Saravanan666/pypi-org huggingface dataset:
Package: chainedci
Chained-ci-pychained-ci-pyis a project to manage CI/CD scenarios with gitlab and
trigger multi subproject pipelines, passing variables and artifacts from
one to the other.⚠️ DOCUMENTATION is to be done ⚠️Here is a few command line:How to use chainedciUsing python package:pipinstallchainedciUsing Docker:dockerrun--rm-ti\registry.gitlab.com/orange-opensource/lfn/ci_cd/chained-ci-pychainedciGenerate .gitlab-ci.yml from scenario fileschainedcigenerate-i<scenario_folder>/inventory-p.vaultwith.vaulta local file containing your vault key that was used to cipher
projects tokens.
Package: chained-recurrence
Taskwarrior Chained Recurrence HookPainlessly adds chained recurrence to Taskwarrior. RequiresPython >= 3.8.In the illustration above, task26ccff69is automatically created when task90e414dlis completed. With the new task having the equivalentdueandwaitattributes, relative to it's ownentryattribute.It's common to havedueorwaitfall on aday boundarysuch as23:59or00:00, this is usually because a named date such aseodorsodwas used
when creating the original task. If this is the case, new tasks will be created
with thedueandwaitattributes automatically falling on the equivalent
day boundaries. If this sounds convoluted, the "Usage" section below
has a simple example.Install$pipinstallchained-recurrence
$chained-recurrenceinstallUsageCreate tasks as you usually would, addingchained:on:$taskaddchained:on'hair cut'When this task's status is changed tocomplete, a new one will be created.Thewaitanddueattributes can also be used, their date and time values
will be updated in relation to the current date and time:$dateMon  1 Jan 18:00:00 GMT 2024$taskaddchained:onwait:1d'workout'Created task 1.$dateSat 20 Jan 09:00:00 GMT 2024$task1doneCompleted task ab566e0f 'workout'.Completed 1 task.Creating new chained task.The newly created chained task will have the following:AttributeValuewait2024-01-21 09:00:00Named Dates$dateMon  1 Jan 18:00:00 GMT 2024$taskaddchained:onwait:sod+1ddue:eod+2d'workout'Created task 1.$dateSat 20 Jan 09:00:00 GMT 2024$task1doneCompleted task ab566e0f 'workout'.Completed 1 task.Creating new chained task.The newly created chained task will have the following:AttributeValuewait2024-01-21 00:00:00due2024-01-22 23:59:59Uninstall$chained-recurrenceuninstall
$pipuninstallchained-recurrence
Package: chainer
Chainer: A deep learning frameworkWebsite|Docs|Install Guide|Tutorials(ja)
|Examples(Official,External)
|Concepts|ChainerXForum(en,ja)
|Slack invitation(en,ja)
|Twitter(en,ja)Chaineris a Python-based deep learning framework aiming at flexibility.
It provides automatic differentiation APIs based on thedefine-by-runapproach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks.
It also supports CUDA/cuDNN usingCuPyfor high performance training and inference.
For more details about Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter.Notice: Asannounced, Chainer is under the maintenance phase and further development will be limited to bug-fixes and maintenance only.InstallationFor more details, see theinstallation guide.To install Chainer, usepip.$pipinstallchainerTo enable CUDA support,CuPyis required.
Refer to theCuPy installation guide.Docker imageWe are providing the official Docker image.
This image supportsnvidia-docker.
Login to the environment with the following command, and run the Python interpreter to use Chainer with CUDA and cuDNN support.$ nvidia-docker run -it chainer/chainer /bin/bashContributionSee thecontribution guide.ChainerXSee theChainerX documentation.LicenseMIT License (seeLICENSEfile).More informationRelease notesReferencesTokui, Seiya, et al. "Chainer: A Deep Learning Framework for Accelerating the Research Cycle."Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019.URLBibTexTokui, S., Oono, K., Hido, S. and Clayton, J.,
Chainer: a Next-Generation Open Source Framework for Deep Learning,Proceedings of Workshop on Machine Learning Systems(LearningSys) in
The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), (2015)URL,BibTexAkiba, T., Fukuda, K. and Suzuki, S.,
ChainerMN: Scalable Distributed Deep Learning Framework,Proceedings of Workshop on ML Systems in
The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), (2017)URL,BibTex
Package: chainerboard
An unofficial visualization tool forchainer, inspired bytensorboard. The toolkit allows visualization of log fromchainer.extensions.LogReport.Example usage:model=L.Classifier(MyModel())optimizer=chainer.optimizers.Adam()optimizer.setup(model)train=create_my_data()train_iter=chainer.iterators.SerialIterator(train,batchsize)updater=training.StandardUpdater(train_iter,optimizer)trainer=training.Trainer(updater,(epochs,'epoch'),out='path/to/output')trainer.extend(extensions.LogReport(log_name='my_log_data')))# optional; allows visualization of parameterstrainer.extend(extensions.ParameterStatistics(model))# Run the trainingtrainer.run()and point chainerboard at the output log file to start local http server.chainerboardpath/to/output/my_log_namenow openhttp://localhost:6006/to view the log.WarningThe author of this project is not a professional web
programmer. Never use the project on remote server since it may
impose serious security risks.DevelopmentTo setup development environment:pipinstall-rrequirements.txtFor testing,toxBuild documentpythonsetup.pybuild_sphinx
Package: chainer-hajo
Chainer Hajo provides utility classes
to be used with the excellent Chainer framework
which are shared across projects here at hajo.me.In particular, this package includes the following helpers:WriteImageallows one to report image arraysto the chainer reporting system as an observation
and to write those to disk as image files.
Enabled using optional parameters,WriteImagecan also display
the image inside an IPython environment, such as Jupyter notebooks.
Together withSecondTriggerthis allows to show the user
regular previews of the training progress
while the associated Jupyter notebook is running.PrintNoLogwill print a set of observations,such as the current loss values, to the command line
without storing them into a file and/or serializing the history to JSON.
For projects with fast training iterations and large epochs,
this can greatly increase training speed by reducing disk IO.SecondTriggerandMinuteTriggerare trigger objectswhich will fire
independently of the number of iterations and epochs,
but at a fixed rate such as once per 30 seconds.
This is useful for providing continuous diagnostics
and performance output for long-running training sessions,
while keeping the evaluation overhead constant.
When using triggers that operate on the iteration or epoch,
then adjusting the number of items per training batch
would increase or reduce the duration per iteration,
thus making it necessary to adjust the trigger timings.
These triggers avoid such problems
by triggering based on the wall clock time.LogHyperparameteris a trainer extensionwhich will copy the current value of a hyperparameter
over into an reporting observation.
This is useful for graphing or logging the training rate,
if modified withExponentialShiftorLinearShift.ClassifyModifyand the associatedClassifyModifyChainwill repeatedly modify the data array withx +=conv_b(Elu(conv_a(x)))which has proven to be an excellent model for learn-able residuals
for video, image and sound data processing.ClassifyModifySequencefurther generalizes this
by allowing the user to specify varying kernel size,
stride, padding, and dilation parameters for the different iterations.SynthAndGateis a reusable generator Chainwhich will convolve the input signal and then calculate
the sigmoid-gated sums of tanh generators.
Usually, the input signal is upsampled to multiple tanh channels
per output element, which allows this operator to use the gating
for choosing between different generators for each output plane.
This approach has proven to work well for many different
likelihood modeling tasks, e.g. generating the distribution
for aF.softmax_cross_entropyloss.
Optionally, the limiting by tanh can be disabled.
This has proven useful for modeling time-continuous image streams,
e.g. videos or continuous EEG signals.Coming soon:Discretizerand the accompanyingdiscretizefunctioncan be used to reduce continuous multi-direction vectors
to a limited number of discrete directions,
while optionally enforcing a given vector norm.
Package: chainerio
ChainerIOChainerIO is an IO abstraction library for Chainer, optimized for deep
learning training with batteries included. It supportsFilesystem API abstraction with unified error semantics,Explicit user-land caching system,IO performance tracing and metrics stats, andFileset container utilities to save metadata.DependencyHDFS client and libhdfs for HDFS accessPython 3Installation and Document buildInstallation$gitclonehttps://github.com/chainer/chainerio.git
$cdchainerio
$pipinstall.Documentation$cdchainerio/docs
$makehtml
$openbuild/html/index.htmlTest$cdchainerio
$pipinstall.[test]$pytestHow to usePlease refer to theDocumentationfor more information about the usage.
Also you can find some examples inexamplesdirectory for usage in Chainer training script examples.
Package: chainer-mask-rcnn
chainer-mask-rcnnChainer Implementation ofMask R-CNN.FeaturesResNet50, ResNet101 backbone.VOC and COCO training examples.Reproduced result of original work (ResNet50, COCO).Weight copy from pretrained model atfacebookresearch/Detectron.Training with batch size >= 2.Support FPN backbones.Keypoint detection.Fig 1. Mask R-CNN, ResNet50, 8GPU, Ours, COCO 31.4 mAP@50:95COCO ResultsModelImplementationN gpu trainingmAP@50:95LogMask R-CNN, ResNet50Ours831.5 - 31.8LogMask R-CNN, ResNet50Detectron831.4 (30.8 after copied)LogFCIS, ResNet50FCIS827.1-Inference# you can use your trained model./demo.pylogs/<YOUR_TRAINING_LOG>--img<IMAGE_PATH_OR_URL># COCO Example: Mask R-CNN, ResNet50, 31.4 mAP@50:95cdexamples/cocoLOG_DIR=logs/20180730_081433
mkdir-p$LOG_DIRpipinstallgdown
gdownhttps://drive.google.com/uc?id=1XC-Mx4HX0YBIy0Fbp59EjJFOF7a3XK0R-O$LOG_DIR/snapshot_model.npz
gdownhttps://drive.google.com/uc?id=1fXHanL2pBakbkv83wn69QhI6nM6KjrzL-O$LOG_DIR/params.yaml
./demo.py$LOG_DIR# copy weight from caffe2 to chainercdexamples/coco
./convert_caffe2_to_chainer.py# or download from https://drive.google.com/open?id=1WOEtVnxqYdHl35pAyIcp-H0HtTjI-l3V./demo.pylogs/R-50-C4_x1_caffe2_to_chainer--imghttps://raw.githubusercontent.com/facebookresearch/Detectron/master/demo/33823288584_1d21cf0a26_k.jpg
./demo.pylogs/R-50-C4_x1_caffe2_to_chainer--imghttps://raw.githubusercontent.com/facebookresearch/Detectron/master/demo/17790319373_bd19b24cfc_k.jpgFig 2. Mask R-CNN, ResNet50, 8GPU, Copied from Detectron, COCO 31.4 mAP@50:95Installation & TrainingSingle GPU Training# Install Chainer Mask R-CNN.pipinstallopencv-python
pipinstall.# Run training!cdexamples/coco&&./train.py--gpu0Multi GPU Training# Install OpenMPIwgethttps://www.open-mpi.org/software/ompi/v3.0/downloads/openmpi-3.0.0.tar.gz
tarzxvfopenmpi-3.0.0.tar.gzcdopenmpi-3.0.0
./configure--with-cuda
make-j4
sudomakeinstall
sudoldconfig# Install NCCL# dpkg -i nccl-repo-ubuntu1404-2.1.4-ga-cuda8.0_1-1_amd64.debdpkg-inccl-repo-ubuntu1604-2.1.15-ga-cuda9.1_1-1_amd64.deb
sudoaptupdate
sudoaptinstalllibnccl2libnccl-dev# Install ChainerMNpipinstallchainermn# Finally, install Chainer Mask R-CNN.pipinstallopencv-python
pipinstall.# Run training!cdexamples/coco&&mpirun-n4./train.py--multi-nodeTestingpipinstallflake8pytest
flake8.
pytest-vtests
Package: chainerui
ChainerUIChainerUI is a visualization and management tool forChainer.InstallationTo install ChainerUI, usepip.$pipinstallchaineruiTo install ChainerUI from source.$gitclonehttps://github.com/chainer/chainerui.git
$cdchainerui/frontend
$npminstall&&npmrunbuild&&cd..
$pipinstall-e.ChainerUI usessqlite3module which is included in the standard Python library. If Python is built from source,sqlite3must be installed before building Python.On Ubuntu, "libsqlite3-dev" must be installed before building Python ($ apt-get install libsqlite3-dev).On Windows, install Visual C++ Build Tools with the Default Install setting before building Python.Usageoutputlogfile withLogReportextension, seeexamples/log-file/train_mnist.pyoutputlogfile manually, seeexamples/log-file/train_mnist_custom_loop.pypost training logs via web API, seeexamples/web-api/train_mnist.pyQuick startInitialize ChainerUI database.$chaineruidbcreate
$chaineruidbupgradeClone examples of train log and create a project.$gitclonehttps://github.com/chainer/chainerui.git
$cdchainerui

$# create example project$chaineruiprojectcreate-dexamples/log-file-nexample-project

$# run ChainerUI server$chaineruiserverOpenhttp://localhost:5000/and select "example-project", then shown a chart of training logs.For more detailed usage, seegetting startedDocker startGet Docker container fromDockerHuband start ChainerUI server. The container has installed ChainerUI module, setup a DB and a command to start the server.$# replace tag to the latest version number$dockerpullchainer/chainerui:latest
$dockerrun-d-p5000:5000--namechaineruichainer/chainerui:latest
$# then ChainerUI server is runningOpenhttp://localhost:5000/, shown empty project list.examples/web-api/train_mnist.pyis an example to setup project and send logs via web API.For more detailed usage, seegetting startedorweb APIBrowser compatibilityChainerUI is supported by the latest stable version of the following browsers.FirefoxChromeLicenseMIT License (seeLICENSEfile).ContributionAny contribution to ChainerUI is welcome!Python codes follow theChainer contribution guide.Run testsInstall "pytest" package and run test$pytestBuild client side JS$cdfrontend
$npminstall
$npmrunbuild:watch
Package: chainexplorer
What is Chain Explore?Chain explorer is an easy to use Python package that allows the exploration of the Bitcoin Blockchain.
It retrieves data through thehttps://www.blockchain.com/explorerAPI.
The focus of the functions is to decode data that was embedded in the blockchain.
Examples of such data are the famous message from Satoshi Nakamoto in the Bitcoin Genesis block.
The Bitcoin white paper. The Satoshi upload and download tool as code on the blockchain.
Or image data stored on the blockchain via the Apertus tool.How to install?Install with pip:pip install chainexplorerAlternatively the package can be installed by cloning the repository as follows.Clone the Git repository:git clone https://github.com/akcarsten/chain_explorerThen go to the folder to which you cloned the repository and run:python setup.py installSome ExamplesBelow some examples are listed that show how the Chain Explorer package can be used to explore the secrets
of the Bitcoin blockchain.Example 1: The secret message in the Genesis blockfromchainexplorerimportexplorerasexp# Download the Genesis blockraw_block=exp.get_by_block(0)# Have a look at the Genesis informationexp.show_block_info(raw_block)# The hidden information is in the input messagein_msg,out_msg=exp.collect_messages(raw_block)# The input message needs to be decoded from hexadecimal to ASCII to be readabledecoded_msg=exp.decode_hex_message(in_msg)print(decoded_msg)b'\x04\xff\xff\x00\x1d\x01\x04EThe Times 03/Jan/2009 Chancellor on brink of second bailout for banks'Example 2: Download the Bitcoin white paperfromchainexplorerimportexplorerasexp# Get the Bitcoin white paper from the blockchainexp.download_data('54e48e5f5c656b26c3bca14a8c95aa583d07ebe84dde3b7dd4a78f4e4186e713','bitcoin.pdf')
Package: chainfury
🦋 NimbleBox ChainFury🦋 Build complex chat apps using LLMs in 4 clicks ⚡️Try it out here. Used in production bychat.nbox.ai.Read theDocsThe documentation page contains all the information on usingchainfuryandchainfury_server.Looking for Inspirations?Here's a few example to get your journey started on Software 2.0:📚 Retrieval Augmented Generation (RAG): Load a PDF and ask it questions, readdocs🏞️ Image generation using Stability: Generate your world, readhere🔐 Private Storage: Privately store the data on AWS S3, readprivacyInstallationThere are two separate packages built into this repository, first ischainfurywhich contains the fury-engine for running
the DAGs andchainfury_serverwhich contains the self hosted server for the GUI.pipinstallchainfury
pipinstallchainfury_server# to launch the serverpython3-mchainfury_serverRun DockerEasiest way to run the server is to use docker. You can use the following command to run ChainFury:dockerbuild.-fDockerfile-tchainfury:latest
dockerrun-p8000:8000chainfury:latestTo pass any env variables you can use the command:dockerrun--envENV_KEY=ENV_VALUE-p8000:8000chainfury:latestCheckout all the:componentenvironment variablesherechainfuryspecific variablesherechainfury_serverspecific variableshereFrom SourceHere's a breakdown of folder:chainfury/contains the chainfury engineserver/contains the chainfury serverclient/contains the frontend code for the GUIapi_docs/contains the documentationTo build the entire system from scratch follow these steps:gitclonehttps://github.com/NimbleBoxAI/ChainFurycdChainFury
python3-mvenvvenvsourcevenv/bin/activateYou will need to haveyarninstalled to build the frontend and move it to the correct location on the servershstories/build_and_copy.shOnce the static files are copied we can now proceed to install dependecies:pipinstallsetuptools
pipinstall-e.# editable install the chainfurycdserver
pipinstall-e.# editable install the chainfury_serverTo start you can now do:cdchainfury_server
python3server.pyYou can now visitlocalhost:8000to see the GUI.ContibutionsChainFury is an open-source project used in production. We are open to contributions to the project in the form of features,
infrastructure or documentation.Ourissuespage is kept up to date with bugs, improvements, and feature requests.If you're looking for help with your code, hop ontoGitHub Discussions boardorDiscord, so that more people can benefit from it.Describing your issue:Try to provide as many details as possible. What exactly goes wrong? How is it failing?
Is there an error? "XY doesn't work" usually isn't that helpful for tracking down problems. Always remember to include
the code you ran and if possible, extract only the relevant parts and don't just dump your entire script. This will make
it easier for us to reproduce the error.Sharing long blocks of code or logs:If you need to include long code, logs or tracebacks, you can wrap them in<details>and</details>. Thiscollapses the contentso it only becomes visible on click, making the issue easier to read and follow.
Package: chaingrep
Chaingrep Python LibraryThe Chaingrep Python library provides convenient access to the Chaingrep API from applications written in Python.DocumentationSee theChaingrep docs.InstallationYou don't need this source code unless you want to modify the package. If you want to use the package, just run:pip install chaingrepInstall from source with:python setup.py installRequirementsPython 3.6+UsageThe library needs to be instantiated with your Chaingrep API key. Here's are a few examples of how the library can be used:fromchaingrepimportChaingrepfromdatetimeimportdatetime,timedeltachaingrep=Chaingrep("<API_KEY>")# Parse a transactiontransaction=chaingrep.transaction("0xc4fd8359894ad78b04a5cd784106bcf6c413db8372492e744433533abc848ac6").parse()print(transaction.transaction_type)# Parse an account's transactionsaccount_transactions=chaingrep.account("0xa4722f1b4B552951828e6A334C5724b34B19A327").parse_transactions()fortransactioninaccount_transactions:print(transaction.time.timeago)# Query all interactions between an account and a contract in the past monthtarget_account="0xa4722f1b4B552951828e6A334C5724b34B19A327"# Metarget_contract="0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48"# USDCcurrent_datetime=datetime.utcnow()past_datetime=current_datetime-timedelta(days=30)# One month ago# Construct the queryquery=chaingrep.query()query.query({"account":target_account,"contract":target_contract})query.timeframe(past_datetime,current_datetime)# Return a countinteractions_count=query.count()# Return resultsinteractions=query.execute()Handling exceptionsUnsuccessful requests raise exceptions. The class of the exception will reflect the sort of error that occurred, and the error message will provide more context. SeeExceptionsfor more.fromchaingrepimportChaingrepchaingrep=Chaingrep("<API_KEY>")transaction=chaingrep.transaction("not_a_valid_hash").parse()# InvalidTransactionHashError: <error message>
Package: chaining
⛓ ChainingPackage that implements functional chaining in Python, which behaves like JavaScript📦 InstallationThis package is not ready to be used in production; There are lots of things still left to be implemented and optimized!# only python 3.6 or newerpip3installchaining🔥 ActionChainList>>>fromchainingimportChainList>>>array=ChainList([1,2,3])>>>array.length3>>>array.map(lambdaitem:item*2)<ChainList[2,4,6]>>>>other_array=ChainList._from('chain').map(lambdaitem,idx:ord(item)+idx)>>>other_array.iterable[99,105,99,108,114]>>>array.concat(other_array)<ChainList[1,2,3,99,105,99,108,114]>
Package: chainit
chainitDocumentation available here:https://lukapeschke.github.io/chainit/This library provides theChainItclass, a wrapper around stdlib'sitertoolsmodule, allowing to chain
operations on iterables, resulting in easier-to-read code.importtypingastdeffib()->t.Iterable[int]:a,b=0,1whileTrue:yieldaa,b=b,a+b# Allows to write things like this...(ChainIt(fib()).filter(lambdax:x%2==0).map(lambdax:x//2).flat_map(range).take_while(lambdax:x<6).collect_list())# ...rather than like thisfromitertoolsimportchainasichain,islice,takewhilelist(takewhile(lambdax:x<6,ichain.from_iterable(map(lambdax:range(x//2),filter(lambdax:x%2==0,fib()))),))Installationpip install chainitExamplesDecoratorIn addition toChainIt, the library provides achainitdecorator. It makes a function returning
an iterable return aChainItinstead:@chainitdeffac():n=0fac=1whileTrue:yieldfacn+=1fac*=nassertfac().enumerate().take(5).collect()==((0,1),(1,1),(2,2),(3,6),(4,24))Using aChainItinstance as an iterableassertlist(fac().take(3))==[1,1,2]foridx,xinfac().enumerate():ifidx>3:breakprint(x)
Package: chainjacking
ChainJackingFind which of your go lang direct GitHub dependencies is susceptible to ChainJacking attackRequirementsGo and it's binaries >= 1.13GitHub token, to run queries on GitHub APIInstallationpip install chainjackingUsageCLIpython -m chainjacking -gt $GH_TOKENArguments-gt <token>- GitHub access token, to run queries on GitHub API (required)-p <path>- Path to scan. (default=current directory)-v- Verbose output mode-url <url>- Scan one or more GitHub URLs-f <path>- Scan one or more GitHub URLs from a file separated by new-line
Package: chainladder
chainladder: Property and Casualty Loss Reserving in PythonWelcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.This package strives to be minimalistic in needing its own API. The syntax mimics popular packagespandasfor data manipulation andscikit-learnfor model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.Chainladder is built by a group of volunteers, and we need YOUR help!This package is written in Python, if you are looking for a similar package written in R, please visitchainladder.Dedicated Documentation SiteWe have a dedicated documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets,  API references, change log history, and more.VisitChainladder-Python on Read the Docs.LicensesThis package is released underMozilla Public License 2.0.