Course information can be found at https://www.udacity.com/course/deep-learning--ud730

docker run -p 8888:8888 -it --rm b.gcr.io/tensorflow-udacity/assignments

On linux, go to: http://127.0.0.1:8888

On mac, find the virtual machine's IP using:

docker-machine ip default

Then go to: http://IP:8888 (likely http://192.168.99.100:8888)

• I'm getting a MemoryError when loading data in the first notebook.

If you're using a Mac, Docker works by running a VM locally (which is controlled by docker-machine). It's quite likely that you'll need to bump up the amount of RAM allocated to the VM beyond the default (which is 1G). This Stack Overflow question has two good suggestions; we recommend using 8G.

In addition, you may need to pass --memory=8g as an extra argument to docker run.

cd tensorflow/examples/udacity
docker build -t $USER/assignments .

To run a disposable container:

docker run -p 8888:8888 -it --rm $USER/assignments

Note the above command will create an ephemeral container and all data stored in the container will be lost when the container stops.

To avoid losing work between sessions in the container, it is recommended that you mount the tensorflow/examples/udacity directory into the container:

docker run -p 8888:8888 -v </path/to/tensorflow/examples/udacity>:/notebooks -it --rm $USER/assignments

This will allow you to save work and have access to generated files on the host filesystem.

V=0.2.0
docker tag $USER/assignments b.gcr.io/tensorflow-udacity/assignments:$V
docker tag $USER/assignments b.gcr.io/tensorflow-udacity/assignments:latest
gcloud docker push b.gcr.io/tensorflow-udacity/assignments

• 0.1.0: Initial release.
• 0.2.0: Many fixes, including lower memory footprint and support for Python 3.