**Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.
The `--train` flag tells ML-Agents to run in training mode. `env_file_path` should be the path to the Unity executable that was just created.
Once the training process completes, and the training process saves the model
(denoted by the `Saved Model` message) you can add it to the Unity project and
use it with agents having an **Internal** brain type.
**Note:** Do not just close the Unity Window once the `Saved Model` message appears. Either wait for the training process to close the window or press Ctrl+C at the command-line prompt. If you simply close the window manually, the .bytes file containing the trained model is not exported into the ml-agents folder.
[Download](https://www.anaconda.com/download/#windows) and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3 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](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).
[Download](https://www.anaconda.com/download/#windows) and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3 is required as we no longer support Python 2. In this guide, we are using Python version 3.5 (Anaconda comes with Python 3.6, we will create an environment with python 3.5) 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).
<palign="center">
<imgsrc="images/anaconda_install.PNG"
</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.
If environment variables were not created, or if you see the error "conda is not recognized as internal or external command", in System Variables, "Path" add the following new paths:
```
C:\ProgramData\Anaconda3\Scripts
C:\ProgramData\Anaconda3\Scripts\conda.exe
C:\ProgramData\Anaconda3
C:\ProgramData\Anaconda3\python.exe
```
## Step 2: Setup and Activate a New Conda Environment
You will create a new [Conda environment](https://conda.io/docs/) 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.
```
conda create -n ml-agents python=3.6
conda create -n ml-agents python=3.5
```
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.
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. In the same Anaconda Prompt, type in the following command _(make sure you are connected to the internet)_:
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 1.4.0. In the same Anaconda Prompt, type in the following command _(make sure you are connected to the internet)_:
pip install tensorflow
pip install tensorflow==1.4.0
```
## Step 3: Install Required Python Packages
```
pip install .
```
This will complete the installation of all the required Python packages to run ML-Agents.
### Install Nvidia CUDA toolkit
[Download](https://developer.nvidia.com/cuda-toolkit-archive) 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)).
[Download](https://developer.nvidia.com/cuda-toolkit-archive) and install the CUDA toolkit from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2015) 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)).
Before installing, please make sure you __close any running instances of Unity or Visual Studio__.
Next, install `tensorflow-gpu` using `pip`. In an Anaconda Prompt with the Conda environment ml-agents activated, type in the following command _(make sure you are connected to the internet)_:
```
pip install tensorflow-gpu
pip install tensorflow-gpu==1.4.0
```
Lastly, you should test to see if everything installed properly and that TensorFlow can identify your GPU. In the same Anaconda Prompt, type in the following command:
```
Found device 0 with properties ...
```
Step Visual Studio 2015: CUDA 8.0 is not compatible with Visual Studio 2017, so you will need an older version. Uninstall Visual Studio 2017 that comes with Unity, download Visual Studio Enterprise 2015 and install it with the Windows SDK. If you don't want/ can't install Visual Studio Enterprise 2015, you will need:
[Visual C++ Redistributable for Visual Studio 2015](https://www.microsoft.com/en-us/download/details.aspx?id=48145),
[Visual Studio Community 2015](https://www.visualstudio.com/vs/older-downloads/) + [Windows SDK](https://msdn.microsoft.com/en-us/library/mt683786.aspx).
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