Batch component added for deployment and inference

assets/common/components/batch_deploy_model/spec.yaml

@@ -33,7 +33,7 @@ command: >-
  $[[--logging_level ${{inputs.logging_level}}]]
  $[[--mini_batch_size ${{inputs.mini_batch_size}}]]
  $[[--instance_count ${{inputs.instance_count}}]]
- --model_deployment_details ${{outputs.model_deployment_details}}
+ --batch_job_output_folder ${{outputs.batch_job_output_folder}}
 
 inputs:
  # Output of registering component
@@ -188,9 +188,9 @@ inputs:
  description: The number of nodes to use for each batch scoring job.
 
 outputs:
- model_deployment_details:
- type: uri_file
- description: Json file to which deployment details will be written
+ batch_job_output_folder:
+ type: uri_folder
+ description: Folder to which batch job outputs will be saved.
 
 tags:
  Preview: ""

assets/common/src/batch_deploy.py

@@ -163,9 +163,9 @@ def parse_args():
  choices=range(1, MAX_INSTANCE_COUNT),
  )
  parser.add_argument(
- "--model_deployment_details",
- type=str,
- help="Json file to which deployment details will be written",
+ "--batch_job_output_folder",
+ type=Path,
+ help="Folder to which batch job outputs will be saved",
  )
  # parse args
  args = parser.parse_args()
@@ -194,18 +194,18 @@ def invoke_endpoint_job(ml_client, endpoint, type, args):
  scoring_job = list(ml_client.jobs.list(parent_job_name=job.name))[0]
 
  ml_client.jobs.download(
- name=scoring_job.name, download_path=".", output_name="score"
+ name=scoring_job.name, download_path=args.batch_job_output_folder, output_name="score"
  )
 
- logger.info(f"Endpoint invoked successfully with {type} test payload.")
+ logger.info(f"Endpoint invoked successfully with {type} test payload. Output stored in {args.output_file_name} file.")
  except Exception as e:
  raise AzureMLException._with_error(
  AzureMLError.create(BatchEndpointInvocationError, exception=e)
  )
 
 
 def get_or_create_compute(ml_client, compute_name, args):
- """Get or create the compute cluster."""
+ """Get or create the compute cluster and return details."""
  try:
  compute_cluster = ml_client.compute.get(compute_name)
  logger.info(f"Using compute cluster {compute_name}.")
@@ -372,7 +372,8 @@ def main():
  "max_concurrency_per_instance": deployment.max_concurrency_per_instance,
  }
  json_object = json.dumps(deployment_details, indent=4)
- with open(args.model_deployment_details, "w") as outfile:
+ file_path = str(args.batch_job_output_folder) + "/model_deployment_details.json"
+ with open(file_path, "w") as outfile:
  outfile.write(json_object)
  logger.info("Saved deployment details in output json file.")