# Installing ML-Agents for Windows

ML-Agents supports Windows 10. While it might be possible to run ML-Agents using other versions of Windows, we only test Windows 10. (We also do not test running Windows on a VM like Bootcamp or Parallels).

To use ML-Agents, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not required for the v0.3 release of ML-Agents. However, training on a GPU might be required by future versions and features.

## Step 1: Install Python via Anaconda

<a href="https://www.anaconda.com/download/#windows" target="_blank">Download</a> and install Anaconda for Windows.  By using Anaconda, you can 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).

<p align="center">
    <img src="images/anaconda_install.PNG" 
        alt="Anaconda Install" 
        width="500" border="10" />
</p>

We recommend the default _advanced installation options_. However, select the options appropriate for your specific situation.

<p align="center">
    <img src="images/anaconda_default.PNG" 
        alt="Anaconda Install" 
        width="500" border="10" />
</p>

After installation, you must open __Anaconda Navigator__ to finish the setup. From the Windows search bar, type _anaconda navigator_. You can close Anaconda Navigator after it opens.

## Step 2: Setup and Activate a New Conda Environment

You will create a new Conda environment to be used with ML-Agents.  This means that all the packages that you install are localized to just this environment.  It will not affect any other installation of Python or other environments.  Whenever you want to run ML-Agents, you will need activate this Conda environment.

To create a new Conda environment, open a new Anaconda Prompt (_Anaconda Prompt_ in the search bar) and type in the following command:

```
conda create -n ml-agents python=3.6
```

You may be asked to install new packages.  Type `y` and press enter _(make sure you are connected to the internet)_.  You will need to install these packages.  This command will create a new Conda environment called ml-agents using Python version 3.6.  

<p align="center">
    <img src="images/conda_new.PNG" 
        alt="Anaconda Install" 
        width="500" border="10" />
</p>

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 Anaconda Prompt, type in the following command:

```
conda activate ml-agents
```

You should see `(ml-agents)` prepended on the last line.

After this, you will need to install `tensorflow` and `tensorflow-gpu`.  This can be installed by using `pip` - which is a package management system used to install Python packages.  In the same Anaconda Prompt, type in the following command _(make sure you are connected to the internet)_:

```
pip install tensorflow tensorflow-gpu
```

## Step 3: 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 Anaconda Prompt (_if you closed the prompt from Step 5, you can activate the ml-agents Conda environment by typing `activate ml-agents`_):

```
git clone git@github.com:Unity-Technologies/ml-agents.git
```

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, from the Anaconda Prompt, change to the python directory inside the ML-agents directory:

```
cd C:\Downloads\ml-agents\python
```

Make sure you are connected to the internet and then type in the Anaconda Prompt:

```
pip install .
```

This will complete the installation of all the required Python packages to run ML-Agents.  

## (Optional) GPU Training using ML-Agents 

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).

## (Optional) Step 1: Install Nvidia CUDA toolkit

<a href="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)).  

_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`

<p align="center">
    <img src="images/install_nvidia_cuda_toolkit.PNG" 
        alt="install nvidia cuda toolkit" 
        width="500" border="10" />
</p>

## (Optional) Step 2: Install Nvidia cuDNN library

<a href="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.

<p align="center">
    <img src="images/cuDNN_membership_required.png" 
        alt="cuDNN membership required" 
        width="500" border="10" />
</p>

Once you've signed up, go back to the cuDNN <a href="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, there are three folders called `bin`, `include`, and `lib`.  

<p align="center">
    <img src="images/cudnn_zip_files.PNG" 
        alt="cuDNN zip files" 
        width="500" border="10" />
</p>

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`

<p align="center">
    <img src="images/cuda_toolkit_directory.PNG" 
        alt="cuda toolkit directory" 
        width="500" border="10" />
</p>

## (Optional) Step 3: Set Environment Variables

You will need to add one environment variable and two path variables.

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__.  

<p align="center">
    <img src="images/edit_env_var.png" 
        alt="edit env variables" 
        width="250" border="10" />
</p>

From here, click the __Environment Variables__ button.  Click __New__ to add a new system variable _(make sure you do this under __System variables__ and not User variables_.  

<p align="center">
    <img src="images/new_system_variable.PNG" 
        alt="new system variable" 
        width="500" border="10" />
</p>

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.

<p align="center">
    <img src="images/system_variable_name_value.PNG" 
        alt="system variable names and values" 
        width="500" border="10" />
</p>

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:

    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\lib\x64
    C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\extras\CUPTI\libx64

Make sure to replace the relevant directory location with the one you have installed.  _Please note that case sensitivity matters_.

<p align="center">
    <img src="images/path_variables.PNG" 
        alt="Path variables" 
        width="500" border="10" />
</p>

Lastly, you should test to see if everything installed properly.  You can do this to see if TensorFlow can identify your GPU. In an Anaconda Prompt, with the ml-agents Conda environment activated, type in the following command: 

```
python

import tensorflow as tf

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
```

You should see something similar to:

```
Found device 0 with properties ...
```

## Acknowledgements

We would like to thank [Jason Weimann](https://unity3d.college/2017/10/25/machine-learning-in-unity3d-setting-up-the-environment-tensorflow-for-agentml-on-windows-10/) and [Nitish S. Mutha](http://blog.nitishmutha.com/tensorflow/2017/01/22/TensorFlow-with-gpu-for-windows.html) for writing the original articles which were used to create this guide.