job.py

# description: train tensorflow CNN model on mnist data distributed via horovod

# For more information on using Horovod with TensorFlow, refer to Horovod documentation:
#
# * [Horovod with TensorFlow](https://github.com/horovod/horovod/blob/master/docs/tensorflow.rst)
# * [Horovod with Keras](https://github.com/horovod/horovod/blob/master/docs/keras.rst)

# imports
import os

from pathlib import Path
from azureml.core import Workspace
from azureml.core import ScriptRunConfig, Experiment, Environment
from azureml.core.runconfig import MpiConfiguration

# get workspace
ws = Workspace.from_config()

# get root of git repo
prefix = Path(__file__).parent

# training script
source_dir = str(prefix.joinpath("src"))
script_name = "train.py"

# environment file
environment_file = str(prefix.joinpath("environment.yml"))

# azure ml settings
environment_name = "tf-gpu-horovod-example"
experiment_name = "tf-mnist-distributed-horovod-example"
compute_name = "gpu-K80-2"

# create environment
env = Environment.from_conda_specification(environment_name, environment_file)

# specify a GPU base image
env.docker.enabled = True
env.docker.base_image = (
    "mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04"
)

# Create a `ScriptRunConfig` to specify the training script & arguments, environment, and cluster to run on.
#
# Create an `MpiConfiguration` to run an MPI/Horovod job.
# Specify a `process_count_per_node` equal to the number of GPUs available per node of your cluster.

# create distributed config
distr_config = MpiConfiguration(process_count_per_node=2, node_count=2)

# create arguments
args = ["--epochs", 30]

# create job config
src = ScriptRunConfig(
    source_directory=source_dir,
    script=script_name,
    arguments=args,
    compute_target=compute_name,
    environment=env,
    distributed_job_config=distr_config,
)

# submit job
run = Experiment(ws, experiment_name).submit(src)
run.wait_for_completion(show_output=True)