Saravanan666/pypi-org huggingface dataset:
Package: aim-mlflow
aimlflowAim-powered supercharged UI for MLFlow logsRun beautiful UI on top of your MLflow logs and get powerful run comparison features.Aboutaimlflow helps to explore various types of metadata tracked during the training with MLFLow, including:hyper-parametersmetricsimagesaudiotextMore about Aim:https://github.com/aimhubio/aimMore about MLFLow:https://github.com/mlflow/mlflowGetting StartedFollow the steps below to set up aimlflow.Install aimlflow on your training environment:pip install aim-mlflowRun live time convertor to sync MLFlow logs with Aim:aimlflow sync --mlflow-tracking-uri={mlflow_uri} --aim-repo={aim_repo_path}Run the Aim UI:aim up --repo={aim_repo_path}Why use aimlflow?Powerful pythonic search to select the runs you want to analyze.Group metrics by hyperparameters to analyze hyperparameters’ influence on run performance.Select multiple metrics and analyze them side by side.Aggregate metrics by std.dev, std.err, conf.interval.Align x axis by any other metric.Scatter plots to learn correlations and trends.High dimensional data visualization via parallel coordinate plot.
Package: ai-models-fourcastnet
ai-models-fourcastnet:warning: This plugin is now deprecated. Please use the newer version that can be found athttps://github.com/ecmwf-lab/ai-models-fourcastnetv2ai-models-fourcastnetis anai-modelsplugin to runNVIDIA's FourCastNet.FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorshttps://arxiv.org/abs/2202.11214The FourCastNet code was developed by the authors of the preprint: Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, Animashree Anandkumar.Version 0.1 of FourCastNet is used as default in ai-models.https://portal.nersc.gov/project/m4134/FCN_weights_v0.1/FourCastNet is released underBSD 3-Clause License, seeLICENSE_fourcastnetfor more details.
Package: ai-models-graphcast
ai-models-graphcastai-models-graphcastis anai-modelsplugin to run Google Deepmind'sGraphCast.GraphCast: Learning skillful medium-range global weather forecasting, arXiv preprint: 2212.12794, 2022.https://arxiv.org/abs/2212.12794GraphCast was created by Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed and Peter Battaglia.The model weights are made available for use under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). You may obtain a copy of the License at:https://creativecommons.org/licenses/by-nc-sa/4.0/.InstallationTo install the package, run:pipinstallai-models-graphcastThis will install the package and most of its dependencies.Then to install graphcast dependencies (and Jax on GPU):Graphcast and JaxGraphcast depends on Jax, which needs special installation instructions for your specific hardware.Please see theinstallation guideto follow the correct instructions.We have prepared tworequirements.txtyou can use. A CPU and a GPU version:For the preferred GPU usage:pip install -r requirements-gpu.txt -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.htmlFor the slower CPU usage:pip install -r requirements.txt
Package: aimodelshare
aimodelshareThe mission of the AI Model Share Platform (website w/ integrated Python library) is to provide a trusted non profit repository for machine learning model prediction APIs (python library + integrated website at modelshare.org.  A beta version of the platform is currently being used by Columbia University students, faculty, and staff to test and improve platform functionality.In a matter of seconds, data scientists can launch a model into this infrastructure and end-users the world over will be able to engage their machine learning models.Launch machine learning models into scalable production ready prediction REST APIs using a single Python function.Details about each model, how to use the model's API, and the model's author(s) are deployed simultaneously into a searchable website at modelshare.org.Deployed models receive an individual Model Playground listing information about all deployed models. Each of these pages includes a fully functional prediction dashboard that allows end-users to input text, tabular, or image data and receive live predictions.Moreover, users can build on model playgrounds by 1) creating ML model competitions, 2) uploading Jupyter notebooks to share code, 3) sharing model architectures and 4) sharing data... with all shared artifacts automatically creating a data science user portfolio.Use the aimodelshare Python library to deploy your model, create a new ML competition, and more.Tutorials for deploying models.Find model playground web-dashboards to generate predictions now.View deployed models and generate predictions at modelshare.orgInstallationYou can then install aimodelshare from PyPipip install aimodelshare
Package: aimodelshare-nightly
aimodelshareThe mission of the AI Model Share Platform (website w/ integrated Python library) is to provide a trusted non profit repository for machine learning model prediction APIs (python library + integrated website at modelshare.org.  A beta version of the platform is currently being used by Columbia University students, faculty, and staff to test and improve platform functionality.In a matter of seconds, data scientists can launch a model into this infrastructure and end-users the world over will be able to engage their machine learning models.Launch machine learning models into scalable production ready prediction REST APIs using a single Python function.Details about each model, how to use the model's API, and the model's author(s) are deployed simultaneously into a searchable website at modelshare.org.Deployed models receive an individual Model Playground listing information about all deployed models. Each of these pages includes a fully functional prediction dashboard that allows end-users to input text, tabular, or image data and receive live predictions.Moreover, users can build on model playgrounds by 1) creating ML model competitions, 2) uploading Jupyter notebooks to share code, 3) sharing model architectures and 4) sharing data... with all shared artifacts automatically creating a data science user portfolio.Use the aimodelshare Python library to deploy your model, create a new ML competition, and more.Tutorials for deploying models.Find model playground web-dashboards to generate predictions now.View deployed models and generate predictions at modelshare.orgInstallationYou can then install aimodelshare from PyPipip install aimodelshare-nightly
Package: ai-models-panguweather
ai-models-panguweatherai-models-panguweatheris anai-modelsplugin to runHuawei's Pangu-Weather.Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast, arXiv preprint: 2211.02556, 2022.https://arxiv.org/abs/2211.02556Pangu-Weather was created by Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu and Qi Tian. It is released by Huawei Cloud.The trained parameters of Pangu-Weather are made available under the terms of the BY-NC-SA 4.0 license.The commercial use of these models is forbidden.Seehttps://github.com/198808xc/Pangu-Weatherfor further details.InstallationTo install the package, run:pipinstallai-models-panguweatherThis will install the package and its dependencies, in particular the ONNX runtime. The installation script will attempt to guess which runtime to install. You can force a given runtime by specifying the theONNXRUNTIMEvariable, e.g.:ONNXRUNTIME=onnxruntime-gpupipinstallai-models-panguweather
Package: aimped
aimpedAimped is a unique python library that provides classes and functions for only exclusively business-tailored AI-based models.In this version, we provide the following features:
API service, Sound processing tools and functions, NLP tools and functions, and a pipeline class for NLP tasks.InstallationpipinstallaimpedAPI UsageConfiguration of the Libraryfromaimped.services.apiimportAimpedAPI# Create new instance Aimpeduser_key=''# user_key received from A3M.user_secret=''# user_secret received from A3M.BASE_URL='https://aimped.ai'# Aimped domain urlapi_service=AimpedAPI(user_key,user_secret,{base_url:BASE_URL})Preparation of the model input datamodel_id=""# ID of the model run. The model ID is available on the model description page under API usage.payload={...}# Model input examples (payload) are available in the api usage tab on the Model description page.Usage of API Functionresult=api_service.run_mode(model_id,input_data)Usage of API Callback Function# return callback functiondefcallback(event,message,time,data=None):ifevent=='start':print(f'Start event at{time}:{message}')elifevent=='proccess':print(f'Progress event at{time}:{message}')elifevent=='error':print(f'Error event at{time}:{message}')elifevent=='end':print(f'End event at{time}:{message}. Data:{data}')result=api_service.run_model_callback(model_id,payload,callback)Usage of API File UploadSome of the models supports file inputs. These inputs are accepted as URIs. Here is the usage of API for file uploads.input=api_service.file_upload(model_id,'/Users/joe/Downloads/xyz.pdf'# sample file path to upload)Usage of API File DownloadSome of the models supports file outputs as result. These outputs are created as URIs. Here is the usage of API for file downloads.output_file=api_service.file_download_and_save('input/application/model_{{modelId}}/user_{{userId}}/file_name',# URI of the model output file in the result'/Users/joe/Downloads/123_file_name'# sample local file path to save)ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: aimrecords
# AimRecords - Record-oriented data storage![GitHub Top Language](https://img.shields.io/github/languages/top/aimhubio/aimrecords)
[![PyPI Package](https://img.shields.io/pypi/v/aimrecords?color=yellow)](https://pypi.org/project/aimrecords/)
[![License](https://img.shields.io/badge/License-Apache%202.0-orange.svg)](https://opensource.org/licenses/Apache-2.0)Library to effectively store the tracked experiment logs.See the documentation [here](docs/README.md).## Getting StartedThese instructions will get you a copy of the project up and running on your
local machine for development and testing purposes.### RequirementsPython 3We suggest to use [virtual
environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments) for managing local dependencies.To start development first install all dependencies:`bash pip install-rrequirements.txt `### Project Structure` ├── aimrecords<-----------main project code │   ├── artifact_storage  <-  manage storage of artifacts │   └── record_storage<---manage records storage of a single artifact ├── docs<-----------------data format documentation ├── examples<-------------example usages of aimrecords └── tests `## Running the testsRun tests via commandpytestin the root folder.### Code Style
We follow [pep8](https://www.python.org/dev/peps/pep-0008/) style guide
for python code.## ContributingPlease read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our
code of conduct, and the process for submitting pull requests to us.
Package: aimrobot
AIM.RobotAIM.Robot is a Python library to run Robotic Assembly Digital Model (RADM) files directly on Universal Robots.RequirementsUniversal Robot (tested on a UR5e and UR10e).Python 3.8 or newerA valid RADM fileInstallationThe easiest way to install AIM.Robot is using pip:pip install aimrobotGetting StartedFirst of all, you need to test the connection to the robot:aimrobot --networktest 192.168.1.1If the connection is established, you can load and run a RADM file:aimrobot --file runme.radmThis will execute all the steps using the wait time  set in the file. If you want to have manual control, you can add the flag --manualaimrobot --file runme.radm --manual
Package: aims-convert
AIMS Roster Data ExtractionThis will primarily be of interest to easyJet pilots. It extracts data from a
“detailed roster” as can be downloaded from AIMS. Pilots from other airlines
that use AIMSmayalso find this useful — it has only been tested against the
files from easyJet’s version of AIMS (as that is all that I have access to), so
its utility will depend on whether whatever version is being used by your
airline produces sufficiently similar output.As well as extracting the data from the AIMS roster, the registration and type
of the aircraft operating the sectors is looked up, and a night flying
calculation is carried out.The three main formats that the data can be extracted to are electronic Flight
Journal (eFJ), iCalendar and CSV.An eFJ is a text file that can be used to store personal flight data in an
intuitive non-tabular form. Full details of the format of this text file can be
found athttps://hursts.org.uk/efjdocs/format.html, and an online tool
capable of converting this format to an FCL.050 compliant logbook in HTML
format can be found athttps://hursts.org.uk/efj.The iCalendar format can be imported into most calendar applications. There is
an option to include full day events such as days off. This is useful for
managing and sharing your future roster.The CSV format is for keeping logbook data in a spreadsheet.The package provides entry points for a command line interface, a Tk based
graphical interface and an AWS lambda function. The AWS function is hooked up
to a website athttps://hursts.org.uk/aims, which requires no installation,
just a browser. The other two interfaces require the installation of a Python
interpreter.
Package: aimsprop
aimspropTo use aimsprop, check out the documentationhere!To develop / contribute to the aimsprop code,
seeCONTRIBUTING.mdto get your environment and branch set up.For a tutorial on the basic functionality of aimsprop, gohere.DescriptionA repository for the representation and manipulation of AIMS-type trajectories, particularly for use in computing
time-dependent properties (bond lengths, angles, torsions, X-ray scattering, UED, photoelectron, UV-Vis, etc) in a
semi-uniform manner.Directoriesaimsprop - the Python AIMS trajectory/property codedocs - mkdocs documentation that is publishedheretests - unit tests for aimsprop code using pytestnotes - notes used in implementingliterature - key papers/book chapters used in implementing certain properties.scripts - utility scripts for common ops like plotting absorption spectra, generating difference densities from Molden
files, etc (no warrenty on these - the usual workflow is to copy these into your own project directories and modify
until they work)examples - examples of use cases of property computations (these are not the most helpful, better to look at
thetutorial).TodosH-Bond coordinatesBetter UED cross sectionsOOP anglesUpdate examplesAdiabatic dynamics are weirdly done in 0.5 fs, non-adiabatic dynamics are in 20 au. These are not the same. We should
fix/accomodate this.AuthorsRob Parrish, Monika Williams, Hayley Weir, Colton Hicks, Alice Walker, Alessio Valentini
Package: aimstack
# aim
#### Version control for AISee the docs [here](https://docs.aimhub.io).## Development### RequirementsPython 3We suggest to use [virtual
environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments) for managing local dependencies.To start development first install all dependencies:`bash pip install-rrequirements.txt `### Project Structure` ├── aim<----------------main project code │   ├── cli<-------------command line interface │   ├── engine<----------business logic │   ├── sdk<-------------Python SDK │   ├── artifacts<-------managing tracked data │   └── version_control <-  managing files and code ├── examples<------------example usages of aim SDK └── tests `### Code Style
We follow [pep8](https://www.python.org/dev/peps/pep-0008/) style guide for python code. We use [autopep8](https://pypi.org/project/autopep8/) and [pycodestyle](https://pypi.org/project/pycodestyle/) to enable checking and formatting Python code.To check code styles, runpycodestyle .in the root folder.To auto format, runautopep8 –in-place –recursive –aggressive –aggressive .in the root folder.
Package: aim-stack
# aim
#### Version control for AISee the docs [here](https://docs.aimhub.io).## Development### RequirementsPython 3We suggest to use [virtual
environment](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments) for managing local dependencies.To start development first install all dependencies:`bash pip install-rrequirements.txt `### Project Structure` ├── aim<----------------main project code │   ├── cli<-------------command line interface │   ├── engine<----------business logic │   ├── sdk<-------------Python SDK │   ├── artifacts<-------managing tracked data │   └── version_control <-  managing files and code ├── examples<------------example usages of aim SDK └── tests `### Code Style
We follow [pep8](https://www.python.org/dev/peps/pep-0008/) style guide for python code. We use [autopep8](https://pypi.org/project/autopep8/) and [pycodestyle](https://pypi.org/project/pycodestyle/) to enable checking and formatting Python code.To check code styles, runpycodestyle .in the root folder.To auto format, runautopep8 –in-place –recursive –aggressive –aggressive .in the root folder.
Package: ain
ainDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nbnx--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallbnxnbdev# activate conda environment$condaactivatebnx# make sure the bnx package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the bnx package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/bnx.gitor fromconda$condainstall-cdsm-72bnxor frompypi$pipinstallbnxDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: aina-gradio-theme
Aina Theme is a custom Gradio theme. Feel free to use this theme to create Gradio apps that have a visual connection to the world of cloud technology.How to edit Aina Gradio Theme colors and properties?In case you would like to change theme properties, just editAinaTheme/aina_class.pyfile.There are custom colors that you can set atutils/custom_colors.py. And if you would like to include extra colors on the theme set them atAinaTheme/__init__.pyHow to use this theme in my Gradio app?First install the theme package.pipinstallaina-gradio-themeOnce you have installed it, add it to the interface/block parameters....
from AinaTheme import theme
with gr.Blocks(theme=theme) as demo:
    ...
Package: aind-codeocean-api
aind-codeocean-apiPython wrapper around CodeOcean's REST API.InstallationTo install fromPyPI, run:pip install aind-codeocean-apiTo install from a clone of the repository, in the root directory, runpip install -e .To install the development libraries of the code, runpip install -e .[dev]UsageExample of getting data asset metadata:from aind_codeocean_api.codeocean import CodeOceanClient

domain = "https://acmecorp.codeocean.com"
token = "AN_API_TOKEN" # Replace with your api token
data_asset_id = "37a93748-ce90-4980-913b-2de0908d5212"
co_client = CodeOceanClient(domain=domain, token=token)
response = co_client.get_data_asset(data_asset_id=data_asset_id)
metadata = response.json()To store credentials locally, run:python -m aind_codeocean_api.credentialsContributingLinters and testingThere are several libraries used to run linters, check documentation, and run tests.Please test your changes using thecoveragelibrary, which will run the tests and log a coverage report:coverage run -m unittest discover && coverage reportUseinterrogateto check that modules, methods, etc. have been documented thoroughly:interrogate .Useflake8to check that code is up to standards (no unused imports, etc.):flake8 .Useblackto automatically format the code into PEP standards:black .Useisortto automatically sort import statements:isort .Pull requestsFor internal members, please create a branch. For external members, please fork the repo and open a pull request from the fork. We'll primarily useAngularstyle for commit messages. Roughly, they should follow the pattern:<type>(<scope>): <short summary>where scope (optional) describes the packages affected by the code changes and type (mandatory) is one of:build: Changes that affect the build system or external dependencies (example scopes: pyproject.toml, setup.py)ci: Changes to our CI configuration files and scripts (examples: .github/workflows/ci.yml)docs: Documentation only changesfeat: A new featurefix: A bug fixperf: A code change that improves performancerefactor: A code change that neither fixes a bug nor adds a featuretest: Adding missing tests or correcting existing testsDocumentationTo generate the rst files source files for documentation, runsphinx-apidoc -o doc_template/source/ srcThen to create the documentation html files, runsphinx-build -b html doc_template/source/ doc_template/build/htmlMore info on sphinx installation can be found here:https://www.sphinx-doc.org/en/master/usage/installation.html
Package: aind-dispim-processing
aind-dispim-processingLibrary for running dispim code ocean pipeline.InstallationTo use the software, in the root directory, runpipinstall-e.To develop the code, runpipinstall-e.[dev]Linters and testingThere are several libraries used to run linters, check documentation, and run tests.Please test your changes using thecoveragelibrary, which will run the tests and log a coverage report:coveragerun-munittestdiscover&&coveragereportUseinterrogateto check that modules, methods, etc. have been documented thoroughly:interrogate.Useflake8to check that code is up to standards (no unused imports, etc.):flake8.Useblackto automatically format the code into PEP standards:black.Useisortto automatically sort import statements:isort.Pull requestsFor internal members, please create a branch. For external members, please fork the repository and open a pull request from the fork. We'll primarily useAngularstyle for commit messages. Roughly, they should follow the pattern:<type>(<scope>): <short summary>where scope (optional) describes the packages affected by the code changes and type (mandatory) is one of:build: Changes that affect build tools or external dependencies (example scopes: pyproject.toml, setup.py)ci: Changes to our CI configuration files and scripts (examples: .github/workflows/ci.yml)docs: Documentation only changesfeat: A new featurefix: A bugfixperf: A code change that improves performancerefactor: A code change that neither fixes a bug nor adds a featuretest: Adding missing tests or correcting existing testsSemantic ReleaseThe table below, fromsemantic release, shows which commit message gets you which release type whensemantic-releaseruns (using the default configuration):Commit messageRelease typefix(pencil): stop graphite breaking when too much pressure appliedPatchFix Release, Default releasefeat(pencil): add 'graphiteWidth' optionMinorFeature Releaseperf(pencil): remove graphiteWidth optionBREAKING CHANGE: The graphiteWidth option has been removed.The default graphite width of 10mm is always used for performance reasons.MajorBreaking Release(Note that theBREAKING CHANGE:token must be in the footer of the commit)DocumentationTo generate the rst files source files for documentation, runsphinx-apidoc-odoc_template/source/srcThen to create the documentation HTML files, runsphinx-build-bhtmldoc_template/source/doc_template/build/htmlMore info on sphinx installation can be foundhere.