# DI-hpc

**Repository Path**: opendilab/DI-hpc

## Basic Information

- **Project Name**: DI-hpc
- **Description**: OpenDILab高性能强化学习算子库
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 3
- **Created**: 2021-10-08
- **Last Updated**: 2023-02-17

## Categories & Tags

**Categories**: machine-learning

**Tags**: None

## README

## DI-HPC: Decision Intelligence - High Performance Computation
**DI-HPC** is an acceleration operator component for general algorithm modules in reinforcement learning algorithms, such as GAE, n-step TD and LSTM, etc. The operators support forward and backward propagation, and can be used in training, data collection, and test modules.

## Requirements
* CUDA 9.2
* PyTorch 1.5 (recommend)
* python 3.6.9
* Linux Platform

*Note: We recommend that DI-HPC and DI-Engine share the same environment, and it should be fine with PyTorch from 1.3.1 to 1.8.*

## Quick Start
#### Install from whl
The easiest way to get DI-HPC is to use pip, and you can get `.whl` from
* [di_hpc_rll-0.0.2-cp36-cp36m-linux_x86_64.whl](http://opendilab.org/download/DI-hpc/di_hpc_rll-0.0.2-cp36-cp36m-linux_x86_64.whl)
* [di_hpc_rll-0.0.2-cp37-cp37m-linux_x86_64.whl](http://opendilab.org/download/DI-hpc/di_hpc_rll-0.0.2-cp37-cp37m-linux_x86_64.whl)
* [di_hpc_rll-0.0.2-cp38-cp38-linux_x86_64.whl](http://opendilab.org/download/DI-hpc/di_hpc_rll-0.0.2-cp38-cp38-linux_x86_64.whl)

and then call
```
$ pip install <YOUR_WHL>
```

#### Install from source code
Alternatively you can install latest DI-HPC from git master branch:
```
$ python3 setup.py install
```

#### Run on Linux
You will get benchmark result by following commands:
```
$ python3 tests/test_gae.py
```