Not required to use v0.3 for ML-Agents. This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
As of ML-Agents v0.3, only CUDA 8 and cuDNN 6 is supported.
<ahref="https://developer.nvidia.com/cuda-toolkit-archive"target="_blank">Download</a> and install the CUDA toolkit from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler and a runtime library and is needed to run ML-Agents. You can select the latest or previous releases. In this guide, we are using version 9.1.85.3 ([direct link](https://developer.nvidia.com/compute/cuda/9.1/Prod/patches/3/cuda_9.1.85.3_windows)).
<ahref="https://developer.nvidia.com/cuda-toolkit-archive"target="_blank">Download</a> and install the CUDA toolkit from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler and a runtime library and is needed to run ML-Agents. In this guide, we are using version 8.0.61 ([direct link](https://developer.nvidia.com/compute/cuda/8.0/Prod2/network_installers/cuda_8.0.61_win10_network-exe)).
Run the installer and select the Express option. Note the directory where you installed the CUDA toolkit. In this guide, we installed in the directory `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1`
Run the installer and select the Express option. Note the directory where you installed the CUDA toolkit. In this guide, we installed in the directory `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`
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Once you've signed up, go back to the cuDNN <ahref="https://developer.nvidia.com/cudnn"target="_blank">downloads page</a>. You may or may not be asked to fill out a short survey. When you get to the list cuDNN releases, __make sure you are downloading the right version for the CUDA toolkit you installed in Step 1.__ In this guide, we are using version 7.1.1 for CUDA toolkit version 9.1+ ([direct link](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.1.1/prod/9.1_20180214/cudnn-9.1-windows10-x64-v7.1)).
Once you've signed up, go back to the cuDNN <ahref="https://developer.nvidia.com/cudnn"target="_blank">downloads page</a>. You may or may not be asked to fill out a short survey. When you get to the list cuDNN releases, __make sure you are downloading the right version for the CUDA toolkit you installed in Step 1.__ In this guide, we are using version 6.0 for CUDA toolkit version 8.0 ([direct link](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v6/prod/8.0_20170307/cudnn-8.0-windows10-x64-v6.0-zip)).
After you have downloaded the cuDNN files, you will need to extract the files into the CUDA toolkit directory. In the cuDNN zip file, there are three folders called `bin`, `include`, and `lib`.
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Copy these three folders into the CUDA toolkit directory. The CUDA toolkit directory is located at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1`
Copy these three folders into the CUDA toolkit directory. The CUDA toolkit directory is located at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`
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For __Variable Name__, enter `CUDA_HOME`. For the variable value, put the directory location for the CUDA toolkit. In this guide, the directory location is `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1`. Press __OK__ once.
For __Variable Name__, enter `CUDA_HOME`. For the variable value, put the directory location for the CUDA toolkit. In this guide, the directory location is `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0`. Press __OK__ once.
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To set the two path variables, inside the same __Environment Variables__ window and under the second box called __System Variables__, find a variable called `PATH` and click __Edit__. You will add two directories to the list. For this guide, the two entries would look like:
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