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pytorch-ignite

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

https://github.com/pytorch/ignite

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    10 of 221 committers (4.5%) from academic institutions
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    Low similarity (10.4%) to scientific vocabulary

Keywords

closember deep-learning hacktoberfest machine-learning metrics neural-network python pytorch

Keywords from Contributors

jax transformer cryptocurrency cryptography analyses gtk qt tk wx agents
Last synced: 6 months ago · JSON representation ·

Repository

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Basic Info
  • Host: GitHub
  • Owner: pytorch
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Homepage: https://pytorch-ignite.ai
  • Size: 52.8 MB
Statistics
  • Stars: 4,691
  • Watchers: 59
  • Forks: 652
  • Open Issues: 166
  • Releases: 24
Topics
closember deep-learning hacktoberfest machine-learning metrics neural-network python pytorch
Created about 8 years ago · Last pushed 8 months ago
Metadata Files
Readme Contributing Funding License Code of conduct Citation

README.md

| ![image](https://img.shields.io/badge/-Tests:-black?style=flat-square) [![image](https://github.com/pytorch/ignite/actions/workflows/unit-tests.yml/badge.svg?branch=master)](https://github.com/pytorch/ignite/actions/workflows/unit-tests.yml) [![image](https://github.com/pytorch/ignite/actions/workflows/gpu-tests.yml/badge.svg)](https://github.com/pytorch/ignite/actions/workflows/gpu-tests.yml) [![image](https://codecov.io/gh/pytorch/ignite/branch/master/graph/badge.svg)](https://codecov.io/gh/pytorch/ignite) [![image](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorch.org/ignite/index.html) | |:--- | ![image](https://img.shields.io/badge/-Stable%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch/ignite/badges/version.svg)](https://anaconda.org/pytorch/ignite) ・ [![image](https://img.shields.io/badge/dynamic/json.svg?label=PyPI&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorch-ignite%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pypi.org/project/pytorch-ignite/) [![image](https://static.pepy.tech/badge/pytorch-ignite)](https://pepy.tech/project/pytorch-ignite) ・ [![image](https://img.shields.io/badge/docker-hub-blue)](https://hub.docker.com/u/pytorchignite) | | ![image](https://img.shields.io/badge/-Nightly%20Releases:-black?style=flat-square) [![image](https://anaconda.org/pytorch-nightly/ignite/badges/version.svg)](https://anaconda.org/pytorch-nightly/ignite) [![image](https://img.shields.io/badge/PyPI-pre%20releases-brightgreen)](https://pypi.org/project/pytorch-ignite/#history)| | ![image](https://img.shields.io/badge/-Community:-black?style=flat-square) [![Twitter](https://img.shields.io/badge/news-twitter-blue)](https://twitter.com/pytorch_ignite) [![discord](https://img.shields.io/badge/chat-discord-blue?logo=discord)](https://discord.gg/djZtm3EmKj) [![numfocus](https://img.shields.io/badge/NumFOCUS-affiliated%20project-green)](https://numfocus.org/sponsored-projects/affiliated-projects) | | ![image](https://img.shields.io/badge/-Supported_PyTorch/Python_versions:-black?style=flat-square) [![link](https://img.shields.io/badge/-check_here-blue)](https://github.com/pytorch/ignite/actions?query=workflow%3A%22PyTorch+version+tests%22)|

TL;DR

Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

PyTorch-Ignite teaser _Click on the image to see complete code_

Features

  • Less code than pure PyTorch while ensuring maximum control and simplicity

  • Library approach and no program's control inversion - Use ignite where and when you need

  • Extensible API for metrics, experiment managers, and other components

Table of Contents

Why Ignite?

Ignite is a library that provides three high-level features:

  • Extremely simple engine and event system
  • Out-of-the-box metrics to easily evaluate models
  • Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics

Simplified training and validation loop

No more coding for/while loops on epochs and iterations. Users instantiate engines and run them.

Example ```python from ignite.engine import Engine, Events, create_supervised_evaluator from ignite.metrics import Accuracy # Setup training engine: def train_step(engine, batch): # Users can do whatever they need on a single iteration # Eg. forward/backward pass for any number of models, optimizers, etc # ... trainer = Engine(train_step) # Setup single model evaluation engine evaluator = create_supervised_evaluator(model, metrics={"accuracy": Accuracy()}) def validation(): state = evaluator.run(validation_data_loader) # print computed metrics print(trainer.state.epoch, state.metrics) # Run model's validation at the end of each epoch trainer.add_event_handler(Events.EPOCH_COMPLETED, validation) # Start the training trainer.run(training_data_loader, max_epochs=100) ```

Power of Events & Handlers

The cool thing with handlers is that they offer unparalleled flexibility (compared to, for example, callbacks). Handlers can be any function: e.g. lambda, simple function, class method, etc. Thus, we do not require to inherit from an interface and override its abstract methods which could unnecessarily bulk up your code and its complexity.

Execute any number of functions whenever you wish

Examples ```python trainer.add_event_handler(Events.STARTED, lambda _: print("Start training")) # attach handler with args, kwargs mydata = [1, 2, 3, 4] logger = ... def on_training_ended(data): print(f"Training is ended. mydata={data}") # User can use variables from another scope logger.info("Training is ended") trainer.add_event_handler(Events.COMPLETED, on_training_ended, mydata) # call any number of functions on a single event trainer.add_event_handler(Events.COMPLETED, lambda engine: print(engine.state.times)) @trainer.on(Events.ITERATION_COMPLETED) def log_something(engine): print(engine.state.output) ```

Built-in events filtering

Examples ```python # run the validation every 5 epochs @trainer.on(Events.EPOCH_COMPLETED(every=5)) def run_validation(): # run validation # change some training variable once on 20th epoch @trainer.on(Events.EPOCH_STARTED(once=20)) def change_training_variable(): # ... # Trigger handler with customly defined frequency @trainer.on(Events.ITERATION_COMPLETED(event_filter=first_x_iters)) def log_gradients(): # ... ```

Stack events to share some actions

Examples Events can be stacked together to enable multiple calls: ```python @trainer.on(Events.COMPLETED | Events.EPOCH_COMPLETED(every=10)) def run_validation(): # ... ```

Custom events to go beyond standard events

Examples Custom events related to backward and optimizer step calls: ```python from ignite.engine import EventEnum class BackpropEvents(EventEnum): BACKWARD_STARTED = 'backward_started' BACKWARD_COMPLETED = 'backward_completed' OPTIM_STEP_COMPLETED = 'optim_step_completed' def update(engine, batch): # ... loss = criterion(y_pred, y) engine.fire_event(BackpropEvents.BACKWARD_STARTED) loss.backward() engine.fire_event(BackpropEvents.BACKWARD_COMPLETED) optimizer.step() engine.fire_event(BackpropEvents.OPTIM_STEP_COMPLETED) # ... trainer = Engine(update) trainer.register_events(*BackpropEvents) @trainer.on(BackpropEvents.BACKWARD_STARTED) def function_before_backprop(engine): # ... ``` - Complete snippet is found [here](https://pytorch.org/ignite/faq.html#creating-custom-events-based-on-forward-backward-pass). - Another use-case of custom events: [trainer for Truncated Backprop Through Time](https://pytorch.org/ignite/contrib/engines.html#ignite.contrib.engines.create_supervised_tbptt_trainer).

Out-of-the-box metrics

Example ```python precision = Precision(average=False) recall = Recall(average=False) F1_per_class = (precision * recall * 2 / (precision + recall)) F1_mean = F1_per_class.mean() # torch mean method F1_mean.attach(engine, "F1") ```

Installation

From pip:

bash pip install pytorch-ignite

From conda:

bash conda install ignite -c pytorch

From source:

bash pip install git+https://github.com/pytorch/ignite

Nightly releases

From pip:

bash pip install --pre pytorch-ignite

From conda (this suggests to install pytorch nightly release instead of stable version as dependency):

bash conda install ignite -c pytorch-nightly

Docker Images

Using pre-built images

Pull a pre-built docker image from our Docker Hub and run it with docker v19.03+.

bash docker run --gpus all -it -v $PWD:/workspace/project --network=host --shm-size 16G pytorchignite/base:latest /bin/bash

List of available pre-built images Base - `pytorchignite/base:latest` - `pytorchignite/apex:latest` - `pytorchignite/hvd-base:latest` - `pytorchignite/hvd-apex:latest` - `pytorchignite/msdp-apex:latest` Vision: - `pytorchignite/vision:latest` - `pytorchignite/hvd-vision:latest` - `pytorchignite/apex-vision:latest` - `pytorchignite/hvd-apex-vision:latest` - `pytorchignite/msdp-apex-vision:latest` NLP: - `pytorchignite/nlp:latest` - `pytorchignite/hvd-nlp:latest` - `pytorchignite/apex-nlp:latest` - `pytorchignite/hvd-apex-nlp:latest` - `pytorchignite/msdp-apex-nlp:latest`

For more details, see here.

Getting Started

Few pointers to get you started:

Documentation

Additional Materials

Examples

Tutorials

Reproducible Training Examples

Inspired by torchvision/references, we provide several reproducible baselines for vision tasks:

  • ImageNet - logs on Ignite Trains server coming soon ...
  • Pascal VOC2012 - logs on Ignite Trains server coming soon ...

Features:

Code-Generator application

The easiest way to create your training scripts with PyTorch-Ignite:

  • https://code-generator.pytorch-ignite.ai/

Communication

User feedback

We have created a form for "user feedback". We appreciate any type of feedback, and this is how we would like to see our community:

  • If you like the project and want to say thanks, this the right place.
  • If you do not like something, please, share it with us, and we can see how to improve it.

Thank you!

Contributing

Please see the contribution guidelines for more information.

As always, PRs are welcome :)

Projects using Ignite

Research papers - [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://github.com/BlackHC/BatchBALD) - [A Model to Search for Synthesizable Molecules](https://github.com/john-bradshaw/molecule-chef) - [Localised Generative Flows](https://github.com/jrmcornish/lgf) - [Extracting T Cell Function and Differentiation Characteristics from the Biomedical Literature](https://github.com/hammerlab/t-cell-relation-extraction) - [Variational Information Distillation for Knowledge Transfer](https://github.com/amzn/xfer/tree/master/var_info_distil) - [XPersona: Evaluating Multilingual Personalized Chatbot](https://github.com/HLTCHKUST/Xpersona) - [CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images](https://github.com/ucuapps/CoronaryArteryStenosisScoreClassification) - [Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog](https://github.com/ictnlp/DSTC8-AVSD) - [Adversarial Decomposition of Text Representation](https://github.com/text-machine-lab/adversarial_decomposition) - [Uncertainty Estimation Using a Single Deep Deterministic Neural Network](https://github.com/y0ast/deterministic-uncertainty-quantification) - [DeepSphere: a graph-based spherical CNN](https://github.com/deepsphere/deepsphere-pytorch) - [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://github.com/lidq92/LinearityIQA) - [Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training](https://github.com/lidq92/MDTVSFA) - [Deep Signature Transforms](https://github.com/patrick-kidger/Deep-Signature-Transforms) - [Neural CDEs for Long Time-Series via the Log-ODE Method](https://github.com/jambo6/neuralCDEs-via-logODEs) - [Volumetric Grasping Network](https://github.com/ethz-asl/vgn) - [Mood Classification using Listening Data](https://github.com/fdlm/listening-moods) - [Deterministic Uncertainty Estimation (DUE)](https://github.com/y0ast/DUE) - [PyTorch-Hebbian: facilitating local learning in a deep learning framework](https://github.com/Joxis/pytorch-hebbian) - [Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks](https://github.com/rpatrik96/lod-wmm-2019) - [Learning explanations that are hard to vary](https://github.com/gibipara92/learning-explanations-hard-to-vary) - [The role of disentanglement in generalisation](https://github.com/mmrl/disent-and-gen) - [A Probabilistic Programming Approach to Protein Structure Superposition](https://github.com/LysSanzMoreta/Theseus-PP) - [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://github.com/auriml/Rx-thorax-automatic-captioning)
Blog articles, tutorials, books - [State-of-the-Art Conversational AI with Transfer Learning](https://github.com/huggingface/transfer-learning-conv-ai) - [Tutorial on Transfer Learning in NLP held at NAACL 2019](https://github.com/huggingface/naacl_transfer_learning_tutorial) - [Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt](https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition) - [Once Upon a Repository: How to Write Readable, Maintainable Code with PyTorch](https://towardsdatascience.com/once-upon-a-repository-how-to-write-readable-maintainable-code-with-pytorch-951f03f6a829) - [The Hero Rises: Build Your Own SSD](https://allegro.ai/blog/the-hero-rises-build-your-own-ssd/) - [Using Optuna to Optimize PyTorch Ignite Hyperparameters](https://medium.com/pytorch/using-optuna-to-optimize-pytorch-ignite-hyperparameters-626ffe6d4783) - [PyTorch Ignite - Classifying Tiny ImageNet with EfficientNet](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f)
Toolkits - [Project MONAI - AI Toolkit for Healthcare Imaging](https://github.com/Project-MONAI/MONAI) - [DeepSeismic - Deep Learning for Seismic Imaging and Interpretation](https://github.com/microsoft/seismic-deeplearning) - [Nussl - a flexible, object-oriented Python audio source separation library](https://github.com/nussl/nussl) - [PyTorch Adapt - A fully featured and modular domain adaptation library](https://github.com/KevinMusgrave/pytorch-adapt) - [gnina-torch: PyTorch implementation of GNINA scoring function](https://github.com/RMeli/gnina-torch)
Others - [Implementation of "Attention is All You Need" paper](https://github.com/akurniawan/pytorch-transformer) - [Implementation of DropBlock: A regularization method for convolutional networks in PyTorch](https://github.com/miguelvr/dropblock) - [Kaggle Kuzushiji Recognition: 2nd place solution](https://github.com/lopuhin/kaggle-kuzushiji-2019) - [Unsupervised Data Augmentation experiments in PyTorch](https://github.com/vfdev-5/UDA-pytorch) - [Hyperparameters tuning with Optuna](https://github.com/optuna/optuna-examples/blob/main/pytorch/pytorch_ignite_simple.py) - [Logging with ChainerUI](https://chainerui.readthedocs.io/en/latest/reference/module.html#external-library-support) - [FixMatch experiments in PyTorch and Ignite (CTA dataaug policy)](https://github.com/vfdev-5/FixMatch-pytorch) - [Kaggle Birdcall Identification Competition: 1st place solution](https://github.com/ryanwongsa/kaggle-birdsong-recognition) - [Logging with Aim - An open-source experiment tracker](https://aimstack.readthedocs.io/en/latest/quick_start/integrations.html#integration-with-pytorch-ignite)

See other projects at "Used by"

If your project implements a paper, represents other use-cases not covered in our official tutorials, Kaggle competition's code, or just your code presents interesting results and uses Ignite. We would like to add your project to this list, so please send a PR with brief description of the project.

Citing Ignite

If you use PyTorch-Ignite in a scientific publication, we would appreciate citations to our project.

@misc{pytorch-ignite, author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani}, title = {High-level library to help with training neural networks in PyTorch}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/pytorch/ignite}}, }

About the team & Disclaimer

PyTorch-Ignite is a NumFOCUS Affiliated Project, operated and maintained by volunteers in the PyTorch community in their capacities as individuals (and not as representatives of their employers). See the "About us" page for a list of core contributors. For usage questions and issues, please see the various channels here. For all other questions and inquiries, please send an email to contact@pytorch-ignite.ai.

Owner

  • Name: pytorch
  • Login: pytorch
  • Kind: organization
  • Location: where the eigens are valued

Citation (CITATION) 

@misc{pytorch-ignite,
  author = {V. Fomin and J. Anmol and S. Desroziers and J. Kriss and A. Tejani},
  title = {High-level library to help with training neural networks in PyTorch},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/pytorch/ignite}},
}

GitHub Events

Total
  • Create event: 53
  • Release event: 1
  • Issues event: 41
  • Watch event: 195
  • Delete event: 52
  • Member event: 1
  • Issue comment event: 148
  • Push event: 233
  • Pull request review comment event: 269
  • Pull request review event: 290
  • Pull request event: 205
  • Fork event: 56
Last Year
  • Create event: 53
  • Release event: 1
  • Issues event: 41
  • Watch event: 195
  • Delete event: 52
  • Member event: 1
  • Issue comment event: 148
  • Push event: 233
  • Pull request review comment event: 269
  • Pull request review event: 290
  • Pull request event: 205
  • Fork event: 56

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 1,800
  • Total Committers: 221
  • Avg Commits per committer: 8.145
  • Development Distribution Score (DDS): 0.613
Past Year
  • Commits: 115
  • Committers: 18
  • Avg Commits per committer: 6.389
  • Development Distribution Score (DDS): 0.661
Top Committers
Name Email Commits
vfdev v****5@g****m 697
Sylvain Desroziers s****s@g****m 86
Jeff Yang y****f@o****m 82
Ahmed Khaled a****1@g****m 68
Sadra Barikbin s****1@y****m 58
Alykhan Tejani a****i@g****m 51
Taras Savchyn 3****n 46
Anmol Joshi a****i@g****m 42
Sergey Epifanov b****o@g****m 36
puhuk w****5@g****m 35
Moh-Yakoub m****b@g****m 26
Ishan Kumar i****6@g****m 23
Jason Kriss j****s@g****m 21
Jeff Yang 3****f 19
Kazuki Adachi k****y@g****m 19
François COKELAER f****r@g****m 18
Elijah Rippeth e****h@g****m 15
John lee j****h@g****m 14
Bibhabasu Mohapatra 6****a 12
Anton Grübel a****l@g****m 12
Justus Schock 1****k 11
Wang Ran (汪然) w****n@o****m 10
Arpan Parikh f****2@p****n 9
Chenglu c****e@g****m 9
Gao, Xiang q****p@g****m 9
Steven u****p@g****m 9
Anton Alekseev t****3@g****m 9
Aryan Gupta 9****6 8
OJASV Kamal 4****1 8
Pranjal Gulati p****l@g****m 7
and 191 more...
Committer Domains (Top 20 + Academic)
campus.technion.ac.il: 2 joo.st: 2 qq.com: 2 pilani.bits-pilani.ac.in: 1 nyu.edu: 1 ist.utl.pt: 1 tum.de: 1 nvidia.com: 1 umich.edu: 1 ifpen.fr: 1 phystech.edu: 1 uw.edu: 1 hanmail.net: 1 me.com: 1 buzzfeed.com: 1 fastmail.com: 1 iith.ac.in: 1 iress.com: 1 unacademy.com: 1 neptune.ai: 1 cornell.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 253
  • Total pull requests: 468
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 24 days
  • Total issue authors: 61
  • Total pull request authors: 60
  • Average comments per issue: 1.77
  • Average comments per pull request: 1.04
  • Merged pull requests: 369
  • Bot issues: 143
  • Bot pull requests: 9
Past Year
  • Issues: 24
  • Pull requests: 206
  • Average time to close issues: 19 days
  • Average time to close pull requests: 5 days
  • Issue authors: 16
  • Pull request authors: 22
  • Average comments per issue: 1.92
  • Average comments per pull request: 0.51
  • Merged pull requests: 157
  • Bot issues: 0
  • Bot pull requests: 1
View more stats
Top Authors
Issue Authors
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Pull Request Authors
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Top Labels
Issue Labels
bug (151) help wanted (41) enhancement (27) question (20) good first issue (14) docs (11) ci (10) module: engine (7) module: metrics (5) Hacktoberfest (5) PyDataGlobal (4) sweep (3) needs-discussion (2) duplicate (1) module: handlers (1) module: distributed (1) high-priority (1) tests (1)
Pull Request Labels
module: metrics (112) ci (111) docs (58) module: handlers (46) examples (32) module: engine (29) module: contrib (29) module: distributed (28) docker (23) module: utils (5) help wanted (2) sweep (2) hacktoberfest-accepted (2) module: base (2) enhancement (1) dependencies (1) github_actions (1)

Packages

  • Total packages: 3
  • Total downloads:
    • pypi 132,600 last-month
  • Total docker downloads: 7,359
  • Total dependent packages: 49
    (may contain duplicates)
  • Total dependent repositories: 766
    (may contain duplicates)
  • Total versions: 2,049
  • Total maintainers: 3
pypi.org: pytorch-ignite

A lightweight library to help with training neural networks in PyTorch.

  • Versions: 2,027
  • Dependent Packages: 49
  • Dependent Repositories: 766
  • Downloads: 132,600 Last month
  • Docker Downloads: 7,359
Rankings
Dependent packages count: 0.3%
Dependent repos count: 0.4%
Downloads: 0.7%
Average: 1.0%
Stargazers count: 1.1%
Docker downloads count: 1.5%
Forks count: 2.1%
Maintainers (3)
trsvchn vfdev-5 soumith
Last synced: about 1 year ago
proxy.golang.org: github.com/pytorch/ignite
  • Versions: 20
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 7.0%
Average: 8.2%
Dependent repos count: 9.3%
Last synced: 6 months ago
conda-forge.org: pytorch-ignite
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Stargazers count: 4.9%
Forks count: 6.3%
Average: 24.1%
Dependent repos count: 34.0%
Dependent packages count: 51.2%
Last synced: 6 months ago

Dependencies

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.github/workflows/discord_issues.yml actions
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.github/workflows/docker-publish.yml actions
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.github/workflows/docs.yml actions
  • actions/cache v3 composite
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  • peaceiris/actions-gh-pages v3 composite
.github/workflows/gpu-hvd-tests.yml actions
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.github/workflows/gpu-tests.yml actions
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.github/workflows/hvd-tests.yml actions
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docs/requirements.txt pypi
  • docutils <0.18
  • sphinx ==5.0.0
  • sphinx-copybutton ==0.4.0
  • sphinx_design *
  • sphinx_togglebutton *
  • sphinxcontrib-katex *
examples/cifar10/requirements.txt pypi
  • clearml *
  • fire *
  • pytorch-ignite *
  • tensorboardX *
  • torchvision *
  • tqdm *
examples/references/classification/imagenet/requirements.txt pypi
  • albumentations *
  • clearml *
  • fire *
  • image-dataset-viz *
  • numpy *
  • opencv-python-headless *
  • py_config_runner >=0.2.0,<1.0.0
  • pytorch-ignite *
  • tensorboard *
  • torch *
  • torchvision *
  • tqdm *
examples/references/segmentation/pascal_voc2012/requirements.txt pypi
  • albumentations *
  • clearml *
  • fire *
  • image-dataset-viz *
  • numpy *
  • opencv-python-headless *
  • py_config_runner >=0.2.0,<1.0.0
  • pytorch-ignite *
  • tensorboard *
  • torch *
  • torchvision *
  • tqdm *
examples/siamese_network/requirements.txt pypi
  • pytorch-ignite *
  • torch *
  • torchvision *
examples/transformers/requirements.txt pypi
  • clearml *
  • datasets *
  • fire *
  • pytorch-ignite *
  • tensorboardX *
  • tqdm *
  • transformers *
pyproject.toml pypi
requirements-dev.txt pypi
  • clearml * development
  • dill * development
  • gymnasium * development
  • markupsafe ==2.0.1 development
  • matplotlib * development
  • mkl * development
  • mlflow * development
  • neptune-client >=0.16.17 development
  • nltk * development
  • numpy * development
  • pandas * development
  • polyaxon * development
  • polyaxon-client * development
  • py-rouge * development
  • pynvml * development
  • pytest * development
  • pytest-cov * development
  • pytest-xdist * development
  • pytorch_fid ==0.1.1 development
  • scikit-image * development
  • scikit-learn * development
  • scipy * development
  • setuptools * development
  • tensorboard * development
  • tensorboardX * development
  • torchvision * development
  • tqdm * development
  • visdom ==0.2.3 development
  • wandb * development
setup.py pypi