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Getting Started

Installation

Prerequisites

Currently, we recommend Python>=3.8, and user can create an virtual environment by 

conda create -n rllte python=3.8

RLLTE has been published as a Python package in PyPi and can be installed with pip, ideally by using a virtual environment. Open up a terminal and install RLLTE with:

pip install rllte-core # basic installation
pip install rllte-core[envs] # for pre-defined environments

with git

Open up a terminal and clone the repository from GitHub witg git: 

git clone https://github.com/RLE-Foundation/rllte.git
After that, run the following command to install package and dependencies: 
pip install -e . # basic installation
pip install -e .[envs] # for pre-defined environments

PyTorch Installation

RLLTE currently supports two kinds of computing devices for acceleration, namely NVIDIA GPU and HUAWEI NPU. Thus users need to install different versions PyTorch for adapting to different devices.

with NVIDIA GPU

Open up a terminal and install PyTorch with: 

pip3 install torch==2.0.0 torchvision
More information can be found in Get Started.

with HUAWEI NPU

Tip

Ascend NPU only supports aarch64!

  • Install the dependencies for PyTorch: 
    pip3 install pyyaml wheel

  • Download the .whl package of PyTorch from Kunpeng file sharing center and install it: 

    wget https://repo.huaweicloud.com/kunpeng/archive/Ascend/PyTorch/torch-1.11.0-cp39-cp39-linux_aarch64.whl
pip3 install torch-1.11.0-cp39-cp39-linux_aarch64.whl

  • Install torch_npu: 

    wget https://gitee.com/ascend/pytorch/releases/download/v5.0.rc1-pytorch1.11.0/torch_npu-1.11.0-cp39-cp39m-linux_aarch64.whl
pip3 install torch_npu-1.11.0-cp39-cp39m-linux_aarch64.whl

  • Install apex [Optional]: 

    wget https://gitee.com/ascend/apex/releases/download/v5.0.rc1-pytorch1.11.0/apex-0.1_ascend-cp39-cp39m-linux_aarch64.whl
pip3 install apex-0.1_ascend-cp39-cp39m-linux_aarch64.whl
    Training with mixed precision can improve the model performance. You can introduce the Apex mixed precision module or use the AMP module integrated in AscendPyTorch 1.8.1 or later based on the scenario. The Apex module provides four function modes to suit different training with mixed precision scenarios. AMP is only similar to one function of the Apex module, but can be directly used without being introduced. For details about how to use the AMP and Apex modules, see "Mixed Precision Description" in the PyTorch Network Model Porting and Training Guide.