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/test/ut/retiarii/_debug_graph_data.json
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# example generated files
/nni_assets/**/data/
# Logs
logs
*.log
......
README.md
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<p align="center">
<img src="docs/img/nni_logo.png" width="300"/>
</p>
<div align="center">
<img src="docs/img/nni_logo.png" width="600"/>
</div>
<br/>
[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE)
[![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&branchName=master)
[![Issues](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)
[![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
[![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)
[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![Join the chat at https://gitter.im/Microsoft/nni](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases)
[![Documentation Status](https://readthedocs.org/projects/nni/badge/?version=stable)](https://nni.readthedocs.io/en/stable/?badge=stable)
[NNI Doc](https://nni.readthedocs.io/) | [简体中文](README_zh_CN.md)
**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering</a>, <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search</a>, <a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html">Hyperparameter Tuning</a> and <a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">Model Compression</a>.
The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <a href="https://nni.readthedocs.io/en/stable/TrainingService/LocalMode.html">Local Machine</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/RemoteMachineMode.html">Remote Servers</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/PaiMode.html">OpenPAI</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/KubeflowMode.html">Kubeflow</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/AMLMode.html">AML (Azure Machine Learning)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a> , other cloud options and even <a href="https://nni.readthedocs.io/en/stable/TrainingService/HybridMode.html">Hybrid mode</a>.
NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in our **[official documentation](https://nni.readthedocs.io/) ([简体中文版点这里](https://nni.readthedocs.io/zh/stable))**. Quick links:
## **Who should consider using NNI**
* [Documentation homepage](https://nni.readthedocs.io/)
* [Installation guide](https://nni.readthedocs.io/en/stable/installation.html)
* [Tutorials](https://nni.readthedocs.io/en/stable/tutorials.html)
* [Python API reference](https://nni.readthedocs.io/en/stable/reference/python_api.html)
* [Releases](https://nni.readthedocs.io/en/stable/Release.html)
* Those who want to **try different AutoML algorithms** in their training code/model.
* Those who want to run AutoML trial jobs **in different environments** to speed up search.
* Researchers and data scientists who want to easily **implement and experiment new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
* ML Platform owners who want to **support AutoML in their platform**.
## **What's NEW!** &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
## What's NEW! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
* **New release**: [v2.6 is available](https://github.com/microsoft/nni/releases/tag/v2.6) - _released on Jan-19-2022_
* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on May-26-2021_
* **New webinar**: [Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html) - _scheduled on June-24-2021_
* **New community channel**: [Discussions](https://github.com/microsoft/nni/discussions)
* **New emoticons release**: [nnSpider](./docs/source/Tutorial/NNSpider.md)
<p align="center">
<a href="#nni-spider"><img width="100%" src="docs/img/emoicons/home.svg" /></a>
</p>
## **NNI capabilities in a glance**
NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiments. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in state-of-the-art AutoML algorithms and out of box support for popular training platforms.
Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution.
<p align="center">
<a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
</p>
<table>
<tbody>
<tr align="center" valign="bottom">
<td>
</td>
<td>
<b>Frameworks & Libraries</b>
<img src="docs/img/bar.png"/>
</td>
<td>
<b>Algorithms</b>
<img src="docs/img/bar.png"/>
</td>
<td>
<b>Training Services</b>
<img src="docs/img/bar.png"/>
</td>
</tr>
</tr>
<tr valign="top">
<td align="center" valign="middle">
<b>Built-in</b>
</td>
<td>
<ul><li><b>Supported Frameworks</b></li>
<ul>
<li>PyTorch</li>
<li>Keras</li>
<li>TensorFlow</li>
<li>MXNet</li>
<li>Caffe2</li>
<a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
</ul>
</ul>
<ul>
<li><b>Supported Libraries</b></li>
<ul>
<li>Scikit-learn</li>
<li>XGBoost</li>
<li>LightGBM</li>
<a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
</ul>
</ul>
<ul>
<li><b>Examples</b></li>
<ul>
<li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
<li><a href="examples/trials/mnist-tfv1">MNIST-tensorflow</li></a>
<li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/GbdtExample.html">Auto-gbdt</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/SklearnExamples.html">Scikit-learn</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/EfficientNet.html">EfficientNet</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrialExample/OpEvoExamples.html">Kernel Tunning</li></a>
<a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
</ul>
</ul>
</td>
<td align="left" >
<a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html">Hyperparameter Tuning</a>
<ul>
<b>Exhaustive search</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Random">Random Search</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GridSearch">Grid Search</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Batch">Batch</a></li>
</ul>
<b>Heuristic search</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Evolution">Naïve Evolution</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Anneal">Anneal</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#PBTTuner">PBT</a></li>
</ul>
<b>Bayesian optimization</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#TPE">TPE</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#DNGOTuner">DNGO Tuner</a></li>
</ul>
</ul>
<a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search (Retiarii)</a>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ENAS.html">ENAS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/DARTS.html">DARTS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/SPOS.html">SPOS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/FBNet.html">FBNet</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Reinforcement Learning</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Regularized Evolution</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">More...</a></li>
</ul>
<a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">Model Compression</a>
<ul>
<b>Pruning</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">More...</a></li>
</ul>
<b>Quantization</b>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#qat-quantizer">QAT Quantizer</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
</ul>
</ul>
<a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering (Beta)</a>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
</ul>
<a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html">Early Stop Algorithms</a>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html#MedianStop">Median Stop</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/LocalMode.html">Local Machine</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/RemoteMachineMode.html">Remote Servers</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/HybridMode.html">Hybrid mode</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
<li><b>Kubernetes based services</b></li>
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/PaiMode.html">OpenPAI</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/KubeflowMode.html">Kubeflow</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a></li>
</ul>
</ul>
</td>
</tr>
<tr align="center" valign="bottom">
</td>
</tr>
<tr valign="top">
<td valign="middle">
<b>References</b>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/autotune_ref.html#trial">Python API</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/installation.html">Supported OS</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html">CustomizeTuner</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Assessor/CustomizeAssessor.html">CustomizeAssessor</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/Tutorial/InstallCustomizedAlgos.html">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/QuickStart.html#define-your-model-space">Define NAS Model Space</a></li>
<li><a href="https://nni.readthedocs.io/en/stable/NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
</ul>
</td>
<td style="border-top:#FF0000 solid 0px;">
<ul>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/Overview.html">Support TrainingService</li>
<li><a href="https://nni.readthedocs.io/en/stable/TrainingService/HowToImplementTrainingService.html">Implement TrainingService</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
## **Installation**
### **Install**
NNI supports and is tested on Ubuntu >= 18.04, Windows 10 >= 21H2, and macOS >= 11.
Simply run the following `pip install` in an environment that has `python 64-bit >= 3.7`.
Linux or macOS
```bash
python3 -m pip install --upgrade nni
```
Windows
```bash
python -m pip install --upgrade nni
```
If you want to try latest code, please [install NNI](https://nni.readthedocs.io/en/stable/installation.html) from source code.
<div align="center">
<a href="#nni-spider"><img width="100%" src="docs/img/emoicons/home.svg" /></a>
</div>
For detail system requirements of NNI, please refer to [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationLinux.html#system-requirements) for Linux & macOS, and [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#system-requirements) for Windows.
## NNI capabilities in a glance
Note:
(TBD: figures and tables)
* If there is any privilege issue, add `--user` to install NNI in the user directory.
* Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install [NNI on Windows](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html).
* If there is any error like `Segmentation fault`, please refer to [FAQ](https://nni.readthedocs.io/en/stable/Tutorial/FAQ.html). For FAQ on Windows, please refer to [NNI on Windows](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#faq).
## Installation
### **Verify installation**
See the [NNI installation guide](https://nni.readthedocs.io/en/stable/installation.html) to install from pip, or build from source.
* Download the examples via clone the source code.
To install the current release:
```bash
git clone -b v2.6 https://github.com/Microsoft/nni.git
```
* Run the MNIST example.
Linux or macOS
```
$ pip install nni
```
```bash
nnictl create --config nni/examples/trials/mnist-pytorch/config.yml
```
To update NNI to the latest version, add `--upgrade` flag to the above commands.
Windows
(TBD: build from soure link)
## Run your first experiment
```powershell
nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml
```
To run this experiment, you need to have [PyTorch](https://pytorch.org/) (as well as [torchvision](https://pytorch.org/vision/stable/index.html)) installed.
* Wait for the message `INFO: Successfully started experiment!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`.
```shell
$ nnictl hello
```
```text
INFO: Starting restful server...
INFO: Successfully started Restful server!
INFO: Setting local config...
INFO: Successfully set local config!
INFO: Starting experiment...
INFO: Successfully started experiment!
-----------------------------------------------------------------------
The experiment id is egchD4qy
The Web UI urls are: http://223.255.255.1:8080 http://127.0.0.1:8080
-----------------------------------------------------------------------
It will generate `nni_hello_hpo` folder in your current working directory, which contains a minimum hyper-parameter tuning example. It will also prompt you to run
You can use these commands to get more information about the experiment
-----------------------------------------------------------------------
commands description
1. nnictl experiment show show the information of experiments
2. nnictl trial ls list all of trial jobs
3. nnictl top monitor the status of running experiments
4. nnictl log stderr show stderr log content
5. nnictl log stdout show stdout log content
6. nnictl stop stop an experiment
7. nnictl trial kill kill a trial job by id
8. nnictl --help get help information about nnictl
-----------------------------------------------------------------------
```shell
python nni_hello_hpo/main.py
```
* Open the `Web UI url` in your browser, you can view detailed information of the experiment and all the submitted trial jobs as shown below. [Here](https://nni.readthedocs.io/en/stable/Tutorial/WebUI.html) are more Web UI pages.
to launch your first NNI experiment. Use the web portal URL shown in the console to monitor the running status of your experiment.
<img src="docs/static/img/webui.gif" alt="webui" width="100%"/>
## **Releases and Contributing**
NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by [filling an issue](https://github.com/microsoft/nni/issues/new/choose).
For more usages, please see [NNI tutorials](https://nni.readthedocs.io/en/stable/tutorials.html).
We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.
## Contribution guidelines
If you plan to contribute new features, new tuners, new training services, etc. please first open an issue or reuse an exisiting issue, and discuss the feature with us. We will discuss with you on the issue timely or set up conference calls if needed.
If you want to contribute to NNI, be sure to review the [contribution guidelines](https://nni.readthedocs.io/en/stable/notes/contributing.html), which includes instructions of submitting feedbacks, best coding practices, and code of conduct.
To learn more about making a contribution to NNI, please refer to our [How-to contribution page](https://nni.readthedocs.io/en/stable/contribution.html).
We use [GitHub issues](https://github.com/microsoft/nni/issues) to track tracking requests and bugs.
Please use [NNI Discussion](https://github.com/microsoft/nni/discussions) for general questions and new ideas.
For questions of specific use cases, please go to [Stack Overflow](https://stackoverflow.com/questions/tagged/nni).
We appreciate all contributions and thank all the contributors!
Participating discussions via the following IM groups is also welcomed.
<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/nni&max=240" /></a>
## **Feedback**
* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
* Open or participate in a [discussion](https://github.com/microsoft/nni/discussions).
* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
Join IM discussion groups:
|Gitter||WeChat|
|----|----|----|
|![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png)| OR |![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png)|
Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull requests from hundreds of contributors.
We appreciate all contributions from community to make NNI thrive.
<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/nni&max=240" width="600" /></a>
## Test status
......@@ -363,6 +122,6 @@ Targeting at openness and advancing state-of-art technology, [Microsoft Research
We encourage researchers and students leverage these projects to accelerate the AI development and research.
## **License**
## License
The entire codebase is under [MIT license](LICENSE)
The entire codebase is under [MIT license](LICENSE).
dependencies/develop.txt
View file @ 51d261e7
......@@ -9,7 +9,8 @@ pytest
pytest-azurepipelines
pytest-cov
rstcheck
sphinx
sphinx >= 4.4
sphinx-argparse-nni >= 0.4.0
sphinx-gallery
sphinxcontrib-bibtex
git+https://github.com/bashtage/sphinx-material.git
dependencies/recommended.txt
View file @ 51d261e7
......@@ -9,6 +9,7 @@ torchvision == 0.11.1+cpu ; sys_platform != "darwin"
torchvision == 0.11.1 ; sys_platform == "darwin"
pytorch-lightning >= 1.5.0
torchmetrics
lightgbm
onnx
peewee
graphviz
......
dependencies/recommended_gpu.txt
View file @ 51d261e7
......@@ -5,6 +5,7 @@ tensorflow
torch == 1.10.0+cu111
torchvision == 0.11.1+cu111
pytorch-lightning >= 1.5.0
lightgbm
onnx
peewee
graphviz
......
dependencies/recommended_legacy.txt
View file @ 51d261e7
......@@ -7,6 +7,7 @@ torchvision == 0.8.2+cpu
pytorch-lightning
torchmetrics
lightgbm
onnx
peewee
graphviz
......
dependencies/required.txt
View file @ 51d261e7
......@@ -6,6 +6,7 @@ hyperopt == 0.1.2
json_tricks >= 3.15.5
numpy < 1.22 ; python_version < "3.8"
numpy ; python_version >= "3.8"
packaging
pandas
prettytable
psutil
......
docs/.gitignore
View file @ 51d261e7
......@@ -2,3 +2,9 @@ build/
# legacy build
_build/
# ignored copied rst in tutorials
/source/tutorials/**/cp_*.rst
# auto-generated reference table
_modules/
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