ctn_benchmarks

Benchmarks and benchmarking software for Nengo

This repository collects together benchmarks and tools for creating benchmarks, all of which are meant for evaluating Nengo models.

Code organization

• ctn_benchmark/*: basic classes
• ctn_benchmark/nengo: a set of test models meant to cover a variety of neural models
• ctn_benchmark/control: benchmarks for adaptive control of embodied systems

Basic usage

All benchmarks are meant to be usable in three different ways:

• From the command line
• From within nengo_gui
• From a separate script

For example, for the Lorenz attractor model, you can run it from the command line as python ctn_benchmark/nengo/lorenz.py. When run in this way, you can use command line arguments to adjust the behaviour of the benchmark. Some of these parameters adjust features of how the model is run, such as whether to display a figure plotting model data after the run (--show_figs) or what nengo backend to use (--backend nengo_ocl or --backend nengo_spinnaker, for example). These parameters are available for all benchmarks using this system. Other parameters are specific to the individual model being run. For example, in the Lorenz model you can set any of the Lorenz variables (sigma, beta, and rho), or the number of neurons (N). For a full list of all parameters, do --help.

To use a benchmark with nengo_gui, create an instance of the class and call make_model() on it. For the Lorenz example, this would be ctn_benchmark.nengo.Lorenz().make_model(). To specify parameters, pass them into the make_model() call. For example, ctn_benchmark.nengo.Lorenz().make_model(N=1000).

Finally, you can run the benchmark using other scripts. To do this, call ctn_benchmark.nengo.Lorenz().run(). This will construct the model, run it, and perform any data analysis defined in the benchmark. You can also pass parameters in to the run() function.

Data analysis

Each time the benchmark is run, data will be stored in the data directory (default: data). Each run is stored in a separate file, with a file name based on the time it was run. These data files store the processed results in text format.

The Data class can be used to read values from these data files. For example, ctn_benchmark.Data(path='data').get('rmse') will return the RMSE value for all the benchmark runs in the data directory.