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Parallel-Image-Processing

Group Project for Distrubuted Systems and Cloud Computing.

Dependencies for Execution

  1. The models git repo for TensorFlow needs to be pulled into every worker and ps node in your system. It should be pulled into the same folder as the test_ps.py and test_worker.py files (located in server/googlenet and node_code repestively).
  2. Python 3.5.3.
  3. A virtual environment with TensorFlow, Paramiko, netifaces installed. See here for more details on installing and using virtualenv.

Instructions for Execution

  1. Run python default_discovery.py in the discovery folder if you are using your own LAN with Pis connected to deploy code to all the Pis on your network.
  2. Run python -m http.server in the client folder to start an HTTP server on the machine you intend to use an PS in tensorflow.
  3. Run python server.py in the server folder to start the Flask server that will send requests to TensorFlow
  4. Run python test_worker.py in /home/pi/cloud_computing/Parallel-Image-Processing/node_code/ on each of your Pis
  5. Load the webpage in client/index.html and upload your image, probabilities of what the image is should appear on the screen.