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:0.4.0
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=0.4.0
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
- 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.