Assignments for Udacity Deep Learning class with TensorFlow

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

Original repo: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/udacity

Running the Jupyter Notebook Locally (Mac instructions)

1. Open Terminal
2. Run the command: sudo pip install jupyter
3. Download the tar.gz files from http://yaroslavvb.com/upload/notMNIST/
4. Move the two tar.gz files into this folder
5. Unzip them in the directory
6. Open Terminal
7. cd into the folder
8. Run the command: jupyter notebook
9. This should open a webpage with the contents of this folder

Note: unzipping notMNIST_large.tar.gz can take especially long, go run or make coffee in the meantime.

###Word2Vec Links: The Udacity class didn't satisfactorily explain the Word2Vec and CBOW models required in Assignment 5. I found some videos from a professor who went over both. It's two lectures.

https://www.youtube.com/watch?v=TsEGsdVJjuA

https://www.youtube.com/watch?v=nuirUEmbaJU

Running the Docker container from the Google Cloud repository

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

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 has two good suggestions; we recommend using 8G.

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

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

Building a local Docker container

cd tensorflow/examples/udacity
docker build -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=0.2.0
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

History

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