Course information can be found at https://www.udacity.com/course/deep-learning--ud730
docker run -p 8888:8888 --name tensorflow-udacity -it b.gcr.io/tensorflow-udacity/assignments:0.5.0
Note that if you ever exit the container, you can return to it using:
docker start -ai tensorflow-udacity
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.
- 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. 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
cd tensorflow/examples/udacity
docker build --pull -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=0.5.0
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
- 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.