We have provided a step by step guide to get ML-Agents working on Windows.
In oreder to get ML-Agents working with Windows, you will need to have Windows 10 installed. While it is possible for ML-Agents to work on other versions of Windows, we have only tested with a local installation of Windows 10 (not using VM like Bootcamp or Parallels).
In order to get ML-Agents working with Windows, you will need to have Windows 10 installed. While it is possible for ML-Agents to work on other versions of Windows, we have only tested with a local installation of Windows 10 (not using VM like Bootcamp or Parallels).
Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
_Before installing, please make sure you __close any running instances of Unity or Visual Studio.___
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`
## Step 2: Install Nvidia cuDNN library
<ahref="https://developer.nvidia.com/cudnn"target="_blank">Download</a> and install the cuDNN library from Nvidia. cuDNN is is a GPU-accelerated library of primitives for deep neural networks. Before you can download, you will need to sign up for free to the Nvidia Developer Program.
width="500" border="10" />
</p>
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 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)).
After you have downloaded the cuDNN files, you will need to extract the files into the CUDA toolkit directory. In the cuDNN zip file, copy 3 folders called `bin`, `include`, and `lib` into the CUDA toolkit directory. In this guide, the CUDA toolkit directory is located at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1`
## Step 4: Set Environment Variables
You will need to add one environment variable and two path variables.
## Step 3: Install Python via Anaconda
<ahref="https://www.anaconda.com/download/#windows"target="_blank">Download</a> and install Anaconda for Windows. By using Anaconda, you can use manage seperate enviornments for different distributions of Python. We **strongly** recommend using Python 3 as we do not guarantee supporting Python 2 in future releases. In this guide, we are using Python version 3.6 and Anaconda version 5.1 ([64-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86_64.exe) or [32-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86.exe) direct links).
To set the environment variable, type `environment variables` in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called __Edit the system environment variables__. From here, click the __Environment Variables__ button. Click __New__ to add a new system variable. 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.
## Step 4: Setup a New Conda Environment
You will create a new Conda enviornment from our installation of Anaconda. This new enviornment will be specifically for ML-Agents. This means that all packages that you install are localized to just this enviornment. It will not effect any other installation of Python. Whenever you want to run ML-Agents, you can do some from this new Conda enviornment.
To set the two path variables, inside the same __Enviornment 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:
Make sure to replace the relevant directory location with the one you have installed.
## Step 4: Install Python via Anaconda
<ahref="https://www.anaconda.com/download/#windows"target="_blank">Download</a> and install Anaconda for Windows. By using Anaconda, you can use manage separate environments for different distributions of Python. We **strongly** recommend using Python 3 as we do not guarantee supporting Python 2 in future releases. In this guide, we are using Python version 3.6 and Anaconda version 5.1 ([64-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86_64.exe) or [32-bit](https://repo.continuum.io/archive/Anaconda3-5.1.0-Windows-x86.exe) direct links).
## Step 5: Setup a New Conda Environment
You will create a new Conda environment from our installation of Anaconda. This new environment will be specifically for ML-Agents. This means that all packages that you install are localized to just this environment. It will not affect any other installation of Python. Whenever you want to run ML-Agents, you can do some from this new Conda environment.
This will create a new Conda environment called ml-agents using Python version 3.6. To use this enviornment, you will need to activate it. _(In the future, if you need to use this enviornment again, you can run the same command)_. In the same CMD or Powershell prompt, type in the following command:
This will create a new Conda environment called ml-agents using Python version 3.6. To use this environment, you will need to activate it. _(In the future, if you need to use this environment again, you can run the same command)_. In the same CMD or Powershell prompt, type in the following command:
activate ml-agents
Found device 0 with properties ...
```
## Step 5: Install Required Python Packages
## Step 6: Install Required Python Packages
ML-Agents includes a list of Python dependencies needed to run. Similarly in Step 4, you will be using `pip` to install these Python packages.
If you haven't already, make sure to clone the repository. You can do this using Git ([download here](https://git-scm.com/download/win)) and running the following commands in a new or same CMD or Powershell window from Step 4:
If you don't want to use Git, you can always directly download all the files [here](https://github.com/Unity-Technologies/ml-agents/archive/master.zip).
In our example, the files are located in C:\Downloads. After you have either cloned or downloaded the files, in the same CMD or Powershell window from Step 4, change to the python directory inside the ML-agents directory (_if you closed this window, you can activate the right Conda enviornment by typing `activate ml-agents`_):
In our example, the files are located in `C:\Downloads`. After you have either cloned or downloaded the files, in the same CMD or Powershell window from Step 4, change to the python directory inside the ML-agents directory (_if you closed this window, you can activate the right Conda environment by typing `activate ml-agents`_):
GitHub
7 年前