- Clone the repository and navigate to the downloaded folder.
git clone https://github.com/udacity/aind2-cnn.git
cd aind2-cnn
-
(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
torequirements/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
torequirements/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
torequirements/dog-windows-gpu.yml
):
conda env create -f requirements/dog-windows.yml activate dog-project
- Linux (to install with GPU support, change
-
(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
torequirements/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
torequirements/requirements-gpu.txt
):
conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt
- Linux or Mac (to install with GPU support, change
-
(Optional) If you are using AWS, install Tensorflow.
sudo python3 -m pip install -r requirements/requirements-gpu.txt
-
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"
- Linux or Mac:
-
(Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the
dog-project
environment.
python -m ipykernel install --user --name dog-project --display-name "dog-project"
- Launch Jupyter notebook.
jupyter notebook
- (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project).