<commit_before># Installation Instructions

## Mac OS X

I've created detailed step-by-step instructions for running the code notebooks for these LiveLessons on a Mac [here](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/step_by_step_MacOSX_install.md).

## Unix

### Where You Already Have the Dependencies

The dependencies are provided in this repository's [Dockerfile](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/Dockerfile). They are: 

* the [Jupyter Notebook Scientific Python Stack](https://github.com/jupyter/docker-stacks/tree/master/scipy-notebook) 
* TensorFlow
* Keras
* tflearn

If you have these packages configured as you like them, you can simply:

`git clone https://github.com/the-deep-learners/TensorFlow-LiveLessons.git`

### Where You Are Missing Dependencies

1. Get Docker CE for, e.g., [Ubuntu](https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/)
2. Follow all the steps in my [Step-by-Step Instructions for Mac](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/step_by_step_MacOSX_install.md) that involve executing code at the command line. That is, execute all steps but one, four and five. 

### In a Fresh Cloud Instance

If you'd like to enjoy the power and flexibility of cloud computing, you can spin up a machine with Google Cloud Compute, Amazon Web Services, Microsoft Azure, or other providers. My step-by-step process for creating an Ubuntu instance with Google Cloud Compute and launching Dockerized Jupyter notebooks is available [here](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/step_by_step_GCP_install.md). 

## Windows

These steps are for users who installed the [Anaconda](https://www.continuum.io/downloads) Python 3 distribution, but other such distributions (e.g., [WinPython](https://winpython.github.io/), [Canopy](https://store.enthought.com/downloads/)) should work too. 

### Install TensorFlow for CPU

1. Create a conda virtual environment: `C:\> conda create -n tf python=3.5`
2. Activate the new environment: `C:\> activate tf`. Your prompt should now change to: `(tf) C:\>`
3. Run `(tf) C:\> pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl`.

### Install TensorFlow for GPU

You shouldn't *need* a GPU for these LiveLessons, but some of the later notebooks in these LiveLessons will run much more quickly if you do. Instructions for setting this up are [here](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/windows_TF_GPU.md).

### Running Dockerized Jupyter Notebooks

Currently, instructions for running the these LiveLessons' Jupyter notebooks from within a Docker container are unavailable on Windows. If you have experience doing this, please feel free to make a pull request and fill it in! 

<commit_msg>add ubuntu GPU note to installation README<commit_after># Installation Instructions

## Mac OS X

I've created detailed step-by-step instructions for running the code notebooks for these LiveLessons on a Mac [here](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/step_by_step_MacOSX_install.md).

## Unix

#### Where You Already Have the Dependencies

The dependencies are provided in this repository's [Dockerfile](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/Dockerfile). They are: 

* the [Jupyter Notebook Scientific Python Stack](https://github.com/jupyter/docker-stacks/tree/master/scipy-notebook) 
* TensorFlow
* Keras
* tflearn

If you have these packages configured as you like them, you can simply:

`git clone https://github.com/the-deep-learners/TensorFlow-LiveLessons.git`

#### Where You Are Missing Dependencies

1. Get Docker CE for, e.g., [Ubuntu](https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/)
2. Follow all the steps in my [Step-by-Step Instructions for Mac](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/step_by_step_MacOSX_install.md) that involve executing code at the command line. That is, execute all steps but one, four and five. 

#### In a Fresh Cloud Instance

If you'd like to enjoy the power and flexibility of cloud computing, you can spin up a machine with Google Cloud Compute, Amazon Web Services, Microsoft Azure, or other providers. My rough notes for creating an Ubuntu instance with Google Cloud Compute and launching Dockerized Jupyter notebooks is available [here](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/step_by_step_GCP_install.md). 

#### Training Models with a GPU

You don't need to train your Deep Learning models with a GPU for these LiveLessons, but some of the later notebooks in these LiveLessons will run much more quickly if you do. My process for running an Nvidia GPU from Dockerized Jupyter notebooks is available [here](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/step_by_step_MacOSX_install.md#training-models-with-a-gpu). 

## Windows

These steps are for users who installed the [Anaconda](https://www.continuum.io/downloads) Python 3 distribution, but other such distributions (e.g., [WinPython](https://winpython.github.io/), [Canopy](https://store.enthought.com/downloads/)) should work too. 

#### Install TensorFlow for CPU

1. Create a conda virtual environment: `C:\> conda create -n tf python=3.5`
2. Activate the new environment: `C:\> activate tf`. Your prompt should now change to: `(tf) C:\>`
3. Run `(tf) C:\> pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.1-cp35-cp35m-win_amd64.whl`.

#### Install TensorFlow for GPU

As mentioned in the Unix section above, you don't need to train your models with a GPU for these LiveLessons, but some of the later notebooks in these LiveLessons will run much more quickly if you do. Instructions for setting this up are [here](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/installation/windows_TF_GPU.md).

#### Running Dockerized Jupyter Notebooks

Currently, instructions for running the these LiveLessons' Jupyter notebooks from within a Docker container are unavailable on Windows. If you have experience doing this, please feel free to make a pull request and fill it in! 


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