Instructions for adding distributed benchmarks to continuous run:

1. You can add your benchmark file under tensorflow/benchmarks/models directory. The benchmark should accept task_id, job_name, ps_hosts and worker_hosts flags. You can copy-paste the following flag definitions:

   tf.app.flags.DEFINE_integer("task_id", 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

3. Create a Dockerfile that sets up dependencies and runs your benchmark. For example, see Dockerfile.alexnet_distributed_test

4. Add the benchmark to 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.

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