Automate issue discovery for your projects against Lightning nightly and releases.

Overview

Logo

Automated Testing for Lightning EcoSystem Projects

CI testing Build Status pre-commit.ci status


Automate issue discovery for your projects against Lightning nightly and releases.
You get CPUs, Multi-GPUs testing for free, and Slack notification alerts if issues arise!

How do I add my own Project?

Pre-requisites

Here are pre-requisites for your project before adding to the Lightning EcoSystem CI:

  • Your project already includes some Python tests with PyTorch Lightning as a dependency
  • You'll be a contact/responsible person to resolve any issues that the CI finds in the future for your project

Adding your own project config

  1. First, fork this project (with CLI or in browser) to be able to create a new Pull Request, and work within a specific branch.
    gh repo fork PyTorchLightning/ecosystem-ci
    cd ecosystem-ci/
  2. Copy the template file in configs folder and call it <my_project_name>.yaml.
    cp configs/template.yaml configs/<my_project_name>.yaml
    
  3. At the minimum, modify the HTTPS variable to point to your repository. See Configuring my project for more options.
    target_repository:
      HTTPS: https://github.com/MyUsername/MyProject.git
    ...
    If your project tests multiple configurations or you'd like to test against multiple Lightning versions such as master and release branches, create a config file for each one of them. As an example, have a look at metrics master and metrics release CI files.
  4. Add your config filename to either/both the GitHub CPU CI file or the Azure GPU CI file.
    • For example, for the GitHub CPU CI file we append our config into the pytest parametrization:
      ...
      jobs:
        pytest:
          ...
              config:
                - "PyTorchLightning/metrics_pl-release.yaml"
                - "PyTorchLightning/transformers_pl-release.yaml"
                - "MyUsername/myproject-release.yaml"
              include:
                - {os: "ubuntu-20.04", python-version: "3.8", config: "PyTorchLightning/metrics_pl-master.yaml"}
                - {os: "ubuntu-20.04", python-version: "3.9", config: "PyTorchLightning/transformers_pl-master.yaml"}
                - {os: "ubuntu-20.04", python-version: "3.9", config: "MyUsername/my_project-master.yaml"}
              exclude:
                - {os: "windows-2019", config: "PyTorchLightning/transformers_pl-release.yaml"}
      ...
    • For example, in the Azure GPU CI file file:
      ...
      jobs:
      - template: testing-template.yml
        parameters:
          configs:
          - "PyTorchLightning/metrics_pl-master.yaml"
          - "PyTorchLightning/metrics_pl-release.yaml"
          - "MyUsername/my_project-master.yaml"
  5. Add the responsible person(s) to CODEOWNERS for your organization folder or just the project.
    # MyProject
    /configs/Myusername/MyProject*    @Myusername
    
  6. Finally, create a draft PR to the repo!

(Optional). [wip] join our Slack channel to be notified if your project is breaking

Configuring my project

The config include a few different sections:

  • target_repository include your project
  • env (optional) define any environment variables required when running tests
  • dependencies listing all dependencies which are taken outside pip
  • testing defines specific pytest arguments and what folders shall be tested

All dependencies as well as the target repository is sharing the same template with the only required field HTTPS and all others are optional:

target_repository:
  HTTPS: https://github.com/PyTorchLightning/metrics.git
  username: my-nick  # Optional, used when checking out private/protected repo
  password: dont-tell-anyone # Optional, used when checking out private/protected repo
  token: authentication-token # Optional, overrides the user/pass when checking out private/protected repo
  checkout: master # Optional, checkout a particular branch or a tag
  install_extras: all # Refers to standard pip option to install some additional dependencies defined with setuptools, typically used as `<my-package>[<install_extras>]`.

# Optional, if any installation/tests require some env variables
env:
   MY_ENV_VARIABLE: "VAR"

copy_tests:
    - integrations # copied folder from the original repo into the running test directory
    # this is copied as we use the helpers inside integrations as regular python package
    - tests/__init__.py
    - tests/helpers

# Optional, additional pytest arguments and control which directory to test on
testing:
  dirs:
    - integrations
  pytest_args: --strict

Note: If you define some files as done above, and they are using internal-cross imports, you need to copy the __init__.py files from each particular package level.

The testing section provides access to the pytest run args and command.

testing:
  # by default pytest is called on all copied items/tests
  dirs:
    - integrations
  # OPTIONAL, additional pytest arguments
  pytest_args: --strict
Owner
Pytorch Lightning
Pytorch Lightning
Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

Debiasing Item-to-Item Recommendations With Small Annotated Datasets This is the code for our RecSys '20 paper. Other materials can be found here: Ful

Microsoft 34 Aug 10, 2022
Determined: Deep Learning Training Platform

Determined: Deep Learning Training Platform Determined is an open-source deep learning training platform that makes building models fast and easy. Det

Determined AI 2k Dec 31, 2022
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 04, 2023
🇰🇷 Text to Image in Korean

KoDALLE Utilizing pretrained language model’s token embedding layer and position embedding layer as DALLE’s text encoder. Background Training DALLE mo

HappyFace 74 Sep 22, 2022
Adaptation through prediction: multisensory active inference torque control

Adaptation through prediction: multisensory active inference torque control Submitted to IEEE Transactions on Cognitive and Developmental Systems Abst

Cristian Meo 1 Nov 07, 2022
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
Homepage of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [PaddlePaddle Implementation] Homepage of paper: Paint Transformer: Fee

442 Dec 16, 2022
Predicting path with preference based on user demonstration using Maximum Entropy Deep Inverse Reinforcement Learning in a continuous environment

Preference-Planning-Deep-IRL Introduction Check my portfolio post Dependencies Gym stable-baselines3 PyTorch Usage Take Demonstration python3 record.

Tianyu Li 9 Oct 26, 2022
Recommendation algorithms for large graphs

Fast recommendation algorithms for large graphs based on link analysis. License: Apache Software License Author: Emmanouil (Manios) Krasanakis Depende

Multimedia Knowledge and Social Analytics Lab 27 Jan 07, 2023
A crossplatform menu bar application using mpv as DLNA Media Renderer.

Macast Chinese README A menu bar application using mpv as DLNA Media Renderer. Install MacOS || Windows || Debian Download link: Macast release latest

4.4k Jan 01, 2023
Predicting future trajectories of people in cameras of novel scenarios and views.

Pedestrian Trajectory Prediction Predicting future trajectories of pedestrians in cameras of novel scenarios and views. This repository contains the c

8 Sep 03, 2022
Constrained Logistic Regression - How to apply specific constraints to logistic regression's coefficients

Constrained Logistic Regression Sample implementation of constructing a logistic regression with given ranges on each of the feature's coefficients (v

1 Dec 29, 2021
Tensor-Based Quantum Machine Learning

TensorLy_Quantum TensorLy-Quantum is a Python library for Tensor-Based Quantum Machine Learning that builds on top of TensorLy and PyTorch. Website: h

TensorLy 85 Dec 03, 2022
An AI Assistant More Than a Toolkit

tymon An AI Assistant More Than a Toolkit The reason for creating framework tymon is simple. making AI more like an assistant, helping us to complete

TymonXie 46 Oct 24, 2022
TensorFlow implementation of Elastic Weight Consolidation

Elastic weight consolidation Introduction A TensorFlow implementation of elastic weight consolidation as presented in Overcoming catastrophic forgetti

James Stokes 67 Oct 11, 2022
[ICCV '21] In this repository you find the code to our paper Keypoint Communities

Keypoint Communities In this repository you will find the code to our ICCV '21 paper: Keypoint Communities Duncan Zauss, Sven Kreiss, Alexandre Alahi,

Duncan Zauss 262 Dec 13, 2022
HyperaPy: An automatic hyperparameter optimization framework ⚡🚀

hyperpy HyperPy: An automatic hyperparameter optimization framework Description HyperPy: Library for automatic hyperparameter optimization. Build on t

Sergio Mora 7 Sep 06, 2022
Simple Tensorflow implementation of "Adaptive Convolutions for Structure-Aware Style Transfer" (CVPR 2021)

AdaConv — Simple TensorFlow Implementation [Paper] : Adaptive Convolutions for Structure-Aware Style Transfer (CVPR 2021) Note This repository does no

Junho Kim 26 Nov 18, 2022
traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation toolbox based on PyTorch.

traiNNer traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation to

202 Jan 04, 2023