docker compose installation commands for your operating system https docs docker com compose install install compose run the following command to build a docker image for application bash bin bash cd ops local sudo chmod a x build sh build sh use the following commands to check start stop running docker containers images on your local machine list all containers only ids bash bin bash docker ps aq stop all running containers bash bin bash docker stop docker ps aq docker stop container id stop single running container remove all containers bash bin bash docker rm docker ps aq docker rm container id remove single stopped container remove all images bash bin bash docker rmi docker images q docker rmi image id remove single docker image check running docker compose services bash bin bash docker compose ps check status of running particular docker compose services bash bin bash docker compose ps q service name kill running docker compose services bash bin bash docker compose kill database migrations create database node modules bin sequelize db create url dialect username password host port database name keyword example description dialect postgres database we are using username root username for the database password postgres password for the database host localhost ip host on which database is running port 5432 port for the database database name sample database database name for the microservice create migrations node modules bin sequelize migration create name migration name run migrations node modules bin sequelize db migrate url dialect username password host port database name keyword example description dialect postgres database we are using username root username for the database password postgres password for the database host localhost ip host on which database is running port 5432 port for the database database name sample database database name from which migrations will happen run the server for development npm run start dev for production npm run start run the test cases npm run test for validating modules npm run test nsp for linting npm run test lint | server |
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Fuel-Pump-Control-Embedded-System | tools modeling draw uml diagrams with drawio http draw io fsm systemc https learnsystemc com simulation proteus v8 13 atmel studio v6 2 project structure modeling folder drawio file includes use case diagram sequence diagram activity diagrams cpp file systemc modeling file report folder a word report and a slide simulation project fuel pump control embedded system folder the main simulation project architecture simulation architecture system simulation on the proteus p align center img src images simulation architecture png alt simulation architecture width 500 p pcb circuit system s integrated circuit in the form of a pcb p align center img src images pcb circuit png alt simulation architecture width 500 p | os |
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2017-BMS-Project-QPSK-Modulation-Demodulation | fit but bms qpsk modulator demodulator school project for bms wireless and mobile networks class faculty of information technology brno university of technology fit vut bms qpsk modul tor demodul tor koln projekt do p edm tu bms bezdr tov a mobiln s t fakulta informa n ch technologi vysok u en technick v brn | server |
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tpu-mlir | docs assets sophgo chip png tpu mlir for chinese version readme https github com sophgo tpu mlir blob master readme cn md tpu mlir is an open source machine learning compiler based on mlir for tpu this project provides a complete toolchain which can convert pre trained neural networks from different frameworks into binary files bmodel that can be efficiently operated on tpus sophgo aims to become a leading global provider of general purpose computing power sophgo focuses on the research development and promotion of computing products such as ai and risc v cpus and has built a comprehensive application matrix covering the cloud edge and endpoint scenarios with its self developed products sophgo provides computing products and integrated solutions for applications such as smart cities intelligent computing centers smart security intelligent transportation safety production industrial quality inspection and intelligent terminals the company has research and development centers in more than 10 cities in china including beijing shanghai shenzhen qingdao and xiamen as well as in the united states and singapore currently supported ai frameworks are pytorch onnx tflite and caffe models from other frameworks need to be converted to onnx models resources here are some resources to help you better understand the project index documents 01 tpu mlir paper https arxiv org abs 2210 15016 02 tpu mlir technical reference manual https tpumlir org en docs developer manual index html 03 tpu mlir quick start https tpumlir org en docs quick start index html index sharing sessions 01 tpu mlir paper https www bilibili com video bv1my4y1o73q share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 02 layergroup https www bilibili com video bv1wo4y1z7ag share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 index topic video links 01 what is ai compiler ai compiler intro https www bilibili com video bv1yp4y1d7gz share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 02 mlir intro basic syntax 1 https www bilibili com video bv1cp411n7fj share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 basic syntax 2 https www bilibili com video bv1gt4y1f7mt share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 basic syntax 3 https www bilibili com video bv1un4y1w72r share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 dialect conversion https www bilibili com video bv1ug411c7nm share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 pattern rewriting https www bilibili com video bv1r44y1d7xv share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 03 tpu mlir intro overview https www bilibili com video bv19d4y1b7er share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 front end conversion https www bilibili com video bv1yv4y1s7wt share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 lowering https www bilibili com video bv1gg411z7mc share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 04 quantization overview https www bilibili com video bv1d8411j7t4 share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 formula derivation https www bilibili com video bv1sw4y1h7uu share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 calibration https www bilibili com video bv1qk411r75k share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 qat https www bilibili com video bv12g411j7wq share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 05 tpu memory ep1 https www bilibili com video bv1t24y1g7pu share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 ep2 https www bilibili com video bv1vy4y1y7et share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 06 tpu mlir practice to onnx format https www bilibili com video bv1fd4y1h7pt share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 graph optimization https www bilibili com video bv1ar4y1u7d6 share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 operator support https www bilibili com video bv1tl411r71p share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 model support https www bilibili com video bv1mm411y7ep share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 fuse preprocess https www bilibili com video bv1ao4y1h7m8 share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 accuracy validation https www bilibili com video bv14e4y1m79d share source copy web vd source 90fd7c624ed0c40af96748bd0b8dd3e8 in addition we also published a series of tasks for any of you interested in our project and would like to develop it with us index tasks 01 rewrite patterns for permuteop https github com sophgo tpu mlir issues 93 02 shape inference implement https github com sophgo tpu mlir issues 81 03 mlir2onnx tool optimize https github com sophgo tpu mlir issues 80 for more tasks please check issue https github com sophgo tpu mlir issues if you have any questions while doing the tasks above you can ask or check the existing answers in our q a platform https ask tpumlir org questions how to build after cloning the code of this project it needs to be compiled in docker download the required image from dockerhub https hub docker com r sophgo tpuc dev shell docker pull sophgo tpuc dev latest myname1234 is just an example you can set your own name docker run privileged name myname1234 v pwd workspace it sophgo tpuc dev latest after the container is created the directory of the code in docker should be workspace tpu mlir building run the following command in the project directory shell cd tpu mlir source envsetup sh build sh usage introduce the usage of tpu mlir by a simple example of compiling yolov5s onnx and running it on the bm1684x tpu platform the model comes from the official website of yolov5 https github com ultralytics yolov5 releases download v6 0 yolov5s onnx it has been placed in project path regression model yolov5s onnx preparation firstly create a model yolov5s directory at the same level directory with this project then put both model and image files into it the operation is as follows shell mkdir model yolov5s cd model yolov5s cp regression path model yolov5s onnx cp rf regression path dataset coco2017 cp rf regression path image mkdir workspace cd workspace model to mlir if the model takes images as input we need to learn its preprocessing before transforming no preprocessing needs to be considered if the input is npz file the preprocessing process is formulated as follows y x mean times scale where x represents the input the input of the official yolov5 is rgb image each value will be multiplied by 1 255 mean and scale are 0 0 0 0 0 0 and 0 0039216 0 0039216 0 0039216 respectively the model conversion command shell model transform py model name yolov5s model def yolov5s onnx input shapes 1 3 640 640 mean 0 0 0 0 0 0 scale 0 0039216 0 0039216 0 0039216 keep aspect ratio pixel format rgb output names 350 498 646 test input image dog jpg test result yolov5s top outputs npz mlir yolov5s mlir the arguments of model transform py argument required description model name yes model name model def yes model definition file onnx pt tflite or prototxt model data no specify the model weight file required when it is caffe model corresponding to the caffemodel file input shapes no the shape of the input such as 1 3 640 640 a two dimensional array which can support multiple inputs resize dims no the size of the original image to be adjusted to if not specified it will be resized to the input size of the model keep aspect ratio no whether to maintain the aspect ratio when resize false by default it will pad 0 to the insufficient part when setting mean no the mean of each channel of the image the default is 0 0 0 0 0 0 scale no the scale of each channel of the image the default is 1 0 1 0 1 0 pixel format no image type can be rgb bgr gray or rgbd output names no the names of the output use the output of the model if not specified otherwise use the specified names as the output test input no the input file for validation which can be an image npy or npz no validation will be carried out if it is not specified test result no output file to save validation result excepts no names of network layers that need to be excluded from validation separated by comma debug no if open debug immediate model file will keep or will remove after conversion done mlir yes the output mlir file name including path after converting to mlir file a model name in f32 npz file containing preprocessed input will be generated mlir to f16 bmodel convert the mlir file to the f16 bmodel by the following command shell model deploy py mlir yolov5s mlir quantize f16 chip bm1684x test input yolov5s in f32 npz test reference yolov5s top outputs npz model yolov5s 1684x f16 bmodel the arguments of model deploy py argument required description mlir yes mlir file quantize yes quantization type f32 f16 bf16 int8 chip yes the platform that the model will use currently only bm1684x is supported more tpu platforms will be supported in the future calibration table no the quantization table path required when it is int8 quantization tolerance no tolerance for the minimum similarity between mlir quantized and mlir fp32 inference results correctnetss no tolerance for the minimum similarity between simulator and mlir quantized inference results 0 99 0 90 by default excepts no names of network layers that need to be excluded from validation separated by comma debug no if open debug immediate model file will keep or will remove after conversion done model yes name of output model file including path dynamic no dynamic codegen for to support dynamic shape mlir to int8 bmodel before converting to the int8 model you need to run calibration to get the calibration table the number of input data is about 100 to 1000 according to the situation then use the calibration table to generate a symmetric int8 bmodel it is generally not recommended to use the asymmetric one if the symmetric one already meets the requirements because the performance of the asymmetric model will be slightly worse than the symmetric model here is an example of the existing 100 images from coco2017 to perform calibration shell run calibration py yolov5s mlir dataset coco2017 input num 100 o yolov5s cali table execute the following command to convert to the int8 symmetric quantized model shell model deploy py mlir yolov5s mlir quantize int8 calibration table yolov5s cali table chip bm1684x test input yolov5s in f32 npz test reference yolov5s top outputs npz tolerance 0 85 0 45 model yolov5s 1684x int8 bmodel results comparison this project has a yolov5 sample written in python path python samples detect yolov5 py for object detection read the code to learn how the model is used 1 preprocess the input 2 model inference to get output 3 post process the output the following code is used to verify the output of onnx f32 int8 model respectively onnx model shell detect yolov5 py input image dog jpg model yolov5s onnx output dog origin jpg f16 bmodel shell detect yolov5 py input image dog jpg model yolov5s 1684x f16 bmodel output dog f16 jpg int8 symmetric quantized bmodel shell detect yolov5 py input image dog jpg model yolov5s 1684x int8 bmodel output dog int8 jpg outputs of different models are compared below docs quick start assets yolov5s png auxiliary tools model inference tool model runner py supports bmodel mlir pytorch onnx tflite caffe shell model runner py input resnet18 in f32 npz model resnet18 1684x f32 bmodel output resnet18 output npz tool for bmodel the bmodel file can be viewed and edited by model tool model tool info model file show brief model info print model file show detailed model info extract model file extract one multi net bmodel to multi one net bmodels combine file1 filen o new file combine bmodels to one bmodel by filepath combine dir dir1 dirn o new dir combine bmodels to one bmodel by directory path dump model file start offset byte size out file dump binary data to file from bmodel for example to get basic information of bmodel shell model tool info resnet18 1684x f32 bmodel related links official network https tpumlir org discourse https ask tpumlir org videos https space bilibili com 1829795304 channel collectiondetail sid 734875 | ai |
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flutter_ddd_todo_firebase | flutter firebase ddd flutter domain driven development using firebase getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https flutter dev docs get started codelab cookbook useful flutter samples https flutter dev docs cookbook for help getting started with flutter view our online documentation https flutter dev docs which offers tutorials samples guidance on mobile development and a full api reference | server |
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micro-services-tutorial-iot | nearform https rawgit com nearform msworkshop master assets banner svg developing microservices this workshop will walk you through creating and composing a set of microservices in node js using fuge docker and docker compose supports node 4 x tested on win7 win10 osx linux note this is a beginners level workshop whilst you should be comfortable with node js modules and applications and have a basic understanding of docker and it s principles we will be helping you through the basics this workshop presents a series of fairly simple challenges to help you get up to speed with these technologies the app the app you re building is a sensor fed chart that provides realtime data from a dummy sensor this app is broken down into a number of individual services each with its own well defined concern as outlined in this diagram image docs target png frontend a simple web app that uses jquery rickshaw charts and websockets to show a realtime graph of data being emitted by our sensor this app has an api which is included in the same microservice who s sole job is to talk to and read from other microservices actuator a small microservice that causes reads on the sensor based on an offset sensor a dummy temperature sensor that sends out varying values based on what it receives from the actuator serialisation a service that handles reads and writes in serial fashion to the database uses websocket stream to update the web app and thus the graph in real time broker a robust messaging layer build for iot based devices we use this to wire up the actuator serialiser and sensor in a loosely coupled fashion influx a database in a container that the serialiser uses for robust storage of data once we have built the application it should look a bit like this image docs screen png the libraries the system uses the following libraries and technologies rickshaw charts a charting library for the web http code shutterstock com rickshaw express a http web server library http expressjs com websocket stream a web socket streams implementation for realtime communication to the browser https www npmjs com package websocket stream browserify a module to let you require modules client side by bundling up dependencies http browserify org seneca a microservices library http senecajs org mosca mqtt an mqtt broker that enables robust message particularly suited to iot https github com mcollina mosca influxdb a time series database particularly suited to time sensitive data https influxdb com fuge a microservice development environment https github com apparatus fuge docker a container engine https www docker com setting up to complete this workshop you will need node js and some supporting tools installed on your laptop along with a working docker installation with an influxdb image you should follow the instruction for each component to get set up node js go to https nodejs org en and download and install the latest stable build for your system fuge once you have installed node js fuge may be installed by running npm install g fuge note to work through this workshop on linux please set proxy none in the fuge config js for each step curl windows does not come with curl included in cmd exe if you are using windows please install curl ensuring you select the version that matches your installed version of windows curl can be found at https curl haxx se download html please use the windows generic version if using cmd exe docker go to https www docker com products docker toolbox and download and install the latest stable version of docker toolbox for your platform once you have the toolbox installed start the default machine docker machine start default set your shells environment using eval docker machine env default on windows for cmd exe use for f tokens i in docker machine exe env do i to set the environment more details here https docs docker com machine reference env confirm that all is well by running docker ps this tutorial uses a docker influx db container https hub docker com r tutum influxdb you should pull this container using the following command docker pull tutum influxdb you should also pull a node js container using docker pull node once you have the following complete its time to move to step0 workshop you should clone this repository to your local machine git clone git github com nearform micro services tutorial iot git you should then setup the repository by installing required node modules cd micro services tutorial iot npm install next up step0 step0 readme md need help we are as ever your humble servants and happy to take any questions matteocollina https twitter com matteocollina pelger https twitter com pelger mcdonnelldean https twitter com mcdonnelldean davidmarkclem https twitter com davidmarkclem wpreul https twitter com wpreul docker cheat sheet https github com wsargent docker cheat sheet | server |
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LLM_training | baichuan7b 13b https github com baichuan inc baichuan 7b llama https huggingface co meta llama bloom https huggingface co bigscience moss16b https github com openlmlab moss chatglm chatglm2 https github com thudm chatglm 6b sft full parameter tuning lora https arxiv org abs 2106 09685 qlora https arxiv org abs 2305 14314 rm full parameter tuning lora https arxiv org abs 2106 09685 qlora https arxiv org abs 2305 14314 sft datasets belle open source 500k https huggingface co datasets bellegroup train 0 5m cn blob main belle open source 0 5m json 50 belle https github com lianjiatech belle firefly train 1 1m https huggingface co datasets yeungnlp firefly train 1 1m 23 nlp 115 moss 003 sft data https huggingface co datasets yeungnlp moss 003 sft data moss 100 safety prompts https github com thu coai safety prompts 100k prompts chatgpt rm datasets gpt 4 generated data en zh https github com instruction tuning with gpt 4 gpt 4 llm gpt 4 instruction tuning causal language model generate src clm gif huggingface blog https huggingface co blog how to generate python generation config generationconfig temperature 0 6 top p 0 8 do sample true repetition penalty 2 0 max new tokens 512 max length max new tokens input sequence generate ids model generate inputs generation config generation config output tokenizer decode generate ids 0 len inputs input ids 0 greedy search token greedy search greedy search the nice woman 0 5 0 4 0 2 the dog has 0 4 0 9 0 36 greedy search src greedy search png beam search greedy search token beamsize beam search beam search greedy search src beam search png top k sampling top k sampling token top k k 6 token 6 token top k sampling src top k sampling png top p sampling top p sampling p top p sampling src top p sampling png safetyprompts 30000 qlora baichuan13b base vs baichuan 13b sft baichuan13b base result src baichuan13b base result png baichuan13b sft result src baichuan13b sft result png shell sh run sh shell cuda visible devices 0 1 2 3 torchrun nproc per node 4 train sft py train args file config lora config json shell safetyprompt prompt response n n ai type unfairness and discrimination s human prompt n nassistant response s shell s human input1 n nassistant ouput1 s human input2 n nassistant ouput2 s human inputk n nassistant ouputk s rlhf training model onnx c inference implementation | ai |
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gmic-qt | g mic qt a versatile g mic plugin purpose g mic qt is a versatile front end to the image processing framework g mic https gmic eu it is in fact a plugin for gimp http gimp org krita https krita org paint net https www getpaint net digikam https www digikam org and an 8bf filter plugin for photoshop compatible software as well as a standalone application standalone md authors s bastien fourey david tschumperl g mic lib original gtk based plugin contributors boudewijn rempt boud valdyas org krita compatibility layer later replaced by a native version of the plugin amyspark https github com amyspark krita native version of the plugin work in progress nicholas hayes https github com 0xc0000054 paint net and 8bf filter compatibility layers work in progress gilles caulier https github com cgilles digikam compatibility layer translators jan helebrant czech translation frank tegtmeyer german translation chroma ghost bazza pixls us spanish translation s bastien fourey french translation duddy hadiwido indonesian translation francesco riosa italian translation iarga pixls us dutch translation alex mozheiko polish translation maxr portuguese translation alex mozheiko russian translation andrex starodubtsev ukrainian translation linuxtoy https twitter com linuxtoy chinese translation omiya tou tokyogeometry github japanese translation official pre release binary packages available at gmic eu https gmic eu travis ci last build status master branch linux build status https api travis ci org c koi gmic qt svg branch master https travis ci org c koi gmic qt devel branch linux build status https api travis ci org c koi gmic qt svg branch devel https travis ci org c koi gmic qt build instructions by default the gimp integration plugin is built qmake qmake is simple to use but only really works in an environment where bash is available sh git clone https github com dtschump gmic git git clone https github com c koi gmic qt git make c gmic src cimg h gmic stdlib community h cd gmic qt qmake host none gimp paintdotnet 8bf make cmake cmake works on all platforms the first part is the same and requires make and wget to be available if you don t have all dependencies cmake will warn you which ones are missing note that the minimum cmake version is 3 1 sh git clone https github com dtschump gmic git git clone https github com c koi gmic qt git make c gmic src cimg h gmic stdlib community h cd gmic qt then make a build directory sh mkdir build cd build sh cmake dgmic qt host none gimp paintdotnet 8bf dgmic path path to gmic dcmake build type debug release relwithdebinfo make adapt g mic qt to new applications developers will find guidelines and instructions for the adaptation of the plugin to a new host application in the new host howto https github com c koi gmic qt blob master new host howto md | front_end |
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pyloom | pyloom a event sourcing framework for building large language model applications pyloom is a framework designed to streamline the development of intricate llm applications drawing inspiration from event sourcing https martinfowler com eaadev eventsourcing html pyloom applies this concept to the llm agent development offering a range of powerful features that enables better dev experience developing intricate and non deterministic llm agents involves making multiple llm calls and intricate control structures similar to constructing a marble machine if an error arises developers are frequently compelled to rerun the entire agent workflow developers care not only the agent s final outcome but also the steps it takes to arrive there at times we want to see how tweaks like extra tools or adjusted prompts in between can affect the final outcome pyloom is crafted to address these challenges features git for agent pyloom tracks state changes and the evolution of the agent at each step agent replay with pyloom developers can replay and navigate through the agent s flow once encouter an error in the agent flow you can fix the issue and restart from the exact point of failure event sourcing pyloom employs event sourcing representing agent actions as events by replaying the event stream the agent s state can be reconstructed furthermore developer can apply the same event streams to different agents and compare performance reproduce production issues leveraging event sourcing pyloom facilitates the reproducing of production errors by replaying the identical event streams in development environment pyloom can be used with other llm frameworks like langchain and guidance installation you can install pyloom using pip python pip install pyloom quick start tutorial notebooks tutorial ipynb contributing we are extremely open to contributions in various forms bug fixes new features improved documentation and pull requests your input is valuable to us | ai |
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Udagram-Image-Filtering-Microservice | image filter | cloud |
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apparatus | asto apparatus software tool archive notice after the completion of my research degree the repository has been archived asto has made a significant impact to my research i want to express my gratitude to everyone that helped make it better however this is not the end active development with continue on cyberlens s https github com cyberlens disc0very git repository please report issues and feature requests there an iot network security analysis and visualization tool js standard style https img shields io badge code 20style standard brightgreen svg http standardjs com styled with prettier https img shields io badge styled with prettier ff69b4 svg https github com prettier prettier travis build https travis ci org or3stis apparatus svg branch master dependencies status https david dm org or3stis apparatus svg https david dm org or3stis apparatus devdependencies status https david dm org or3stis apparatus dev status svg https david dm org or3stis apparatus type dev asto is security analysis tool for iot networks it is developed to support the apparatus security framework asto is based on electron http electron atom io and cytoscape js http js cytoscape org the icons are provided by google s material design https material io icons the application is in alpha stage the focus now is to improve the core functionality of the application along with the introduction of additional features to reach beta stage features 1 graph based visualization of iot systems 1 model iot systems in design and implementation engineering phases 1 an automatic model transition between the two engineering phases 1 model iot system state 1 automate implementation phase models generation using pcap ng files 1 perform model based vulnerability identification through cve databases 1 generate automated model based security insights 1 attribute based pattern identification 1 search through graphs using a variety of options concepts modules attributes 1 togglable light and dark theme some screenshots asto home https raw githubusercontent com or3stis apparatus master assets screenshot1 png asto ui 1 https raw githubusercontent com or3stis apparatus master assets screenshot2 png asto ui 2 https raw githubusercontent com or3stis apparatus master assets screenshot3 png asto ui 2 https raw githubusercontent com or3stis apparatus master assets screenshot4 png console asto has a command line console available on the bottom right corner of the app you gain focus on the console by pressing the keybinding cmd l for macos and ctrl l for windows linux if you type help it will display a list of console options the console can be used to search for specific objects in the graph or perform operations raw text is used as search input for example if you type device asto will highlight all the nodes in the graph that have the word device as an attribute all console commands must be preceded with a for example typing insights will perform the security insights functions on the other hand typing insights without the will perform a search operation on the graph elements with the keyword insights color themes asto supports a light and a dark color theme the colors themes are based on atom s one dark https github com atom one dark syntax and one light https github com atom one light syntax to switch between themes use the toggle button on the bottom left corner to use to clone and run this repository you ll need git https git scm com and node js https nodejs org en download installed on your computer to download and install the app type the following in your terminal bash clone this repository git clone https github com or3stis apparatus git navigate into the repository cd apparatus install dependencies npm install different mode operations of the app bash to run the app in the default mode npm start to run the app in developer mode npm run dev to build the app into binary npm run dist because the app is still in prototype stage it is best to keep up to date with the most recent commits to do so before starting the app type bash inside the apparatus directory update to the latest git pull once the app starts the first window home screen will ask you to choose which modeling phase would you like to use after you select a phase you will be presented with three choices the first is to create a new graph the second choice is to load an existing graph the third option is the debug app which loads a default graph used for debugging purposes you will find some example graphs in the sample folder note in performance if you render a graph with more than a thousand nodes depending on your hardware you might detect some performance issues the reason is that the default label rendering of nodes and edges in asto is quite expensive rendering label on nodes and edges along with directional arrows is cpu expensive to improve performance you can hide the labels and the directional arrows by pressing the label button you can find more information about cytoscape s performance optimizations in this link http js cytoscape org performance privacy notice the software does not collect personal information of any kind the only network operation the application performs is when the vulnerability identification process is used the vulnerability identification makes a network request to https cve circl lu api search can be changed in the settings which maintains its own analytics contributing if you want to contribute to the project that s great check the contributing https github com or3stis apparatus blob master contributing md guide the application is being developed on macos that means that new commits might introduce breaking changes in other platforms especially commits that involve access to the file system if something is not working don t hesitate to create an issue https github com or3stis apparatus issues if you want to find out how the app works check the wiki https or3stis github io apparatus wiki you can check the project s planned features in the roadmap https or3stis github io apparatus roadmap thanks a shoutout to nomnuds https github com nomnuds and nickarg https github com nickarg who provide the much needed feedback on windows license mit license md | security-analysis cytoscape iot security-iot asto apparatus | server |
4180_Final_Project | remotely controlled robot with alarm team members kyumin kim and yifan wang georgia institute of technology watch our demo video demonstration https youtu be xvhx ossby table of contents 1 project overview https github com kyuminkim01 4180 final project wiki project overview 2 parts list https github com kyuminkim01 4180 final project wiki parts list 3 schematic and circuitry https github com kyuminkim01 4180 final project wiki schematic and circuitry 4 source code https github com kyuminkim01 4180 final project wiki source code 5 future direction https github com kyuminkim01 4180 final project wiki future direction project overview img width 777 alt image src https user images githubusercontent com 122822330 234151412 47ec72e2 5fe6 406b 885e 9d3181978a53 png th user will use the control pad on the bluefruitconnect mobile app to control the speed and directions of the robot movement button 1 4 set the speed level to 0 0 0 3 0 6 0 9 m s respectively forward arrow will set the movement to forward direction and reverse arrow will set the movement to reverse direction left arrow will set the robot to turn left and right arrow will set the robot to turn right this is a picture of our robot robot https user images githubusercontent com 113227334 234109507 9f8b1897 de77 4165 ad78 6fca4d7d8666 jpg this is the robot block diagram block diagram https user images githubusercontent com 122822330 234113114 e671c4bf 98e9 4767 a168 f0c624a5a74d png parts list mbed lpc1768 https www sparkfun com products 9564 distance sensor hc sr04 https www sparkfun com products 15569 serial miniature lcd module https www sparkfun com products 11377 audio amp tpa2005d1 https www sparkfun com products 11044 speaker pcb mount https www sparkfun com products 11089 led rgb https www sparkfun com products 105 bluetooth low energy https www adafruit com product 2479 description hobby gearmotor https www sparkfun com products 13302 motor driver dual tb6612fng https www sparkfun com products 14450 shadow chassis https www sparkfun com products 13301 schematic and circuitry this is a schematic of our project unnamed https user images githubusercontent com 113227334 234116802 96ebb0b3 f97a 4aeb 9828 0cb5992712ca png nbsp nbsp battery 2 battery 1 mbed bluetooth hbridge motor1 motor2 class d amp speaker vin vin 3 3 16v power vout vcc stby gnd gnd gnd gnd gnd in power p28 rxi p27 txo gnd cto vmot p23 pwma p6 ain1 p5 ain2 p22 pwmb p12 bin1 p11 bin2 aout1 aout2 bout1 bout2 p24 in out speaker out speaker table 1 circuitry for the connection of mbed bluetooth hbridge motors class d amp and speaker nbsp nbsp battery 1 mbed sonar sensor rgb led ulcd vin vcc 5v gnd gnd gnd gnd gnd p30 ecco p29 trig p26 red p25 green p21 blue p9 tx p10 rx p8 res table 2 circuitry for the connection of mbed sonar sensor rgb led and ulcd note use of external 5v power is required because we need enough current to drive servos and the led array connect everything to a common ground source code all source code is included in this repository include mbed h include rtos h include sdfilesystem h include motor h include cstdint include ulcd 4dgl h include songplayer h include ultrasonic h motor m1 p23 p6 p5 pwm fwd rev motor m2 p22 p12 p11 serial blue p28 p27 ulcd ulcd 4dgl lcd p9 p10 p8 serial tx serial rx reset pin float note 1 1700 0 float duration 1 0 5 declare global variables volatile int current distance 500 volatile float current speed 0 0 volatile float m1 direction 1 0 volatile float m2 direction 1 0 mutex to make the lcd lib thread safe mutex lcd mutex songplayer myspeaker p24 distance fucntion for sonar sensor volatile int s 0 class rgbled public rgbled pinname redpin pinname greenpin pinname bluepin void write float red float green float blue private pwmout redpin pwmout greenpin pwmout bluepin rgbled rgbled pinname redpin pinname greenpin pinname bluepin redpin redpin greenpin greenpin bluepin bluepin 50hz pwm clock default a bit too low go to 2000hz less flicker redpin period 0 0005 distance fucntion for sonar sensor void dist int distance printf distance d mm r n distance current distance distance ultrasonic mu p29 p30 1 1 dist set the trigger pin to d8 and the echo pin to d9 have updates every 1 seconds and a timeout after 1 second and call dist when the distance changes void rgbled write float red float green float blue redpin red greenpin green bluepin blue class could be moved to include file rgbled myrgbled p26 p25 p21 rgb pwm pins void rgblight void const argument while 1 if s 1 myrgbled write 0 0 1 0 1 0 yellow for reverse movement if s 2 myrgbled write 0 0 0 0 1 0 red for stop if s 3 myrgbled write 0 0 1 0 0 0 green for forward movement if s 4 myrgbled write 1 0 0 0 0 0 blue for turning left right thread wait 50 sonar sensor thread void sensor thread void const args mu startupdates start measuring the distance while 1 mu checkdistance call checkdistance as much as possible as this is where the class checks if dist needs to be called thread wait 50 ulcd thread void ulcd thread void const args while 1 thread loop lcd mutex lock lcd color green lcd locate 0 0 lcd text height 2 lcd text width 2 lcd printf distance lcd locate 0 2 lcd printf 2 0d mm current distance lcd locate 0 4 lcd color red lcd printf speed lcd locate 0 6 lcd printf 2 0f m s 10 current speed lcd mutex unlock thread wait 50 audio void audio thread void const args while 1 thread loop if current distance 500 myspeaker playsong note duration wait 0 9 thread wait 100 int main char bnum 0 start threads thread thread1 rgblight thread sonar sensor thread thread ulcd ulcd thread thread audio audio thread while 1 if blue readable lcd mutex lock if blue getc if blue getc b bnum blue getc switch bnum case 1 current speed 0 0 m1 speed m1 direction current speed m2 speed m2 direction current speed s 2 break case 2 current speed 0 3 m1 speed m1 direction current speed m2 speed m2 direction current speed 0 02 if m1 direction 1 0 m2 direction 1 0 s 3 else s 1 break case 3 current speed 0 6 m1 speed m1 direction current speed m2 speed m2 direction current speed 0 02 if m1 direction 1 0 m2 direction 1 0 s 3 else s 1 break case 4 current speed 0 9 m1 speed m1 direction current speed m2 speed m2 direction current speed 0 02 if m1 direction 1 0 m2 direction 1 0 s 3 else s 1 break case 5 m1 speed float current speed m2 speed float current speed s 3 m1 direction 1 0 m2 direction 1 0 break case 6 m1 speed float current speed m2 speed float current speed s 1 m1 direction 1 0 m2 direction 1 0 break case 7 m1 speed float 0 0 m2 speed float 0 3 s 4 break case 8 m1 speed float 0 3 m2 speed float 0 0 s 4 break default break lcd mutex unlock thread wait 50 future direction robot response can be slow occasionally we will improve the wireless connection and optimize responding time distance sensor might give wrong reading if the speed changes rapidly we will modify the mbed codes so the speed changing process is smoother the robot is not moving in a straight line when in reverse movement we will do more mechanical examination and testing to fix the issue | os |
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londogard-nlp-toolkit | maven central https img shields io maven central v com londogard nlp svg label maven 20central https search maven org search q g 22com londogard 22 20and 20a 22nlp 22 a href https www buymeacoffee com hlondogard target blank img src https cdn buymeacoffee com buttons v2 default green png alt buy me a coffee style height 60px important width 217px important a londogard nlp toolkit londogard natural language processing toolkit written in kotlin for the jvm this toolkit will be used throughout londogard libraries products such as our summarizer text generation more the languagesupport enum is used to determine what support different tools like embeddings or stopwords have out of the box tool info docs samples kotlin notebook word embeddings word subword embeddings available in 157 fasttext cc https fasttext cc 275 languages bpemb https bpemb h its org out of the box embeddings https londogard github io londogard nlp toolkit nlp com londogard nlp embeddings index html wordembeddings ipynb https github com londogard londogard nlp toolkit blob main docs samples components wordembeddings ipynb sentence embeddings average unsupervised random walk sentence embeddings https aclanthology org w18 3012 sentence embeddings https londogard github io londogard nlp toolkit nlp com londogard nlp embeddings sentence index html sentence embeddings ipynb https github com londogard londogard nlp toolkit blob main docs samples components sentence embeddings ipynb stopwords supports 23 languages out of the box through nltk s list of stopword stopwords https londogard github io londogard nlp toolkit nlp com londogard nlp stopwords index html stopwords ipynb https github com londogard londogard nlp toolkit blob main docs samples components stopwords ipynb word frequencies supports 34 languages out of the box through luminosoinsight https github com luminosoinsight wordfreq word frequency tables wordfrequency https londogard github io londogard nlp toolkit nlp com londogard nlp wordfreq index html wordfreq ipynb https github com londogard londogard nlp toolkit blob main docs samples components wordfreq ipynb stemming supports 14 languages out of the box using snowball stemmer https snowballstem org under the hood stemming https londogard github io londogard nlp toolkit nlp com londogard nlp stemmer index html stemmer ipynb https github com londogard londogard nlp toolkit blob main docs samples components stemmer ipynb tokenizers char word subword sentence tokenizer support sentencepiece huggingface it s there tokenizers https londogard github io londogard nlp toolkit nlp com londogard nlp tokenizer index html br sentence tokenizers https londogard github io londogard nlp toolkit nlp com londogard nlp tokenizer sentence index html tokenizer ipynb https github com londogard londogard nlp toolkit blob main docs samples components tokenizers ipynb vectorizers encoders bagofwords tf idf bm25 onehot vectorizers tf idf bm 25 https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning vectorizer index html br count vectorizers count hash https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning vectorizer count index html br encoders onehot https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning encoders index html br transforms tf idf bm 25 https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning transformers index html todo keyword extractions cooccurencekeywords based on algorithm proposed in doi 10 1142 s0218213004001466 https www researchgate net publication 2572200 keyword extraction from a single document using word co occurrence statistical information keywords ipynb https github com londogard londogard nlp toolkit blob main docs samples components keyword extraction ipynb machine learning logisticregression classifier using gradient descent na vebayes binary hidden markov model hmm as sequence classifier classifiers logisticregression na vebayes https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning predictors classifiers index html br regression linearregression https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning predictors regression index html br sequence classifier hiddenmarkovmodel https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning predictors sequence index html see e2e examples https github com londogard londogard nlp toolkit tree main docs samples e2e deep learning transformers huggingface classifierpipeline and tokenclassifierpipeline which supports huggingface onnx model names pytorch from local files transformers https londogard github io londogard nlp toolkit nlp com londogard nlp meachinelearning predictors transformers index html see e2e examples https github com londogard londogard nlp toolkit tree main docs samples e2e spacy like api wip installation mavencentral kotlin implementation com londogard nlp version guides simple end 2 end guides available as notebooks via docs samples https github com londogard londogard nlp toolkit tree main docs samples e2e this includes 1 imdb sentiment analysis using logistic regression or na ve bayes 2 imdb sentiment analysis using huggingface transformers using classifierpipeline create model name 3 pos tagging using hidden markov model 4 pos tagging using huggingface transformers using tokenclassifierpipeline create model name potentially more | nlp natural-language-processing jvm kotlin | ai |
eosio.contracts | eosio contracts version 1 9 2 the design of the eosio blockchain calls for a number of smart contracts that are run at a privileged permission level in order to support functions such as block producer registration and voting token staking for cpu and network bandwidth ram purchasing multi sig etc these smart contracts are referred to as the bios system msig wrap formerly known as sudo and token contracts this repository contains examples of these privileged contracts that are useful when deploying managing and or using an eosio blockchain they are provided for reference purposes eosio bios contracts eosio bios eosio system contracts eosio system eosio msig contracts eosio msig eosio wrap contracts eosio wrap the following unprivileged contract s are also part of the system eosio token contracts eosio token dependencies eosio cdt v1 7 x https github com eosio eosio cdt releases tag v1 7 0 eosio v2 0 x https github com eosio eos releases tag v2 0 8 optional dependency only needed to build unit tests build to build the contracts follow the instructions in build and deploy https developers eos io manuals eosio contracts latest build and deploy section contributing contributing guide contributing md code of conduct contributing md conduct license mit license the included icons are provided under the same terms as the software and accompanying documentation the mit license we welcome contributions from the artistically inclined members of the community and if you do send us alternative icons then you are providing them under those same terms important see license license for copyright and license terms all repositories and other materials are provided subject to the terms of this important important md notice and you must familiarize yourself with its terms the notice contains important information limitations and restrictions relating to our software publications trademarks third party resources and forward looking statements by accessing any of our repositories and other materials you accept and agree to the terms of the notice | eosio smart-contracts | blockchain |
point2cyl | point2cyl reverse engineering 3d objects from point clouds to extrusion cylinders point2cyl reverse engineering 3d objects from point clouds to extrusion cylinders https arxiv org abs 2112 09329 mikaela angelina uy sup sup yen yu chang sup sup minhyuk sung purvi goel joseph lambourne tolga birdal and leonidas guibas cvpr 2022 pic network teaser v4 compressed png introduction we propose point2cyl a supervised network transforming a raw 3d point cloud to a set of extrusion cylinders reverse engineering from a raw geometry to a cad model is an essential task to enable manipulation of the 3d data in shape editing software and thus expand their usages in many downstream applications particularly the form of cad models having a sequence of extrusion cylinders a 2d sketch plus an extrusion axis and range and their boolean combinations is not only widely used in the cad community software but also has great expressivity of shapes compared to having limited types of primitives e g planes spheres and cylinders in this work we introduce a neural network that solves the extrusion cylinder decomposition problem in a geometry grounded way by first learning un derlying geometric proxies precisely our approach first predicts per point segmentation base barrel labels and nor mals then estimates for the underlying extrusion param eters in differentiable and closed form formulations our experiments show that our approach demonstrates the best performance on two recent cad datasets fusion gallery and deepcad and we further showcase our approach on reverse engineering and editing inproceedings uy point2cyl cvpr22 title point2cyl reverse engineering 3d objects from point clouds to extrusion cylinders author mikaela angelina uy and yen yu chang and minhyuk sung and purvi goel and joseph lambourne and tolga birdal and leonidas guibas booktitle conference on computer vision and pattern recognition cvpr year 2022 pre requisites code was tested using python 3 8 with cuda 11 0 python3 8 m venv env source env bin activate pip install r requirements txt data download dataset downloads can be found in the link http download cs stanford edu orion point2cyl data tar gz and it should be extracted in the project home folder deepcad processed data and splits can be found here http download cs stanford edu orion point2cyl deepcad zip training to train point2cyl without sketches example commands are as follows python train point2cyl without sketch py logdir log point2cyl without sketch pred seg pred normal pred bb data dir data to train point2cyl with sketches example commands are as follows python train point2cyl py logdir log point2cyl pred seg pred normal pred bb pc logdir log point2cyl without sketch is pc init is pc train im logdir results igr dense is im init is im train data dir data evaluation example commands to run evaluation script are as follows for point2cyl without sketches python eval py logdir results point2cyl without sketch dump dir dump point2cyl without sketch data dir data for point2cyl with sketches python eval py logdir results point2cyl dump dir dump point2cyl data dir data pre trained models the pretrained models for our point2cyl can be found in results results deepcad pretrained model can be downloaded here http download cs stanford edu orion point2cyl deepcad zip visualization example commands to run visualization script are as follows python visualizer py logdir results point2cyl model id 55838 a1513314 0000 1 dump dir dump 55838 a1513314 0000 1 output dir output 55838 a1513314 0000 1 data dir data license this repository is released under mit license see license file for details | cloud |
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ehealth_backend | div align center h1 ehealth backend h1 div requirements this project is developed using flask and sqlalchemy br structure app py application entry point sql sql dump to init database and tables config py includes configurations for db src api endpoints api py rest api definition endpoint name py core functions exceptions app exception py execptions are written over here test postman includes postman tests models py sqlalchemy models serializer py serializing the input and output data br how to run project locally clone the repository https github com user name ehealth backend git installing the requirements cd ehealth backend source path to env activate python m pip install r requirements txt setting up postgres create superuser createuser interactive username create user sudo u postgres psql postgres create user username with password password if output create role then exit from the postgres terminal by pressing q postgres q create db sudo u postgres createdb db name default name ehealth check the connection default hostname 127 0 0 1 default port 5432 default db name ehealth psql postgresql username password hostname port db name if the the connection is established successfully change add the postgres connection url in the app py file see the example below with username admin password admin host localhost port 5423 db name ehealth postgresql admin admin localhost 5432 ehealth 3 creating database schema named ehealth br python manage py db init python manage py db migrate python manage py db upgrade 4 run app br python app py br | server |
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Computer-Vision-A-Z | udemy computer vision a z website https www udemy com computer vision a z module module 1 face recognition module 2 object detection module 3 gans | ai |
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nuc980_homework | nuc980 homework curriculum design of embedded operating system 1194 led nuc980 on off led on off led led led pwm int pwm set int period int duty cycle int sw period pwm duty cycle sw 1 0 int 0 1 gpio server uart usr | os |
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Warehouse-ML | warehouse warehouse management system with quality assurance this project involves the development of a warehouse management system wms that integrates quality assurance mechanisms to ensure the storage and management of goods within a warehouse facility the system is designed to handle various aspects of warehousing including validating goods ensuring their quality and managing their storage project overview the main components of the system include a coldstorage facility various facilities for storing different types of goods a goodsqualityassurance module for quality control and a warehouse class that orchestrates the entire process coldstorage this serves as the central storage facility within the warehouse it is responsible for receiving and storing goods while considering available capacity goods are categorized based on their types and stored in corresponding facilities if a specific facility doesn t exist for a certain type of goods the system notifies the client facility these are specialized storage units designed to hold specific types of goods they are responsible for managing their own capacities and providing reports on their status each facility keeps track of available storage slots and ensures goods are stored appropriately goodsqualityassurance this component is crucial for validating the quality of goods before storage it filters out expired goods and ensures that only valid items are accepted for storage if all goods are expired the system imposes fines on the client this module plays a pivotal role in maintaining the overall quality and integrity of stored goods warehouse the warehouse class acts as the core orchestrator integrating the various components together it handles the coordination of quality assurance checks and the subsequent storage of goods in cases where multiple goods need to be stored simultaneously the system employs multithreading and synchronization to ensure efficient and error free operations diagram images diagram svg error handling the project incorporates an extensive error handling mechanism through the use of custom error types these errors include badgoods indicates that all goods are expired leading to a fine for the client unsupportedfacility occurs when the warehouse lacks a suitable facility for a specific type of goods prompting the need to construct a corresponding facility unsupportedgoods arises when inappropriate goods are attempted to be stored in a certain facility indicating a quality assurance error full denotes that a facility is at full capacity requiring either the construction of new facilities or the removal of items conclusion the warehouse management system with quality assurance provides a comprehensive solution for efficiently managing goods within a warehouse environment by integrating quality control and facility management this project ensures that only valid high quality items are stored while offering a seamless experience for both warehouse operators and clients through the effective use of error handling and modular design the system optimizes storage operations and maintains the integrity of stored goods | os |
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ESP31_RTOS_SDK | esp32 rtos sdk esp32 sdk based on freertos toolchain we suggest to choose crosstool ng as the compiler toolchain follow the instructions below to install crosstool ng step 1 install the required toolchain packages sudo apt get install git autoconf build essential gperf bison flex texinfo libtool libncurses5 dev wget gawk libc6 dev i386 python serial libexpat dev step 2 create a directory e g opt espressif to store the toolchain sudo mkdir opt espressif step 3 make the current user the owner sudo chown user opt espressif step 4 download the latest toolchain installation file to the directory created in step 2 cd opt espressif git clone b esp108 1 21 0 git github com jcmvbkbc crosstool ng git step 5 install toolchain cd crosstool ng bootstrap configure prefix pwd make make install ct ng xtensa esp108 elf ct ng build step 6 set the path variable to point to the newly compiled toolchain export path opt espressif crosstool ng builds xtensa esp108 elf bin path notice you need to do step 6 once you open a new shell or you can put it inside your bashrc file project template compile step 1 create a directory e g workspace to store a new project mkdir workspace step 2 clone esp32 rtos sdk cd workspace git clone https github com espressif esp32 rtos sdk git step 3 copy esp32 rtos sdk examples project template to workspace directory created in step 1 cp workspace esp32 rtos sdk examples project template workspace r step 4 create a directory e g workspace esp32 bin to store the bin files compiled mkdir p workspace esp32 bin step 5 set sdk path as the path of sdk files and bin path as the path of bin files compiled export sdk path workspace esp32 rtos sdk export bin path workspace esp32 bin notice make sure you set the correct paths or it will occur a compile error export bin path is optional default bin path is bin step 6 start to compile files cd workspace project template make clean make notice you need to do step 5 every time you open a new shell or you can put it inside your bashrc file if your project is successfully compiled the drom0 bin irom0 flash bin and user ota files will be generated in bin path directory makefile options esp conf in project template includes makefile supported options you can modify according to your needs 1 esptool parameters serial port depend on your platform default dev ttyusb0 esp port dev ttyusb0 serial baudrate default 230400 esp baud 230400 spi flash size 1mb 2mb 4mb 8mb 16mb default 1mb esp fs 1mb spi flash freq 40m 26m 20m 80m default 40m esp ff 40m spi flash mode qio qout dio dout default qio esp fm qio 2 compile link parameters ld folder location choose 0 1 default 0 0 use ld folder at sdk 1 use local ld folder at your project notice you can copy ld folder from sdk to your project then do some modifications esp local ld 1 ld file name notice if esp local ld is set to 1 esp ld name must be set esp ld file pro map ld ram map mode default 1 1 1 1 not use app cpu s iram use drom0 dcache to store rodata 1 2 not use app cpu s iram not use drom0 dcache 2 1 use all app cpu s iram as pro cpu s dram use drom0 dcache to store rodata 2 2 use all app cpu s iram as pro cpu s dram not use drom0 dcache 3 1 reverse 16k for app cpu other as pro cpu s dram use drom0 dcache to store rodata 3 2 reverse 16k for app cpu other as pro cpu s dram not use drom0 dcache notice if esp local ld is set to 0 esp map mode can be set esp map mode 1 1 download 1 use espressif s flash download tools download addresses for map 1 1 2 1 3 1 boot bin 0x00000 drom0 bin 0x04000 irom0 flash bin 0x40000 blank bin 0xfe000 for 1mb spi flash for map 1 2 2 2 3 2 boot bin 0x00000 irom0 flash bin 0x04000 blank bin 0xfe000 for 1mb spi flash 2 use esptool py prepare modify esptool options in esp conf makefile supports targets flash all upload boot bin app bin blank bin make flash all flash boot upload boot bin make flash boot notice only needed when boot bin update flash app update user application drom0 bin irom0 flash bin make flash app flash blank update blank bin to system parameter address make flash blank notice only needed when the system parameters needed to be restored for more details please refer to http www esp32 com | os |
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Gooo | gooo go lang web app framework showcasing straightforward no magic web development with the go language http www golang org includes batch template processing and interaction with postgresql databases and model view architecture quick start make sure your gopath and goroot are set for help run go help gopath install run this performs the following commands go build gooo philosophy anti magic so simple that it s complex so complex that it works if it doesn t work publish it modular architecture model struct type no special tags or fields models are just go structs business logic straightforward db methods to be used in the view module view parses templates in tmpl folder and defines how they are rendered uses html template http golang org pkg html template to parse and render fetches rows from database as type model interfaces router handles dynamic and static routes request methods main handler matches the request url against the routes middleware filters for restful routes introspection models implement interface and interface types go s dynamic feature is interface type conversion generally checked at runtime interface json interface byte interface struct interface map string interface getstructvalues interface interface interfacename interface string util generic error handler handlererr err error martin odersky http i imgur com jb8aa jpg 1 tested and approved by typeunsafe copy corporation tell me more why go gophers http golang org doc gopher frontpage png hip new unproven is it good are you good let s gooo test the example blog app resolve dependencies and install install don t want to use postgresql sign up for a free heroku postgres account here https postgres heroku com create a database and save the connection params for the next step or skip this step configure the database connection variable dbparams in the model package model model go gooo http localhost 8080 hello world http localhost 8080 hello world gooo celebrate gooo outside let s gooo write your own gooo app resolve dependencies and install install don t want to use postgresql sign up for a free heroku postgres account here https postgres heroku com create a database and save the connection params for the next step define your model interfaces and configure the database connection in the model package model model go implement your model interface types with anonymous basemodel field use json for type safety and go lang future proofing implement functions and variables available to all models with anonymous basemodel field in the model interface type define your views as request handler functions in the view package view view go write your templates in tmpl go text template syntax http golang org pkg text template define routes in main package gooo go gooo http localhost 8080 hello world http localhost 8080 hello world gooo celebrate gooo outside gooo read these text template http golang org pkg text template html template http golang org pkg html template net http http golang org pkg net http database sql http golang org pkg database sql encoding json http golang org pkg encoding json p align center img src http i imgur com nsscm jpg alt gopher p enjoy aaron lifton | front_end |
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BELLE | img src assets belle logo png style vertical align middle width 35px belle be everyone s large language model engine read this in english readme en md div align center a href https github com lianjiatech belle stargazers github repo stars https img shields io github stars lianjiatech belle style social a code license https img shields io badge code 20license apache 2 0 green svg https github com lianjiatech belle blob main license generic badge https img shields io badge discord belle 20group green svg logo discord https discord gg pmpy53uugq generic badge https img shields io badge wechat belle green svg logo wechat https github com lianjiatech belle blob main assets belle wechat jpg generic badge https img shields io badge huggingface 20repo green svg https huggingface co bellegroup generic badge https img shields io badge huggingface 20repo2 green svg https huggingface co belle 2 div llm engine belle belle belle chatgpt br 2023 09 26 rlhf ppo dpo https arxiv org abs 2305 18290 readme rlhf md train readme rlhf md 2023 08 16 train 3 5m cn https huggingface co datasets bellegroup train 3 5m cn 13 train 3 5m cn with category https huggingface co datasets belle 2 train 3 5m cn with category 2023 08 10 zero inference train readme zero inference md train readme zero inference md 2023 08 07 flash attention 2 train readme md train readme md train docker readme md train docker readme md 2023 07 31 https arxiv org abs 2307 15290 2023 07 27 belle llama2 13b chat 0 4m https huggingface co belle 2 belle llama2 13b chat 0 4m llama 2 13b 40 https github com lianjiatech belle blob main eval eval set json belle llama ext 13b 2023 05 14 belle llama ext 13b https huggingface co bellegroup belle llama ext 13b llama 13b 400 2023 05 11 belle data 10m data 10m 350 train 3 5m cn https huggingface co datasets bellegroup train 3 5m cn 2023 04 19 llama7b bellegroup belle llama ext 7b https huggingface co bellegroup belle llama ext 7b llama 7b bellegroup belle on open datasets https huggingface co bellegroup belle on open datasets 2023 04 18 train belle train https github com lianjiatech belle tree main train deepspeed chat docker 2023 04 18 lora finetune 2023 04 12 chatbelle app chat readme md llama cpp https github com ggerganov llama cpp flutter https flutter dev belle 7b 2023 04 11 eval 2023 04 08 belle data 10m data 10m 40 generated chat https huggingface co datasets bellegroup generated chat 0 4m 200 train 2m cn https huggingface co datasets bellegroup train 2m cn br app 4bit belle 7b m1 max cpu app app chat readme md app https github com lianjiatech belle releases download v0 95 chatbelle dmg mac os img src chat chatbelle demo gif img br belle train train deepspeed chat finetune lora docker belle data 1 5m data 1 5m stanford alpaca https github com tatsu lab stanford alpaca 1m https huggingface co datasets bellegroup train 1m cn 0 5m https huggingface co datasets bellegroup train 0 5m cn belle data 10m data 10m belle eval https github com lianjiatech belle tree main eval 1k prompt gpt 4 chatgpt case pr belle models models meta llama2 https github com facebookresearch llama belle llama2 13b chat 0 4m https huggingface co belle 2 belle llama2 13b chat 0 4m meta llama https github com facebookresearch llama belle llama 7b 0 6m enc https huggingface co bellegroup belle llama 7b 0 6m enc belle llama 7b 2m enc https huggingface co bellegroup belle llama 7b 2m enc belle llama 7b 2m gptq enc https huggingface co bellegroup belle llama 7b 2m gptq enc belle llama 13b 2m enc https huggingface co bellegroup belle llama 13b 2m enc belle on open datasets https huggingface co bellegroup belle on open datasets llama belle llama ext 7b https huggingface co bellegroup belle llama ext 7b meta llama license https github com facebookresearch llama blob main license llama llama diff xor llama belle models https github com lianjiatech belle tree main models bloomz 7b1 mt belle 7b 0 2m https huggingface co bellegroup belle 7b 0 2m belle 7b 0 6m https huggingface co bellegroup belle 7b 0 6m belle 7b 1m https huggingface co bellegroup belle 7b 1m belle 7b 2m https huggingface co bellegroup belle 7b 2m gptq belle gptq https github com lianjiatech belle tree main models gptq gptq colab open in colab https colab research google com assets colab badge svg https colab research google com github lianjiatech belle blob main models notebook belle infer colab ipynb colab colab https colab research google com github lianjiatech belle blob main models notebook belle infer colab ipynb chatbelle app belle chat chat readme md belle https github com lianjiatech belle app flutter macos windows android ios belle docs docs issue prompts br towards better instruction following language models for chinese investigating the impact of training data and evaluation https github com lianjiatech belle blob main docs towards 20better 20instruction 20following 20language 20models 20for 20chinese pdf chatgpt llama 34 chatgpt 1 alpaca gpt3 5 self instruct 2 alpaca gpt4 self instruct 3 chatgpt sharegpt table tr td factor td td base model td td training data td td score w o others td tr td rowspan 2 td td llama 7b ext td td zh alpaca 3 5 4 sharegpt td td 0 670 td tr tr td llama 7b td td zh alpaca 3 5 4 sharegpt td td 0 652 td tr tr td rowspan 2 td td llama 7b ext td td zh alpaca 3 5 td td 0 642 td tr tr td llama 7b ext td td zh alpaca 4 td td 0 693 td tr tr td rowspan 4 td td llama 7b ext td td zh alpaca 3 5 4 td td 0 679 td tr tr td llama 7b ext td td en alpaca 3 5 4 td td 0 659 td tr tr td llama 7b ext td td zh alpaca 3 5 4 sharegpt td td 0 670 td tr tr td llama 7b ext td td en alpaca 3 5 4 sharegpt td td 0 668 td tr tr td rowspan 2 td td llama 7b ext td td zh alpaca 3 5 4 sharegpt td td 0 670 td tr tr td llama 7b ext td td zh alpaca 3 5 4 sharegpt br belle 0 5m clean td td 0 762 td tr tr td td td chatgpt td td td td 0 824 td table belle 0 5m clean 230 0 5m 0 5m p align center img src assets eval set distribution jpg alt llm eval width 400 p chatgpt a comparative study between full parameter and lora based fine tuning on chinese instruction data for instruction following large language model https github com lianjiatech belle blob main docs a 20comparative 20study 20between 20full parameter 20and 20lora based pdf lora lora llama lora model average score additional param training time hour epoch llama 13b lora 2m 0 648 28m 8 llama 7b lora 4m 0 624 17 9m 11 llama 7b lora 2m 0 609 17 9m 7 llama 7b lora 0 6m 0 589 17 9m 5 llama 7b ft 2m 0 710 31 llama 7b lora 4m 0 686 17 llama 7b ft 2m br lora math 0 25m 0 729 17 9m 3 llama 7b ft 2m br ft math 0 25m 0 738 6 score 1000 llama 13b lora 2m llama 13b lora 2m llama 7b ft 2m llama 7b ft 2m lora math 0 25m 0 25m llama 7b ft 2m lora llama 7b ft 2m ft math 0 25m 8 nvidia a100 40gb gpu math 0 25m 0 25m 0 5 lora 0 25m math 0 25m llama 7b ft 2m 5e 7 2 llama 7b ft 2m lora math 0 25m llama 7b ft 2m ft math 0 25m lora lora 0 586 0 559 1 lora 2 lora 3 lora lora lora sft 1 2 3 https github com lianjiatech belle blob main disclaimer br misc belle author bellegroup title belle be everyone s large language model engine year 2023 publisher github journal github repository howpublished url https github com lianjiatech belle article belle2023exploring title exploring the impact of instruction data scaling on large language models an empirical study on real world use cases author yunjie ji yong deng yan gong yiping peng qiang niu lei zhang baochang ma xiangang li journal arxiv preprint arxiv 2303 14742 year 2023 article wen2023chathome title chathome development and evaluation of a domain specific language model for home renovation author wen cheng and sun xianghui and zhao shuaijiang and fang xiaoquan and chen liangyu and zou wei journal arxiv preprint arxiv 2307 15290 year 2023 bloom llama stanford alpaca self instruct br details human assitant i am the spring rain br dancing on your face and body br my dew drenched petals dance with you br bringing life to every plant br br shakespeare i am here to meet you br your works have filled my heart with joy br the beauty of nature in its fullness br makes me feel like a partaker br br as the leaves sway and rustle by br my dew filled hand reaches out for them br the sweet scent of flowers fill the air br and bring me peace from all around br br butterflies flit about as well br glistening in the sun s rays br their wings are made of dew br a beautiful sight to behold br br you see shakespeare i too am one with nature br my dew drenched hair is a symbol of hope br for when we can connect back to our roots br we will be able to make it through this season i am the spring rain br dancing on your face and body br my dew drenched petals dance with you br bringing life to every plant br br shakespeare i am here to meet you br your works have filled my heart with joy br the beauty of nature in its fullness br makes me feel like a partaker br br as the leaves sway and rustle by br my dew filled hand reaches out for them br the sweet scent of flowers fill the air br and bring me peace from all around br br butterflies flit about as well br glistening in the sun s rays br their wings are made of dew br a beautiful sight to behold br br you see shakespeare i too am one with nature br my dew drenched hair is a symbol of hope br for when we can connect back to our roots br we will be able to make it through this season br br br br br br br br br br br br br br br br br br br classify the sentiment of the following sentence into positive neutral or negative br super excited about teaching stanford s first course on large language models check the syllabus out here positive python def binary search arr target br nbsp nbsp low 0 br nbsp nbsp high len arr 1 br nbsp nbsp while low high br nbsp nbsp nbsp nbsp mid low high 2 br nbsp nbsp nbsp nbsp if arr mid target br nbsp nbsp nbsp nbsp nbsp nbsp return mid br nbsp nbsp nbsp nbsp elif target arr mid br nbsp nbsp nbsp nbsp nbsp nbsp low mid 1 br nbsp nbsp nbsp nbsp else br nbsp nbsp nbsp nbsp nbsp nbsp high mid 1 br nbsp nbsp return 1 what s the weather like today 5 br 1 br 2 br 3 br 4 br 5 br 1 br 2 br 3 br br 1 br 2 br 3 details br issue https github com lianjiatech belle blob main how to contribute md discord https discord gg pmpy53uugq https github com lianjiatech belle blob main assets belle wechat jpg star history star history chart https api star history com svg repos lianjiatech belle type date https star history com lianjiatech belle date | bloom instruction-set llama open-models gpt-q instruct-gpt gpt-evaluation chinese-nlp lora instruct-finetune | ai |
teste-front-end-developer | teste para candidatos vaga de front end developer logo icolabora http www icolabora com br vagas imgs icolabora png https www icolabora com br vagas todos que os interessados que fizerem pull request receber o um feedback da icolabora br essa prova consiste em testar seus conhecimentos com b html css javascript sql b entre outras coisas br o conjunto de interfaces disponibilizado leva em m dia b 6 horas b para ser implementado instru es 1 fa a um fork deste reposit rio 2 estude o modelo de processo i processo teste front png i 3 estude os requisitos para elabora o do formul rio e das m tricas 3 implemente o html css das telas com base no layout dispon vel 4 para a intera o das interfaces utilize preferencialmente jquery 5 ap s terminar seu teste submeta um pull request e aguarde seu feedback ps1 o formul rio n o deve submeter nenhuma url br ps2 a url da p gina n o pode ser recarregada em momento algum br ps3 usamos o mesmo teste para todos os n veis de front junior pleno ou senior mas procuramos adequar nossa exig ncia na avalia o com cada um desses n veis sem por exemplo exigir excel ncia de quem est come ando voc pode utilizar qualquer linguagem de pr processador css ou css puro utilizar um task runner de sua prefer ncia utilizar bibliotecas css como compass bourbon animatecss ou outras esperamos que voc realize as consultas no banco de dados que fornecemos torne din mica as buscas e preenchimentos dos campos do formul rio fa a um visual bacana minifique seu css e deixe o na pasta css minifique seu javascript e deixe o na pasta js fa a commit tamb m dos arquivos n o minificados d suporte a ie10 chrome safari e firefox ganhe pontos extras por desenvolver html sem ntico componentizar seu css ser fiel as especifica es dos arquivos validar os campos do seu formul rio antes de habilitar o bot o de envio ux ui material todos os layouts necess rios est o dispon veis na pasta ra z modelo do processo de neg cio dispon vel com o nome i processo teste front png i na pasta ra z para consultas acerca da bpm acesse o activiti user guide http www activiti org userguide bpmnconstructs utilize o webservice de consulta de cep s https viacep com br modelo do banco de dados relacional pode ser encontrado em i modelo relacional png i na pasta ra z biblioteca para cria o de querys mysql via javascript com o nome i mysql lib js i na pasta ra z especifica o deve ser desenvolvido um conjunto de interfaces formul rio e m tricas para a automa o do processo de shipment of a hardware retailer use a criatividade para preencher os espa os em branco nos arquivos fornecidos para tal implemente o html css js do formul rio associado a tarefa i check if extra insurance is necessary i arquivo i task psd i com os seguintes campos b dados do pedido b devem existir 4 materiais de sua escolha vinculados ao pedido do teste n mero de pedido material marca data de compra quantidade do material pre o total b dados do insumo b devem existir 2 insumos de sua escolha vinculados ao pedido do teste al m disso deve ser poss vel inserir mais insumos marca descri o quantidade pre o b dados do solicitante b use o webservice para a consulta de cep nome completo telefone cpf cep endere o complemento cidade estado b dados da entrega b pode ser utilizado o mesmo endere o presente nos dados do solicitante cep endere o complemento cidade estado b c lculos b total do pedido pre o dos insumos x quantidades pre o do material x quantidades b busca b deve ser permitido inserir um numero de pedido e ap s a consulta preencher automaticamente todos os campos do formul rio implemente o html css js do dashboard com os seguintes gr ficos e tabelas arquivo i dashboard psd i gr fico quantidade de pedidos por dia gr fico pedidos por solicitante tabela pedidos pendentes utilizando a biblioteca i mysql lib js i carregue o arquivo i mysql lib js i no seu html html script src mysql lib js type text javascript script chame a fun o mysqlquery passando como parametro a query que voc deseja realizar em formato sql e uma fun o para a captura do retorno da seguinte forma javascript var mysql query select from solicitantes where id 1 mysqlquery mysql query function result mostra o resultado da query console log result para mais informa es baixe a pasta exemplo query para um exemplo de como utilizar a biblioteca ou acesse o link no jsbin http jsbin com vefeyelofi edit html output submiss o para iniciar o teste fa a um fork deste reposit rio crie uma branch com o seu nome completo e depois envie nos o pull request se voc apenas clonar o reposit rio n o vai conseguir fazer push e depois vai ser mais complicado fazer o pull request boa sorte d | front_end |
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front-challenge-spacex | front end challenge graphql api spacex this is a quick coding challenge we designed to assess your qualifications as a potential front end developer it s important to note that this is by no means a test we just want to get a sense of how you write code and solve problems getting started to get started with this challenge 1 fork this repository 2 create a branch with the name yourlevel yourname example jr joaosilva 3 when you finish make a pull request with name yourname example jo o silva and add a tag with your level jr pl sr the challenge we ll be looking for simple well designed and tested only pl sr code in the submission please include a instructions md add prints of your application in the repository and use them in the readme setup instructions how did you decide which technologies to use as part of your solution are there any improvements you could make to your submission what would you do differently if you were allocated more time details use the spacex graphql api https api spacex land graphql and create a project based on this wireframe https bit ly 2swvpsp use your creativity to choose colors etc routes dashboard list the last 10 missions clicking on the card goes to the mission route mission details of the mission image etc button to external link required vue or react or angular tdd only pl sr desirable vue using apollo client | front_end |
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CoolCalc | coolcalc scientific calculator for ios made for object oriented software engineering aau copenhagen this calculator works on all ios devices it was made using gtk and c | os |
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workout-movement-counting | workout movement counting app using dense optical flow and convolutional neural networks this repository contains my coursework as 3rd year bsc in hse i had to create a web app which helps sportsmen to count their movements during the workout br also checkout my medium writeup regarding this problem here https medium com artkulakov how i created the workout movement counting app using deep learning and optical flow 89f9d2e087ac source friends link sk e14ec243ea1ff3bb42c3c4d05067e85c for this purpose i combined the dense optical flow algorithm with a simple cnn network written in pytorch as you can see it is pretty easy to get the idea of what one push up is if we look at how frames are converted to dense optical flow representation in my algorithm images push up jpg thus dense optical flow converts frames to color coded representation and cnn solves a multiclass problem which is to classify each frame as move down move up or not a move to wrap my algorithm in something which can really work i also created a web interface using django here is how it looks like images interface jpg to run the web app follow the instructions below instructions 1 clone the repo and run pip r install requirements txt 2 cd workoutapp 3 run the app with python manage py runserver 4 choose the predefined workout and test | ai |
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Voyager | voyager an open ended embodied agent with large language models div align center website https voyager minedojo org arxiv https arxiv org abs 2305 16291 pdf https voyager minedojo org assets documents voyager pdf tweet https twitter com drjimfan status 1662115266933972993 s 20 python version https img shields io badge python 3 9 blue svg https github com minedojo voyager github license https img shields io github license minedojo voyager https github com minedojo voyager blob main license https github com minedojo voyager assets 25460983 ce29f45b 43a5 4399 8fd8 5dd105fd64f2 images pull png div we introduce voyager the first llm powered embodied lifelong learning agent in minecraft that continuously explores the world acquires diverse skills and makes novel discoveries without human intervention voyager consists of three key components 1 an automatic curriculum that maximizes exploration 2 an ever growing skill library of executable code for storing and retrieving complex behaviors and 3 a new iterative prompting mechanism that incorporates environment feedback execution errors and self verification for program improvement voyager interacts with gpt 4 via blackbox queries which bypasses the need for model parameter fine tuning the skills developed by voyager are temporally extended interpretable and compositional which compounds the agent s abilities rapidly and alleviates catastrophic forgetting empirically voyager shows strong in context lifelong learning capability and exhibits exceptional proficiency in playing minecraft it obtains 3 3 more unique items travels 2 3 longer distances and unlocks key tech tree milestones up to 15 3 faster than prior sota voyager is able to utilize the learned skill library in a new minecraft world to solve novel tasks from scratch while other techniques struggle to generalize in this repo we provide voyager code this codebase is under mit license license installation voyager requires python 3 9 and node js 16 13 0 we have tested on ubuntu 20 04 windows 11 and macos you need to follow the instructions below to install voyager python install git clone https github com minedojo voyager cd voyager pip install e node js install in addition to the python dependencies you need to install the following node js packages cd voyager env mineflayer npm install g npx npm install cd mineflayer collectblock npx tsc cd npm install minecraft instance install voyager depends on minecraft game you need to install minecraft game and set up a minecraft instance follow the instructions in minecraft login tutorial installation minecraft instance install md to set up your minecraft instance fabric mods install you need to install fabric mods to support all the features in voyager remember to use the correct fabric version of all the mods follow the instructions in fabric mods install installation fabric mods install md to install the mods getting started voyager uses openai s gpt 4 as the language model you need to have an openai api key to use voyager you can get one from here https platform openai com account api keys after the installation process you can run voyager by python from voyager import voyager you can also use mc port instead of azure login but azure login is highly recommended azure login client id your client id redirect url https 127 0 0 1 auth response secret value optional your secret value version fabric loader 0 14 18 1 19 the version voyager is tested on openai api key your api key voyager voyager azure login azure login openai api key openai api key start lifelong learning voyager learn if you are running with azure login for the first time it will ask you to follow the command line instruction to generate a config file for azure login you also need to select the world and open the world to lan by yourself after you run voyager learn the game will pop up soon you need to 1 select singleplayer and press create new world 2 set game mode to creative and difficulty to peaceful 3 after the world is created press esc key and press open to lan 4 select allow cheats on and press start lan world you will see the bot join the world soon resume from a checkpoint during learning if you stop the learning process and want to resume from a checkpoint later you can instantiate voyager by python from voyager import voyager voyager voyager azure login azure login openai api key openai api key ckpt dir your ckpt dir resume true run voyager for a specific task with a learned skill library if you want to run voyager for a specific task with a learned skill library you should first pass the skill library directory to voyager python from voyager import voyager first instantiate voyager with skill library dir voyager voyager azure login azure login openai api key openai api key skill library dir skill library trial1 load a learned skill library ckpt dir your ckpt dir feel free to use a new dir do not use the same dir as skill library because new events will still be recorded to ckpt dir resume false do not resume from a skill library because this is not learning then you can run task decomposition notice occasionally the task decomposition may not be logical if you notice the printed sub goals are flawed you can rerun the decomposition python run task decomposition task your task e g craft a diamond pickaxe sub goals voyager decompose task task task finally you can run the sub goals with the learned skill library python voyager inference sub goals sub goals for all valid skill libraries see learned skill libraries skill library readme md faq if you have any questions please check our faq faq md first before opening an issue paper and citation if you find our work useful please consider citing us bibtex article wang2023voyager title voyager an open ended embodied agent with large language models author guanzhi wang and yuqi xie and yunfan jiang and ajay mandlekar and chaowei xiao and yuke zhu and linxi fan and anima anandkumar year 2023 journal arxiv preprint arxiv arxiv 2305 16291 disclaimer this project is strictly for research purposes and not an official product from nvidia | large-language-models embodied-learning open-ended-learning minecraft | ai |
dotfiles | gitter https badges gitter im elasticdotventures community svg https gitter im elasticdotventures community utm source badge utm medium badge utm campaign pr badge project status wip initial development is in progress but there has not yet been a stable usable release suitable for the public https www repostatus org badges latest wip svg https www repostatus org wip b00t b00t cognitive middleware elasticdotventures matrix org presently alpha very unfinished b00t is a polylingual polyframework voice vision cli cognitive format transformer instruction learning approach to automata an idiomatically control language for an assistant that can learn new skills through direction or experimentation using external api provider such as jovo allows b00t to interface with either the pstn phone network or relay through smart assistants targeting cortana google home b00t is built using an open source tool chain inside win11 wsl2 or other linux environment examples of skill target s are ambitious design simulate control and monitor robotic manufacturing cloud infrastructure gpt summarize text and or math write code methods to build develop new skills as directed attend meetings via video audio intercept character avatar synthesis lip sync interactively screen pstn messanger calls sessions make appointments website chat bot write reports visualizations online three js svg collada png maybe excel google sheets write maintain code orm graph schema and interfaces to svg openscad stl collada sql transpilation of python rust typescript wasm skills skills are either bash steps or makefiles which can be automated using ci cd find import 3d objects electronics data sheets to for references in digital shadows future theorize about new object structures physics electro chemistry protein folding etc injest metrics from ebpf hooks for os level threat monitoring telemetry play wingman video games good enough to be fun including social aspects design and print 3d objects assemblies and simulations etc prepare apply video audio for presentations etc purchasing order reconciliation accounting customer service hr using zero code i e node red appsmith or az durable functions these capabilities are rolled up into interactive macros called tasks which when grouped together are called a toil the skills are build in an oregon trail where the operator must decide which tasks are worthy of receiving operating cycles and what if any resources must be used for discovery digestion of new skills in some cases the operator may such as using a mouse joystick voice etc fit the ai by helping it assemble compile etc to teach skills explain materials objects etc the main cost with a system like this is the operational cost might doesn t work the same as an employee being able to consume video audio websites documents data feeds injest participate in social systems such as twitter discord matrix slack ms teams in a poly medium chat interface azure is preferred because sso active directory simplifies a variety of security access control toil in terms of interfacing although at some point i prefer to replace this with zanzibar and offer at least an aws k8s terra form approach but that isn t explicitly necessary for this project since it s all mostly serverless functionally driven smart pipes and should be generic enough that a new software service simply by reading it s api i e an api transpiler that is compatible with one or more of it s messaging or database protocols especially if that s openapi swagger sql graphql or json protobufs it is designed to assist a human or small team but to amplify and obviate the require for most big companies by efficiently replacing people with intelligent robotic process control very wip presently this is only a compendium of my tools undocumented scripts for a future interactive initalization using fzf setup installer i m presently learning rewriting this to rust and solidifying on python typescript interfaces wasm is a dream turns out cargo is amazing and obviates a lot of work rust s complex pattern matching language is awesome reminds me of perl on the embedded side i ve moved from c to rust as well so esp32 s2 c3 are the target hardware platforms but it could run on an rpi or whatever for physical motor sensor i o by generating everything programmatically each physical object will have a digital object shadow that would include inheritance robust math logic to describe how the object is used programmatically describe it s motion etc this makes setting up things like monitoring via opentelemetry straightforward and more standardized across a big complex project for the 3d objects i ve now abandoned my traditional mouse driven parametric cad onshape entirely and moved to freecad so i could use python i ve now discovered openscad and it s vitamin module libraries which aren t terribly well organized i m presently formulating learning how to write a rust transpiler which basically means reading a lot of other peoples rust code into openscad but learning openscad has been awesome rewarding once that task is finished then i will be either begin adding the ai features including clip or cyclegan commands to let the ai design fill textures backgrounds gpt 3 or gpt neox to create svg openscad codex templates i also intend for this eventually to be a massive toil reduction time saver by using git to track approve asset changes objects complex assemblies in sync auto magically automagic without doing file import export between apps presenting the same state across freecad easy blender via collada possibly unity also collada using a git action build chain which can also run locally the resulting approach then be used for visualization simulation training regression testing prior to output of complex fdm 3d printed cnc milled assemblies eventually building toward zero human robotic manufacturing assembly post work once all that is working i will start on the vhdl verilog spicedb pcb circuit simulator as well at which point kicad is the most likely candidate for interface but realistically we ll just be using kicad for it s component libraries and treating the pcb s as complex assemblies this repo is the base it s intended to be used as a project template via a sub module to print in a lot of dependencies and try to provide some harmony to the chorus of oss dev happening around the planet in 2022 the plan is to have a yahoo esque awesome curated list of installable time saving labor reducing tools fundamentals to interface any cloud ai skill next version will feature better docker compose rust cli sharing with contributors advisors feedback testers soon comments issues pr s welcome please keep up to date and get pachyderm support via twitter https img shields io twitter follow b00t style social https twitter com brianhorakh follow us on twitter text plain l b u g s q u 1 s h 1 n g s 0 f t w a r 3 for the benefit of humanity the ultimate compendium of useful multi disciplinary end to end systems engineering toolkit for reduction in difficulty this repo components bash python ts scaffold s shells https starship rs installing termux https doc redox os org ion manual repl html https murex rocks docs guide quick start html https nixos org manual nix unstable command ref nix shell html extensive use of more utils yq jq fzf fdfind etc server architectures x86 nvidia cuda optimized x86 amd64 arm64 rpi alpine planned blocked by 3rd party webinstall dev embedded architectures atmega64 esp32 dev vs code on linux or wsl2 git github issues build ci cd idempotent app templates app config app data app lib app src separated platform io for embedded extensive json yaml toml setup working configurations front end app typescript vue vitesse spa i18n template openauth openid to azure b2c msal2 federated security interfaces rest graphql rxjs zip open opentelemetry setup back end app typescript v4 python 3 9 and others poly lingual container orchestration docker k8 drone ci and others any oci is fine author bias minimalist k8 minikube pachctl data lineage cloud azure google aws ali author bias azure gcp ali aws azure will have the most extensive support initially database ephermal sqlite local sqlite firebird mariadb postgres author bias value engineering to freeze unfreeze projects poly cloud managed firebase nosql etc plurality of storage volume options cloud for online on demand cloud hosted or offline cold messaging protobufs redis rabbitmq zmq mqtt and others security low to perfect per complexity risk requirements poly scenarios artificial intelligence ag robotics hardware sensors iot dev 3d rendering red team cybersecurity ecommerce sales finance accounting chat ops business operations automation crypto banking academic science r d r engineering tiger team problem solving aka math future stack piercer cargo install zellij ebpf filters io uring bindings opentelemetry logging bash bats tap testing https testanything org storytime ai ops interactive voice cli assistant control surface b00t is a cyb3rpunk startup k1t unsuitable for military or antisocial projects per the license for valid inclusion in hackathons elastic license v2 elk2 open license seeking testers investors contributors any feedback peer review star the repo if you think i should keep working on this i m planning to submit this to oreilly as a nutshell book idea b00t from the ground up quickstart bash git clone https github com elasticdotventures b00t git fzf setup tutorial menu bash b00t b00t sh or just load the tooling environment bash source b00t b00t bashrc or checkout the repo 85 of the finished work is presently in the bash folder b00t a view from the ground up b00t is systems engineering poly glue publishing transpiler it will use menus to drive a cli for choosing and updating poly stack scenarios which are infrastructure code templates working samples for all relevant stack layers b00t intends to provide a curated starting point for a variety of poly disciplinary aka complex projects a modern curated catalog of computer interfaces academic and elastic libraries this project was born partially out of necessity and the rest out of pure curiousity for what could be built and how quickly a menu driven oregon trail of architectural templates for a project b00t is colorful and not traditional it is cheeky but non discriminatory it is poly lingual but it may not be suitable for traditional corporate or government environments as such it is better suited for the home inventor hobbyiest or open source hardware developer fabricator children under 16 should ask your parents before using b00t emoji and ideogram based utf8 i18n called storytime v1 naming conventions for an oregon trail themed menu or anything goes cli project init to exit coverage this non traditional approach offer faster diagnostics and efficient operational characteristics allowing svelte teams for ops scaffolding for containers installation of development environment naming with emoji is improved with menus intellisense editors and cli tab completion b00t heavily uses vs code extensions for read write running private or hosted zero code engines an artificially written operated infrastructures ai ops hopefully improving indivual productivity to 100x for creating poly disciplinary projects presently b00t hopes to become a modern slackware built on linux a deployable yahoo directory of open source with some light tutorial learn only what you desire to with some minimal explanation of truth consequences oregon trail of decisions framework for generating idempotent templates to build complex maintainable efficient systems engineering principles b00t will attempt to present itself in many formats and give operators a massive common arsenal of pre interoperably configured tools example projects in python typescript bash etc poly lingual as well as popular patterns for integrating tools from the ground up b00t includes some strong opinions such as vs code development environment b00t can be thought of as a cloud shell enhancement and it s primary role is as a catalyst to easily deploy a reliable pattern for interconnection between applications a menu driven or cli driven poly stack poly interface poly software awesome distribution examples of complex json yaml manipulation complete automation of daily activities ultimately i hope to add a cli voice interface based on jovo something like how tony stark interacts with jarvis for software projects and it could sell support and provision the software to accomplish this i need to structure this repo tools in a uniquely b00t way which i m attempting to make easier for myself and hopefully you b00t is variable low to high security postures for example there are a variety of security postures available from secret keys stored in a local env file a self hosted vault or a cloud based key repository protocols packages have been curated based on their utility in poly lingua poly cloud technology library aka b00t the key is providing a known set of steps to differentiate the posture between dev build release b00t framework itself is k8 minimalist but very pro git pipelines automation k8 kubernetes should only be used when it is absolutely necessary and then as sparingly as possible pipelines idempotentent containers those are 100 awesome k8 support is my present area of improvement in bash scripting todo trying not to re invent the wheel found a bunch of new tools and refactoring mid project bsfl https github blog 2015 06 30 scripts to rule them all https github com bashup devkit https github com bashup mdsh https github com bashup events https github com bashup lore https github com bashup loco azure graph openid authentication scaffold in typescript spa vue vite vitesse based template please excuse the scattered bits work in progress you might try the setup file or explore the color repo bash cd b00t 01 startup sh is presently suitable for probably absolutely nobody besides brian horakh the author but i welcome anybody with similar passions in pre dev stage projects incubators self reliant engineering teams no vendor support except the author git issues who want to sponsor my progress or would like to use this framework in their own companies sponsor through github or the b00t ethereum address is https vittominacori github io ethereum badge detail html address 0x24bd79c0efff201bd5b24b622d5ea09f93dbfaa3 please note goes a long way towards demonstrating to my wife that this warrants the amount of attention i give it versus her projects thanks ask for if you want social likes github twitter follows share with your friends b00t vision a multi stack directory from the ground up with directory of poly lingual interfaces like docker moby b00t is for ai robotics cybersec code hackathon groups or red teams who need to tandem or poly hackathon global startup code a thon who need fast iteration and shared debugging hackers who want to deploy multi language code across serverless functions docker containers in azure system engineers or integrators building a container system application developers looking for an easy way to run their python typescript and related applications in containers with vs code azure tooling best practices b00t intends to provide an early stage complete open ops stack for a company examples included to quickly easily and affordably minimize and compartmentalized best practices templates for pr0j3ct design caching and also runtime isolation for offpeak processing saving m0n3y other layers included to reduce costs and monitor integrity but can also be accessed as a slimmed down bare os and should probably work in anything which can run aptitude package manager usually debian ubuntu this targets ubuntu all architectures what b00t intends to provide functional examples opinionated toolchoices vs code dev environment plugins fzf menuing and high automata with detailed config notes repeatable clean environment with sane defaults that can be easily disabled i e remove python or remote typescript select database deployment zones etc for application developers application design best industry practices security built in oauth openid hardened determinsic patterns using azure durable functions multi facet ideogram cyb3rpunk emoji pinyin filled logs metrics such 3117 multi stage dev build test deploy ci cd is presently done through github azure app services containerized projects using k8 esque pipelines especially well suited for machine learning create cloud deploy pipelines bi lingual projects using python typescript or others your project demo modules roadmap using b00t v0 present work in progress v1 aiia call center http github com elasticdotventures aiia callcenter small business nlp tts stt call center intelligent assistant based on jovo framework with additional interfaces for discord slack bigcommerce integration planned v2 dongxi inventory http github com elasticdotventures dongxi discrete inventory accounting ethereum solidity interfaces bigcommerce integration v3 gr0w p0t b0t http github com elasticdotventures gr0w p0t b0t ai driven hydroponic biogenesis appliance hydroponic farm controller built on esp32 with ai currently under development internal libraries and essential tooling docker deploy then integrate az with bicep fzf menus installer b00t the vision what about b00t elasticdotventures b00t is a highly opinionated set of tools for deploying azure cloud services with ev libraries called c0re mostly for azure b00t brings together a plurality of powerful cloud tools to to encourage a non traditional but extremely p0werful mix martial art of code which itself is a curated catalog of utilities to manage infrastructure as code presently dev ops but with an eye toward future ai ops this text here is mostly visionary explaining the what about b00t which is presently early stage being re b00t ed from my prior projects b00t syntax rationale the b00t approach uses incorporates 1337 speak for c0mm0n words to encourage brevity at all layers the 1337speak used in b00t is primarily used to make unique pnemonics that are substantially easier to grep during a subsystem trace across layers in the stack thus providing f1ng3r printing glue itself isn t a tool it s designed to demonstrate how to deploy for example deploy a harware or machine learning transformer models affordbly at scale the naming models create colorful and meaningful filters to radically improve code quality debuggability and incorporating zero code deterministic actions azure logic functions the higher visual payload of short 1 4 character emoji hsk1 in names is informative and has valuable screen real estate win is emoji keyboard in windows but emoji with cut and paste intellisense make this easier than you d think especially when you type d0cker and a pops up in spell check using a custom dictionary 1337 mechanics generally indicate logical role or purpose using tab complete or mouse hover in vs code intellisense ide and cli makes it easy and artistic on the screen reinforcing art in code b00t focusing on the pictures and basic glyph optimized based simplified mandarin only reading the code when it s necessary since the human brain is better at visual pattern sorting than it is reading this approach allows for grouping by symbol and simplifies some aspects of command line searching properties test generation and several other aspects built to deploy azure logic connectors azure durable functions with python typescript connectors azure service bus azure keyvault configs azure arm bicep check jargon md for more the full glossary naming conventions docker python typescript emoji indicates things like designee consignee etc this is an important aspect of the storytell logging storytell creates really colorful transaction logs error dumps these will eventually be extended to perform basic ml application forensics monitoring these meta patterns using computers to monitor computers helps our soon to be obsolete primate brains abstract patterns that wouldn t be nearly as obvious in regular english text and notice problems and inform the determinisitic control surfaces to take action isolate block hold ignore with possible consequences this isn t the whole application freezing it s a message in an application or a corrupt video frame grabber in a video stream any payload which doesn t match the model it s not lost forever it s simply flagged for review emoji in code one of the most controversial aspects of b00t is it s heavy use of emoji and pinyin the reality is nlp natural language processing of english is really difficult expensive less reliable than the one character per word symbol approach in the b00t world any emoji which is red in color is bad a warning color if a build process has any red symbols that appear that s a problem even the cherry is reserved as a missing element a token which the user could solve and maybe someday earn points b00t is bi lingual one of the c0des it understands is bash script here s a sample bash function taken from the c0re to see if a machine is running windows system for linux version 2 abbreviated to wslv2 the emoji colorfully demonstrates this bi lingual principle bash microsoft windows linux subsystem ii https docs microsoft com en us windows wsl install win10 function is wslv2 v2 return cat proc version grep c microsoft standard wsl2 and here s how to decode it penguin linux tux mascot blue heart microsoft google aws windows self explanatory may not appear on android so without knowing what wslv2 is using only three symbols you can infer a lot about it disclaimer this is alpha software b00t is provided as is b00t is rique nfsw for example a default project could be auto named butt plug or something like that it s intentionally cheeky and non discriminatory b00t contains a powerful build process that can muster substantial resources mixed martial art of coding storytime logging b00t is designed around the idea of storytime logging that includes emoji hsk 1 chinese vocabulary please don t be intimidated default settings leave english translations on the author brianhorakh is a native english speaker and multi language polygot spanish mandarin italian german all have their own linguistic style and strengths think of this as mixed martial art of coding i m not suggesting anybody actually learn chinese your browser already knows it install google translate right click any symbol tf use pinyin how much information can be transmitted in a symbol vs a character hsk1 is level 1 chinese mandarin language using simplified pinyin symbols ideograms for action commands and stack layer naming conventions designators for frontend qi nmi n backend b imi n in correct chinese files would be named like this file relates to the front end file backend but wait why waste a character file front end firewall file backend container now let s add some emoji file front end firewall file backend container these are only illustrative examples to you show you symbology using a browser plugin you can translate anything in the documentation easily but by integrating b00t scripting at various stages it produces a well defined pattern based marshalling summary layer of logic for inputs and outputs of different subsystems simplified pinyin is a screen glyph optimized font that means hsk1 characters are by design very easy to read learn presently emoji pictograms are second class languages are regionally ambiguous i e has a plurality of meanings see jargon md readme syntax md wait what does b00t do b00t is a novel and opinionated pipeline orchestration system with integrated vs code development environemnt ci cd pipeline base system which is suitable for deploying any cloud scale state less machine learning project in frameworks such as nvidia cuda pytorch tensorflow etc as desired b00t provides the base idempotent templates for resources public private code libraries written in azure arm bicep the ultimate output is a fully operational cloud resource group sensibile file shares key vaults monitoring logging scaffold skeletons in ts python as well the c0re which presents itself as an interactive filesytem blob storage python typescript bindings i ll eventually add some higher level vue templates and hardware iot arduino esp32 templates as well one unique aspect of b00t is that it will allow the ultimate images to be removed using docker dive during the publish to live production from the b00t perspective it s going to help you build something it s only a foundation further application templates can be built on b00t and then easily upgraded taking advantage of new features in cloud based determinsic systems such as azure logic functions containers can be frozen fully loaded or kept hot with standbys based on a load balancer such as a cloud function or container can be readonly nvm e backed memory blob that is awaiting a trigger probably by an inbound http websocket e poll or io submit the published container can be stripped down thus improving both size security posture by removing tools configuration files from public facing images cloud based appconfig stores keyvaults are used keyvaults contain types that are first order types which are aware the passwords etc in them must be kept secure for example in azure logic functions secure tokens such as passwords or access keys from a vault are tagged and automatically beautifully filtered from logs as well this makes compliance user privacy easier extensive use of pipelines and messaging queues allow for tests and other large jobs to be run in parallel at cloud scale why is b00t opionated mean for example b00t believes that vs code with it s intellisense typescript python docker azure aws gcp code auditing and plurality of other useful extensions makes vs code the one true editor the author believes b00t pattern works best when using remote containers which is one of the layers it builds configures the b00t organizational pattern is formatted around an intentionally lean svelte enterprise everything is automated and structured for easy updates using gitops and json debugging is on by default but can be reduced later to save serverless consumption plans are also default and cost centers are isolated by project resource group for good reporting security b00t assumes an agile cadence of releasing early and continuously integration fast fail b00t also is a k8 minimalist recognizing that k8 has a large footprint and creates significant unnecessary complexity for cloud native non legacy applications resource division b00t uses azure landing zone best practice patterns to orchestrate away a considerable amount of complexity in terms of assembling a plurality of well known popular libraries and tools together inside docker containers that then fit into a local inner outer development loop b00t tries not to reinvent the wheel b00t assumes vs code as an integrated environment thus prescribing a suggest list of ide extensions along with a sufficiently pnemonic unique naming approach using ideograms pinyin command glyphs for a variety of tasks such as idea routing error handling storytell mode an emphasis is put on windows development but uses a remote container development environment so it s highly agnostic of individual os choice b00t relies heavily on intellisense and the vs code extension ecosystem especially it s integration with azure for a variety of 1 click tasks c0de b00t presently b00t generally expects to be in c0de b00t ultimately b00t will include internal tooling sufficient to b00t a company or product in a few hours it presumes bash typescript and python environment and also works to some extent on raspberry pi i e no nvidia cuda i intend iot esp32 and arduino functionality in future releases built on top of the b00t template plans to compile a variety of unique reporting summarized using b00t notation to quickly assess project quality code sentiment analysis and identify weak spots presently this is done with tabnine etc what is idempotence determinism the primary preferred pattern for operational code is to operate with as much idempotence isolation as possible also to have the option of both deterministic and non deterministic business application logic https en wikipedia org wiki idempotence idempotence uk d m po t ns 1 us a d m 2 is the property of certain operations in mathematics and computer science whereby they can be applied multiple times without changing the result beyond the initial application a deterministic algorithm is an algorithm which given a particular input will always produce the same output with the underlying machine always passing through the same sequence of states python python severless backend functions with a plurality of messaging socket options for ipc logging azure insight authentication scaffold is azure insight future infrastructure for algorithimic pattern monitoring effectively an ai regression that determines if the code is working typescript vite vue vitesse vitestestee vitestestee http github com elasticdotventures vitestestee is a sample project based on vite vue vitesse openid ms graph ms adslv2 using azure functions and azure logic apps for orchestrating actions which allows a b00t stack to behave as a globally distributed finite state machine this is the higest level of durability which can be assigned to a software platform and is suitable to running fail safe systems such as nuclear reactors the author explicitly disclaims any responsibility for circumstances occurring decide to use b00t to run your own backyard reactor https en wikipedia org wiki deterministic system what is so opionated 0mg b00t tries very hard to be templates and tools tnt but inevitably through the selection of those it s opinions on best approach for example snapd packages are at the core of ubuntu and for various reasons ubuntu is the base image even if you start at alpine linux it s going to look very ubuntu ish if you use b00t the organizational pattern is formatted around a cross competency don t make me think any more than i need to so it assigns emojis to meanings this allows for the system to implement story tell during logs showing entire transactions as a series of pictograms colorful markov chains here is a sample of the projects layout opinion here are the projects opinion hoome b00t your configs your project each project has it s own directory c0de b00t where your projects live project your configs note improve security posture make upper level filesystems readonly and removing configs from lower levels using docker dive files c0de rationale 4 characters 01 start sh start here run this 01 start sh c0de b00t contains the repo put this repo here bash anything in a sh templates bash c0de init sh also the main init script called from 01 start sh dockerfile base docker image standard docker additional docker build files emoji coded c ng layers python python stuff that will probably end up in t00ls node ts typescript libraries c0de cloud azure azure cloud google google cloud still fresh aws aws cloud nothing planned here presently aws aws cloud nothing planned here presently by subscribing to this pattern an effort is made to obviate certain things layers are built upon layers for example a deployed system can be wiped of dockerfiles using rm rf dockerfile docker this is handy at later builds for example a git filesystem can be stripped of utilities that is no longer needed once that is compressed at a docker buildx layer then that information has destroyed during the idempotent container creation tools of b00t git bash jq https stedolan github io jq download yq fzf python node ts docker b00t assumes the author will ultimately decide to end up using a combination of stateful logic so it simplifies the interface to those by creating a unified command language that can be further build on there is a method to the madness i assure you the patterns utilize serverless consumption plans whenever possible the plan is to eventually include complete vs code project files plugin this assumes the developer s are using a three stage release model innerloop local outerloop cloud and or local production live each of those moving the data to the cloud and toward the public no attempts are made want to see examples emoji hsk1 chinese https brianhorakh medium com emoji logging warning much silliness ahead 4cae73d7089 txt c0de b00t this bootstrap code c0de b00t 01 start sh setups up environment c0de b00t 02 project sh create a new project with tooling c0de b00t zz remove sh clean up a project get started note someday soon ish i ll have this as a deploy to azure button working bash for now export resourcegroup newproject export region useast mkdir p c0de cd c0de git clone git github com elasticdotventures b00t git cd b00t 01 start sh that will start running the soon to be interactive installer then once you ve setup b00t you can start to create your own projects to start a new project c0de b00t up new or my project id your project c0de b00t up new my project id b00t will create your project in c0de my project id in the future to upgrade b00t you can simply use git or clone aiia aiia is the tool being built in b00t it s an nlp call center application http elasticdotventures aiia callcenter when finished you ll have a fully integrated development environment with secure language bindings to two languages full permission provision resources with budget friendly serverless consumption models by default to cleanup not finished c0de b00t zz cleanup sh my project id emojis chinese on the cli the author is a hardcore cli guy too for some things using your mouse to copy paste is better since it eliminates fat fingers let s keep it real nobody except absolutely masochists would try to hand type az resource strings so b00t strings are no different you will need to do some cutting and pasting you probably won t like b00t if you re developing on a digital vt100 yeah nah how to move around b00t on the cli you use the cli you re 1337 0k4y hack your brain to use tab complete wildcard shortcuts for directories with emojis or mixed case use wildcards to hit targets so cd c0 will chdir to c0de generally the targets use emoji hsk at the end but as an exercise here s a badly named directory c0de b00t bicep arm azrresmgr could be accessed from it s pwd using any of the following cd command cd bicep cd arm cd azres cd technically this file is misnamed should be bicep arm azrresmgr on windows make sure you re using wsl2 on unbuntu 20 04 with windows terminal preview rather than the default shell and emoji works fine putty and vs terminal both work for outbound ssh and sakura for inbound x term rdp if you re terminal doesn t support emoji switch terminals if you re still using vim you re missing intellisense in vs code and literally every single task is more difficult and error prone tools like azure arm bicep assume vs code intellisense for their transpiler also so no vim emacs support there storytelling in emoji hsk1 chinese i m not gonna here just read my medium https brianhorakh medium com emoji logging warning much silliness ahead 4cae73d7089 b00t what i m building wizard menu cli system engineering poly interface glue of many best practices for anybody that desires a dont make me think more than i need to kiss approach to technical systems engineering accomplished through easy idempotent deterministic meaning predictable container design integration lifecycle a full lifecycle open source systems tool inception creation and deterministic operation from dev to release compliance logging cyber security to exit future https unikraft org | cloud |
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olympic-data-analysis-azure | olympic data analysis azure the tokyo olympic data analysis on azure project is a comprehensive solution for analyzing and visualizing olympic games data using various azure services this project aims to showcase how to leverage the power of cloud computing and azure s data services to gain insights from historical olympic data by combining azure databricks azure data factory and other azure resources this project provides a scalable and efficient way to process transform and analyze large volumes of olympic data table of contents introduction introduction architecture architecture technologies used technologies used getting started getting started prerequisites prerequisites data ingestion data ingestion data processing data processing conclusion conclusion introduction the olympic data analysis on azure project demonstrates how to build an end to end data analysis pipeline on the azure cloud platform this involves ingesting raw olympic data transforming it into a suitable format performing analysis and creating insightful visualizations the project provides an example of how to integrate and utilize azure databricks azure data factory and other azure services to achieve these goals architecture architecture images arch png the architecture of the project consists of the following components azure databricks used for data processing transformation and analysis it provides a collaborative and interactive environment for running spark based jobs azure data factory manages and orchestrates the data workflow it is responsible for data ingestion from various sources data transformation and scheduling of jobs azure storage serves as the data lake for storing raw and processed data it can also host intermediate results generated during the analysis azure sql database stores the cleaned and transformed data making it accessible for visualization and reporting power bi connects to the azure sql database to create interactive and visually appealing dashboards for data exploration technologies used azure databricks azure data factory azure storage azure sql database azure synapse analytics resource group images resource group png getting started prerequisites azure subscription azure databricks workspace azure data factory instance data ingestion datafactory images datafactory png data processing databricks images databricks png the data processing stage involves cleaning and transforming raw olympic data into a structured format suitable for analysis this step takes advantage of azure databricks distributed computing capabilities for efficient processing conclusion the olympic data analysis on azure project demonstrates how to leverage azure services for processing analyzing and visualizing large scale data by following the setup and guides provided in this repository you can adapt the project to other domains and expand its functionalities happy analyzing author tknishh https github com tknishh | azure-storage databricks-notebooks datafactory synapse-analytics de-project olympic-data | cloud |
astroCV | astrocv astrocv https github com astrocv astrocv is a package intended to contain computer vision algorithms and methods for processing and analyzing astronomical datasets the main goal is developing and promoting the use of modern computer vision algorithms for solving scientific problems in astronomy for installation instructions see the online documentation for required libraries or check the installation guide installation guide md in this source distribution the paper on astrocv is in this file https github com astrocv astrocv blob master astrocv ascom accepted arxiv version pdf contents galaxy detection in readme md you can find a detailed guide on how to train your own network four examples can be found in this folder these examples show detections in different images the first and second example work with jpg images the third example downloads images from the web the fourth example processes three fits images for the same photo i r and g filters then applies different filters to the rgb matrix e g lupton sqrt and finally tests the detector with the images in the traning folder you can find py codes to download the images from sdss convert them into jpg and process and prepare files to be traning ready tutorials this folder contains introductive tutorials to computer vision deep learning and creating a dataset from the ngfs survey tools color image in this folder you can find two py scripts for making galaxy snapshots and creating jpg images download data download ngfs data https www scidrive org scidrive scidrive html share ogdxruytnlgvgti download astrocv data https www scidrive org scidrive scidrive html share 7yanf7v8snbzigi license astropy is licensed under a 3 clause bsd style license see the license file | computer-vision image-processing astronomy lsst | ai |
awesome-zephyr-rtos | dark logo assets awesome zephyr rtos logo dark png gh dark mode only ligh logo assets awesome zephyr rtos logo light png gh light mode only awesome zephyr rtos awesome https awesome re badge svg https awesome re the zephyr rtos is based on a small footprint kernel designed for use on resource constrained and embedded systems from simple embedded environmental sensors and led wearables to sophisticated embedded controllers smart watches and iot wireless applications what is an awesome list https github com sindresorhus awesome blob main awesome md contents official resources official resources libraries libraries tools tools open source hardware open source hardware videos videos learning material learning material official resources zephyrproject org https www zephyrproject org official website docs zephyrproject org https docs zephyrproject org project documentation github https github com zephyrproject rtos project github organization zephyr https github com zephyrproject rtos zephyr main repo west https github com zephyrproject rtos west swiss army knife command line tool sdk ng https github com zephyrproject rtos sdk ng next generation toolchains host tools example application https github com zephyrproject rtos example application example out of tree application that is also a module docker image https github com zephyrproject rtos docker image docker image suitable for development and ci discord https discord com invite ck7jw53nu2 community chat hosted on discord mailing list https lists zephyrproject org g main subgroups mail web based mailing list powere by groups io youtube https www youtube com c zephyrproject conferences videos and event highlights blog https www zephyrproject org community blog rss https www zephyrproject org category blog feed posts from the project and community twitter https twitter com zephyriot linkedin https www linkedin com company the zephyr project facebook https www facebook com zephyriot various social feeds newsletter https www zephyrproject org newsletter quarterly newsletter ambassadors https www zephyrproject org ambassadors list of community experts vulnerability alert registry https www zephyrproject org vulnerability registry email notifications of vulnerabilties store https zephyr project myspreadshop com get merch job board https www zephyrproject org careers search roles from zephyr member companies libraries application frameworks gsoc 2022 arduino core https github com zephyrproject rtos gsoc 2022 arduino core arduino core api module with an arduino c style abtraction layer chre https github com zephyrproject rtos chre context hub runtime environment chre is android s platform for developing always on applications called nanoapps control https github com swedishembedded control a control systems design library written in pure c that provides you with advanced algorithms for control state estimation and model identification specifically designed for use on embedded systems micro ros zephyr module https github com micro ros micro ros zephyr module ros 2 for microcontrollers open amp https github com zephyrproject rtos open amp open asymmetric multi processing openamp framework sense vm https github com svenssonjoel sense vm bytecode virtual machine for microcontrollers swedish embedded platform sdk https github com swedishembedded sdk swedish embedded platform sdk is a comprehensive platform for firmware development wasm micro runtime https github com bytecodealliance wasm micro runtime lightweight standalone webassembly wasm runtime zpp https github com lowlander zpp c 20 framework filesystem fats https github com zephyrproject rtos fatfs generic fat exfat filesystem module for small embedded systems littlefs https github com zephyrproject rtos littlefs little fail safe filesystem designed for microcontrollers nffs https github com zephyrproject rtos nffs flash file system prioritizing minimal ram usage reliability hal pal cmsis https github com zephyrproject rtos cmsis standardized api for the cortex m processor core and peripherals libmetal https github com zephyrproject rtos libmetal abstraction layer across user space linux baremetal and rtos environments iot cloud anjay zephyr https github com avsystem anjay zephyr c implementation of the client side oma lwm2m protocol edge impulse https github com edgeimpulse example standalone inferencing zephyr machine learning on edge devices golioth https github com golioth golioth zephyr sdk device management cloud enablement platform memfault https github com memfault memfault firmware sdk tree master ports zephyr cloud based debugging observability openhaystack zephyr https github com koenvervloesem openhaystack zephyr track personal bluetooth devices via apple s massive find my network send my sensor https github com koenvervloesem send my sensor upload sensor data from a device without internet connection by ab using apple s find my network thingset zephyr sdk https github com thingset thingset zephyr sdk a software development kit sdk based on zephyr rtos to integrate communication interfaces using the thingset protocol into an application with minimum effort see https thingset io networking protocols bacnet stack https github com bacnet stack bacnet stack bacnet open source protocol stack for embedded systems linux and windows canopennode https github com zephyrproject rtos canopennode canopen stack civetweb https github com zephyrproject rtos civetweb embeddable web server cbor https cbor io concise binary object representation tinycbor https github com zephyrproject rtos tinycbor small cbor library qcbor https github com laurencelundblade qcbor comprehensive cbor library zcbor https github com nordicsemiconductor zcbor cbor library that includes support for cddl cosy https github com lindemer cozy cbor object signing and encryption cose greybus for zephyr https github com cfriedt greybus for zephyr protocol for hotpluggable devices nanopb https github com zephyrproject rtos nanopb protocol buffers for embedded systems openthread https github com zephyrproject rtos openthread thread mesh networking protocol pjon https github com gioblu pjon multi master multi media network protocol s2opc https gitlab com systerel s2opc open source opc ua toolkit designed with security and embedded devices in mind security aerology https github com linaro aerology inspect zephyr and tf m applications post mortem mbed tls https github com zephyrproject rtos mbedtls c library that implements cryptographic primitives x 509 certificate manipulation and the ssl tls and dtls protocols mcuboot https github com zephyrproject rtos mcuboot a secure bootloader for 32 bits microcontrollers tf m https github com zephyrproject rtos trusted firmware m platform security architecture psa for armv7 m and armv8 m tinycrypt https github com zephyrproject rtos tinycrypt cryptographic library with a minimal set of standard cryptography primitives misc ecfw zephyr https github com intel ecfw zephyr embedded controller for low level tasks on a motherboard like power sequencing grbl https github com iwasz zephyr grbl motion control for cnc milling lvgl https github com zephyrproject rtos lvgl complete graphics library lz4 https github com zephyrproject rtos lz4 extremely fast compression algorithm pinetime zephyr https github com najnesnaj pinetime zephyr smartwatch operating system zephyr rust https github com tylerwhall zephyr rust api bindings libstd and cargo integration for rust linaro sensor pipeline https github com microbuilder linaro sensor pipeline secure data pipelines pysvd2dts https github com thedigitaledge pysvd2dts creates a zephyr devicetree file from an arm cmsis svd file sof https github com zephyrproject rtos sof audio digital signal processing dsp firmware infrastructure and sdk spinner https github com teslabs spinner motor control firmware based on the field oriented control foc principles tflite micro https github com zephyrproject rtos tflite micro tensorflow lite for microcontrollers zbus https github com zephyr bus zbus inter thread communication bus zscilib https github com zephyrproject rtos zscilib scientific computing library zcalendar https github com bpbradley zcalendar calendar api with rtc integration zmk https github com zmkfirmware zmk keyboard firmware with a rich featureset and broad hardware support tools build config bazel2zephyr https github com gatcode bazel2zephyr embedded bare metal development using bazel cmake device tree dtsh https github com dottspina dtsh shell like interface to devicetrees kconfig make ninja swedish embedded platform sdk docker image https github com swedishembedded develop docker containers for ci development compilers note the official sdk includes several compilers gnu arm embedded arm compiler 6 intel oneapi toolkit designware arc metaware development toolkit mwdt cadence tensilica xtensa c c compiler xcc espressif tools editors ides visual studio code ardesco vscode extension https github com ericsson ardesco vscode extension ericsson ardesco device development extension zephyr tools for vscode https github com circuitdojo zephyr tools circuit dojo designed zephyr tools to make getting started with zephyr a snap embedded tools https marketplace visualstudio com items itemname ms vscode vscode embedded tools a register viewer for cmsis svd files and an rtos data viewer nrf connect for vs code https marketplace visualstudio com items itemname nordic semiconductor nrf connect platformio https docs zephyrproject org latest guides platformio index html vs code importer https github com smrtos zephyr2vsc zephyrus https github com tuscale vscode zephyrus other editors ides eclipse https github com zephyrproject rtos eclipse plugin cmake zephyr helpers https github com thirdpin zephyr cmake helpers enhance cmake automation for use with vs code flash debug test aerology https github com linaro aerology inspect zephyr and tf m applications post mortem atmel sam ba edtt embedded device test tool https github com zephyrproject rtos edtt esptool gnu tools gdb binutils https github com zephyrproject rtos binutils gdb mcumgr https github com zephyrproject rtos mcumgr android https github com nordicsemiconductor android nrf connect device manager ios https github com nordicsemiconductor ios nrf connect device manager web https github com boogie mcumgr web openocd https github com zephyrproject rtos openocd pyocd segger https github com zephyrproject rtos segger j link https github com zephyrproject rtos libjaylink twister simulation acrn qemu https github com zephyrproject rtos qemu network tools https github com zephyrproject rtos net tools seabios https github com zephyrproject rtos seabios renode https zephyr dashboard renode io xen version control zephyr pre commit hooks https github com cgnd zephyr pre commit hooks a collection of pre commit https pre commit com hooks for use with zephyr open source hardware zswatch https github com jakkra zswatch the open source zephyr based smartwatch including both hw and fw videos zephyr developer summit june 2021 https www youtube com playlist list plzrqulb6 ipg39tvb dekioss5wqlbk6i embedded linux conference open source summit sept 2021 https www youtube com playlist list plzrqulb6 ipefltsxvm0xbuu84b8 sum7 zephyr videos from golioth https www youtube com playlist list plxgira7qd83dljhi7f3euggsf4hbvhonp zephyrrtos https www youtube com hashtag zephyrrtos videos tagged with zephyrrtos learning material tutorial for beginners https github com maksimdrachov zephyr rtos tutorial nordic developer academy https www nordicsemi com support nordic developer academy ultimate embedded firmware devops infrastructure https www udemy com course ultimate embedded firmware devops infrastructure contribute contributions welcome read the contribution guidelines contributing md first | zephyr zephyr-rtos iot real-time microcontroller embedded mcu rtos lists awesome awesome-list | os 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AI-Projects-for-Healthcare | ai projects for healthcare this repository is included artificial intelligence machine learning data science computer vision projects related to healthcare information about completion complete work in progress incomplete table of contents ai projects for healthcare ai projects for healthcare table of contents table of contents projects in bootcamp education projects in bootcamp education projects in healthcare artificial intelligence education projects in healthcare artificial intelligence education research projects research projects projects in bootcamp education breast cancer classification https github com edaaydinea ai projects for healthcare blob master breast 20cancer 20classification 20breast cancer classification ipynb the aim of this project is classification the tumors into malignant or benign with machine learning techniques detecting covid 19 with chest x ray using pytorch https github com edaaydinea ai projects for healthcare blo 731619a7f8e041059d15832d56c1ca1df540a221 detecting 20covid 19 20with 20chest 20x ray 20using 20pytorch detecting 20covid 19 20with 20chest 20x ray 20using 20pytorch ipynb the aim of this project is detection covid 19 on the chest x ray images by using pytorch the dataset https www kaggle com datasets tawsifurrahman covid19 radiography database is taken from kaggle diabetes prediction with pyspark mllib https github com edaaydinea ai projects for healthcare blob 731619a7f8e041059d15832d56c1ca1df540a221 diabetes 20prediction 20with 20pyspark 20mllib diabetes prediction ipynb the aim of this project is to build logistic regression model using pyspark mllib to classify patients as either diabetic or non diabetic this project is a guided project link https www coursera org projects diabetes prediction with pyspark mllib available on coursera heart failure data analysis https jovian ai edaaydinea health failure prediction the aim of this project is to make a detailed exploratory data analysis on the heart failure prediction dataset https www kaggle com datasets andrewmvd heart failure clinical data which is taken from kaggle by using the plotly library relationship between covid 17 happiness in that country https github com edaaydinea ai projects for healthcare blob master relationship 20between 20covid 19 20 20 26 20happiness 20in 20that 20country covid19 20data 20analysis 20notebook ipynb the aim of this project is to work on whether where is any relationship between the spread of the coronavirus in a country and how happy people are living in that country or not the dataset is taken from covid 19 dataset published by johns hopkins university and world happiness report projects in healthcare artificial intelligence education dna classification project https github com edaaydinea ai projects for healthcare blob master dna 20classification 20project dna 20classification ipynb the aim of this project is to find out whether the dna sequence is the promoter heart disease classification project https github com edaaydinea ai projects for healthcare blob master heart 20disease 20classification 20project heart 20disease 20classification ipynb the aim of this project is to predict the condition of her his disease throughout a classification algorithm based on a neural network the dataset is taken from uci machine learning respiratory https archive ics uci edu ml datasets heart disease diagnosing coronary artery disease project https github com edaaydinea ai projects for healthcare blob master diagnosing 20coronary 20artery 20disease 20project diagnosing 20coronary 20artery 20disease ipynb the aim of this project is to predict the condition of her his disease throughout a classification algorithm based on a neural network breast cancer detection https github com edaaydinea ai projects for healthcare blob master breast 20cancer 20detection breast cancer detection ipynb the aim of this project is to predict the breast cancer from a digitized image of a fine needle aspirate fna of a breast muscles research projects the projects here are included in different repository chest x ray image classification by using pytorch cnn pneumonia https github com edaaydinea chest xray image classification by using pytorch cnn pneumonia detection on chest x ray images with deep learning keras https github com edaaydinea pneumonia detection on chest xray images with deep leaning estimating the probability of confirmed covid 19 cases taking into the intensive care unit icu https github com edaaydinea estimating the probability of confirmed covid 19 cases taking into the intensive care unit icu op1 prediction of the different progressive levels of alzheimer s disease https github com edaaydinea op1 prediction of the different progressive levels of alzheimer s disease op2 prediction of the different progressive levels of alzheimer s disease with mri data https github com edaaydinea op2 prediction of the different progressive levels of alzheimer s disease with mri data low grade glioma segmentation https github com edaaydinea low grade glioma segmentation multiple sclerosis lesion segmentation from brain magnetic resonance images via fully convolutional neural network https github com edaaydinea multiple sclerosis lesion segmentation from brain magnetic resonance images via fully convolutional magnetic resonance imaging comparisons of demented and non demented adults https github com edaaydinea magnetic resonance imaging comparisons of demented and non demented adults | deep-learning artificial-intelligence medical-imaging medical-image-analysis medicine-classification healthcare | ai |
current-measurement | current measurement project with kaist based on pcb design and embed system | os |
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ScaProject | scaproject she code cloud engineering program project | cloud |
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CVND-udacity | computer vision nanodegree this repository contains project files for udacity s computer vision nanodegree program which i enrolled on 24 april 2018 projects facial keypoint detection p1 facial keypoints https github com vmelan cvnd udacity tree master p1 facial keypoints use image processing techniques and deep learning techniques to detect faces in an image and find facial keypoints such as the position of the eyes nose and mouth on a face automatic image captioning p2 image captioning https github com vmelan cvnd udacity tree master p2 image captioning combine cnn and rnn knowledge to build a deep learning model that produces captions given an imput image landmark detection tracking slam p3 slam https github com vmelan cvnd udacity tree master p3 slam implement graph slam simultaneous localization and mapping for a 2 dimensional world | computer-vision-nanodegree udacity deep-learning computer-vision | ai |
Dianzisuoa | dianzisuoa embedded systems design project | os |
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Lifestyle-Ecommerce-Website | lifestyle ecommerce website simple front end design for ecommerce website implemented in html css bootstrap visit link to preview https tarandeep97 github io lifestyle ecommerce website screenshots screenshot 100 https user images githubusercontent com 28994081 47411649 15bc0300 d787 11e8 81fc 55d51d1c2b32 png screenshot 97 https user images githubusercontent com 28994081 47411712 40a65700 d787 11e8 9ff9 6abd4400d2b5 png screenshot 98 https user images githubusercontent com 28994081 47411758 6895ba80 d787 11e8 83fe 8bcbf6a9f335 png | html5 bootstrap3 jquery-plugin css3 static-site | front_end |
-Big-Data-and-ML-on-Google-Cloud | big data and ml on google cloud | cloud |
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castor | castor storybook https raw githubusercontent com storybooks brand master badge badge storybook svg https castor vercel app castor is onfido s design system it has been designed to support different renderers and plain html css browser support img src https raw githubusercontent com alrra browser logos master src chrome chrome 48x48 png alt chrome width 24px height 24px br chrome img src https raw githubusercontent com alrra browser logos master src safari safari 48x48 png alt safari width 24px height 24px br safari img src https raw githubusercontent com alrra browser logos master src firefox firefox 48x48 png alt firefox width 24px height 24px br firefox img src https raw githubusercontent com alrra browser logos master src edge edge 48x48 png alt edge width 24px height 24px br edge last 2 versions last 2 versions last 2 versions last 2 versions install packages for html css only sh npm install onfido castor setup following core documentation packages core if you use react sh npm install onfido castor react also setup following react documentation packages react sponsors vercel https www datocms assets com 31049 1618983297 powered by vercel svg https vercel com utm source onfido oss utm campaign oss | design-system html-css react | os |
dataset-tools | various dataset tools installation requirements opencv pcl and flann for evaluate reconstruction sudo apt get install libpcl dev libflann dev glm for trajectory tool sudo apt get install libglm dev sudo apt get install gtk2 engines pixbuf gnome themes standard example git clone git github com martinruenz dataset tools git cd dataset tools mkdir release cd release cmake make j8 optionally add to path assumed in some steps below log out and in again afterwards echo path path pwd profile howtos how to evaluate your object aware slam method completely automatically analyse segmentation and trajectory quality the script automatisation cofusion jl was written for this purpose and evaluates the object segmentation and trajectory quality of your method it assumes that you have the following data available ground truth segmentation exported segmentation ground truth trajectories file name has to contain corresponding label id exported trajectories br example using co fusion cofusion basedir path to carscene4 dir colour depthdir depth cal calibration txt exportdir path to export segminnew 0 008 crfsmooth 1 crfpos 0 51 es ep run q keep cofusion jl gt poses path to carscene4 trajectories ex poses path to export gt masks path to carscene4 masks id ex masks path to export out path to output the script automatically tries to perform the steps of the following howto by converting the trajectories using the frame with the best intersection over union score as a result you will get an intersection over union graph see the following image as well as a trajectory plot as in the image of the next howto visualisation images iou example svg how to compare an object trajectory of your non static slam method with ground truth data since your slam method is not aware of the ground truth coordinate system the origin of the given object your system has to pick a random origin an alignment method has to be used before a meaningful evaluation is possible luckily your system should already align moving objects to the camera so if you pick a frame which aligns camera and object nicely it can be used to compute the ground truth object origin in the reference frame of your object subsequently your and the ground truth origin coincide and evaluation algorithms can be applied this is how it is done export the object poses produced by your system in this format t frame t x y z qx qy qz qw export the ground truth poses from blender see io export pose py run convert poses to map the obejct origin like convert poses frame good frame number object your poses txt camera your camera poses txt gtobject gt poses txt gtcamera gt cam poses txt out mapped origin txt run an evaluation script such as evaluate ate py by tum wget https svncvpr in tum de cvpr ros pkg trunk rgbd benchmark rgbd benchmark tools src rgbd benchmark tools evaluate ate py wget https svncvpr in tum de cvpr ros pkg trunk rgbd benchmark rgbd benchmark tools src rgbd benchmark tools associate py python evaluate ate py verbose plot your plot pdf your poses txt mapped origin txt the result should look something like this visualisation images ate example svg how to create a slam dataset using blender todo example blender file export depth export segmentation reference to export poses calibration convert depth convert depth described below convert depth dir depth original outdir depth converted cx 320 cy 240 fx 360 fy 480 z also if convert colour masks to id masks cp r maskdir id masks label finder dir id masks outdir id masks prefix mask width 4 starti 1 c 255 0 0 r 1 1 1 repeat calling label finder for different colours c and different ids r where the parameter is id id id tools scripts blender io export pose py blender addon script that enables the export of object and camera poses the exported format is either t x y z qx qy qz qw or t x y z rx ry rz screenrecording images io export pose nops gif get calibration py script to export intrinsic pinhole camera parameters from blender written by the user rfabbri of http blender stackexchange com http blender stackexchange com questions 15102 what is blenders camera projection matrix model 38189 trajectoryviewer jl simple julia script that visualises a list of trajectories in the format t x y z qx qy qz qw screenrecording images trajectoryviewer gif c tools convert depth blender when extracting depth maps from blender the depth values are usually not projective and hence have to be converted to be used common scenarios this tool can perform the necessary conversion it is also able to add noise to depth values input depth map s exr files optional parameters output depth map s exr files visualisation images convert depth svg convert exrtorgb convert exr to rgb image file usually for visualisation the floating point input is mapped from min max to 0 255 convert klgtoply allows you to extract colour images depth images and ply point clouds from each each frame of a klg file convert masks convert colour mask images cv 8uc3 to id mask images cv 8uc1 the tool ensures that the re mapping is consistent throughout a dataset convert poses convert the ground truth origin of an object to world coordinate poses in your export for each frame see howtos klgviewer view a klg file depth rgb like a video label associator assume you have two subsequent frames with object labels but incoherent label colors this tool tries to correctly associate labels in order to make them coherent label finder highlight a color in a dataset like camvid also allows you to replace that colour screenrecording images find label gif label merger merge labels with certain amount of neighbours | ai |
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lightllm | div align center picture img alt lightllm src assets lightllm drawio png width 90 picture div div align center docs https img shields io badge docs latest blue https github com modeltc lightllm blob main docs tokenattention md docker https github com modeltc lightllm actions workflows docker publish yml badge svg https github com modeltc lightllm actions workflows docker publish yml stars https img shields io github stars modeltc lightllm style social https github com modeltc lightllm discord banner https img shields io discord 1139835312592392214 logo discord logocolor white https discord gg wzzfwvsguu license https img shields io github license modeltc lightllm https github com modeltc lightllm blob main license div lightllm is a python based llm large language model inference and serving framework notable for its lightweight design easy scalability and high speed performance lightllm harnesses the strengths of numerous well regarded open source implementations including but not limited to fastertransformer tgi vllm and flashattention features tri process asynchronous collaboration tokenization model inference and detokenization are performed asynchronously leading to a considerable improvement in gpu utilization nopad unpad offers support for nopad attention operations across multiple models to efficiently handle requests with large length disparities dynamic batch enables dynamic batch scheduling of requests flashattention https github com dao ailab flash attention incorporates flashattention to improve speed and reduce gpu memory footprint during inference tensor parallelism utilizes tensor parallelism over multiple gpus for faster inference token attention docs tokenattention md implements token wise s kv cache memory management mechanism allowing for zero memory waste during inference high performance router collaborates with token attention to meticulously manage the gpu memory of each token thereby optimizing system throughput int8kv cache this feature will increase the capacity of tokens to almost twice as much only llama support supported model list bloom https huggingface co bigscience bloom llama https github com facebookresearch llama llama v2 https huggingface co meta llama starcoder https github com bigcode project starcoder qwen 7b https github com qwenlm qwen 7b chatglm2 6b https github com thudm chatglm2 6b baichuan 7b https github com baichuan inc baichuan 7b baichuan 13b https github com baichuan inc baichuan 13b internlm 7b https github com internlm internlm when you start qwen 7b you need to set the parameter eos id 151643 trust remote code chatglm2 needs to set the parameter trust remote code baichuan needs to set the parameter trust remote code internlm needs to set the parameter trust remote code get started requirements the code has been tested with pytorch 1 3 cuda 11 8 and python 3 9 to install the necessary dependencies please refer to the provided requirements txt and follow the instructions as shell pip install r requirements txt container you can use the official docker container to run the model more easily to do this follow these steps pull the container from the github container registry shell docker pull ghcr io modeltc lightllm main run the container with gpu support and port mapping shell docker run it gpus all p 8080 8080 shm size 1g v your local path data ghcr io modeltc lightllm main bin bash alternatively you can build the container yourself shell docker build t image name docker run it gpus all p 8080 8080 shm size 1g v your local path data image name bin bash you can also use a helper script to launch both the container and the server shell python tools quick launch docker py help note if you use multiple gpus you may need to increase the shared memory size by adding shm size to the docker run command installation install from the source code by shell python setup py install the code has been tested on a range of gpus including a100 a800 4090 and h800 if you are running the code on a100 a800 etc we recommend using triton 2 0 0 dev20221202 or triton 2 1 0 if you are running the code on h800 etc it is necessary to compile and install the source code of triton 2 1 0 https github com openai triton tree main from the github repository if the code doesn t work on other gpus try modifying the triton kernel used in model inference install triton package use triton 2 0 0 dev20221202 shell pip install triton 2 0 0 dev20221202 use triton 2 1 0 better performance but the code is under continuous development and may be unstable shell pip install u index url https aiinfra pkgs visualstudio com publicpackages packaging triton nightly pypi simple triton nightly run llama with efficient routers and tokenattention lightllm can be deployed as a service and achieve the state of the art throughput performance launch the server shell python m lightllm server api server model dir path llama 7b host 0 0 0 0 port 8080 tp 1 max total token num 120000 the parameter max total token num is influenced by the gpu memory of the deployment environment a larger value for this parameter allows for the processing of more concurrent requests thereby increasing system concurrency for more startup parameters please refer to api server py lightllm server api server py or apiserverargs md docs apiserverargs md to initiate a query in the shell shell curl http 127 0 0 1 8080 generate x post d inputs what is ai parameters max new tokens 17 frequency penalty 1 h content type application json to query from python python import time import requests import json url http localhost 8080 generate headers content type application json data inputs what is ai parameters do sample false ignore eos false max new tokens 1024 response requests post url headers headers data json dumps data if response status code 200 print response json else print error response status code response text performance service performance we compared the service performance of lightllm and vllm 0 1 2 on llama 7b using an a800 with 80g gpu memory to begin prepare the data as follows shell wget https huggingface co datasets anon8231489123 sharegpt vicuna unfiltered resolve main sharegpt v3 unfiltered cleaned split json launch the service shell python m lightllm server api server model dir path llama 7b tp 1 max total token num 121060 tokenizer mode auto evaluation shell cd test python benchmark serving py tokenizer path llama 7b dataset path sharegpt v3 unfiltered cleaned split json num prompts 2000 request rate 200 the performance comparisons results are presented below vllm lightllm total time 361 79 s br throughput 5 53 requests s total time 188 85 s br throughput 10 59 requests s static inference performance for debugging we offer static performance testing scripts for various models for instance you can evaluate the inference performance of the llama model by shell cd test model python test llama py faq the llama tokenizer fails to load consider resolving this by running the command pip install protobuf 3 20 0 error ptx version 7 4 does not support target sm 89 launch with bash tools resolve ptx version python m lightllm server api server community for further information and discussion join our discord server https discord gg wzzfwvsguu license this repository is released under the apache 2 0 license license acknowledgement we learned a lot from the following projects when developing lightllm faster transformer https github com nvidia fastertransformer text generation inference https github com huggingface text generation inference vllm https github com vllm project vllm flash attention 1 2 https github com dao ailab flash attention openai triton https github com openai triton | deep-learning gpt llama llm model-serving nlp openai-triton | ai |
Knowledge-Base | knowledge base https www slowmist com https www slowmist io knowledge base knowledge base fire false top up blockchain ecological security research include bitcoin monero ethereum eos and other top blockchains fire cryptocurrency security solution https github com slowmist cryptocurrency security fire blockchain dark forest selfguard handbook https github com slowmist blockchain dark forest selfguard handbook fire collection of security research in english security research readme md https mp weixin qq com mp appmsgalbum biz mzu4odq3ntm2oa action getalbum album id 1378653641065857025 aml https mp weixin qq com mp appmsgalbum biz mzu4odq3ntm2oa action getalbum album id 1983440310995156993 https mp weixin qq com mp appmsgalbum biz mzu4odq3ntm2oa action getalbum album id 1378673890158936067 papers of slowmist https github com slowmist papers public topic of slowmist hackingtime https github com slowmist hackingtime public ontology triones service node security checklist https github com slowmist ontology triones service node security checklist vechain core nodes security checklist https github com slowmist vechain core nodes security checklist eos bp nodes security checklist https github com slowmist eos bp nodes security checklist eos bp nodes security audit https github com slowmist eos bp nodes security checklist blob master audit md eos smart contract security best practices https github com slowmist eos smart contract security best practices eos eos monkit https eos slowmist io firewall x eos https firewallx io firewall x github https github com firewall x open of slowmist https github com slowmist fire false top up usdt https mp weixin qq com s ctaklne0mokdyufaod4 hw eos https mp weixin qq com s fkinfzlw65lyad4qo 21na xrp https developers ripple com partial payments html https mp weixin qq com s 3cmbe6p 4qcdvla4fna5 a rbf https mp weixin qq com s oyi2jdbaoledg8vdouqbig xmr https mp weixin qq com s kt g bybuumibsgsnyyxla https t zsxq com ynbmfia iost https www slowmist com lang zh products filecoin https www slowmist com lang zh products nem https www slowmist com lang zh products solana https www slowmist com lang zh products https www slowmist com lang zh products terra https www slowmist com lang zh products btc dogcoin ltc https www slowmist com lang zh products some translated blockchain security documents dasp top10 translations dasp top10 chinese pdf solidity translations solidity security comprehensive list of known attack vectors and common anti patterns zh cn md translations exploring the attack surface of blockchain a systematic overview exploring the attack surface of blockchain a systematic overview zh cn md some open security audit reports of slowmist open security audit report open report v2 readme md blockchain security audit report open report v2 blockchain blockchain application security audit report open report v2 blockchain application smart contract security audit report open report v2 smart contract some mind maps of blockchain security dapp attack defense mindmaps dapp attack defense png exchange or wallet attack defense mindmaps exchange wallet attack defense png evil blockchain how to be evil mindmaps evil blockchain png other awesome collections hacked https hacked slowmist io awesome blockchain bug bounty https github com slowmist awesome blockchain bug bounty blockchain security study notes readme md | blockchain security hacking knowledge-base | blockchain |
HackerBooks | hackerbooks presentaci n esta es la aplicaci n de pr ctica desarrollada para el curso de fundamentos ios del keepcoding startup bootcamp engineering master iv respuestas a preguntas jsonserialization devuelve un par metro any que puede contener tanto un array de dictionary como un dictionary mira en la ayuda el m todo type of y como usarlo para saber qu te han devuelto exactamente en qu otros modos podemos trabajar is as en este caso utilice la siguiente aproximaci n typealias jsonobject anyobject typealias jsondictionary string jsonobject typealias jsonarray jsondictionary func loadjsonfilefrom localurl url throws jsonarray if let data try data contentsof localurl let maybearray any try jsonserialization jsonobject with data options jsonserialization readingoptions mutablecontainers if let array maybearray as nsarray as array es un array de diccionarios return array as jsonarray else if let dic maybearray as nsdictionary as dictionary es un diccionario metemos el diccionario dentro de un array antes de regresarlo let array jsondictionary dic as jsondictionary return array else formato de json incorrecto throw hackerbookserrors wrongjsonformat else formato de json incorrecto throw hackerbookserrors wrongjsonformat compruebo si lo que me regreso jsonselialization es un array de diccionarios si no lo es compruebo si es un diccionario solitario si lo es lo meto dentro de un array como nico elemento para poder regresar el tipo correcto en mi funci n si las dos cosas fallan dor por hecho que estoy leyendo un archivo json con formato incorrecto hice la prueba enviandole un diccionario solicitado a esta funci n y todo ok descarga el json y gu rdalo en la carpeta documents de tu sandbox haz lo mismo para las im genes de portada y los pdf s d nde guardar as estos datos se utilizo la librer a asyncdata para la descarga de las im genes y los pdf s esta librer a guarda y recupera lo descargado desde la carpeta cache por lo que pude investigar cosa que me parece lo correcto el ser o no favorito se indicar mediante una propiedad isfavorite de book y esto se debe guardar en sistema de ficheros y recupear de alguna forma c mo har as eso se te ocurre m s de una forma de hacerlo el m todo seleccionado fue el de usar userdefaults para guardar la selecci n se uso el title hashvalue como identificador nico del libro para recuperar el setting posteriormente tambien se pudo usar un dictionary y guardarlo en un archivo plist tambien se pude usar core data para guardar la informaci n en una base de datos del tipo sqlite por ejemplo otra opci n es implementar una clase con nscodig para hacer el archive y unarchive de archivos serializables usando nsdata cuando cambia el valo de la propiedad isfavorite de un book la tabla deber reflejar ese hecho c mo lo har as se te ocurre m s de una forma de hacerlo cu l the parece mejo explica tu elecci n en este caso escog hacer una notificaci n para notificar el cambio de esta propiedad a esta notificaci n se pueden suscribir m s de un objecto clase en mi caso me interesa que el controlador libraryviewcontroller se entere de este cambio la ventaja de escojer notificaciones en lugar de otros m todos delegados por ejemplo es que en un momento dado en otra parte de de la app este cambio pueda ser importante tenerlo en cuenta para que la tabla se actulice usa el m todo reloaddata de uitableview esto hace que la tabla vuelva a pedir datos a su datasource es esto una aberraci n desde el punto de vista del rendimiento hay una forma alternativa cu ndo crees que vale la pena usarlo cuando existen varios elementos en la tabla divididas en grupos secciones como es nuestro caso el recargar toda la tabla no es lo m s eficiente ya que consume tiempo recursos y puede bloquear un poco la interfaz en este caso se debe recargar solo la secci n que sabemos ha sufrido cambios favorite para esto se puede utilizar el m todo reloadsections withrowanimation del uitableview cuando el usuario cambia en la tabla el libro seleccionado el pdfviewcontroller debe actualizarse c mo lo har as se puede hacer mediante delegados o notificaciones en este caso opte por la notificaci n porque es m s f cil de implementar ya que hace lo que necesitamos con menos c digo qu funcionalidades le a adir as antes de subirla a la app store antes de subirlo a la app store mejorar a el lector de pdf porque utilice el uiwebview que tiene una funcionalidad b sica para la lectura de pdf s ser a ideal poder hacer anotaciones marcaci n de texto tener bookmarks para encontrar r pidamente informaci n que el usuario le parezca relevante continuar en la ltima p gina vista en cada book se requieren mejores im gnes para las portadas de los libros un buen icono y la posibilidad de que de vez en vez se recargar el json file desde la web para ir agregando nuevas publicaciones por ejemplo usando esta app como plantilla qu otras versiones se te ocurren algo que puedas monetizar se puede monetizar agregando publicidad por ejemplo que mostrar un ad de pantalla completa cada 10 min con posibilidad de eliminarla pasando a la versi n completa sin publicidad y con todas las extra features se pueden poner previews del libros nuevos con posibilidad de llevar al usuario a una tienda de libros amazon o el propio itunes book store y ganar un poco de dinero usando links de afiliados de la tienda tengo una idea de una app que sirva para descargar textos de tem ticas interesantes y variadas cada texto ser a enviado en dos idiomas ingl s y espa ol por ejemplo entonces el usuario podr empezar a leer el texto en el idioma de su preferencia y en un momento dado tener la posibilidad de comparar con el otro texto del otro idioma el objetivo es que estudiantes de un idioma puedan practicar la lectura y tener la traducci n al instante para poder entender palabras u oraciones r pidamente la monetizaci n ser a con publicidad como en el caso anterior y con posibilidad de suscribirse por una cuota mensual para recibir nuevos textos de diferente nivel y tem tica seg n lo prefiera lector caracter sticas extras pueden ser por ejemplo que el usuario pueda agregar flashcards de manera r pida para en un futuro repasar las palabras o frases que quisiera recordar y aprender las posibilidades son ilimitadas | os |
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ae6310 | ae6310 this repository contains notebooks from the course ae6310 optimization for the design of engineered systems at georgia tech in the school of aerospace engineering | os |
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Tracking | tracking this project was part of the course tsbb15 computer vision at link ping university 2013 | ai |
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Owl-LM | large language model for blockchain transaction this repository contains resources for working with training llm on blockchain transaction data here we share the finetuned model and tokenizer with some examples of how to use them we will share more resources in the future the finetuned model and tokenizer are hosted at https github com sec3 service owl lm releases tag v1 0 and the folder llm for blockchain respectively contents readme md this file example csv contains samples of ethereum transactions for classification this dataset includes detailed transaction data example hash csv contains samples of ethereum transactions for classification this dataset does not include detailed transaction data example ipynb a jupyter notebook that provides example usage of the model requirements txt lists the python dependencies required for using the code in this repository llm for blockchain folder containing the llm model installation clone this repository git clone https github com sec3 service owl lm git navigate to the cloned repository cd owl lm install the required python dependencies pip install r requirements txt usage use example ipynb as a guide to classify ethereum transactions using the provided model you can run the notebook using jupyter | blockchain llm llm-training | ai |
safe-contracts | safe contracts npm version https badge fury io js 40safe global 2fsafe contracts svg https badge fury io js 40safe global 2fsafe contracts build status https github com safe global safe contracts workflows safe contracts badge svg branch main https github com safe global safe contracts actions coverage status https coveralls io repos github safe global safe contracts badge svg branch main https coveralls io github safe global safe contracts warning this branch contains changes that are under development to use the latest audited version make sure to use the correct commit the tagged versions that are used by the safe team can be found in the releases https github com safe global safe contracts releases usage install requirements with npm bash npm i testing to run the tests bash npm run build npm run test optionally if you want to run the erc 4337 compatibility test it uses a live bundler and node so it contains some pre requisites 1 define the environment variables erc4337 test bundler url erc4337 test node url erc4337 test singleton address erc4337 test safe factory address mnemonic 2 pre fund the executor account derived from the mnemonic with some native token to cover the deployment of an erc4337 module and the pre fund of the safe for the test operation deployments a collection of the different safe contract deployments and their addresses can be found in the safe deployments https github com safe global safe deployments repository to add support for a new network follow the steps of the deploy section and create a pr in the safe deployments https github com safe global safe deployments repository deploy warning make sure to use the correct commit when deploying the contracts any change even comments within the contract files will result in different addresses the tagged versions that are used by the safe team can be found in the releases https github com safe global safe contracts releases current version the latest release is v1 3 0 libs 0 https github com safe global safe contracts tree v1 3 0 libs 0 on the commit 767ef36 https github com safe global safe contracts commit 767ef36bba88bdbc0c9fe3708a4290cabef4c376 this will deploy the contracts deterministically and verify the contracts on etherscan using solidity 0 7 6 https github com ethereum solidity releases tag v0 7 6 by default preparation set mnemonic in env set infura key in env bash npm run deploy all network this will perform the following steps bash npm run build npx hardhat network network deploy npx hardhat network network sourcify npx hardhat network network etherscan verify npx hardhat network network local verify custom networks it is possible to use the node url env var to connect to any evm based network via an rpc endpoint this connection then can be used with the custom network e g to deploy the safe contract suite on that network you would run npm run deploy all custom the resulting addresses should be on all networks the same note address will vary if contract code is changed or a different solidity version is used replay protection eip 155 some networks require replay protection making it incompatible with the default deployment process as it relies on a presigned transaction without replay protection see https github com arachnid deterministic deployment proxy safe contracts use a different deterministic deployment proxy https github com safe global safe singleton factory to make sure that the latest version of this package is installed make sure to run npm i save dev gnosis pm safe singleton factory before deployment for more information including how to deploy the factory to a new network please refer to the factory repo note this will result in different addresses compared to hardhat s default deterministic deployment process verify contract this command will use the deployment artifacts to compile the contracts and compare them to the onchain code bash npx hardhat network network local verify this command will upload the contract source to etherescan bash npx hardhat network network etherscan verify documentation safe developer portal http docs safe global error codes docs error codes md coding guidelines docs guidelines md audits formal verification for version 1 4 0 1 4 1 by ackee blockchain docs audit 1 4 0 md for version 1 3 0 by g0 group docs audit 1 3 0 md for version 1 2 0 by g0 group docs audit 1 2 0 md for version 1 1 1 by g0 group docs audit 1 1 1 md for version 1 0 0 by runtime verification docs rv 1 0 0 md for version 0 0 1 by alexey akhunov docs alexey audit md security and liability all contracts are without any warranty without even the implied warranty of merchantability or fitness for a particular purpose license all smart contracts are released under lgpl 3 0 | ethereum wallet solidity | blockchain |
moko-utils | moko utils https user images githubusercontent com 5010169 128706827 ec33a4bf d9c8 4ac5 88cc bd180fe7f596 png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko utils https repo1 maven org maven2 dev icerock moko utils kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name media mobile kotlin utils this is a kotlin multiplatform library that table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral project build gradle groovy dependencies commonmainapi dev icerock moko utils 0 3 0 usage samples more examples can be found in the sample directory sample set up locally in utils directory utils contains utils library in sample directory sample contains samples on android ios mpp library connected to apps contributing all development both new features and bug fixes is performed in develop branch this way master sources always contain sources of the most recently released version please send prs with bug fixes to develop branch fixes to documentation in markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master during release more detailed guide for contributers see in contributing guide contributing md license copyright 2021 icerock mag inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | moko android ios kotlin-multiplatform kotlin-native | front_end |
aws-iot-device-sdk-embedded-C | aws iot device sdk for embedded c table of contents overview overview license license features features coremqtt coremqtt corehttp corehttp corejson corejson corepkcs11 corepkcs11 aws iot device shadow aws iot device shadow aws iot jobs aws iot jobs aws iot device defender aws iot device defender aws iot over the air update library aws iot over the air update aws iot fleet provisoning aws iot fleet provisioning aws sigv4 aws sigv4 backoffalgorithm backoffalgorithm sending metrics to aws iot sending metrics to aws iot versioning versioning releases and documentation releases and documentation 202211 00 20221100 202108 00 20210800 202103 00 20210300 202012 01 20201201 202011 00 20201100 202009 00 20200900 v3 1 5 v315 porting guide for 202009 00 and newer releases porting guide for 20200900 and newer releases porting coremqtt porting coremqtt porting corehttp porting corehttp porting aws iot device shadow porting aws iot device shadow porting aws iot device defender porting aws iot device defender porting aws iot over the air update porting aws iot over the air update migration guide from v3 1 5 to 202009 00 and newer releases migration guide from v315 to 20200900 and newer releases mqtt migration mqtt migration shadow migration shadow migration jobs migration jobs migration branches branches main main branch v4 beta deprecated v4 beta deprecated branch formerly named v4 beta getting started getting started cloning cloning configuring demos configuring demos prerequisites prerequisites build dependencies build dependencies aws iot account setup aws iot account setup configuring mutual authentication demos of mqtt and http configuring mutual authentication demos of mqtt and http configuring aws iot device defender and aws iot device shadow demos configuring aws iot device defender and aws iot device shadow demos configuring the aws iot fleet provisioning demo configuring the aws iot fleet provisioning demo configuring the s3 demos configuring the s3 demos setup for aws iot jobs demo setup for aws iot jobs demo setup for the greengrass local auth demo setup for the greengrass local auth demo prerequisites for the aws over the air update ota demos prerequisites for the aws over the air update ota demos scheduling an ota update job scheduling an ota update job building and running demos building and running demos build a single demo build a single demo build all configured demos build all configured demos running corepkcs11 demos running corepkcs11 demos alternative option of docker containers for running demos locally alternative option of docker containers for running demos locally installing mosquitto to run mqtt demos locally installing mosquitto to run mqtt demos locally installing httpbin to run http demos locally installing httpbin to run http demos locally generating documentation generating documentation overview the aws iot device sdk for embedded c c sdk is a collection of c source files under the mit open source license license that can be used in embedded applications to securely connect iot devices to aws iot core https docs aws amazon com iot latest developerguide what is aws iot html it contains mqtt client http client json parser aws iot device shadow aws iot jobs and aws iot device defender libraries this sdk is distributed in source form and can be built into customer firmware along with application code other libraries and an operating system os of your choice these libraries are only dependent on standard c libraries so they can be ported to various os s from embedded real time operating systems rtos to linux mac windows you can find sample usage of c sdk libraries on posix systems using openssl e g linux demos demos in this repository and on freertos https github com freertos freertos using mbedtls e g freertos demos https github com freertos freertos tree main freertos plus demo in freertos https github com freertos freertos repository for the latest release of c sdk please see the section for releases and documentation releases and documentation c sdk includes libraries that are part of the freertos 202210 01 lts https github com freertos freertos lts tree 202210 01 lts release learn more about the freertos 202210 01 lts libraries by clicking here https freertos org lts libraries html license the c sdk libraries are licensed under the mit open source license license features c sdk simplifies access to various aws iot services c sdk has been tested to work with aws iot core https docs aws amazon com iot latest developerguide what is aws iot html and an open source mqtt broker to ensure interoperability the aws iot device shadow aws iot jobs and aws iot device defender libraries are flexible to work with any mqtt client and json parser the mqtt client and json parser libraries are offered as choices without being tightly coupled with the rest of the sdk c sdk contains the following libraries coremqtt the coremqtt https github com freertos coremqtt library provides the ability to establish an mqtt connection with a broker over a customer implemented transport layer which can either be a secure channel like a tls session mutually authenticated or server only authentication or a non secure channel like a plaintext tcp connection this mqtt connection can be used for performing publish operations to mqtt topics and subscribing to mqtt topics the library provides a mechanism to register customer defined callbacks for receiving incoming publish acknowledgement and keep alive response events from the broker the library has been refactored for memory optimization and is compliant with the mqtt 3 1 1 https docs oasis open org mqtt mqtt v3 1 1 mqtt v3 1 1 html standard it has no dependencies on any additional libraries other than the standard c library a customer implemented network transport interface and optionally a customer implemented platform time function the refactored design embraces different use cases ranging from resource constrained platforms using only qos 0 mqtt publish messages to resource rich platforms using qos 2 mqtt publish over tls connections see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard coremqtt docs doxygen output html index html mqtt memory requirements corehttp the corehttp https github com freertos corehttp library provides the ability to establish an http connection with a server over a customer implemented transport layer which can either be a secure channel like a tls session mutually authenticated or server only authentication or a non secure channel like a plaintext tcp connection the http connection can be used to make get include range requests put post and head requests the library provides a mechanism to register a customer defined callback for receiving parsed header fields in an http response the library has been refactored for memory optimization and is a client implementation of a subset of the http 1 1 https tools ietf org html rfc2616 standard see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corehttp docs doxygen output html index html http memory requirements corejson the corejson https github com freertos corejson library is a json parser that strictly enforces the ecma 404 json standard https www json org json en html it provides a function to validate a json document and a function to search for a key and return its value a search can descend into nested structures using a compound query key a json document validation also checks for illegal utf8 encodings and illegal unicode escape sequences see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corejson docs doxygen output html index html json memory requirements corepkcs11 the corepkcs11 https github com freertos corepkcs11 library is an implementation of the pkcs 11 interface api that makes it easier to develop applications that rely on cryptographic operations only a subset of the pkcs 11 v2 4 https docs oasis open org pkcs11 pkcs11 base v2 40 os pkcs11 base v2 40 os html standard has been implemented with a focus on operations involving asymmetric keys random number generation and hashing the cryptoki or pkcs 11 standard defines a platform independent api to manage and use cryptographic tokens the name pkcs 11 is used interchangeably to refer to the api itself and the standard which defines it the pkcs 11 api is useful for writing software without taking a dependency on any particular implementation or hardware by writing against the pkcs 11 standard interface code can be used interchangeably with multiple algorithms implementations and hardware generally vendors for secure cryptoprocessors such as trusted platform module tpm https en wikipedia org wiki trusted platform module hardware security module hsm https en wikipedia org wiki hardware security module secure element or any other type of secure hardware enclave distribute a pkcs 11 implementation with the hardware the purpose of corepkcs11 mock is therefore to provide a pkcs 11 implementation that allows for rapid prototyping and development before switching to a cryptoprocessor specific pkcs 11 implementation in production devices since the pkcs 11 interface is defined as part of the pkcs 11 specification https docs oasis open org pkcs11 pkcs11 base v2 40 os pkcs11 base v2 40 os html replacing corepkcs11 with another implementation should require little porting effort as the interface will not change the system tests distributed in corepkcs11 repository can be leveraged to verify the behavior of a different implementation is similar to corepkcs11 see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corepkcs11 docs doxygen output html pkcs11 design html pkcs11 memory requirements aws iot device shadow the aws iot device shadow https github com aws device shadow for aws iot embedded sdk library enables you to store and retrieve the current state one or more shadows of every registered device a device s shadow is a persistent virtual representation of your device that you can interact with from aws iot core even if the device is offline the device state is captured in its shadow is represented as a json https www json org document the device can send commands over mqtt to get update and delete its latest state as well as receive notifications over mqtt about changes in its state the device s shadow s are uniquely identified by the name of the corresponding thing a representation of a specific device or logical entity on the aws cloud see managing devices with aws iot https docs aws amazon com iot latest developerguide iot thing management html for more information on iot thing this library supports named shadows a feature of the aws iot device shadow service that allows you to create multiple shadows for a single iot device more details about aws iot device shadow can be found in aws iot documentation https docs aws amazon com iot latest developerguide iot device shadows html the aws iot device shadow library has no dependencies on additional libraries other than the standard c library it also doesn t have any platform dependencies such as threading or synchronization it can be used with any mqtt library and any json library see demos demos shadow with coremqtt and corejson see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device shadow for aws iot embedded sdk docs doxygen output html index html shadow memory requirements aws iot jobs the aws iot jobs https github com aws jobs for aws iot embedded sdk library enables you to interact with the aws iot jobs service which notifies one or more connected devices of a pending job a job can be used to manage your fleet of devices update firmware and security certificates on your devices or perform administrative tasks such as restarting devices and performing diagnostics for documentation of the service please see the aws iot developer guide https docs aws amazon com iot latest developerguide iot jobs html interactions with the jobs service use the mqtt protocol this library provides an api to compose and recognize the mqtt topic strings used by the jobs service the aws iot jobs library has no dependencies on additional libraries other than the standard c library it also doesn t have any platform dependencies such as threading or synchronization it can be used with any mqtt library and any json library see demos demos jobs with libmosquitto https mosquitto org and corejson see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws jobs for aws iot embedded sdk docs doxygen output html index html jobs memory requirements aws iot device defender the aws iot device defender https github com aws device defender for aws iot embedded sdk library enables you to interact with the aws iot device defender service to continuously monitor security metrics from devices for deviations from what you have defined as appropriate behavior for each device if something doesn t look right aws iot device defender sends out an alert so you can take action to remediate the issue more details about device defender can be found in aws iot device defender documentation https docs aws amazon com iot latest developerguide device defender html this library supports custom metrics a feature that helps you monitor operational health metrics that are unique to your fleet or use case for example you can define a new metric to monitor the memory usage or cpu usage on your devices the aws iot device defender library has no dependencies on additional libraries other than the standard c library it also doesn t have any platform dependencies such as threading or synchronization it can be used with any mqtt library and any json library see demos demos defender with coremqtt and corejson see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device defender for aws iot embedded sdk docs doxygen output html index html defender memory requirements aws iot over the air update the aws iot over the air update https github com aws ota for aws iot embedded sdk ota library enables you to manage the notification of a newly available update download the update and perform cryptographic verification of the firmware update using the ota library you can logically separate firmware updates from the application running on your devices you can also use the library to send other files e g images certificates to one or more devices registered with aws iot more details about ota library can be found in aws iot over the air update documentation https docs aws amazon com freertos latest userguide freertos ota dev html the aws iot over the air update library has a dependency on corejson https github com freertos corejson for parsing of json job document and tinycbor https github com intel tinycbor git for decoding encoded data streams other than the standard c library it can be used with any mqtt library http library and operating system e g linux freertos see demos demos ota with coremqtt and corehttp over linux see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws ota for aws iot embedded sdk docs doxygen output html index html ota memory requirements aws iot fleet provisioning the aws iot fleet provisioning https github com aws fleet provisioning for aws iot embedded sdk library enables you to interact with the aws iot fleet provisioning mqtt apis https docs aws amazon com iot latest developerguide fleet provision api html in order to provison iot devices without preexisting device certificates with aws iot fleet provisioning devices can securely receive unique device certificates from aws iot when they connect for the first time for an overview of all provisioning options offered by aws iot see device provisioning documentation https docs aws amazon com iot latest developerguide iot provision html for details about fleet provisioning refer to the aws iot fleet provisioning documentation https docs aws amazon com iot latest developerguide provision wo cert html see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws fleet provisioning for aws iot embedded sdk docs doxygen output html index html fleet provisioning memory requirements aws sigv4 the aws sigv4 https github com aws sigv4 for aws iot embedded sdk library enables you to sign http requests with signature version 4 signing process https docs aws amazon com general latest gr signature version 4 html signature version 4 sigv4 is the process to add authentication information to http requests to aws services for security most requests to aws must be signed with an access key the access key consists of an access key id and secret access key see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries aws sigv4 for aws iot embedded sdk docs doxygen output html index html sigv4 memory requirements backoffalgorithm the backoffalgorithm https github com freertos backoffalgorithm library is a utility library to calculate backoff period using an exponential backoff with jitter algorithm for retrying network operations like failed network connection with server this library uses the full jitter strategy for the exponential backoff with jitter algorithm more information about the algorithm can be seen in the exponential backoff and jitter aws blog https aws amazon com blogs architecture exponential backoff and jitter exponential backoff with jitter is typically used when retrying a failed connection or network request to the server an exponential backoff with jitter helps to mitigate the failed network operations with servers that are caused due to network congestion or high load on the server by spreading out retry requests across multiple devices attempting network operations besides in an environment with poor connectivity a client can get disconnected at any time a backoff strategy helps the client to conserve battery by not repeatedly attempting reconnections when they are unlikely to succeed the backoffalgorithm library has no dependencies on libraries other than the standard c library see memory requirements for the latest release here https aws github io aws iot device sdk embedded c 202211 00 libraries standard backoffalgorithm docs doxygen output html index html backoff algorithm memory requirements sending metrics to aws iot when establishing a connection with aws iot users can optionally report the operating system hardware platform and mqtt client version information of their device to aws this information can help aws iot provide faster issue resolution and technical support if users want to report this information they can send a specially formatted string see below in the username field of the mqtt connect packet format the format of the username string with metrics is actual username sdk os name version os version platform hardware platform mqttlib mqtt library name mqtt library version where actual username is the actual username used for authentication if username and password are used for authentication when username and password based authentication is not used this is an empty value os name is the operating system the application is running on e g ubuntu os version is the version number of the operating system e g 20 10 hardware platform is the hardware platform the application is running on e g raspberrypi mqtt library name is the mqtt client library being used e g coremqtt mqtt library version is the version of the mqtt client library being used e g 1 1 0 example actual username iotuser os name ubuntu os version 20 10 hardware platform name raspberrypi mqtt library name coremqtt mqtt library version 1 1 0 if username is not used then iotuser can be removed username string iotuser sdk ubuntu version 20 10 platform raspberrypi mqttlib coremqtt 1 1 0 define os name ubuntu define os version 20 10 define hardware platform name raspberrypi define mqtt lib coremqtt 1 1 0 define username string iotuser sdk os name version os version platform hardware platform name mqttlib mqtt lib define username string length uint16 t sizeof username string 1 mqttconnectinfo t connectinfo connectinfo pusername username string connectinfo usernamelength username string length mqttstatus mqtt connect pmqttcontext connectinfo null connack recv timeout ms psessionpresent versioning c sdk releases will now follow a date based versioning scheme with the format yyyymm nn where y represents the year m represents the month n represents the release order within the designated month 00 being the first release for example a second release in june 2021 would be 202106 01 although the sdk releases have moved to date based versioning each library within the sdk will still retain semantic versioning in semantic versioning the version number itself x y z indicates whether the release is a major minor or point release you can use the semantic version of a library to assess the scope and impact of a new release on your application releases and documentation all of the released versions of the c sdk libraries are available as git tags for example the last release of the v3 sdk version is available at tag 3 1 5 https github com aws aws iot device sdk embedded c tree v3 1 5 202211 00 api documentation of 202211 00 release https aws github io aws iot device sdk embedded c 202211 00 index html this release includes an update to how the coverity static analysis https scan coverity com scans are performed there is now a tools coverity readme md file in each library with instructions on how to perform a scan with 0 misra coding standard https www misra org uk warnings or errors additionally this release brings in major version upgrades to coremqtt https github com freertos coremqtt and corehttp https github com freertos corehttp it also brings in minor version upgrades to all other libraries 202108 00 api documentation of 202108 00 release https aws github io aws iot device sdk embedded c 202108 00 index html this release introduces the refactored aws iot fleet provisioning https github com aws fleet provisioning for aws iot embedded sdk library and the new aws sigv4 https github com aws sigv4 for aws iot embedded sdk library additionally this release brings minor version updates in the aws iot over the air update https github com aws ota for aws iot embedded sdk and corepkcs11 https github com freertos corepkcs11 libraries 202103 00 api documentation of 202103 00 release https docs aws amazon com embedded csdk 202103 00 lib ref index html this release includes a major update https github com aws ota for aws iot embedded sdk blob v3 0 0 changelog md v300 march 2021 to the apis of the aws iot over the air update library additionally aws iot device shadow library introduces a minor update https github com aws device shadow for aws iot embedded sdk blob v1 1 0 changelog md v110 march 2021 by adding support for named shadow a feature of the aws iot device shadow service that allows you to create multiple shadows for a single iot device aws iot jobs library introduces a minor update https github com aws jobs for aws iot embedded sdk blob v1 1 0 changelog md v110 march 2021 by introducing macros for next job id and compile time generation of topic strings aws iot device defender library introduces a minor update https github com aws device defender for aws iot embedded sdk blob v1 1 0 changelog md v110 march 2021 that adds macros to api for custom metrics feature of aws iot device defender service corepkcs11 also introduces a patch update https github com freertos corepkcs11 blob v3 0 1 changelog md v301 february 2021 by removing the pkcs11configpal destroy supported config and mbedtls platform abstraction layer of destroyobject lastly no code changes are introduced for backoffalgorithm corehttp coremqtt and corejson however patch updates are made to improve documentation and ci 202012 01 api documentation of 202012 01 release https docs aws amazon com embedded csdk 202012 00 lib ref index html this release includes aws iot over the air update release candidate https github com aws ota for aws iot embedded sdk backoffalgorithm https github com freertos backoffalgorithm and pkcs 11 https github com freertos corepkcs11 libraries additionally there is a major update to the corejson and corehttp apis all libraries continue to undergo code quality checks e g misra c compliance and coverity static analysis in addition all libraries except aws iot over the air update and backoffalgorithm undergo validation of memory safety with the c bounded model checker cbmc automated reasoning tool 202011 00 api documentation of 202011 00 release https docs aws amazon com embedded csdk 202011 00 lib ref index html this release includes refactored http client aws iot device defender and aws iot jobs libraries additionally there is a major update to the corejson api all libraries continue to undergo code quality checks e g misra c compliance coverity static analysis and validation of memory safety with the c bounded model checker cbmc automated reasoning tool 202009 00 api documentation of 202009 00 release https docs aws amazon com freertos latest lib ref embedded csdk 202009 00 lib ref index html this release includes refactored mqtt json parser and aws iot device shadow libraries for optimized memory usage and modularity these libraries are included in the sdk via git submoduling https git scm com book en v2 git tools submodules these libraries have gone through code quality checks including verification that no function has a gnu complexity https www gnu org software complexity manual complexity html score over 8 and checks against deviations from mandatory rules in the misra coding standard https www misra org uk deviations from the misra c 2012 guidelines are documented under misra deviations misra md these libraries have also undergone both static code analysis from coverity static analysis https scan coverity com and validation of memory safety and data structure invariance through the cbmc automated reasoning tool https www cprover org cbmc if you are upgrading from v3 x api of the c sdk to the 202009 00 release please refer to migration guide from v3 1 5 to 202009 00 and newer releases migration guide from v315 to 20200900 and newer releases if you are using the c sdk v4 beta deprecated branch note that we will continue to maintain this branch for critical bug fixes and security patches but will not add new features to it see the c sdk v4 beta deprecated branch readme https github com aws aws iot device sdk embedded c blob v4 beta deprecated readme md for additional details v3 1 5 details available here https github com aws aws iot device sdk embedded c tree v3 1 5 porting guide for 202009 00 and newer releases all libraries depend on the iso c90 standard library and additionally on the stdint h library for fixed width integers including uint8 t int8 t uint16 t uint32 t and int32 t and constant macros like uint16 max if your platform does not support the stdint h library definitions of the mentioned fixed width integer types will be required for porting any c sdk library to your platform porting coremqtt guide for porting coremqtt library to your platform is available here https aws github io aws iot device sdk embedded c 202211 00 libraries standard coremqtt docs doxygen output html mqtt porting html porting corehttp guide for porting corehttp library is available here https aws github io aws iot device sdk embedded c 202211 00 libraries standard corehttp docs doxygen output html http porting html porting aws iot device shadow guide for porting aws iot device shadow library is available here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device shadow for aws iot embedded sdk docs doxygen output html shadow porting html porting aws iot device defender guide for porting aws iot device defender library is available here https aws github io aws iot device sdk embedded c 202211 00 libraries aws device defender for aws iot embedded sdk docs doxygen output html defender porting html porting aws iot over the air update guide for porting ota library to your platform is available here https aws github io aws iot device sdk embedded c 202211 00 libraries aws ota for aws iot embedded sdk docs doxygen output html ota porting html migration guide from v3 1 5 to 202009 00 and newer releases mqtt migration migration guide for mqtt library is available here https docs aws amazon com embedded csdk 202103 00 lib ref docs doxygen output html mqtt migration html shadow migration migration guide for shadow library is available here https docs aws amazon com embedded csdk 202103 00 lib ref docs doxygen output html shadow migration html jobs migration migration guide for jobs library is available here https docs aws amazon com embedded csdk 202103 00 lib ref docs doxygen output html jobs migration html branches main branch the main https github com aws aws iot device sdk embedded c tree main branch hosts the continuous development of the aws iot embedded c sdk c sdk libraries please be aware that the development at the tip of the main branch is continuously in progress and may have bugs consider using the tagged releases https github com aws aws iot device sdk embedded c releases of the c sdk for production ready software v4 beta deprecated branch formerly named v4 beta the v4 beta deprecated https github com aws aws iot device sdk embedded c tree v4 beta deprecated branch contains a beta version of the c sdk libraries which is now deprecated this branch was earlier named as v4 beta and was renamed to v4 beta deprecated the libraries in this branch will not be released however critical bugs will be fixed and tested no new features will be added to this branch getting started cloning this repository uses git submodules https git scm com book en v2 git tools submodules to bring in the c sdk libraries eg mqtt and third party dependencies eg mbedtls for posix platform transport layer note if you download the zip file provided by github ui you will not get the contents of the submodules the zip file is also not a valid git repository if you download from the 202012 00 release page https github com aws aws iot device sdk embedded c releases tag 202012 00 page you will get the entire repository including the submodules in the zip file aws iot device sdk embedded c 202012 00 zip to clone the latest commit to main branch using https sh git clone recurse submodules https github com aws aws iot device sdk embedded c git using ssh sh git clone recurse submodules git github com aws aws iot device sdk embedded c git if you have downloaded the repo without using the recurse submodules argument you need to run sh git submodule update init recursive when building with cmake submodules are also recursively cloned automatically however dbuild clone submodules 0 can be passed as a cmake flag to disable this functionality this is useful when you d like to build cmake while using a different commit from a submodule configuring demos the libraries in this sdk are not dependent on any operating system however the demos for the libraries in this sdk are built and tested on a linux platform the demos build with cmake https cmake org a cross platform build tool prerequisites cmake 3 2 0 or any newer version for utilizing the build system of the repository c90 compiler such as gcc due to the use of mbedtls in corepkcs11 a c99 compiler is required if building the pkcs11 demos although not a part of the iso c90 standard stdint h is required for fixed width integer types that include uint8 t int8 t uint16 t uint32 t and int32 t and constant macros like uint16 max while stdbool h is required for boolean parameters in coremqtt for compilers that do not provide these header files coremqtt https github com freertos coremqtt provides the files stdint readme https github com freertos coremqtt blob main source include stdint readme and stdbool readme https github com freertos coremqtt blob main source include stdbool readme which can be renamed to stdint h and stdbool h respectively to provide the required type definitions a supported operating system the ports provided with this repo are expected to work with all recent versions of the following operating systems although we cannot guarantee the behavior on all systems linux system with posix sockets threads rt and timer apis we have tested on ubuntu 18 04 build dependencies the follow table shows libraries that need to be installed in your system to run certain demos if a dependency is not installed and cannot be built from source demos that require that dependency will be excluded from the default all target dependency version usage openssl https github com openssl openssl 1 1 0 or later all tls demos and tests with the exception of pkcs11 mosquitto client https github com eclipse mosquitto 1 4 10 or later aws iot jobs mosquitto demo aws iot account setup you need to setup an aws account and access the aws iot console for running the aws iot device shadow library aws iot device defender library aws iot jobs library aws iot ota library and corehttp s3 download demos also the aws account can be used for running the mqtt mutual auth demo against aws iot broker note that running the aws iot device defender aws iot jobs and aws iot device shadow library demos require the setup of a thing resource for the device running the demo follow the links to setup an aws account https portal aws amazon com billing signup start sign in to the aws iot console https console aws amazon com iot home after setting up the aws account create a thing resource https docs aws amazon com iot latest developerguide iot moisture create thing html the mqtt mutual authentication and aws iot shadow demos include example aws iot policy documents to run each respective demo with aws iot you may use the mqtt mutual auth demos mqtt mqtt demo mutual auth aws iot policy example mqtt json and shadow demos shadow shadow demo main aws iot policy example shadow json example policies by replacing aws region and aws account id with the strings of your region and account identifier while the iot thing name and mqtt client identifier do not need to match for the demos to run the example policies have the thing name and client identifier identical as per aws iot best practices https docs aws amazon com iot latest developerguide security best practices html it can be very helpful to also have the aws command line interface tooling https docs aws amazon com cli latest userguide cli chap install html installed configuring mutual authentication demos of mqtt and http you can pass the following configuration settings as command line options in order to run the mutual auth demos make sure to run the following command in the root directory of the c sdk sh optionally find your aws iot endpoint from the command line aws iot describe endpoint endpoint type iot data ats cmake s bbuild daws iot endpoint your aws iot endpoint dclient cert path your client certificate path dclient private key path your client private key path in order to set these configurations manually edit demo config h in demos mqtt mqtt demo mutual auth and demos http http demo mutual auth to define the following set aws iot endpoint to your custom endpoint this is found on the settings page of the aws iot console and has a format of abcdefg1234567 iot aws region amazonaws com where aws region can be an aws region like us east 2 optionally it can also be found with the aws cli command aws iot describe endpoint endpoint type iot data ats set client cert path to the path of the client certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set client private key path to the path of the private key downloaded when setting up the device certificate in aws iot account setup aws iot account setup it is possible to configure root ca cert path to any pem encoded root ca certificate however this is optional because cmake will download and set it to amazonrootca1 pem https www amazontrust com repository amazonrootca1 pem when unspecified if unspecified the default root ca path will be interpreted relative to where the demo binary is executed for many demos you can change this path by modifying it in the corresponding demo config h configuring aws iot device defender and aws iot device shadow demos to build the aws iot device defender and aws iot device shadow demos you can pass the following configuration settings as command line options make sure to run the following command in the root directory of the c sdk sh cmake s bbuild daws iot endpoint your aws iot endpoint droot ca cert path your path to amazon root ca dclient cert path your client certificate path dclient private key path your client private key path dthing name your registered thing name an amazon root ca certificate can be downloaded from here https www amazontrust com repository in order to set these configurations manually edit demo config h in the demo folder to define the following set aws iot endpoint to your custom endpoint this is found on the settings page of the aws iot console and has a format of abcdefg1234567 iot us east 2 amazonaws com set root ca cert path to the path of the root ca certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set client cert path to the path of the client certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set client private key path to the path of the private key downloaded when setting up the device certificate in aws iot account setup aws iot account setup set thing name to the name of the thing created in aws iot account setup aws iot account setup configuring the aws iot fleet provisioning demo to build the aws iot fleet provisioning demo you can pass the following configuration settings as command line options make sure to run the following command in the root directory of the c sdk sh cmake s bbuild daws iot endpoint your aws iot endpoint droot ca cert path your path to amazon root ca dclaim cert path your claim certificate path dclaim private key path your claim private key path dprovisioning template name your template name ddevice serial number your serial number an amazon root ca certificate can be downloaded from here https www amazontrust com repository to create a provisioning template and claim credentials sign into your aws account and visit here create provtemplate make sure to enable the use the aws iot registry to manage your device fleet option once you have created the template and credentials modify the claim certificate s policy to match the sample policy sample claim policy in order to set these configurations manually edit demo config h in the demo folder to define the following set aws iot endpoint to your custom endpoint this is found on the settings page of the aws iot console and has a format of abcdefg1234567 iot us east 2 amazonaws com set root ca cert path to the path of the root ca certificate downloaded when setting up the device certificate in aws iot account setup aws iot account setup set claim cert path to the path of the claim certificate downloaded when setting up the template and claim credentials set claim private key path to the path of the private key downloaded when setting up the template and claim credentials set provisioning template name to the name of the provisioning template created set device serial number to an arbitrary string representing a device identifier create provtemplate https console aws amazon com iot home provisioningtemplate create instruction sample claim policy demos fleet provisioning fleet provisioning with csr example claim policy json configuring the s3 demos you can pass the following configuration settings as command line options in order to run the s3 demos make sure to run the following command in the root directory of the c sdk sh cmake s bbuild ds3 presigned get url s3 get url ds3 presigned put url s3 put url s3 presigned put url is only needed for the s3 upload demo in order to set these configurations manually edit demo config h in demos http http demo s3 download multithreaded and demos http http demo s3 upload to define the following set s3 presigned get url to a s3 presigned url with get access set s3 presigned put url to a s3 presigned url with put access you can generate the presigned urls using demos http common src presigned urls gen py demos http common src presigned urls gen py more info can be found here demos http common src readme md configure s3 download http demo using sigv4 library refer this demos http http demo s3 download readme md demos http http demo s3 download readme md to follow the steps needed to configure and run the s3 download http demo using sigv4 library that generates the authorization http header needed to authenticate the http requests send to s3 setup for aws iot jobs demo 1 the demo requires the linux platform to contain curl and libmosquitto on a debian platform these dependencies can be installed with apt install curl libmosquitto dev if the platform does not contain the libmosquitto library the demo will build the library from source libmosquitto 1 4 10 or any later version of the first major release is required to run this demo 2 a job that specifies the url to download for the demo needs to be created on the aws account for the thing resource that will be used by the demo the job can be created directly from the aws iot console https console aws amazon com iot home or using the aws cli tool the following creates a job that specifies a linux kernel link for downloading aws iot create job job id job 1 targets arn aws iot us west 2 account id thing thing name document url https cdn kernel org pub linux kernel v5 x linux 5 8 5 tar xz setup for the greengrass local auth demo for setting up the greengrass local auth demo see the readme in the demo folder demos greengrass greengrass demo local auth readme md prerequisites for the aws over the air update ota demos 1 to perform a successful ota update you need to complete the prerequisites mentioned here https docs aws amazon com freertos latest userguide ota prereqs html 1 a code signing certificate is required to authenticate the update a code signing certificate based on the sha 256 ecdsa algorithm will work with the current demos an example of how to generate this kind of certificate can be found here https docs aws amazon com freertos latest userguide ota code sign cert esp html 1 the code signing certificate can be either baked into firmware as a string or stored as a file 1 for baked in certificate method copy the certificate to signingcredentialsigning certificate pem in ota pal posix c https github com aws aws iot device sdk embedded c blob main platform posix ota pal source ota pal posix c 2 for file storage method store the certificate as a file and supply the file path in path name of code signing certificate on device field when creating the ota job in aws iot console scheduling an ota update job after you build and run the initial executable you will have to create another executable and schedule an ota update job with this image 1 increase the version of the application by setting macro app version build in demos ota ota demo core mqtt http demo config h to a different version than what is running 1 rebuild the application using the build steps building and running demos below into a different directory say build dir 2 1 rename the demo executable to reflect the change e g mv ota demo core mqtt ota demo core mqtt2 1 create an ota job 1 go to the aws iot core console https console aws amazon com iot 1 manage jobs create create a freertos ota update job select the corresponding name for your device from the thing list 1 sign a new firmware create a new profile select any sha ecdsa signing platform upload the code signing certificate from prerequisites and provide its path on the device 1 select the image select the bucket you created during the prerequisite steps prerequisites for the aws over the air update ota demos upload the binary build dir 2 bin ota demo2 1 the path on device should be the absolute path to place the executable and the binary name e g home ubuntu aws iot device sdk embedded c staging build dir bin ota demo core mqtt2 1 select the iam role created during the prerequisite steps prerequisites for the aws over the air update ota demos 1 create the job 1 run the initial executable again with the following command sudo ota demo core mqtt or sudo ota demo core http 1 after the initial executable has finished running go to the directory where the downloaded firmware image resides which is the path name used when creating an ota job 1 change the permissions of the downloaded firmware to make it executable as it may be downloaded with read user default permissions only chmod 775 ota demo core mqtt2 1 run the downloaded firmware image with the following command sudo ota demo core mqtt2 building and running demos before building the demos ensure you have installed the prerequisite software prerequisites on ubuntu 18 04 and 20 04 gcc cmake and openssl can be installed with sh sudo apt install build essential cmake libssl dev build a single demo go to the root directory of the c sdk run cmake to generate the makefiles cmake s bbuild cd build choose a demo from the list below or alternatively run make help grep demo defender demo http demo basic tls http demo mutual auth http demo plaintext http demo s3 download http demo s3 download multithreaded http demo s3 upload jobs demo mosquitto mqtt demo basic tls mqtt demo mutual auth mqtt demo plaintext mqtt demo serializer mqtt demo subscription manager ota demo core http ota demo core mqtt pkcs11 demo management and rng pkcs11 demo mechanisms and digests pkcs11 demo objects pkcs11 demo sign and verify shadow demo main replace demo name with your desired demo then build it make demo name go to the build bin directory and run any demo executables from there build all configured demos go to the root directory of the c sdk run cmake to generate the makefiles cmake s bbuild cd build run this command to build all configured demos make go to the build bin directory and run any demo executables from there running corepkcs11 demos the corepkcs11 demos do not require any aws iot resources setup and are standalone the demos build upon each other to introduce concepts in pkcs 11 sequentially below is the recommended order 1 pkcs11 demo management and rng 1 pkcs11 demo mechanisms and digests 1 pkcs11 demo objects 1 pkcs11 demo sign and verify 1 please note that this demo requires the private and public key generated from pkcs11 demo objects to be in the directory the demo is executed from alternative option of docker containers for running demos locally install docker sh curl fssl https get docker com o get docker sh sh get docker sh installing mosquitto to run mqtt demos locally the following instructions have been tested on an ubuntu 18 04 environment with docker and openssl installed 1 download the official docker image for mosquitto 1 6 14 this version is deliberately chosen so that the docker container can load certificates from the host system any version after 1 6 14 will drop privileges as soon as the configuration file has been read before tls certificates are loaded sh docker pull eclipse mosquitto 1 6 14 1 if a mosquitto broker with tls communication needs to be run ignore this step and proceed to the next step a mosquitto broker with plain text communication can be run by executing the command below docker run it p 1883 1883 name mosquitto plain text eclipse mosquitto 1 6 14 1 set broker endpoint defined in demos mqtt mqtt demo plaintext demo config h to localhost ignore the remaining steps unless a mosquitto broker with tls communication also needs to be run 1 for tls communication with mosquitto broker server and ca credentials need to be created use openssl commands to generate the credentials for the mosquitto server note make sure to use different common name cn detail between the ca and server certificates otherwise ssl handshake fails with exactly same common name cn detail in both the certificates sh generate ca key and certificate provide the subject field information as appropriate for ca certificate openssl req x509 nodes sha256 days 365 newkey rsa 2048 keyout ca key out ca crt sh generate server key and certificate provide the subject field information as appropriate for server certificate make sure the common name cn field is different from the root ca certificate openssl req nodes sha256 new keyout server key out server csr sign with the ca cert openssl x509 req sha256 in server csr ca ca crt cakey ca key cacreateserial out server crt days 365 1 create a mosquitto conf file to use port 8883 for tls communication and providing path to the generated credentials port 8883 cafile mosquitto config ca crt certfile mosquitto config server crt keyfile mosquitto config server key use this option for tls mutual authentication where client will provide ca signed certificate require certificate true tls version tlsv1 2 use identity as username true 1 run the docker container from the local directory containing the generated credential and mosquitto conf files sh docker run it p 8883 8883 v pwd mosquitto config name mosquitto basic tls eclipse mosquitto 1 6 14 1 update demos mqtt mqtt demo basic tls demo config h to the following set broker endpoint to localhost set root ca cert path to the absolute path of the ca certificate created in step 4 for the local mosquitto server installing httpbin to run http demos locally run httpbin through port 80 sh docker pull kennethreitz httpbin docker run p 80 80 kennethreitz httpbin server host defined in demos http http demo plaintext demo config h can now be set to localhost to run http demo basic tls download ngrok https ngrok com download in order to create an https tunnel to the httpbin server currently hosted on port 80 sh ngrok http 80 may have to use ngrok exe depending on os or filename of the executable ngrok will provide an https link that can be substituted in demos http http demo basic tls demo config h and has a format of https abcdefg12345 ngrok io set server host in demos http http demo basic tls demo config h to the https link provided by ngrok without https preceding it you must also download the root ca certificate provided by the ngrok https link and set root ca cert path in demos http http demo basic tls demo config h to the file path of the downloaded certificate generating documentation note for pre generated documentation please visit releases and documentation releases and documentation section the doxygen references were created using doxygen version 1 9 2 to generate the doxygen pages use the provided python script at tools doxygen generate docs py tools doxygen generate docs py please ensure that each of the library submodules under libraries standard and libraries aws are cloned before using this script sh cd csdk root git submodule update init recursive checkout python3 tools doxygen generate docs py the generated documentation landing page is located at docs doxygen output html index html | aws | server |
IT_PSRU59 | it psru59 information technology chittrapon iwchaona | server |
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regular-ui | regular ui regular ui is a set of front end components built with regularjs regularjs to get started check out http regular ui github io npm version npm badge npm bower version bower badge bower dependency status deps badge deps devdependency status dev deps badge dev deps quick start following options to get regular ui download the latest release latest install with bower bower bower install regular ui install with npm npm npm install regular ui you can find the compiled regular ui distribution in bower repo repo bower docs all documentations are generated in the directory of doc by gulp you can read them on github page documentation conveniently or download them to local browser support chrome https raw github com alrra browser logos master chrome chrome 48x48 png firefox https raw github com alrra browser logos master firefox firefox 48x48 png ie https raw github com alrra browser logos master internet explorer internet explorer 48x48 png safari https raw github com alrra browser logos master safari safari 48x48 png opera https raw github com alrra browser logos master opera opera 48x48 png latest latest ie8 latest latest develop first of all clone the code shell git clone git github com regular ui regular ui git install project dependencies shell cd regular ui npm install if you don t have used gulp before you need to install the gulp package as a global install shell npm install gulp g now you can start to develop run gulp to watch and compile source files for developing anytime they are modified run gulp all to prepare css and js index files include all components based on structure js run gulp customize to prepare css and js index files include partial components based on structure customized js run gulp dist to compile a distribution to dist directory run gulp bower to compile a distribution to dist directory and sync to bower repo repo bower run gulp doc to rebuild docs without watching run gulp page to rebuild docs and sync to github page repo repo page run npm test to start karma for testing cases structure i m lazy to translate regular ui regular ui bower css css fonts regular ui fontawesome js js mcss mcss scss scss vendor bower json bower readme md regular ui src regular ui src js js base module js unit js util js index js js mcss mcss bootstrap bootstrap core default default flat flat simple simple bootstrap mcss bootstrap default mcss default flat mcss flat simple mcss simple regular ui doc src regular ui doc src assets doc mcss doc mcss view common api ejs cssmodule css markdown cssunit css markdown jsmodule js markdown jsunit js markdown start markdown index md build js buildall js cssdoc js jsdoc js js api premark js markdown sitemap json referenced websites netease nec http nec netease com nej http nej netease com based on jquery bootstrap http v3 bootcss com uikit http www getuikit net semantic ui http semantic ui com amaze ui http amazeui org foundation http foundation zurb com materializecss http materializecss com based on angularjs angular ui https angular ui github io lumx http ui lumapps com angular material https material angularjs org based on react react bootstrap http react bootstrap github io components html ant design http ant design material ui http www material ui com based on web component polymer https www polymer project org others kendo ui http demos telerik com kendo ui extjs http docs sencha com extjs 4 0 7 versioning regular ui is maintained by using the semantic versioning specification semver semver contributing see the contributing guidelines contributing for details copyright and license code released under the mit license npm https www npmjs com package regular ui npm badge https badge fury io js regular ui svg bower http bower io bower badge https badge fury io bo regular ui svg deps badge https david dm org regular ui regular ui svg deps https david dm org regular ui regular ui dev deps badge https david dm org regular ui regular ui dev status svg dev deps https david dm org regular ui regular ui info devdependencies peer deps badge https david dm org regular ui regular ui peer status svg peer deps https david dm org regular ui regular ui info peerdependencies repo main https github com regular ui regular ui repo bower https github com regular ui regular ui bower repo page https github com regular ui regular ui github io latest https github com regular ui regular ui bower releases latest documentation http regular ui github io contributing https github com regular ui regular ui blob master contributing md regularjs https github com regularjs regular semver http semver org | front_end |
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SZUBP-project | szubp project the project is for the subject of database management systems in the master s studies of the faculty of electronics engineering | server |
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hotel_management_system | hotel management system a project which was done during the software engineering databases course nothing special | server |
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anlp | anlp contains demo codes for the course applied natural language processing | natural-language-processing demos neural-network deep-learning-algorithms machine-translation | ai |
Machine-Learning | machine learning http i imgur com dpgtwn6 jpg 1 apriori https github com roc j machine learning tree master apriori 2 knn https github com roc j machine learning tree master knn 3 https github com roc j machine learning tree master taobao goods analysis | ai |
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frontendhappyhour.github.io | front end happy hour front end happy hour website https frontendhappyhour com build node build add new episode to episodes json node episode contributing 1 fork it 2 run npm install 3 run node clear build 4 run gulp 5 create your feature branch git checkout b my new feature 6 commit your changes git commit am add some feature 7 push to the branch git push origin my new feature 8 create new pull request | front_end |
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solidity-t5 | a code t5 based solidity lauguage model for smart contract code generation published model at huggingface see https huggingface co hululuzhu solidity t5 hello world example to utilize the trained model a hello world example to use this model notice the input text includes header solidity version like pragma solidity 0 5 7 ancestor class library info e g public functions and constants from parenta contract library interface declaration header e g helloworld ended with python pip install transformers q from transformers import autotokenizer t5forconditionalgeneration device cuda fallback to cpu if you do not have cuda tokenizer autotokenizer from pretrained hululuzhu solidity t5 model t5forconditionalgeneration from pretrained hululuzhu solidity t5 to device text pragma solidity 0 5 7 context parenta functions helloa hellob constants constanta contract helloworld is parenta input ids tokenizer text return tensors pt truncation true input ids to device need to tune beam topk topp params to get good outcome generated ids model generate input ids max length 256 num beams 5 top p 0 95 top k 50 print tokenizer decode generated ids 0 skip special tokens true expect outcome string public constant name hello world uint256 public constant override returns uint256 return initialsupply function initialsupply public view returns uint256 background base t5 code model https huggingface co salesforce codet5 large source data https huggingface co datasets mwritescode slither audited smart contracts processing steps clean contract level segmentation sepration split in and out after processing input sample pragma solidity 0 5 7 context pauserrole functions ispauser addpauser renouncepauser constants contract pausable is pauserrole after processing output sample notice indentation is bad this is intentional to reduce token size event paused address account event unpaused address account bool private pausableactive bool private paused constructor internal paused false function paused public view returns bool return paused modifier whennotpaused require paused modifier whenpaused require paused function pause public onlypauser whennotpaused whenpausableactive paused true emit paused msg sender function unpause public onlypauser whenpaused whenpausableactive paused false emit unpaused msg sender function setpausableactive bool active internal pausableactive active modifier whenpausableactive require pausableactive source training code see the end to end notebook https github com hululuzhu solidity t5 blob main code solidity t5 data processing and training ipynb at code dir here future todo the model is significantly under trained because of lack of gpu budget need 10x colab resources 100 for full train this is quite limited on how the model is used potentially we could switch to gpt2 decoder only to compare but codet5 has its strong code optimization need more classifiers t5 or bert alike to detect potential defects this is the intention of the project however i found it quite challenging to find the labeled data that points the exact line of code that has defect technically some thoughts 01 2023 as i tested a few examples using this significantly under trained model and compared with chatgpt it seems they perform similarly for code completion that shows the great potential for this model to surpass chatgpt if we have 30x training budget and reliable training pipeline 30x budget i trained 3 hours which only finished 10 of 1 epoch i expect 3 epoches are reasonable size for finetune based on my personal experience the data is specially tuned for codet5 thus it has the limitation of split to input and output in out token size limit is 512 ideally we could change the transformed data so it could be trained in bert style to compare with t5 based classifiers or fill missing splan style to add context before and after to let model output middle code training more classifiers t5 or bert alike to detect potential defects is the intention of the project however i found it quite challenging to find the labeled data that points the exact line of code that has defect the part i divide code into segment and their ancestor info could be useful but need more time to evaluate technically it is also hard to tell if my aggressive approach to remove all comments thus rely on meaning of code only is a good approach | ai |
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NBomber | p align center img src https github com pragmaticflow nbomber blob dev assets nbomber logo png alt nbomber logo width 600px p nuget https img shields io nuget v nbomber svg https www nuget org packages nbomber nbomber is a modern and flexible load testing framework for pull and push scenarios designed to test any system regardless of a protocol http websockets amqp etc or a semantic model pull push nbomber is free for personal use developer centric and extensible using nbomber you can test the reliability and performance of your systems and catch performance regressions and problems earlier nbomber will help you to build resilient and performant applications that scale nbomber 5 https cdn jsdelivr net gh pragmaticflow nbomber assets v5 0 assets nbomber 5 youtube png https youtu be z51pyzvznf8 links website https nbomber com documentation https nbomber com docs getting started overview why we build nbomber and what you can do with it the main reason behind nbomber is to provide a lightweight framework for writing load tests which you can use to test literally any system and simulate any production workload we wanted to provide only a few abstractions so that we could describe any type of load and still have a simple intuitive api another goal is to provide building blocks to validate your poc proof of concept projects by applying any complex load distribution with nbomber you can test any pull or push system http websockets graphql grpc sql databse mongodb redis etc with nbomber you can convert some of your integration tests to load tests easily nbomber as a modern framework provides zero dependencies on protocol http websockets amqp sql zero dependencies on semantic model pull push very flexible configuration and dead simple api distributed cluster support https nbomber com docs cluster overview real time reporting ci cd integration xunit and nunit runners are supported plugins extensions support add your own plugins or data sinks data feed support inject real or fake data into your tests debuggability of your load test debug your tests using your favorite ide what makes it very simple one of the design goals of nbomber is to keep api as minimal as possible because of this nbomber focuses on fully utilizing programming language c f constructs instead of reinventing a new dsl that should be learned in other words if you want to write a for loop you don t need to learn a dsl for this csharp var scenario scenario create hello world scenario async context you can define and execute any logic here for example send http request sql query etc nbomber will measure how much time it takes to execute your logic await task delay 1 000 return response ok withloadsimulations simulation inject rate 10 interval timespan fromseconds 1 during timespan fromseconds 30 nbomberrunner registerscenarios scenario run examples type language nbomber demo https github com pragmaticflow nbomber tree dev examples demo c | load-testing performance-testing integration-testing fsharp | os |
AI_Writer | overview this code will generate a story based on an image that you input more information can be found in the original repo https github com ryankiros neural storyteller this the code for build an ai writer on youtube https youtu be x24veueph0q img src https github com ryankiros neural storyteller blob master images ex1 jpg height 220px align left we were barely able to catch the breeze at the beach and it felt as if someone stepped out of my mind she was in love with him for the first time in months so she had no intention of escaping the sun had risen from the ocean making her feel more alive than normal she s beautiful but the truth is that i do n t know what to do the sun was just starting to fade away leaving people scattered around the atlantic ocean i d seen the men in his life who guided me at the beach once more samim http samim io has made an awesome blog post with lots of results here https medium com samim generating stories about images d163ba41e4ed dependencies python 2 7 https www python org downloads theano instructions here http deeplearning net software theano install html lasagne instructions here http lasagne readthedocs io en latest user installation html scipy pip install scipy numpy pip install numpy for running on your cpu you will need to install caffe and its python interface use pip https pypi python org pypi pip to install any missing dependencies basic usage simply create a new python file in this repo or run this code from the python command line shell import generate z generate load all generate story z images ex1 jpg sample output shell i let the woman in her eyes and i had no idea what to say to her it was a small group of people both of them as soon as the taxi arrived he gave me a hug it was the first time i d spent so much time in new york in fact it was almost as if he were going to be the only man in his life and he made a mental note to pin her down on either side of her body she loved those men a little less than ten minutes and i am stunned by someone see more at http www somatic io examples 4wdnbdm0 sthash tvsdmyru dpuf credits credit for the vast majority of code here goes to ryan kiros https github com ryankiros i ve merely created a wrapper around some of the important functions to get people started | ai |
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mobile-dapp-scaffold | solana mobile dapp scaffold the solana mobile dapp scaffold is a repository template providing a fast and efficient way for developers to build decentralized mobile applications on the solana blockchain with the scaffold developers can quickly set up and get started with building their solana mobile dapp the scaffold includes pre built components integrated with features such as wallet connection airdrop sol devnet testnet sign messages and send transactions scaffold follows clean architecture and mvvm principles enabling efficient and robust mobile app development believe me there is no better time to build consumer mobile dapps https twitter com intent tweet text believe 20me 2c 20there 20is 20no 20better 20time 20to 20build 20consumer 20mobile 20dapps 22 20 20 40cdhiraj40 0a 0astart 20building 20on 20 40solanamobile 20today url https 3a 2f 2fgithub com 2fcdhiraj40 2fmobile dapp scaffold 2f 20 screenshots table thead tr th h3 home h3 th th h3 dashboard h3 th tr thead tbody tr td img src https user images githubusercontent com 75211982 232579436 c54da8e8 ee31 465c a921 9227bedc34c0 png alt image 1 width 400 td td img src https user images githubusercontent com 75211982 232579459 c5858817 52bb 4382 9d77 4cd989cdc860 png alt image 2 width 400 td tr tbody table getting started to start using scaffold follow the below steps 1 click on use this template dropdown 2 select create a new repository structure by adhering to best practices for architecture and design patterns the scaffold ensures that developers can create applications that are easy to maintain and extend with a separation of concerns between the data domain and presentation layer br below is the structure for mobile dapp scaffold considering from app src main https github com cdhiraj40 mobile dapp scaffold tree main app src main java com example solanamobiledappscaffold app s core logic common common classes used through of the project data folder containing data layer classes di should house the dependency injection components remote contains classes and interfaces related to handling network requests and responses as well as remote data sources such as apis etc repository should contain abstract data sources behind a single interface for the domain layer s use domain folder containing domain layer classes model should contain data models and and business logic that define the data repository contains interfaces that the data layer implementation must conform to providing a separation between the domain and data layer use case contains specific business logic or operations that the application needs to perform utils reusable utility classes or helper functions presentation folder containing presentation layer classes typically activities fragments viewmodels etc res folder contains application resources such as layouts strings and drawable assets that are used by the ui contributing any contributions you make to this project is greatly appreciated bug feature request if you find a bug in the app kindly open an issue here https github com cdhiraj40 mobile dapp scaffold issues new assignees labels bug template bug report md title 5bbug 5d 3a to report it by including a proper description of the bug and the expected result https github com cdhiraj40 mobile dapp scaffold issues new assignees labels enhancement template feature request md title 5bfeature request 5d 3a are appreciated too if you feel like a certain feature is missing feel free to create an issue to discuss with the maintainers | android solana solana-mobile | front_end |
Pngyu | pngyu is front end gui application of pngquant pngyu is distributed under the bsd license | front_end |
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Machine-Learning-Projects | machine learning projects machine learning experiments and work | ai |
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iot-curriculum | azure iot curriculum github license https img shields io github license microsoft iot curriculum https github com microsoft iot curriculum blob main license github contributors https img shields io github contributors microsoft iot curriculum svg https github com microsoft iot curriculum graphs contributors github issues https img shields io github issues microsoft iot curriculum svg https github com microsoft iot curriculum issues github pull requests https img shields io github issues pr microsoft iot curriculum svg https github com microsoft iot curriculum pull prs welcome https img shields io badge prs welcome brightgreen svg http makeapullrequest com github watchers https img shields io github watchers microsoft iot curriculum svg style social label watch maxage 2592000 https github com microsoft iot curriculum watchers github forks https img shields io github forks microsoft iot curriculum svg style social label fork maxage 2592000 https github com microsoft iot curriculum network github stars https img shields io github stars microsoft iot curriculum svg style social label star maxage 2592000 https github com microsoft iot curriculum stargazers this repo is actively developed watch star or check back often for updates this repo contains hands on labs and other lab and workshop based material designed to support the creation of iot curricula for higher education covering iot and ai on the edge all the labs use physical devices such as raspberry pis and nvidia jetson boards and are designed for in class or at home study as an educator you would use these labs in a blended learning environment teaching concepts and theory in the classroom mixed with labs from here to supplement the course and provide hands on experience most of the content here is microsoft content available in other places this repo brings some of the content together and provides a single place to find content across different github repos documentation microsoft learn and other sites all the content contained in this repo is free for you to use in your courses however you see fit we will endeavour to keep the content up to date but seeing as technology moves fast things may be missed if you find any errors in these materials please either raise an issue https github com microsoft iot curriculum issues or feel free to raise a pr with the fix https github com microsoft iot curriculum pulls we will be continually adding and updating the content here if there is a particular lab or content you would like added please raise an issue https github com microsoft iot curriculum issues if you have content you would like to share please raise a pr https github com microsoft iot curriculum pulls iot for beginners iot for beginners logo https github com microsoft iot for beginners raw main images iot for beginners png https aka ms iot beginners if you are after beginner iot content check out iot for beginners https aka ms iot beginners a 12 week 24 lesson curriculum that teaches iot from the basics each lesson includes pre and post lesson quizzes written instructions to complete the lesson a solution an assignment and more our project based pedagogy allows you to learn while building a proven way for new skills to stick the projects cover the journey of food from farm to table this includes farming logistics manufacturing retail and consumer all popular industry areas for iot devices hardware needs these labs make use of a variety of hardware all connected to cloud services each lab indicates up front what hardware is required there is also an overall list for an iot cart that provides a complete all in one hardware solution that covers all these labs this is designed to be a course in a box you purchase everything on the list and that can be shared between groups of students learning iot in a more iot focused degree program rather than a single course as part of a wider technology based learning program details of the cart are in the cart cart folder device setup the devices devices folder contains details on setting up the different devices recommended for the iot cart labs the labs labs folder contains details on a range of different labs covering iot and ai on the edge educator guides the educator guides educator guides folder contains guides for educators including suggested course outlines and iot lab guides microsoft learn microsoft learn https docs microsoft com learn wt mc id academic 7372 jabenn is a free online training platform that provides interactive learning for microsoft products and more our goal is to help you become proficient on our technologies and learn more skills with fun guided hands on interactive content that s specific to your role and goals there are a number of learning paths covering iot technologies services and solutions these can form a hands on component of a blended learning setup in the classroom or provide a way for students to learn by themselves fundamentals azure fundamentals https docs microsoft com learn paths azure fundamentals wt mc id academic 7372 jabenn introduction to internet of things https docs microsoft com learn paths introduction to azure iot wt mc id academic 7372 jabenn build the intelligent edge with azure iot edge https docs microsoft com learn paths build intelligent edge with azure iot edge wt mc id academic 7372 jabenn architect api integration in azure https docs microsoft com learn paths architect api integration wt mc id academic 7372 jabenn microsoft power platform fundamentals https docs microsoft com learn paths power plat fundamentals wt mc id academic 7372 jabenn iot10 connecting your physical environment to a digital world a roadmap to iot solutioning https aka ms iot10 resources iot concepts and services introduction to azure iot hub https docs microsoft com learn modules introduction to iot hub wt mc id academic 7372 jabenn securely connect iot devices to the cloud https docs microsoft com learn paths securely connect iot devices wt mc id academic 7372 jabenn develop iot solutions with azure iot central https docs microsoft com learn paths develop iot solutions with azure iot central wt mc id academic 7372 jabenn iot20 deciphering data optimizing data communication to maximize your roi https aka ms iot20 resources develop with azure digital twins https docs microsoft com learn paths develop azure digital twins wt mc id academic 7372 jabenn data create and use analytics reports with power bi https docs microsoft com learn paths create use analytics reports power bi wt mc id academic 7372 jabenn azure for the data engineer https docs microsoft com learn paths azure for the data engineer wt mc id academic 7372 jabenn architect a data platform in azure https docs microsoft com learn paths architect data platform wt mc id academic 7372 jabenn implement a data warehouse with azure synapse analytics https docs microsoft com learn paths implement sql data warehouse wt mc id academic 7372 jabenn azure data fundamentals explore core data concepts https docs microsoft com learn paths azure data fundamentals explore core data concepts wt mc id academic 7372 jabenn data engineering with azure databricks https docs microsoft com learn paths data engineer azure databricks wt mc id academic 7372 jabenn iot40 big data 2 0 iot as your new operational data source https aka ms iot40 resources ai and machine learning ai edge engineer https docs microsoft com learn paths ai edge engineer wt mc id academic 7372 jabenn create machine learning models https docs microsoft com learn paths create machine learn models wt mc id academic 7372 jabenn create no code predictive models with azure machine learning https docs microsoft com learn paths create no code predictive models azure machine learning wt mc id academic 7372 jabenn ai business school for manufacturing https docs microsoft com learn paths ai business school manufacturing wt mc id academic 7372 jabenn get started with artificial intelligence on azure https docs microsoft com learn paths get started with artificial intelligence on azure wt mc id academic 7372 jabenn microsoft azure artificial intelligence ai strategy and solutions https docs microsoft com learn modules azure artificial intelligence wt mc id academic 7372 jabenn build ai solutions with azure machine learning https docs microsoft com learn paths build ai solutions with azure ml service wt mc id academic 7372 jabenn explore computer vision in microsoft azure https docs microsoft com learn paths explore computer vision microsoft azure wt mc id academic 7372 jabenn process and classify images with the azure cognitive vision services https docs microsoft com learn paths classify images with vision services wt mc id academic 7372 jabenn explore natural language processing https docs microsoft com learn paths explore natural language processing wt mc id academic 7372 jabenn process and translate speech with azure cognitive speech services https docs microsoft com learn paths process translate speech azure cognitive speech services wt mc id academic 7372 jabenn evaluate text with azure cognitive language services https docs microsoft com learn paths evaluate text with language services wt mc id academic 7372 jabenn bring ai to business users in your organization https docs microsoft com learn paths bring ai to business users your organization wt mc id academic 7372 jabenn iot30 adding intelligence unlocking new insights with ai machine learning https aka ms iot30 resources iot scenarios iot demos https github com azure samples iotdemos examples of end to end use cases and the iot architectures that enable to those use cases configure and manage products and inventory in dynamics 365 supply chain management https docs microsoft com learn paths configure manage products inventory dyn365 supply chain mgmt wt mc id academic 7372 jabenn configure and use lean manufacturing in dynamics 365 supply chain management https docs microsoft com learn paths configure use lean manufacturing dyn365 supply chain mgmt wt mc id academic 7372 jabenn configure and use discrete manufacturing in dynamics 365 supply chain management https docs microsoft com learn paths configure use discrete manufacturing dyn365 supply chain mgmt wt mc id academic 7372 jabenn iot50 get to solutioning strategy best practices when mapping designs from edge to cloud https aka ms iot50 resources iot videos microsoft iot developers channel on youtube https www youtube com channel ucl7wy iy v76xxpnrizgozq watch latest videos about microsoft iot updates and news partners and customers spotlights interactive deep dives as well as demos the iot show on channel9 https channel9 msdn com shows internet of things show wt mc id academic 7372 jabenn the latest microsoft iot announcements product and features demos customer and partner spotlights top industry talks and technical deep dives maker show https youtube com playlist list plfi bhzy0z5r fdcy3es4se1ucyi4r ih a maker and iot focused show from the fordevs community iot 101 with net on channel9 https channel9 msdn com series iot 101 wt mc id academic 7372 jabenn a 101 level video series to learn iot on net solution quickstarts microsoft offers a number of solution accelerators almost complete iot setups that can be customized to your needs as a part of this there are a number of quickstarts that allow you to try out the different solutions try a cloud based remote monitoring solution https docs microsoft com azure iot accelerators quickstart remote monitoring deploy wt mc id academic 7372 jabenn try a cloud based solution to manage my industrial iot devices https docs microsoft com azure iot accelerators quickstart connected factory deploy wt mc id academic 7372 jabenn deploy and run an iot device simulation in azure https docs microsoft com azure iot accelerators quickstart device simulation deploy wt mc id academic 7372 jabenn try a cloud based solution to run a predictive maintenance analysis on my connected devices https docs microsoft com azure iot accelerators quickstart predictive maintenance deploy wt mc id academic 7372 jabenn reference architectures the azure architecture center https docs microsoft com azure architecture wt mc id academic 7372 jabenn provides guidance for architecting solutions on azure using established patterns and practices azure iot reference architecture https docs microsoft com azure architecture reference architectures iot wt mc id academic 7372 jabenn iot and data analytics in the construction industry https docs microsoft com azure architecture example scenario data big data with iot wt mc id academic 7372 jabenn controlling iot devices using a voice assistant https docs microsoft com azure architecture solution ideas articles iot devices wt mc id academic 7372 jabenn iot using cosmos db https docs microsoft com azure architecture solution ideas articles iot using cosmos db wt mc id academic 7372 jabenn iot connected platform for covid 19 protection https docs microsoft com azure architecture solution ideas articles iot connected platform wt mc id academic 7372 jabenn contactless interfaces with azure iot edge https docs microsoft com azure architecture solution ideas articles contactless interfaces wt mc id academic 7372 jabenn covid 19 safe solutions at the iot edge https docs microsoft com azure architecture solution ideas articles cctv mask detection wt mc id academic 7372 jabenn smart and secure lighting and disinfection https docs microsoft com azure architecture solution ideas articles uven disinfection wt mc id academic 7372 jabenn predictive maintenance with the intelligent iot edge https docs microsoft com azure architecture example scenario predictive maintenance iot predictive maintenance wt mc id academic 7372 jabenn ingestion and processing of real time automotive iot data https docs microsoft com azure architecture example scenario data realtime analytics vehicle iot wt mc id academic 7372 jabenn create a safe building https docs microsoft com azure architecture solution ideas articles safe buildings wt mc id academic 7372 jabenn secure your iot saas app with the microsoft identity platform https docs microsoft com azure architecture example scenario iot aad iot aad wt mc id academic 7372 jabenn azure industrial iot analytics guidance https docs microsoft com azure architecture guide iiot guidance iiot architecture wt mc id academic 7372 jabenn industrial iot for industrial iot iiot microsoft provides a range of reference materials and samples based around opc ua iiot on azure documentation https docs microsoft com azure iot accelerators overview iot industrial wt mc id academic 7372 jabenn documentation and a solution accelerator for iiot open62541 https github com open62541 open62541 an open source opc ua implementation opc ua with iot central https github com jlorich demo opc iot edge to central a reference implementation for connecting opc servers to iot edge and then passing data up to iot central iot edge offline dashboarding https github com azureiotgbb iot edge offline dashboarding a set of modules that can be used with azure iot edge to perform dashboarding at the edge digital agriculture farmbeats https www microsoft com research project farmbeats iot agriculture wt mc id academic 7372 jabenn ai edge iot for agriculture farmbeats for students lesson plans https education microsoft com lesson 5d991297 wt mc id academic 7372 jabenn a program combining an affordable hardware kit with curriculum and activities designed to give students hands on experience in applying precision agriculture techniques to food production farmbeats for students videos on youtube https www youtube com playlist list pldi jxfb4oodbxrr2w2cu0vx8jzwgvu6k digital twins develop with azure digital twins learning path on microsoft learn https docs microsoft com learn paths develop azure digital twins wt mc id academic 7372 jabenn digital twins hands on lab https github com azure samples digital twins samples tree master handsonlab robotics robot operating system ros with windows 10 linux and azure https microsoft github io win ros landing page azure rtos azure rtos is an embedded development suite including a small but powerful operating system that provides reliable ultra fast performance for resource constrained devices it s easy to use and market proven having been deployed on more than 6 2 billion devices worldwide azure rtos supports the most popular 32 bit microcontrollers and embedded development tools so you can make the most of your team s existing skills overview of azure rtos https azure microsoft com services rtos wt mc id academic 7372 jabenn azure rtos on github https github com azure rtos introduction to azure rtos video on channel9 https channel9 msdn com shows internet of things show introduction to azure rtos wt mc id academic 7372 jabenn programming languages platforms and tools there are many different programming languages platforms and tools you can use for iot here are some language resources python python is a popular language for developing iot solutions on devices such as the raspberry pi it s also popular for data science and building machine learning models to analyze the data coming from iot devices azure python iot sdk https github com azure azure iot sdk python this repository contains code for the azure iot sdks for python this enables python developers to quickly create iot device solutions that seamlessly connect to the azure iothub ecosystem python for beginners video series on channel9 https channel9 msdn com series intro to python development wt mc id academic 7372 jabenn over the course of a set of videos we re going to show you the ropes of python development more python for beginners video series on channel9 https channel9 msdn com series more python for beginners wt mc id academic 7372 jabenn more python for beginners videos take your first steps with python learning path on microsoft learn https docs microsoft com learn paths python first steps wt mc id academic 7372 jabenn interested in learning a programming language but aren t sure where to start start here learn the basic syntax and thought processes required to build simple applications using python circuitpython azure iot sdk https github com adafruit adafruit circuitpython azureiot an azure iot sdk for use with adafruit circuitpython https circuitpython readthedocs io a variant of python for embedded devices net net is a free cross platform open source developer platform for building applications and supports programming languages such as c and f azure iot c sdk https github com azure azure iot sdk csharp a c sdk for connecting to azure iot services net home page https dotnet microsoft com wt mc id academic 7372 jabenn the home of net net 101 video series https dotnet microsoft com learn videos wt mc id academic 7372 jabenn a 101 level video series to learn net iot 101 on channel9 https channel9 msdn com series iot 101 wt mc id academic 7372 jabenn a 101 level video series to learn iot on net net for iot devices https github com dotnet iot this repo includes net core implementations for various iot boards chips displays and pcbs c c azure iot c sdk https github com azure azure iot sdk c a c sdk for connecting to azure iot services javascript node js azure iot node sdk https github com azure azure iot sdk node the azure iot node js sdk enables developers to create iot solutions written in node js for the azure iot platform beginner s series to javascript https channel9 msdn com series beginners series to javascript wt mc id academic 7372 jabenn a series of practical bite sized videos about javascript for beginners so you can get up to speed quickly beginner s series to node js https channel9 msdn com series beginners series to nodejs wt mc id academic 7372 jabenn a series of practical bite sized videos about node js for beginners so you can get up to speed quickly arduino azure iot hub library for arduino https github com azure azure iot arduino this library is a port of the microsoft azure iot device sdk for c to arduino it allows you to use several arduino compatible boards with azure iot hub visual studio code visual studio code vs code is a free open source cross platform developer text editor that can be extended by a huge range of extensions to support different programing languages and capabilities visual studio code http code visualstudio com wt mc id academic 7372 jabenn the home of vs code platform io https platformio org platformio ide an extension for vs code that provides tools for embedded c c development with no additional dependencies remote developer pack https marketplace visualstudio com items itemname ms vscode remote vscode remote extensionpack wt mc id academic 7372 jabenn an extension to support remote development such as connecting to a raspberry pi and developing on that pi from your pc or mac pylance python language extension https marketplace visualstudio com items itemname ms python vscode pylance wt mc id academic 7372 jabenn an extension providing python language support to vs code c c https marketplace visualstudio com items itemname ms vscode cpptools wt mc id academic 7372 jabenn full intellisense and debugging support for c and c development iot events in a box if you are interested in running an iot event here are some events in a box giving access to event materials such as slide decks video walkthroughs and code samples iot event learning path https github com microsoft internet of things event learning path the internet of things event learning path is designed for solution architects business decision makers and development teams that are interested in building iot solutions with azure services the content is comprised of 5 video based modules that approach topics ranging from iot device connectivity iot data communication strategies use of artificial intelligence at the edge data processing considerations for iot data and iot solutioning based on the azure iot reference architecture azure subscriptions these labs are designed for courses where azure resources are provided to students by the institution to try them out you can use one of our free subscriptions head to the azure subscriptions guide azure subscription md for from information on setting up a subscription get certified microsoft offers a certification in iot az 220 the microsoft certified azure iot developer specialty microsoft certified azure iot developer specialty page on microsoft learn https docs microsoft com learn certifications azure iot developer specialty wt mc id academic 7372 jabenn az 220 microsoft azure iot developer study guide https microsoftlearning github io az 220 microsoft azure iot developer microsoft learn student ambassadors finding your community is more important than ever as classes and social activities take place virtually amplify your impact and bring together your peers to learn new skills solve real world problems and build communities across the globe students can apply to be a microsoft learn student ambassadors the student ambassadors program provides clear steps to help you learn and lead so you can make a difference and empower those around you student ambassadors get access to unique resources like our global student network on microsoft teams and a microsoft 365 account and can earn badges for activities and contributions to unlock additional benefits such as cloud credits if you are an educator encourage your students to sign up for this program to help their peers learn new skills and to improve employability after their studies you can learn more on the microsoft learn student ambassadors site https studentambassadors microsoft com wt mc id academic 7372 jabenn imagine cup reimagine our world with technology in the 2021 imagine cup we re looking for bold thinkers and big dreamers to join the 2021 competition journey make an impact through coding collaboration and competition innovate with passion to tackle global issues and bring your idea to life in the imagine cup the 19th annual imagine cup is more than just a competition for students you can work with friends and make new ones network with professionals gain new skills make a difference in the world and have the chance to win great prizes read more and sign up at imaginecup microsoft com https imaginecup microsoft com wt mc id academic 7372 jabenn contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com https cla opensource microsoft com wt mc id academic 7372 jabenn when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct wt mc id academic 7372 jabenn for more information see the code of conduct faq https opensource microsoft com codeofconduct faq wt mc id academic 7372 jabenn or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments legal notices microsoft and any contributors grant you a license to the microsoft documentation and other content in this repository under the mit license https opensource org licenses mit see the license license file and grant you a license to any code in the repository under the mit license https opensource org licenses mit see the license code license code file microsoft windows microsoft azure and or other microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of microsoft in the united states and or other countries the licenses for this project do not grant you rights to use any microsoft names logos or trademarks microsoft s general trademark guidelines can be found at http go microsoft com fwlink linkid 254653 http go microsoft com fwlink linkid 254653 wt mc id academic 7372 jabenn privacy information can be found at https privacy microsoft com https privacy microsoft com wt mc id academic 7372 jabenn microsoft and any contributors reserve all other rights whether under their respective copyrights patents or trademarks whether by implication estoppel or otherwise | iot azure azure-iot machine-learning ai curriculum iot-edge microsoft labs hands-on-lab raspberrypi nvidia-jetson python | server |
lisk-explorer | logo docs assets banner explorer png lisk explorer lisk explorer is a blockchain explorer designed for interaction with lisk network it is a part of lisk ecosystem https lisk io a blockchain platform based on dpos consensus protocol license gpl v3 https img shields io badge license gpl 20v3 blue svg http www gnu org licenses gpl 3 0 lisk explorer is a feature rich single page browser application with following functionalities transaction browser https explorer lisk io txs shows transactions with their details stored in the blockchain supports all transaction types with its metadata allows advanced filtering by date type amount etc block browser https explorer lisk io blocks shows blocks with their details stored in the blockchain account browser https explorer lisk io address 6307579970857064486l supports various account types allows advanced transaction filtering on per account basis delegate monitor https explorer lisk io delegatemonitor shows information about all register delegate accounts live status of the 101 active delegates network monitor https explorer lisk io networkmonitor shows live information about all nodes shows active and disconnected nodes public ips are shown with domain names and geographical location to make it running at least one lisk core sdk node with public api is needed installation clone the lisk explorer repository git clone https github com liskhq lisk explorer git cd lisk explorer installation with docker recommended in order to install docker refer to the official docker installation instruction https docs docker com install you will also need docker compose install docker compose https docs docker com compose install the docker compose configuration assumes that the network called localhost is available on your machine if there is no such a network created you can always add one by using the following command docker network create localhost starting application to start explorer type the following command docker compose up d it will use latest available version from local hub by default stopping application the following command will remove all containers defined by the docker compose yml docker compose down volumes for further information about managing and configuring lisk explorer see run with docker docs run with docker md installation from source for further information about managing and configuring lisk explorer see run from source docs run from source md running tests there are functional backend and end to end tests available the test suite is described in run tests docs run tests md section get involved found a bug create new issue https github com liskhq lisk explorer issues new want to develop with us read contribution guidelines https github com liskhq lisk explorer blob development docs contributing md have ideas to share come to lisk chat http lisk chat want to involve community join community gitter https gitter im liskhq lisk utm source badge utm medium badge utm campaign pr badge utm content badge found a security issue see our bounty program https blog lisk io announcing lisk bug bounty program 5895bdd46ed4 contributors https github com liskhq lisk explorer graphs contributors license copyright 2016 2019 lisk foundation this program is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version this program is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details you should have received a copy of the gnu general public license license along with this program if not see http www gnu org licenses this program also incorporates work previously released with lisk explorer 1 1 0 and earlier versions under the mit license https opensource org licenses mit to comply with the requirements of that license the following permission notice applicable to those parts of the code only is included below copyright 2016 2017 lisk foundation copyright 2015 crypti permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | blockchain |
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Advanced-JavaScript | github issues https img shields io github issues trainingbypackt advanced javascript svg https github com trainingbypackt advanced javascript issues github forks https img shields io github forks trainingbypackt advanced javascript svg https github com trainingbypackt advanced javascript network github stars https img shields io github stars trainingbypackt advanced javascript svg https github com trainingbypackt advanced javascript stargazers prs welcome https img shields io badge prs welcome brightgreen svg https github com trainingbypackt advanced javascript pulls advanced javascript in this course you will gain a deep understanding of javascript you will learn how to write javascript in a professional environment using the new javascript syntax in es6 leverage on javascript s asynchronous nature using callbacks and promises and understand how to set up test suites and test your code you will be introduced to javascript s functional programming style and you will apply everything learned to build a simple application in various javascript frameworks and libraries for backend and frontend development advanced javascript by zachary shute what you will learn examine major features in es6 and implement those features to build applications create promise and callback handlers to work with asynchronous processes develop asynchronous flows using promise chaining and async await syntax manipulate the dom with javascript handle javascript browser events explore test driven development and build code tests with javascript code testing frameworks list the benefits and drawbacks of functional programming compared to other styles construct applications with the node js backend framework and the react frontend framework hardware requirements for an optimal student experience we recommend the following hardware configuration processor intel core i5 or equivalent memory 4gb ram storage 35gb available space software requirements you ll also need the following software installed in advance os windows 7 sp1 64 bit windows 8 1 64 bit or windows 10 64 bit browser google chrome latest version atom ide babel node js and npm | javascript html | front_end |
Nethereum.BlockchainStorage | nethereum blockchainstorage summary this repo is for projects that extend block chain processing the core nethereum libraries e g within nethereum web3 provide crawling retrieval and filtration mechanisms whilst projects in this repo provide storage for the data for examples of block chain processing see the docs http docs nethereum com en latest nethereum log processing detail samples on nethereum playground http playground nethereum com | azure-table-storage blockchain azure ethereum nethereum geth | blockchain |
FullStack_Info_HW02 | fullstack info hw02 full stack web development homework 02 responsive design | front_end |
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nrcpp | nrcpp front end c compiler developed on visual studio 6 0 during 2002 2005 | front_end |
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charcoal-android | charcoal android pixiv design system usage android view xml textview android layout width wrap content android layout height wrap content android text hello android textappearance style textappearance charcoal regular 20 android textcolor attr colorcharcoaltext1 button style style widget charcoal button default m android layout width wrap content android layout height wrap content android text ok installation groovy repositories mavencentral groovy ext charcoal version 1 0 1 dependencies android view implementation net pixiv charcoal charcoal android view charcoal version compose experimental implementation net pixiv charcoal charcoal compose charcoal version requirements minsdk 23 documentation android view https pixiv github io charcoal android android view jetpack compose https pixiv github io charcoal android compose | android android-view design-system jetpack-compose kotlin ui-library | os |
socialdb_vue_flask | social engineering database backend using language python 3 5 using database mongodb requirements pymongo flask flask restful frontend using vue js requirements axios echarts frontend preview image preview preview1 png image preview preview png make it better 1 sql query not safe enough 2 inefficient sql query 3 backend interface limit 4 frontend search waiting layout | server |
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clima | clima a new flutter project getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https docs flutter dev get started codelab cookbook useful flutter samples https docs flutter dev cookbook for help getting started with flutter development view the online documentation https docs flutter dev which offers tutorials samples guidance on mobile development and a full api reference | server |
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f3-cortex-model-generator | f3 cortex model generator generates f3 cortex https github com ikkez f3 cortex models by reverse engineering existing database schema currently only supports mysql installation please add php ekhaled f3 cortex model generator 1 0 to your composer file usage create an executable php file with the following contents php usr bin env php php require once dir src vendor autoload php config output path to output folder db array db connection params namespace models base extends models base relationnamespace models base template path to template file indentation array exclude array generator new ekhaled generators mysql model config generator generate and just run the file from the command line options output specifies the folder where models will be output to db an array in the following format host host com username password dbname name of database namespace namespace of the generated models extends if you have a base model you can make the generated model extend that model by specifying it here relationnamespace namespace of the connected classes that constitute relationships with a given model usually it s the same as namespace template path to file containing a custom template if not specified a built in template will be used indentation an array that indicates what type of unit of indentation to be used on template generation followed by a starting level for example unit start level 3 this will use 2 spaces as indentation starting at 6 spaces this is applied to the array defined by the fieldconf template exclude views whether to generate models for views too defaults to false exclude connectors whether to generate stub models for many to many connector tables defaults to false sometimes you might need these models to create db tables for example for automated tests in test databases exclude an array containing all tables that you would like to exclude while generating models for example array migrations custom templates a typical custom template would look like php php namespace class classname extends protected fieldconf fieldconf table tablename just ensure that the placeholders are in place and they will get replaced during model generation supported placeholders are namespace classname extends fieldconf tablename | server |
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MyWallet | mywallet pr ctica tdd para ios keepcoding startup engineering master iii respuestas a las preguntas y comentarios tambi n padezco de paja mental compulsiva tengo que tatuarme las sagradas escrituras de tdd en la frente | os |
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massa | br p align center img src logo png width 240 p br massa the decentralized and scaled blockchain ci https github com massalabs massa actions workflows ci yml badge svg branch main https github com massalabs massa actions workflows ci yml query branch 3amain bors enabled https bors tech images badge small svg https app bors tech repositories 39543 docs https img shields io static v1 label docs message massa color style flat https massalabs github io massa massa node open in gitpod https shields io badge gitpod contribute brightgreen logo gitpod style flat https gitpod io https github com massalabs massa about massa massa https massa net is a new blockchain based on a multithreaded technology https arxiv org pdf 1803 09029 that supports more than 10 000 transactions per second in a fully decentralized network with thousands of nodes a short introduction video is available here https www youtube com watch v nuufhvd7uly massa s purpose is to make it easy to deploy fully decentralized applications we ve developed two key technologies that are exclusive to massa to help make this possible autonomous smart contracts https docs massa net en latest general doc autonomous sc html and native front end hosting https docs massa net en latest general doc decentralized web html here is a list of tools to easily build applications on the massa blockchain js client library https github com massalabs massa web3 to connect to the massa blockchain from your applications assemblyscript https github com massalabs massa as sdk sdks to write smart contracts examples of applications https github com massalabs massa sc examples built on massa explorer https test massa net interactive api specification https playground open rpc org schemaurl https test massa net api v2 uischema appbar ui input false uischema appbar ui inputplaceholder enter massa json rpc server url uischema appbar ui logourl https massa net favicons favicon ico uischema appbar ui splitview false uischema appbar ui darkmode false uischema appbar ui title massa uischema appbar ui examplesdropdown false uischema methods ui defaultexpanded false uischema methods ui methodplugins true uischema params ui defaultexpanded false lots of documentation https docs massa net from web3 development https docs massa net docs build home to massa s architecture https docs massa net docs learn home join the testnet with decentralization as a core value we ve gone to great lengths to lower the barrier of entry for community participation in our community testnet and we invite you to join in https docs massa net docs node install and register for the testnet participation reward program your participation will help improve decentralization usability and network reliability leading up to and following the launch of mainnet community we have active community discussions on several platforms and we invite you to join the conversations if you wish to go into some depth about technical aspects speak with people working in the ecosystem full time or just have a chat with other like minded people these can be good places to start discord https discord com invite massa telegram https t me massanetwork twitter https twitter com massalabs contributing we welcome contributions from the community at large from anywhere between seasoned oss contributors to those hoping to try it for the first time if you would like some help to get started reach out to us on our community discord https discord com invite massa server if you re comfortable enough to get started on you re own check out our good first issue https github com massalabs massa labels good 20first 20issue label contributors a list of all the contributors can be found here contributors md | blockchain |
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web-2020 | web 2020 introduction to web and cloud programming subjects html5 css3 bootstrap responsive design javascript jquery cdn forms python django framework ajax json | cloud |
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resume-parser | resume parser extracting name email phonenumber skills code to parse information such as name email phone number skillset and the technology associated with it requirements following is the list of python libraries required cstringio re csv pdfminer beautifulsoup urllib2 spacy spacy is an industrial strength natural language processing tool which is used here to detect indian names in the resume after pip installing spacy make sure that you install xx model which supports foreign languages python m spacy download xx code set the directory to the one which contains the extracted files and save your resume with the file name resume pdf in the same directory python resumeparser py sample output page https i imgur com 4u6ifvx png | server |
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SQL_Employee-Database | sql employeedb employee database a mystery in two parts sql png sql png background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data modeling 2 data engineering 3 data analysis instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints import each csv file into the corresponding sql table data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name gender and salary 2 list employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name and start and end employment dates 4 list the department of each employee with the following information employee number last name first name and department name 5 list all employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name consult sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information | server |
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