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README.md
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## Requirements and Preparation ## Requirements and Preparation
Please install `Python>=3.6` and `PyTorch>=1.3.0`. (You could also run this project in lower versions of Python and PyTorch, but may have bugs). Please install `Python>=3.6` and `PyTorch>=1.5.0`. (You could also run this project in lower versions of Python and PyTorch, but may have bugs).
Some visualization codes may require `opencv`. Some visualization codes may require `opencv`.
CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`. CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.

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docs/README_CN.md
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[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md) [![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md)
自动深度学习库 (AutoDL-Projects) 是一个开源的，轻量级的，功能强大的项目。 自动深度学习库 (AutoDL-Projects) 是一个开源的，轻量级的，功能强大的项目。
台项目目前实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。 该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。
**谁应该考虑使用AutoDL-Projects** **谁应该考虑使用AutoDL-Projects**
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## 准备工作 ## 准备工作
Please install `Python>=3.6` and `PyTorch>=1.3.0`. (You could also run this project in lower versions of Python and PyTorch, but may have bugs). 请使用`3.6`以上的`Python`，更多的Python包参见[requirements.txt](docs/requirements.txt).
Some visualization codes may require `opencv`.
CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`. 请下载并且解压缩`CIFAR`和`ImageNet`到`$TORCH_HOME`.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Drive](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
## 引用 ## 引用

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docs/requirements.txt
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nats_bench>=1.4 nats_bench>=1.4
torchvision torchvision
torch torch
# Optional
opencv