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Bitfusion Ubuntu 14 Theano AMI Readme

Getting started - Launch the AMI

Subscribe to and launch the AMI from here:

Launch the AMI

EC2 Instance Access

To get started, launch an AWS instances using this AMI from the EC2 Console. If you are not familiar with this process please review the AWS documentation provided here:

http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/launching-instance.html

Accessing the instance via SSH:

ssh -i <path to your pem file> ubuntu@{ EC2 Instance Public IP }

Jupyter Notebook - http://{ EC2 Instance Public IP }:8888

Logging In

You can login to the notebook at:

  • http://{EC2 Instance Public IP}:8888
  • The login PASSWORD is set to the Instance ID.

You can get the Instance ID (Jupyter Notebook Password) from the EC2 console by clicking on the running instance, or if you are logged in via ssh you can obtain it by executing the following command:

  ec2metadata --instance-id

Updating the HASHED Jupyter Login Password:

It is highly recommended that you change the Jupyter login password.

When logged in via ssh you can update the hashed password using the function notebook.auth.security.passwd():

  ipython
  In [1]: from notebook.auth import passwd
  In [2]: passwd()
  Enter password:
  Verify password:
  Out[2]: 'sha1:67c9e60bb8b6:9ffede0825894254b2e042ea597d771089e11aed'
  exit()

You can then add the hashed password to your Jupyter config file, the default location for this file is ~/.jupyter/jupyter_notebook_config.py

Example:

  c.NotebookApp.password = u'sha1:67c9e60bb8b6:9ffede0825894254b2e042ea597d771089e11aed'

To have the new password take effect restart the Jupyter:

  sudo service ipython-notebook restart

Notebook Location

The default notebook directory is /home/ubuntu/pynb. This directory is required for Jupyter to function. If it is deleted you will need recreate it and ensure it is owned by the ubuntu user.

Theano

From the Theano Documentation:

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

  • tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
  • transparent use of a GPU – Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)
  • efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs.
  • speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
  • dynamic C code generation – Evaluate expressions faster.
  • extensive unit-testing and self-verification – Detect and diagnose many types of errors.

Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (University of Montreal’s deep learning/machine learning classes).

Theano - Tutorials & References

Theano - Minimal Examples

Example 1

The example is taken from http://www.marekrei.com/blog/theano-tutorial/

  import theano
  import numpy

  x = theano.tensor.fvector('x')
  W = theano.shared(numpy.asarray([0.2, 0.7]), 'W')
  y = (x * W).sum()

  f = theano.function([x], y)

  output = f([1.0, 1.0])
  print output
Example 2

This example is taken from: http://deeplearning.net/software/theano/tutorial/adding.html

  import numpy
  import theano.tensor as T
  x = T.dscalar('x')
  y = T.dscalar('y')
  z = x + y
  numpy.allclose(z.eval({x : 16.3, y : 12.1}), 28.4)

Theano - Verifying GPU Usage

If you are an AWS G2 EC2 instance we have included a test script to confirm that Theano is using the GPU. The script comes from the Theano documentation:

http://deeplearning.net/software/theano/tutorial/using_gpu.html

To verify GPU Usage:

   python2 theano_gpu_check.py

Keras

This AMI has Keras set to use Theano as it's backend. Keras is compatible with Python 2.7 to 3.5.

Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

  • allows for easy and fast prototyping (through total modularity, minimalism, and extensibility).
  • supports both convolutional networks and recurrent networks, as well as combinations of the two.
  • supports arbitrary connectivity schemes (including multi-input and multi-output training).
  • runs seamlessly on CPU and GPU.

You can read the documentation at http://keras.io/

Keras - Getting Started:

Getting started: 30 seconds to Keras:

http://keras.io/#getting-started-30-seconds-to-keras

Supported AWS Instances

t2.medium	t2.large
m3.medium	m3.large	m3.xlarge	m3.2xlarge
m4.large	m4.xlarge	m4.2xlarge	m4.4xlarge	m4.10xlarge
c3.large	c3.xlarge	c3.2xlarge	c3.4xlarge	c3.8xlarge
c4.large	c4.xlarge	c4.2xlarge	c4.4xlarge	c4.8xlarge
r3.large	r3.xlarge	r3.2xlarge	r3.4xlarge	r3.8xlarge
i2.xlarge	i2.2xlarge	i2.4xlarge	i2.8xlarge
d2.xlarge	d2.2xlarge	d2.4xlarge	d2.8xlarge
g2.2xlarge	g2.8xlarge
x1.32xlarge

Version History

v0.01

  • Nvidia Driver Version 352
  • Cuda Toolkit Version 7.5
  • Nvidia cuDNN Version 5
  • theano Version 0.82
  • bfboost 0.1.0+1532

Support

Please send all comments and support request to support@bitfusion.io