Easy Jupyter notebook setup on AWS GPU EC2 with machine learning AMI

The Amazon machine learning AMI (link may change in the future) is set up for CUDA/GPU support and preinstalled: TensorFlow, Keras, MXNet, Caffe, Caffe2, PyTorch, Theano, CNTK, and Torch.

I chose the least expensive g2.2xlarge EC2 instance type with a GPU and used the One Click Launch option (you will need to specify a key file pem file for the AWS region where you are starting the instance). to have an instance running and available in about a minute. This GPU instance costs $0.65/hour so remember to either stop it (if you want to reuse it later and don't mind paying a small cost of persistent local storage) or terminate it if you don't want to be charged for the 60GB of SSD storage space associated with the EC2.

I am very comfortable working in SSH shells using Emacs, screen, etc. When an instance boots up, the Actions -> Connect menu shows you the temporary public address which you can use to SSH in:

ssh -i "~/.ssh/key-pair.pem" ec2-user@ec2-54-201-219-212.compute-1.amazonaws.com

I keep my pem files in ~/.ssh, you might store them in a different place. If you haven't used EC2 instances before and don't already have an pem access files, follow these directions.

Anaconda is installed so jupyter is also pre-installed and can be started from any directory on your EC2 using:

jupyter notebook

After some printout, you will see a local URI to access the Jupyter notebook that will look something like this:

    Copy/paste this URL into your browser when you connect for the first time,

    to login with a token:

        http://localhost:8888/?token=f25c0cdf24b128c0c7ae2ce92bc0583934bc0c1293f83ccf

In another terminal window start another SSH session but this time map the local port 8888 to port 8888 on the EC2:

ssh -L 8888:127.0.0.1:8888 -i ".ssh/key-pair.pem" ec2-user@ec2-54-201-219-212.compute-1.amazonaws.com

Now on your laptop you can attach to the remote Jupyter instance using (your token will be different):

http://localhost:8888/?token=f25c0cdf24b128c0c7ae2ce92bc0583934bc0c1293f83ccf

Alternative to using SSH tunnel:

A nice alternative is to install (on your laptop - no server side installation is required) and use sshuttle. Assuming I have a domain name attached to the sometimes running EC2, I use the following aliases in my bash configuration file:

alias gpu='ssh -i "~/.ssh/key-pair.pem" ec2-user@MYDOMAIN.COM'
alias tun="sshuttle -r ec2-user@MYDOMAIN.COM 0/0 -e 'ssh -i \".ssh/key-pair.pem\"'"

Note: Keeping an Elastic IP Address attached to a EC2 when the EC2 is usually not running will cost you about $3.40/month, but I find having a "permanent" IP address assigned to a domain name is convenient.

Goodies in the AWS machine learning AMI:

There are many examples installed for each of these frameworks. I usually use Keras and I was pleased to see the following examples ready to run:

addition_rnn.py

antirectifier.py

babi_memnn.py

babi_rnn.py

cifar10_cnn.py

conv_filter_visualization.py

conv_lstm.py

deep_dream.py

image_ocr.py

imdb_bidirectional_lstm.py

imdb_cnn.py

imdb_cnn_lstm.py

imdb_fasttext.py

imdb_lstm.py

lstm_benchmark.py

lstm_text_generation.py

mnist_acgan.py

mnist_cnn.py

mnist_hierarchical_rnn.py

mnist_irnn.py

mnist_mlp.py

mnist_net2net.py

mnist_siamese_graph.py

mnist_sklearn_wrapper.py

mnist_swwae.py

mnist_tfrecord.py

mnist_transfer_cnn.py

neural_doodle.py

neural_style_transfer.py

pretrained_word_embeddings.py

reuters_mlp.py

reuters_mlp_relu_vs_selu.py

stateful_lstm.py

variational_autoencoder.py

variational_autoencoder_deconv.py

There are many other examples for the other frameworks TensorFlow, MXNet, Caffe, Caffe2, PyTorch, Theano, CNTK, and Torch.