Subscribe to and launch the AMI from here:
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 }
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
It is highly recommended that you change the Jupyter login password.
When logged in via ssh you can update the hashed password using one of the following functions:
- for iPython 5
IPython.lib.passwd - for any earlier release of iPython
notebook.auth.security.passwd():
iPython 5 example:
ipython
In [1]: from IPython.lib import passwd
In [2]: passwd()
Enter password:
Verify password:
Out[2]: 'sha1:67c9e60bb8b6:9ffede0825894254b2e042ea597d771089e11aed'
exit()
iPython 4 and earlier example:
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 outputed hashed password, which should look similar to sha1:67c9e60bb8b6:9ffede0825894254b2e042ea597d771089e11aed
,to your Jupyter config file. The default location for this file is ~/.jupyter/jupyter_notebook_config.py. If you scroll
to the bottom of the file you will see the configuration entry that needs to be updated:
Example:
c.NotebookApp.password = u'sha1:67c9e60bb8b6:9ffede0825894254b2e042ea597d771089e11aed'
Place your update string in place the old one and restart Jupyter to have the password take effect.
sudo service jupyter restart
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.
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).
- Official: http://deeplearning.net/software/theano/tutorial/index.html
- http://www.marekrei.com/blog/theano-tutorial/
- http://ir.hit.edu.cn/~jguo/docs/notes/a_simple_tutorial_on_theano.pdf
- http://outlace.com/Beginner-Tutorial-Theano/
CUDNN Warning: You will see a warning regarding cuDNN - this should be harmless, we have tested with the examples and in house models and have not run into any issues. If you do see any issues please contact us at support@bitfusion.io
/usr/local/lib/python2.7/dist-packages/theano/sandbox/cuda/__init__.py:600:
UserWarning: Your cuDNN version is more recent than the one Theano officially supports.
If you see any problems, try updating Theano or downgrading cuDNN to version 5.
warnings.warn(warn)
When you run this AMI on a non-GPU instance you may see the following warning when running Theano. This warning can be safely ignored, it simply indicates that no GPU devices were found on the system:
WARNING (theano.sandbox.cuda): CUDA is installed, but device gpu is not available
(error: Unable to get the number of gpus available: no CUDA-capable device is detected)
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
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)
If you are using an AWS GPU 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
Lasagne is a lightweight wrapper built around Theano for building and training neural networks. This AMI has the latest development version (As of the publish date of the AMI)
cd /home/ubuntu/lasagne-example
python lasagne-mnist-example.py
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/
Getting started: 30 seconds to Keras:
http://keras.io/#getting-started-30-seconds-to-keras
We installed the Keras examples in the ubuntu user folder:
~/keras-examples
You can execute these examples directly from the command line:
python2 mnist_cnn.py
t2.nano t2.micro t2.medium t2.large
m3.medium m3.large m3.xlarge m3.2xlarge
m4.large m4.xlarge m4.2xlarge m4.4xlarge m4.10xlarge m4.16xlarge
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
p2.xlarge p2.8xlarge p2.16xlarge
x1.16xlarge x1.32xlarge
v2016.03
- Set image_dim_ordering to "th"
v2016.02
- Updated CuDNN to 5.1
- Updated to ipython 5
- Updated to nvidia driver 352.99 (p2 instance support)
- Updated to latest version Bitfusion Boost 0.1.0+1562
- Updated to Keras 1.1.0
- Added Python 3 Theano 0.8.2 module
- Added Lasagne 0.2.dev1 (Python 2 and Python 3)
- Added Scikit-learn 0.18 (Python 2 and Python 3)
- Added Enum34 (Python 2 and Python 3)
- Added pycuda 2016.1.2 (Python 2 and Python 3)
- Added Pandas 0.19.0 (Python 2 and Python 3)
- Added Python 3 kernel to Jupyter
- Added support to compile pycuda kernels for Jupyter
- Jupyter start script is now called jupyter (sudo service jupyter restart)
v0.01
- Keras 1.0.5
- Nvidia Driver Version 352
- Cuda Toolkit Version 7.5
- Nvidia cuDNN Version 5
- theano Version 0.82
- bfboost 0.1.0+1532
Please send all comments and support request to support@bitfusion.io