Assignments for Udacity Deep Learning class with TensorFlow
===========================================================

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

Running the Docker container from the Google Cloud repository
-------------------------------------------------------------

    docker run -p 8888:8888 -it --rm b.gcr.io/tensorflow-udacity/assignments:0.5.0

Accessing the Notebooks
-----------------------

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)

FAQ
---

* **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](http://stackoverflow.com/questions/32834082/how-to-increase-docker-machine-memory-mac)
has two good suggestions; we recommend using 8G.

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

* **I want to create a new virtual machine instead of the default one.**

`docker-machine` is a tool to provision and manage docker hosts, it supports multiple platform (ex. aws, gce, azure, virtualbox, ...). To create a new virtual machine locally with built-in docker engine, you can use

    docker-machine create -d virtualbox --virtualbox-memory 8196 tensorflow
    
`-d` means the driver for the cloud platform, supported drivers listed [here](https://docs.docker.com/machine/drivers/). Here we use virtualbox to create a new virtual machine locally. `tensorflow` means the name of the virtual machine, feel free to use whatever you like. You can use

    docker-machine ip tensorflow
    
to get the ip of the new virtual machine. To switch from default virtual machine to a new one (here we use tensorflow), type

    eval $(docker-machine env tensorflow)
    
Note that `docker-machine env tensorflow` outputs some environment variables such like `DOCKER_HOST`. Then your docker client is now connected to the docker host in virtual machine `tensorflow`


Notes for anyone needing to build their own containers (mostly instructors)
===========================================================================

Building a local Docker container
---------------------------------

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

Running the local container
---------------------------

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.

Pushing a Google Cloud release
------------------------------

    V=0.5.0
    docker tag $USER/assignments b.gcr.io/tensorflow-udacity/assignments:$V
    gcloud docker push b.gcr.io/tensorflow-udacity/assignments
    docker tag -f $USER/assignments b.gcr.io/tensorflow-udacity/assignments:latest
    gcloud docker push b.gcr.io/tensorflow-udacity/assignments

History
-------

* 0.1.0: Initial release.
* 0.2.0: Many fixes, including lower memory footprint and support for Python 3.
* 0.3.0: Use 0.7.1 release.
* 0.4.0: Move notMMNIST data for Google Cloud.
* 0.5.0: Actually use 0.7.1 release.
