Use TensorFlow and convolutional neural networks with PowerAI to classify radio signals from outer space
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README.md

Search for extra terrestrial intelligence (SETI) with Tensorflow on PowerAI

Each night, using the Allen Telescope Array (ATA) in northern California, the SETI Institute scans the sky at various radio frequencies, observing star systems with known exoplanets, searching for faint but persistent signals. The current signal detection system is programmed to search only for particular kinds of signals: narrow-band carrier waves. However, the detection system sometimes triggers on signals that are not narrow-band signals (with unknown efficiency) and are also not explicitly-known radio frequency interference (RFI). There seems to be various categories of these kinds of events that have been observed in the past.

Our goal is to classify these accurately in real-time. This may allow the signal detection system to make better observational decisions, increase the efficiency of the nightly scans, and allow for explicit detection of these other signal types.

For more information refer to SETI hackathon page.

When you’ve completed this pattern, you will understand how to:

  • Convert signal data into image data
  • Build and train a convolutional neural network
  • Display and share results in Jupyter Notebooks

This pattern will assist application developers who need to efficiently build powerful deep learning applications and use GPUs to train the model quickly.

architecture

Flow

  1. The developer loads the provided notebooks to run on a PowerAI system on Nimbix Cloud.
  2. The SETI dataset demonstrates a use case of recognizing different classes of radio signals from outer space.
  3. The training notebook uses TensorFlow with convolutional neural networks to train a model and build a classifier.
  4. The prediction notebook demonstrates the accuracy of the classifier.

Steps

Follow these steps to setup and run this code pattern. The steps are described in detail below.

  1. Get 24-hours of free access to the PowerAI platform
  2. Access and start the Jupyter notebooks
  3. Run the notebooks
  4. Analyze the results
  5. Save and share
  6. End your trial

1. Get 24-hours of free access to the PowerAI platform

IBM has partnered with Nimbix to provide cognitive developers a trial account that provides 24-hours of free processing time on the PowerAI platform. Follow these steps to register for access to Nimbix to try the PowerAI code patterns and explore the platform.

  • Go here and follow the instructions to register for your free trial.

  • Use the welcome page (or confirmation email) to determine when your container is "ACTIVE" and collect the following information:

    • IP Address (might be fully qualified domain name)
    • User Id
    • Password
  • Take the IP Address (FQDN) and use your local browser to go to https://<IP Address>.

  • Login with the User Id and Password.

    welcome

2. Access and start the Jupyter notebooks

  • Get a new terminal window by clicking on the New pull-down and selecting Terminal.

    powerai-notebook-terminal

  • When using the free trial, a 4-hour timeout will cause you to lose data that is not in the /data directory. Create a patterns directory under /data and a symbolic link for that directory under /usr/local/samples/ as follows:

    mkdir /data/patterns
    ln -s /data/patterns /usr/local/samples/
    
  • Use git clone to download the example notebook, dataset, and retraining library into /data/patterns:

    cd /data/patterns
    git clone https://github.com/IBM/powerai-seti-signal-classification
    

    clone

  • Once done, you can exit the terminal and return to the notebook browser. Use the Files tab. From the root folder, click on patterns, powerai-seti-signal-classification, and then notebooks.

    powerai-notebook-open

  • If your container is paused (after 4 hours) and you resume it, your data will still be under /data. Recreate the symbolic link for it to show up in the Jupyter files tree.

    ln -s /data/patterns /usr/local/samples/
    

3. Run the notebooks

Introduction to running notebooks

When a notebook is executed, what is actually happening is that each code cell in the notebook is executed, in order, from top to bottom.

Each code cell is selectable and is preceded by a tag in the left margin. The tag format is In [x]:. Depending on the state of the notebook, the x can be:

  • A blank, this indicates that the cell has never been executed.
  • A number, this number represents the relative order this code step was executed.
  • A *, this indicates that the cell is currently executing.

There are several ways to execute the code cells in your notebook:

  • One cell at a time.
    • Select the cell, and then press the Play button in the toolbar.
  • Batch mode, in sequential order.
    • From the Cell menu bar, there are several options available. For example, you can Run All cells in your notebook, or you can Run All Below, that will start executing from the first cell under the currently selected cell, and then continue executing all cells that follow.

The SETI notebooks

To complete the code pattern, run the training and prediction notebooks in this order:

  1. seti_cnn_tf_training.ipynb
  2. seti_predition.ipynb
Preparing the dataset

First, we read the Basic 4 dataset, converted signals into images, and saved them in MNIST format. Now our signal classification problem has become an image classification problem. We split the MNIST data into train and test datasets and stored the results.

signal_as_image.png

The training and prediction notebooks use our stored results, so you can skip the data preparation step. You can review an example notebook from the data preparation step here.

Training the model

In the seti_cnn_tf_training.ipynb notebook, we use the famous SETI Dataset to build a convolutional neural network (CNN) able to perform signal classification. The CNN will determine, with some associated error, what type of signal is presented.

The notebook combines code with documentation to describe the steps and the training of the CNN model.

Run this notebook. The PowerAI free trial does not include GPUs, so training is slower. We'll use a lower number of epochs to finish faster, but this will hurt accuracy.

epochs.png loss_values.png

To create a production model for a problem of this size, you would want to run a higher number of epochs using PowerAI with one or more GPUs.

Prediction

In the seti_prediction.ipynb notebook, we will use the trained model to predict the signal class. Run this notebook after the training notebook has completed.

evaluation.png

Notice that the accuracy started at 25% (1 in 4) and improved considerably. Accuracy will continue to improve with more training time (more epochs). See below for some benchmarks that we ran with GPUs.

4. Analyze the results

Performance

Convolutional neural networks involve a lot of matrix and vector multiplications that can be parallelized. GPUs can improve performance, because GPUs were designed to handle these operations in parallel!

GPU vs. CPU

A single core CPU takes a matrix operation in serial, one element at a time, but a single GPU could have hundreds or thousands of cores, while a CPU typically has no more than a few cores.

How to use GPU with TensorFlow?

It is important to notice that if both CPU and GPU are available on the machine that you are running the notebook, and if a TensorFlow operation has both CPU and GPU implementations, the GPU devices will be given priority when the operation is assigned to a device.

In our case, as we are running this notebook on IBM PowerAI, you may have access to multiple GPUs, but let's use one of the GPUs in this notebook, for the sake of simplicity.

Note: If you are running the free trial, you would expect to have zero GPUs. This notebook will work, but the training will be slow.

Benchmarks

The accuracy will start at 25% (1 in 4 classes) and gradually improves with training. With the free trial service on Nimbix Cloud, we're seeing accuracy over 50% after 75 minutes with 50 epochs.

The performance improves with GPUs -- allowing us to improve accuracy considerably. We've captured some benchmarks after more epochs running with single and multiple GPUs.

PowerAI with Single GPU
CPU Architecture CPU cores  Memory  GPU  Step time (sec/batch)   Accuracy
POWER8 40 256 GB 1 x Tesla K80 ~0.127 ~72% at 75K steps (3 hours)
POWER8 32 128 GB 1 x Tesla P100 w/NVLink np8g4 ~0.035 ~72% at 75K steps (1 hour)
PowerAI with Multiple GPUs
CPU Architecture CPU cores  Memory  GPU  Step time (sec/batch)   Accuracy
POWER8 160 1 TB 4 x Tesla K80 ~0.066 ~72% at 75K steps (83 minutes)
POWER8 64 256 GB 2 x Tesla P100 w/NVLink np8g4 ~0.033 ~72% at 75K steps (40 minutes)
POWER8 128 512 GB 4 x Tesla P100 w/NVLink np8g4 ~0.017 ~72% at 75K steps (20 minutes)

5. Save and share

How to save your work

Because this notebook is running temporarily on a Nimbix Cloud server, use the following options to save your work:

Under the File menu, there are options to:

  • Download as... will download the notebook to your local system.
  • Print Preview will allow you to print the current state of the notebook.

6. End your trial

When you are done with your work, please cancel your subscription by visiting the Manage link on the My Products and Services page.

License

This code pattern is licensed under the Apache License, Version 2. Separate third-party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 and the Apache License, Version 2.

Apache License FAQ