Path6.Mod2.c - Deploy and Consume Models - Invoke and Troubleshoot Batch Endpoints, Debug Pipelines

Question 1

Example code for invoking a Batch Endpoint for a Pipeline Job

Answer 1

A Pipeline Job that expects an Input instance parameter pointing to the dataset you want to score:

input = Input(type=AssetTypes.URI_FOLDER,  path="azureml:new-data:1")
job = ml_client.batch_endpoints.invoke(
    endpoint_name=endpoint.name,
    inputs={
        "input_dataset": input,
    }
)

Question 2

Example code for invoking a Batch Endpoint that serves a Model deployment

Answer 2

if your endpoint serves a Model deployment, use the short form that supports a single input:

input = Input(type=AssetTypes.URI_FOLDER,  path="azureml:new-data:1")
job = ml_client.batch_endpoints.invoke(
   endpoint_name=endpoint.name,
   input=input,
)

Question 3

Where to troubleshoot the Batch Pipeline Job

Answer 3

Since Batch endpoints generally run pipeline jobs, you’re going to have a bunch of Child Jobs plus your main pipeline Job, hence you’ll go to each individual Child Job details > Outputs + Logs tab, then the invidual log files, as well as the Outputs + Logs tab for the main job:

Question 4

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The three log files used for troubleshooting Batch Pipeline Jobs
The two functions that have their errors logged from your Scoring script.

Answer 4

• job_error.txt: Summarize errors from your script.
• job_progress_overview.txt: High-level information about the number of mini-batches processed so far.
• job_result.txt: Errors from calling the init() and run() function in the Scoring Script.

Question 5

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Where to find the code logs

Answer 5

Outputs + Logs > user_logs folder > std_log.txt

Question 6

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Where to find Azure ML general logs

Answer 6

Outputs + Logs > system_logs folder

Question 7

Where to view the status of your Job

Answer 7

Under Jobs and Experiments views (any Authoring pages won’t have Job status)