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llama.LLM.check_job_status

Check the status of a job

status = LLM.check_job_status(job_id)

Parameters

  • job_id: str - unique job id

Returns

status: dict - a dictionary with status information

Scheduled jobs will have the following returned

{'status': 'SCHEDULED'}

Just starting jobs will have the following format returned

{
    'status': 'RUNNING',
    'progress': 'Starting Run.',
    'starttime': 1680724301.7032173
}

While jobs that have made some progress will have the following format returned

{
    'status': 'RUNNING',
    'progress': 'Progress: 1 iterations out of 2.',
    'starttime': 1680724301.7032173,
    'time_elapsed': '8.602318525314331',
    'average_runtime': '8.602318525314331',
    'estimated_total_time': '16.602318525314331',
    'estimated_time_remaining': '8.602318525314331'
}

Completed jobs will have the following format returned

{
    'status': 'DONE',
    'progress': 'Progress: 3 iterations out of 3.',
    'starttime': 1680724434.2409794,
    'time_elapsed': '20.019465446472168',
    'average_runtime': '6.673155148824056',
    'estimated_total_time': '20.019465446472168',
    'estimated_time_remaining': '0.0'
}

Statuses

Possible statuses include

'NOT_SCHEDULED'
'SCHEDULED'
'RUNNING'
'DONE'
'ERRORED'
'CANCELED'

Running Information

  • progress - A description of the progress made in terms of iterations. Each iteration represents an equal subset of the data.
  • starttime - Job starttime, unixtime in seconds
  • time_elapsed - Amount of time elapsed since the start time
  • average_runtime - Average runtime per iteration in seconds thus far
  • estimated_total_time - Estimated total runtime based on average runtime in seconds
  • estimated_time_remaining - Estimated total time remaining based on average runtime in seconds