def launch(
environment: str,
job: str,
trace: bool,
kill_on_sigterm: bool,
existing_runs: str,
as_run_submit: bool,
tags: List[str],
parameters: List[str],
parameters_raw: Optional[str],
):
dbx_echo(f"Launching job {job} on environment {environment}")
api_client = prepare_environment(environment)
additional_tags = parse_multiple(tags)
if parameters_raw:
prepared_parameters = parameters_raw
else:
override_parameters = parse_multiple(parameters)
prepared_parameters = sum([[k, v]
for k, v in override_parameters.items()],
[])
filter_string = generate_filter_string(environment)
run_info = _find_deployment_run(filter_string, additional_tags,
as_run_submit, environment)
deployment_run_id = run_info["run_id"]
with mlflow.start_run(run_id=deployment_run_id) as deployment_run:
with mlflow.start_run(nested=True):
artifact_base_uri = deployment_run.info.artifact_uri
if not as_run_submit:
run_launcher = RunNowLauncher(job, api_client,
artifact_base_uri, existing_runs,
prepared_parameters)
else:
run_launcher = RunSubmitLauncher(job, api_client,
artifact_base_uri,
existing_runs,
prepared_parameters,
environment)
run_data, job_id = run_launcher.launch()
jobs_service = JobsService(api_client)
run_info = jobs_service.get_run(run_data["run_id"])
run_url = run_info.get("run_page_url")
dbx_echo(f"Run URL: {run_url}")
if trace:
if kill_on_sigterm:
dbx_echo("Click Ctrl+C to stop the run")
try:
dbx_status = _trace_run(api_client, run_data)
except KeyboardInterrupt:
dbx_status = "CANCELLED"
dbx_echo("Cancelling the run gracefully")
_cancel_run(api_client, run_data)
dbx_echo("Run cancelled successfully")
else:
dbx_status = _trace_run(api_client, run_data)
if dbx_status == "ERROR":
raise Exception(
"Tracked run failed during execution. Please check Databricks UI for run logs"
)
dbx_echo("Launch command finished")
else:
dbx_status = "NOT_TRACKED"
dbx_echo(
"Run successfully launched in non-tracking mode. Please check Databricks UI for job status"
)
deployment_tags = {
"job_id": job_id,
"run_id": run_data.get("run_id"),
"dbx_action_type": "launch",
"dbx_status": dbx_status,
"dbx_environment": environment,
}
mlflow.set_tags(deployment_tags)
def _get_run_status(api_client: ApiClient,
run_data: Dict[str, Any]) -> Dict[str, Any]:
jobs_service = JobsService(api_client)
run_status = jobs_service.get_run(run_data["run_id"])
return run_status