def _create_jobs(jobs: List[Dict[str, Any]],
api_client: ApiClient) -> Dict[str, int]:
deployment_data = {}
for job in jobs:
dbx_echo(f'Processing deployment for job: {job["name"]}')
jobs_service = JobsService(api_client)
all_jobs = jobs_service.list_jobs().get("jobs", [])
matching_jobs = [
j for j in all_jobs if j["settings"]["name"] == job["name"]
]
if not matching_jobs:
job_id = _create_job(api_client, job)
else:
if len(matching_jobs) > 1:
raise Exception(
f"""There are more than one jobs with name {job["name"]}.
Please delete duplicated jobs first""")
job_id = matching_jobs[0]["job_id"]
_update_job(jobs_service, job_id, job)
deployment_data[job["name"]] = job_id
return deployment_data
def _update_job(jobs_service: JobsService, job_id: str, job: Dict[str,
Any]) -> str:
dbx_echo(
f'Updating existing job with id: {job_id} and name: {job["name"]}')
try:
jobs_service.reset_job(job_id, job)
except HTTPError as e:
dbx_echo("Failed to update job with definition:")
dbx_echo(json.dumps(job, indent=4))
raise e
return job_id
def launch(self) -> Tuple[Dict[Any, Any], Optional[str]]:
dbx_echo("Launching job via run now API")
jobs_service = JobsService(self.api_client)
all_jobs = jobs_service.list_jobs().get("jobs", [])
matching_jobs = [
j for j in all_jobs if j["settings"]["name"] == self.job
]
if not matching_jobs:
raise Exception(f"Job with name {self.job} not found")
if len(matching_jobs) > 1:
raise Exception(
f"Job with name {self.job} is duplicated. Please make job name unique."
)
job_data = matching_jobs[0]
job_id = job_data["job_id"]
active_runs = jobs_service.list_runs(job_id,
active_only=True).get("runs", [])
for run in active_runs:
if self.existing_runs == "pass":
dbx_echo("Passing the existing runs status check")
if self.existing_runs == "wait":
dbx_echo(
f'Waiting for job run with id {run["run_id"]} to be finished'
)
_wait_run(self.api_client, run)
if self.existing_runs == "cancel":
dbx_echo(f'Cancelling run with id {run["run_id"]}')
_cancel_run(self.api_client, run)
if self.prepared_parameters:
dbx_echo(
f"Default launch parameters are overridden with the following: {self.prepared_parameters}"
)
# we don't do a null-check here since the job existence will be already done during listing above.
job_settings = job_data.get("settings")
# here we define the job type to correctly pass parameters
extra_payload_key = _define_payload_key(job_settings)
extra_payload = {extra_payload_key: self.prepared_parameters}
run_data = jobs_service.run_now(job_id, **extra_payload)
else:
run_data = jobs_service.run_now(job_id)
return run_data, job_id
def launch(self) -> Tuple[Dict[Any, Any], Optional[str]]:
dbx_echo("Launching job via run now API")
jobs_service = JobsService(self.api_client)
all_jobs = jobs_service.list_jobs().get("jobs", [])
matching_jobs = [
j for j in all_jobs if j["settings"]["name"] == self.job
]
if not matching_jobs:
raise Exception(f"Job with name {self.job} not found")
if len(matching_jobs) > 1:
raise Exception(
f"Job with name {self.job} is duplicated. Please make job name unique."
)
job_id = matching_jobs[0]["job_id"]
active_runs = jobs_service.list_runs(job_id,
active_only=True).get("runs", [])
for run in active_runs:
if self.existing_runs == "pass":
dbx_echo("Passing the existing runs status check")
if self.existing_runs == "wait":
dbx_echo(
f'Waiting for job run with id {run["run_id"]} to be finished'
)
_wait_run(self.api_client, run)
if self.existing_runs == "cancel":
dbx_echo(f'Cancelling run with id {run["run_id"]}')
_cancel_run(self.api_client, run)
if self.override_parameters:
_prepared_parameters = sum(
[[k, v] for k, v in self.override_parameters.items()], [])
dbx_echo(
f"Default launch parameters are overridden with the following: {_prepared_parameters}"
)
run_data = jobs_service.run_now(job_id,
python_params=_prepared_parameters)
else:
run_data = jobs_service.run_now(job_id)
return run_data, job_id
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
def _cancel_run(api_client: ApiClient, run_data: Dict[str, Any]):
jobs_service = JobsService(api_client)
jobs_service.cancel_run(run_data["run_id"])
_wait_run(api_client, run_data)
def launch(
environment: str,
job: str,
trace: bool,
kill_on_sigterm: bool,
existing_runs: str,
tags: List[str],
parameters: List[str],
):
dbx_echo(f"Launching job {job} on environment {environment}")
api_client = prepare_environment(environment)
additional_tags = parse_multiple(tags)
override_parameters = parse_multiple(parameters)
filter_string = generate_filter_string(environment, additional_tags)
runs = mlflow.search_runs(filter_string=filter_string, max_results=1)
if runs.empty:
raise EnvironmentError(
f"""
No runs provided per given set of filters:
{filter_string}
Please check experiment UI to verify current status of deployments.
"""
)
run_info = runs.iloc[0].to_dict()
dbx_echo("Successfully found deployment per given job name")
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
deployments = _load_deployments(api_client, artifact_base_uri)
job_id = deployments.get(job)
if not job_id:
raise Exception(
f"Job with name {job} not found in the latest deployment" % job
)
jobs_service = JobsService(api_client)
active_runs = jobs_service.list_runs(job_id, active_only=True).get(
"runs", []
)
for run in active_runs:
if existing_runs == "pass":
dbx_echo("Passing the existing runs status check")
if existing_runs == "wait":
dbx_echo(
f'Waiting for job run with id {run["run_id"]} to be finished'
)
_wait_run(api_client, run)
if existing_runs == "cancel":
dbx_echo(f'Cancelling run with id {run["run_id"]}')
_cancel_run(api_client, run)
if override_parameters:
_prepared_parameters = sum(
[[k, v] for k, v in override_parameters.items()], []
)
dbx_echo(
f"Default launch parameters are overridden with the following: {_prepared_parameters}"
)
run_data = jobs_service.run_now(
job_id, python_params=_prepared_parameters
)
else:
run_data = jobs_service.run_now(job_id)
if trace:
dbx_echo("Tracing job run")
if kill_on_sigterm:
dbx_echo("Click Ctrl+C to stop the job 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 job failed during execution. "
"Please check Databricks UI for job logs"
)
dbx_echo("Launch command finished")
else:
dbx_status = "NOT_TRACKED"
dbx_echo(
"Job successfully launched in non-tracking mode. Please check Databricks UI for job status"
)
deployment_tags = {
"job_id": job_id,
"run_id": run_data["run_id"],
"dbx_action_type": "launch",
"dbx_status": dbx_status,
"dbx_environment": environment,
}
mlflow.set_tags(deployment_tags)