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.
- CUDA 9.2
- PyTorch 1.5 (recommend)
- python 3.6 or python 3.7 or python3.8
- Linux Platform
- CUDA 9.0
- gcc 5.4.0
- PyTorch 1.1.0
- python 3.6 or python 3.7
- Linux Platform
Note: We recommend that DI-HPC and DI-Engine share the same environment, and it should be fine with PyTorch from 1.1.0 to 1.10.0.
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
- di_hpc_rll-0.0.2-cp37-cp37m-linux_x86_64.whl
- di_hpc_rll-0.0.2-cp38-cp38-linux_x86_64.whl
and then call
$ pip install <YOUR_WHL>
Alternatively you can install latest DI-HPC from git master branch:
$ python3 setup.py install
You will get benchmark result by following commands:
$ python3 tests/test_gae.py
- [] Trition Kernel for Reinfocement Learning
- File an issue on Github
- Discuss on DI-engine's (also for DI-hpc) discord server
- Contact our email (opendilab@pjlab.org.cn)
We appreciate all the feedbacks and contributions to improve DI-engine, both algorithms and system designs. And CONTRIBUTING.md offers some necessary information.
DI-hpc released under the Apache 2.0 license.