def register_model(model_name, run_id):
"""Register the model to the AML Workspace"""
cli_auth = AzureCliAuthentication()
experiment = Experiment.from_directory(".", auth=cli_auth)
run = Run(experiment, run_id)
run.register_model(model_name,
model_path='outputs/final_model.hdf5',
model_framework='TfKeras',
model_framework_version='1.13')
def main(epochs, iterations, compute_target, concurrent_runs):
cli_auth = AzureCliAuthentication()
experiment = Experiment.from_directory(".", auth=cli_auth)
ws = experiment.workspace
cluster = ws.compute_targets[compute_target]
food_data = ws.datastores['food_images']
script_arguments = {"--data-dir": food_data.as_mount(), "--epochs": epochs}
tf_est = TensorFlow(source_directory=".",
entry_script='code/train/train.py',
script_params=script_arguments,
compute_target=cluster,
conda_packages=['pillow', 'pandas'],
pip_packages=['click', 'seaborn'],
use_docker=True,
use_gpu=True,
framework_version='1.13')
# Run on subset of food categories
tf_est.run_config.arguments.extend(
['apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio'])
param_sampler = RandomParameterSampling({
'--minibatch-size':
choice(16, 32, 64),
'--learning-rate':
loguniform(-9, -6),
'--optimizer':
choice('rmsprop', 'adagrad', 'adam')
})
# Create Early Termination Policy
etpolicy = BanditPolicy(evaluation_interval=2, slack_factor=0.1)
# Create HyperDrive Run Configuration
hyper_drive_config = HyperDriveConfig(
estimator=tf_est,
hyperparameter_sampling=param_sampler,
policy=etpolicy,
primary_metric_name='acc',
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,
max_total_runs=iterations,
max_concurrent_runs=concurrent_runs)
# Submit the Hyperdrive Run
print("Submitting Hyperdrive Run")
hd_run = experiment.submit(hyper_drive_config)
hd_run.wait_for_completion(raise_on_error=True, show_output=True)
print("Finishing Run")
best_run = hd_run.get_best_run_by_primary_metric()
print(f'##vso[task.setvariable variable=run_id]{best_run.id}')
