These pages document the python code for the SpiNNakerGraphFrontEnd module which is part of the SpiNNaker Project.

This code depends on SpiNNUtils, SpiNNMachine, SpiNNMan, PACMAN, DataSpecification, SpiNNFrontEndCommon (Combined_documentation).

SpiNNakerGraphFrontEnd

The API for running SpiNNaker simulations based on a basic (non-neural) graph.

The general usage pattern for this API something like is:

import spinnaker_graph_front_end as gfe

# Uses information from your configuration file
# You might need to specify how many SpiNNaker boards to allocate
gfe.setup()

# Make the bits that do the computation
for each vertex to add:
    gfe.add_machine_vertex_instance(vertex)

# Connect them together so computations are coordinated
for each edge to add:
    gfe.add_machine_edge_instance(edge)

# Actually plan and run the simulation
gfe.run(number_of_steps)

# Get the results back; what this means can be complex
for each vertex:
    results += vertex.retrieve_relevant_results()

# Shut everything down
# Only your retrieved results really exist after this
gfe.stop()

# Analyse/render the results; totally application-specific!

It is possible to use GFE-style vertices in a neural graph (e.g., to simulate the external world). Talk to the SpiNNaker team for more details.

Contents:

Indices and tables