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Oct 11, 2018
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pull/1294 from has-taiar
xiaomaogy Oct 9, 2018
78e1f4e
removed the left bracket
xiaomaogy Oct 9, 2018
2605447
moved the windows link position
xiaomaogy Oct 9, 2018
f891ff7
update the windows doc
xiaomaogy Oct 10, 2018
671afa3
resolved the comments, changed the pip install . to pip install -e . …
xiaomaogy Oct 10, 2018
7d56ac7
Resolved the comments
xiaomaogy Oct 11, 2018
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add the 'the'
xiaomaogy Oct 11, 2018
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23 changes: 16 additions & 7 deletions docs/Installation-Windows.md
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Expand Up @@ -7,8 +7,8 @@ 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 required for the v0.4 release of
the ML-Agents toolkit. However, training on a GPU might be required by future
(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
Expand Down Expand Up @@ -126,19 +126,28 @@ git clone https://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).

The `UnitySDK` subdirectory contains the Unity Assets to add to your projects.
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Why are we referring to the SDK as assets?

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I copied over the same sentences in the Installation doc to this Installation windows doc. Also they can be considered as Assets since they are something we can put in the Asset store. (And are compatible with the Asset usage, while the python package isn't).

It also contains many [example environments](Learning-Environment-Examples.md)
to help you get started.

The `ml-agents` subdirectory contains Python packages which provide
trainers and a Python API to interface with Unity.

The `gym-unity` subdirectory contains a package to interface with OpenAI Gym.

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:
cloned or downloaded the files, from the Anaconda Prompt, change to the ml-agents
subdirectory inside the ml-agents directory:

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

Make sure you are connected to the internet and then type in the Anaconda
Prompt:
Prompt within `ml-agents` subdirectory:

```sh
pip install .
pip install -e .
```

This will complete the installation of all the required Python packages to run
Expand All @@ -152,7 +161,7 @@ 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](https://developer.nvidia.com/cuda-gpus).

As of the ML-Agents toolkit v0.4, only CUDA v9.0 and cuDNN v7.0.5 is supported.
Currently for the ML-Agents toolkit, only CUDA v9.0 and cuDNN v7.0.5 is supported.

### Install Nvidia CUDA toolkit

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38 changes: 19 additions & 19 deletions docs/Installation.md
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Expand Up @@ -16,7 +16,14 @@ Build Support_ component when installing Unity.
width="500" border="10" />
</p>

## Clone the ML-Agents Toolkit Repository
## Windows Users
For setting up your environment on Windows, we have created a [detailed
guide](Installation-Windows.md) to setting up your env. For Mac and Linux,
continue with this guide.

## Mac and Unix Users

### Clone the ML-Agents Toolkit Repository

Once installed, you will want to clone the ML-Agents Toolkit GitHub repository.

Expand All @@ -26,14 +33,14 @@ git clone https://github.com/Unity-Technologies/ml-agents.git

The `UnitySDK` subdirectory contains the Unity Assets to add to your projects.
It also contains many [example environments](Learning-Environment-Examples.md)
that can be used to help get you familiar with Unity.
to help you get started.

The `ml-agents` subdirectory contains Python packages which provide
trainers and a Python API to interface with Unity.

The `gym-unity` subdirectory contains a package to interface with OpenAI Gym.

## Install Python and mlagents Package
### Install Python and mlagents Package

In order to use ML-Agents toolkit, you need Python 3.6 along with the
dependencies listed in the [requirements file](../ml-agents/requirements.txt).
Expand All @@ -42,21 +49,6 @@ Some of the primary dependencies include:
- [TensorFlow](Background-TensorFlow.md)
- [Jupyter](Background-Jupyter.md)

### NOTES

- We do not currently support Python 3.7 or Python 3.5.
- If you are using Anaconda and are having trouble with TensorFlow, please see
the following
[note](https://www.tensorflow.org/install/install_mac#installing_with_anaconda)
on how to install TensorFlow in an Anaconda environment.

### Windows Users

If you are a Windows user who is new to Python and TensorFlow, follow [this
guide](Installation-Windows.md) to set up your Python environment.

### Mac and Unix Users

[Download](https://www.python.org/downloads/) and install Python 3.6 if you do not
already have it.

Expand All @@ -68,12 +60,20 @@ To install the dependencies and `mlagents` Python package, enter the
`ml-agents/` subdirectory and run from the command line:

```sh
pip3 install .
pip3 install -e .
```

If you installed this correctly, you should be able to run
`mlagents-learn --help`

**Notes:**

- We do not currently support Python 3.7 or Python 3.5.
- If you are using Anaconda and are having trouble with TensorFlow, please see
the following
[note](https://www.tensorflow.org/install/install_mac#installing_with_anaconda)
on how to install TensorFlow in an Anaconda environment.

## Docker-based Installation

If you'd like to use Docker for ML-Agents, please follow
Expand Down
8 changes: 5 additions & 3 deletions docs/Migrating.md
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Expand Up @@ -54,13 +54,14 @@ using the search bar of the `Hierarchy` window with the word `Agent`.
python3 learn.py 3DBall --train
```

from the `python/` directory, we now run
from the `python` subdirectory (which is changed to `ml-agents` subdirectory
in v0.5), we now run

```sh
mlagents-learn config/trainer_config.yaml --env=3DBall --train
```

from the directory where we installed the ML-Agents Toolkit.
from the root directory where we installed the ML-Agents Toolkit.

* It is now required to specify the path to the yaml trainer configuration file
when running `mlagents-learn`. For an example trainer configuration file, see
Expand Down Expand Up @@ -88,7 +89,8 @@ using the search bar of the `Hierarchy` window with the word `Agent`.
### Python API

* We've changed some of the Python packages dependencies in requirement.txt
file. Make sure to run `pip3 install .` within your `ml-agents/python` folder
file. Make sure to run `pip3 install -e .` within your `ml-agents/python`
folder
to update your Python packages.

## Migrating from ML-Agents toolkit v0.2 to v0.3
Expand Down
2 changes: 1 addition & 1 deletion docs/Training-on-Amazon-Web-Service.md
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Expand Up @@ -66,7 +66,7 @@ After launching your EC2 instance using the ami and ssh into it:
```sh
git clone https://github.com/Unity-Technologies/ml-agents.git
cd ml-agents/ml-agents/
pip3 install .
pip3 install -e .
```

### Setting up X Server (optional)
Expand Down