(Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step.

(Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment.

• Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml):

conda env create -f requirements/dog-linux.yml
source activate dog-project

• Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml):

conda env create -f requirements/dog-mac.yml
source activate dog-project

• Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml):

conda env create -f requirements/dog-windows.yml
activate dog-project

(Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment.

• Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt):

conda create --name dog-project python=3.5
source activate dog-project
pip install -r requirements/requirements.txt

• Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt):

conda create --name dog-project python=3.5
activate dog-project
pip install -r requirements/requirements.txt

(Optional) If you are using AWS, install Tensorflow.

Switch Keras backend to TensorFlow.

• Linux or Mac:

   KERAS_BACKEND=tensorflow python -c "from keras import backend"
  

• Windows:

   set KERAS_BACKEND=tensorflow
   python -c "from keras import backend"
  

(Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment.