Raw Benchmark Data for Popular Machine Learning Frameworks
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Deep Learning Benchmarks


This repository contains structured performance characteristics of the most popular Machine and Deep Learning Frameworks forward and backward performance for entire models, single layers and more. Deep Learning Benchmarks is inspired by soumith's convnet-benchmarks and zer0n's deepframeworks.

Deep Learning Benchmarks goal is to provide raw, structured, comparable data about the performance (operational speed, operational memory usage) of different Machine Learning Frameworks in various environments and machines. Like the other two repositories, Deep Learning Benchmarks highest goal is gaining objective, reproducible benchmarks, which are reviewed by the community. New benchmark data is introduced through PRs, its process is described in Submitting Benchmark Data.

View Data

One advantage of structured, raw benchmark data is, that it can be easily queried, visualized and compared. [Autumn's Benchmark page][autumn-benchmark-page], provides an open interface for querying, visualizing and later sharing the Deep Learning Benchmark data.

Raw Data

The repository contains this README and benchmark.toml, where all the benchmark data is stored.

You can consume the data like a JSON REST-API, by fetching

  .then(function(response) {
    return response.json()
  }).then(function(json) {
    console.log('parsed json', json)
  }).catch(function(ex) {
    console.log('parsing failed', ex)

We use TOML for the original benchmark data file format (instead of JSON), because it is more convenient for merging data. Out of convenience we convert the TOML into a JSON file locally as well before we merge a PR, this increases the usability of the data for client-side applications.

Data Structure


Submitting Benchmark Data