https://github.com/feifeibear/slurm-system-tensorflow-benchmark

https://github.com/feifeibear/slurm-system-tensorflow-benchmark

Last synced: 9 months ago
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• Host: GitHub
• URL: https://github.com/feifeibear/slurm-system-tensorflow-benchmark
• Owner: feifeibear
• License: apache-2.0
• Created: 2018-02-07T21:55:15.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-03-15T21:45:52.000Z (almost 8 years ago)
• Last Synced: 2025-01-23T00:41:15.719Z (11 months ago)
• Language: Python
• Size: 789 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Instructions for adding distributed benchmarks to continuous run:

1. You can add your benchmark file under

   [tensorflow/benchmarks/scripts](https://github.com/tensorflow/benchmarks/tree/master/scripts) directory. The benchmark should accept `task_index`, `job_name`, `ps_hosts` and `worker_hosts` flags. You can copy-paste the following flag definitions:

    ```python

    tf.app.flags.DEFINE_integer("task_index", None, "Task index, should be >= 0.")

    tf.app.flags.DEFINE_string("job_name", None, "job name: worker or ps")

    tf.app.flags.DEFINE_string("ps_hosts", None, "Comma-separated list of hostname:port pairs")

    tf.app.flags.DEFINE_string("worker_hosts", None, "Comma-separated list of hostname:port pairs")

    ```

2. Report benchmark values by calling `store_data_in_json` from your benchmark

   code. This function is defined in

   [benchmark\_util.py](https://github.com/tensorflow/benchmarks/blob/master/scripts/util/benchmark_util.py).

3. Create a Dockerfile that sets up dependencies and runs your benchmark. For

   example, see [Dockerfile.tf\_cnn\_benchmarks](https://github.com/tensorflow/benchmarks/blob/master/scripts/Dockerfile.tf_cnn_benchmarks).

4. Add the benchmark to

   [benchmark\_configs.yml](https://github.com/tensorflow/benchmarks/blob/master/scripts/benchmark_configs.yml)

   * Set `benchmark_name` to a descriptive name for your benchmark and make sure

     it is unique.

   * Set `worker_count` and `ps_count`.

   * Set `docker_file` to the Dockerfile path starting with `benchmarks/`

     directory.

   * Optionally, you can pass flags to your benchmark by adding `args` list.

5. Send PR with the changes to annarev.

Currently running benchmarks:

https://benchmarks-dot-tensorflow-testing.appspot.com/

For any questions, please contact annarev@google.com.