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Installing ML-Agents Toolkit for Windows (Deprecated)
⚠️ Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you.
The ML-Agents Toolkit supports Windows 10. While it might be possible to run the ML-Agents Toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents Toolkit has not been tested on a Windows VM such as Bootcamp or Parallels.
To use the ML-Agents Toolkit, 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 currently required for the ML-Agents Toolkit. However, training on a GPU might be required by future versions and features.
Step 1: Install Python via Anaconda
Download and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3.6.1 or higher is required as we no longer support Python 2. In this guide, we are using Python version 3.6 and Anaconda version 5.1 (64-bit or 32-bit direct links).
We recommend the default advanced installation options. However, select the options appropriate for your specific situation.
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.
If environment variables were not created, you will see error "conda is not
recognized as internal or external command" when you type conda into the
command line. To solve this you will need to set the environment variable
correctly.
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. Double click "Path" under System variable to edit the "Path" variable, click New to add the following new paths.
%UserProfile%\Anaconda3\Scripts
%UserProfile%\Anaconda3\Scripts\conda.exe
%UserProfile%\Anaconda3
%UserProfile%\Anaconda3\python.exe
Step 2: Setup and Activate a New Conda Environment
You will create a new Conda environment to be used with the ML-Agents Toolkit. 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 must install these required packages.
The new Conda environment is called ml-agents and uses Python version 3.6.
To use this environment, you must activate it. (To use this environment In the future, you can run the same command). In the same Anaconda Prompt, type in the following command:
activate ml-agents
You should see (ml-agents) prepended on the last line.
Next, install tensorflow. Install this package using pip - which is a
package management system used to install Python packages. Latest versions of
TensorFlow won't work, so you will need to make sure that you install version
1.7.1. In the same Anaconda Prompt, type in the following command (make sure
you are connected to the Internet):
pip install tensorflow==1.7.1
Step 3: Install Required Python Packages
The ML-Agents Toolkit depends on a number of Python packages. Use pip to
install these Python dependencies.
If you haven't already, clone the ML-Agents Toolkit Github repository to your
local computer. You can do this using Git
(download here) and running the following
commands in an Anaconda Prompt (if you open a new prompt, be sure to activate
the ml-agents Conda environment by typing activate ml-agents):
git clone --branch release_13 https://github.com/Unity-Technologies/ml-agents.git
The --branch release_13 option will switch to the tag of the latest stable
release. Omitting that will get the master branch which is potentially
unstable.
If you don't want to use Git, you can find download links on the releases page.
The com.unity.ml-agents subdirectory contains the core code to add to your
projects. The Project subdirectory contains many
example environments to help you get
started.
The ml-agents subdirectory contains a Python package which provides deep
reinforcement learning trainers to use with Unity environments.
The ml-agents-envs subdirectory contains a Python API to interface with Unity,
which the ml-agents package depends on.
The gym-unity subdirectory contains a package to interface with OpenAI Gym.
Keep in mind where the files were downloaded, as you will need the trainer
config files in this directory when running mlagents-learn. Make sure you are
connected to the Internet and then type in the Anaconda Prompt:
pip install mlagents
This will complete the installation of all the required Python packages to run the ML-Agents Toolkit.
Sometimes on Windows, when you use pip to install certain Python packages, the pip will get stuck when trying to read the cache of the package. If you see this, you can try:
pip install mlagents --no-cache-dir
This --no-cache-dir tells the pip to disable the cache.
Installing for Development
If you intend to make modifications to ml-agents or ml-agents-envs, you
should install the packages from the cloned repo rather than from PyPi. To do
this, you will need to install ml-agents and ml-agents-envs separately.
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
ml-agents subdirectory inside the ml-agents directory:
cd C:\Downloads\ml-agents
From the repo's main directory, now run:
cd ml-agents-envs
pip install -e .
cd ..
cd ml-agents
pip install -e .
Running pip with the -e flag will let you make changes to the Python files
directly and have those reflected when you run mlagents-learn. It is important
to install these packages in this order as the mlagents package depends on
mlagents_envs, and installing it in the other order will download
mlagents_envs from PyPi.
(Optional) Step 4: GPU Training using The ML-Agents Toolkit
GPU is not required for the ML-Agents Toolkit and won't speed up the PPO algorithm a lot during training(but something in the future will benefit from GPU). 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.
Currently for the ML-Agents Toolkit, only CUDA v9.0 and cuDNN v7.0.5 is supported.
Install Nvidia CUDA toolkit
Download and install the CUDA toolkit 9.0 from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2017) compiler and a runtime library and is needed to run the ML-Agents Toolkit. In this guide, we are using version 9.0.176).
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\v9.0
Install Nvidia cuDNN library
Download and install the cuDNN library from Nvidia. cuDNN 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.
Once you've signed up, go back to the cuDNN downloads page. 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 7.0.5 for CUDA toolkit version 9.0 (direct link).
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.
Copy these three folders into the CUDA toolkit directory. The CUDA toolkit
directory is located at
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0
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.
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.
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\v9.0. Press OK once.
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:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\lib\x64
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\extras\CUPTI\libx64
Make sure to replace the relevant directory location with the one you have installed. Please note that case sensitivity matters.
Install TensorFlow GPU
Next, install tensorflow-gpu using pip. You'll need version 1.7.1. In an
Anaconda Prompt with the Conda environment ml-agents activated, type in the
following command to uninstall TensorFlow for cpu and install TensorFlow for gpu
(make sure you are connected to the Internet):
pip uninstall tensorflow
pip install tensorflow-gpu==1.7.1
Lastly, you should test to see if everything installed properly and that TensorFlow can identify your GPU. In the same Anaconda Prompt, open Python in the Prompt by calling:
python
And then type the following commands:
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 ...
Acknowledgments
We would like to thank Jason Weimann and Nitish S. Mutha for writing the original articles which were used to create this guide.