def main():
cluster_id = os.environ.get("DATABRICKS_CLUSTER_ID", None)
# If databricks_cluster_id is not None, but it's an empty string: its None
if cluster_id is not None and not cluster_id:
cluster_id = None
workspace_name = os.environ.get("AML_WORKSPACE_NAME", None)
resource_group = os.environ.get("RESOURCE_GROUP", None)
subscription_id = os.environ.get("SUBSCRIPTION_ID", None)
tenant_id = os.environ.get("TENANT_ID", None)
app_id = os.environ.get("SP_APP_ID", None)
app_secret = os.environ.get("SP_APP_SECRET", None)
experiment_subfolder = os.environ.get("EXPERIMENT_FOLDER",
'aml_service/experiment')
sources_directory = os.environ.get("SOURCES_DIR", None)
experiment_folder = os.path.join(sources_directory, experiment_subfolder)
train_script_path = os.environ.get("TRAIN_SCRIPT_PATH", None)
databricks_workspace_name = os.environ.get("DATABRICKS_WORKSPACE_NAME",
None)
databricks_access_token = os.environ.get("DATABRICKS_ACCESS_TOKEN", None)
databricks_compute_name_aml = os.environ.get("DATABRICKS_COMPUTE_NAME_AML",
None)
model_dir = os.environ.get("MODEL_DIR", 'dbfs:/model')
model_name = os.environ.get("MODEL_NAME", 'torchcnn')
path_components = model_dir.split("/", 1)
model_path = "/dbfs/" + path_components[1] + "/" + model_name + ".pth"
print("The model path will be %s" % (model_path))
aml_workspace = get_workspace(workspace_name, resource_group,
subscription_id, tenant_id, app_id,
app_secret)
print(aml_workspace)
databricks_compute = get_compute(aml_workspace,
databricks_compute_name_aml,
resource_group, databricks_workspace_name,
databricks_access_token)
print(databricks_compute)
step1 = DatabricksStep(name="DBPythonInLocalMachine",
num_workers=1,
python_script_name=train_script_path,
source_directory=sources_directory,
run_name='DB_Python_Local_demo',
existing_cluster_id=cluster_id,
compute_target=databricks_compute,
allow_reuse=False,
python_script_params=['--MODEL_PATH', model_path])
step2 = DatabricksStep(name="RegisterModel",
num_workers=1,
python_script_name="register_model.py",
source_directory=experiment_folder,
run_name='Register_model',
existing_cluster_id=cluster_id,
compute_target=databricks_compute,
allow_reuse=False,
python_script_params=[
'--MODEL_PATH', model_path, '--TENANT_ID',
tenant_id, '--APP_ID', app_id, '--APP_SECRET',
app_secret, '--MODEL_NAME', model_name
])
step2.run_after(step1)
print("Step lists created")
pipeline = Pipeline(
workspace=aml_workspace,
# steps=[step1])
steps=[step1, step2])
print("Pipeline is built")
pipeline.validate()
print("Pipeline validation complete")
pipeline_run = pipeline.submit(experiment_name="pipetest")
print("Pipeline is submitted for execution")
pipeline_details = pipeline_run.get_details()
pipeline_run_id = pipeline_details['runId']
azure_run_url = get_experiment_run_url(subscription_id, resource_group,
workspace_name, pipeline_run_id)
print("To check details of the Pipeline run, go to " + azure_run_url)
pipeline_status = pipeline_run.get_status()
timer_mod = 0
while pipeline_status == 'Running' or pipeline_status == 'NotStarted':
timer_mod = timer_mod + 10
time.sleep(10)
if (timer_mod % 30) == 0:
print("Status: %s. %s seconds have passed." %
(pipeline_status, timer_mod))
pipeline_status = pipeline_run.get_status()
if pipeline_status == 'Failed':
print("AML Pipelne failed. Check %s for details." % (azure_run_url))
sys.exit(1)
else:
print(pipeline_status)
print("Pipeline completed")
def main(cluster_id, workspace_name, resource_group, subscription_id,
tenant_id, app_id, app_secret, experiment_folder, project_folder,
train_script_path, databricks_workspace, databricks_access_token,
databricks_compute_name, mdl_dir, mdl_name):
mdl_file_name = "%s.pth" % (mdl_name)
mdl_path = os.path.join(mdl_dir, mdl_file_name)
print("The model path will be %s" % (mdl_path))
ws = get_workspace(workspace_name, resource_group, subscription_id,
tenant_id, app_id, app_secret)
print(ws)
databricks_compute = get_compute(ws, databricks_compute_name,
resource_group, databricks_workspace,
databricks_access_token)
print(databricks_compute)
step1 = DatabricksStep(name="DBPythonInLocalMachine",
num_workers=1,
python_script_name=train_script_path,
source_directory=project_folder,
run_name='DB_Python_Local_demo',
existing_cluster_id=cluster_id,
compute_target=databricks_compute,
allow_reuse=False,
python_script_params=['--MODEL_PATH', mdl_path])
step2 = DatabricksStep(name="RegisterModel",
num_workers=1,
python_script_name="Register.py",
source_directory=experiment_folder,
run_name='Register_model',
existing_cluster_id=cluster_id,
compute_target=databricks_compute,
allow_reuse=False,
python_script_params=[
'--MODEL_PATH', mdl_path, '--TENANT_ID',
tenant_id, '--APP_ID', app_id, '--APP_SECRET',
app_secret, '--MODEL_NAME', mdl_name
])
step2.run_after(step1)
print("Step lists created")
pipeline = Pipeline(
workspace=ws,
# steps=[step1])
steps=[step1, step2])
print("Pipeline is built")
pipeline.validate()
print("Pipeline validation complete")
pipeline_run = pipeline.submit(experiment_name="pipetest")
print("Pipeline is submitted for execution")
pipeline_details = pipeline_run.get_details()
pipeline_run_id = pipeline_details['runId']
azure_run_url = get_experiment_run_url(subscription_id, resource_group,
workspace_name, pipeline_run_id)
print("To check details of the Pipeline run, go to " + azure_run_url)
pipeline_status = pipeline_run.get_status()
timer_mod = 0
while (pipeline_status == 'Running'):
timer_mod = timer_mod + 10
time.sleep(10)
if ((timer_mod % 30) == 0):
print("Still running. %s seconds have passed." % (timer_mod))
pipeline_status = pipeline_run.get_status()
if pipeline_status == 'Failed':
sys.exit("AML Pipeline failed")
else:
print(pipeline_status)
print("Pipeline completed")
