def run_databricks(uri, entry_point, version, parameters, experiment_id,
cluster_spec, git_username, git_password):
"""
Runs the project at the specified URI on Databricks, returning a `SubmittedRun` that can be
used to query the run's status or wait for the resulting Databricks Job run to terminate.
"""
_check_databricks_auth_available()
if cluster_spec is None:
raise ExecutionException(
"Cluster spec must be provided when launching MLflow project runs "
"on Databricks.")
# Fetch the project into work_dir & validate parameters
work_dir = _fetch_project(uri=uri,
use_temp_cwd=True,
version=version,
git_username=git_username,
git_password=git_password)
project = _load_project(work_dir)
project.get_entry_point(entry_point)._validate_parameters(parameters)
# Upload the project to DBFS, get the URI of the project
dbfs_project_uri = _upload_project_to_dbfs(work_dir, experiment_id)
# Create run object with remote tracking server. Get the git commit from the working directory,
# etc.
tracking_uri = tracking.get_tracking_uri()
remote_run = _create_databricks_run(
tracking_uri=tracking_uri,
experiment_id=experiment_id,
source_name=_expand_uri(uri),
source_version=tracking._get_git_commit(work_dir),
entry_point_name=entry_point)
# Set up environment variables for remote execution
env_vars = {}
if experiment_id is not None:
eprint("=== Using experiment ID %s ===" % experiment_id)
env_vars[tracking._EXPERIMENT_ID_ENV_VAR] = experiment_id
if remote_run is not None:
env_vars[tracking._TRACKING_URI_ENV_VAR] = tracking.get_tracking_uri()
env_vars[tracking._RUN_ID_ENV_VAR] = remote_run.run_info.run_uuid
eprint("=== Running entry point %s of project %s on Databricks. ===" %
(entry_point, uri))
# Launch run on Databricks
with open(cluster_spec, 'r') as handle:
try:
cluster_spec = json.load(handle)
except ValueError:
eprint(
"Error when attempting to load and parse JSON cluster spec from file "
"%s. " % cluster_spec)
raise
fuse_dst_dir = os.path.join(
"/dbfs/",
_parse_dbfs_uri_path(dbfs_project_uri).lstrip("/"))
final_run_id = remote_run.run_info.run_uuid if remote_run else None
command = _get_databricks_run_cmd(fuse_dst_dir, final_run_id, entry_point,
parameters)
db_run_id = _run_shell_command_job(uri, command, env_vars, cluster_spec)
run_id = remote_run.run_info.run_uuid if remote_run else None
return DatabricksSubmittedRun(db_run_id, run_id)
def run_databricks(uri, entry_point, version, parameters, experiment_id, cluster_spec,
git_username, git_password):
"""
Runs the project at the specified URI on Databricks, returning a `SubmittedRun` that can be
used to query the run's status or wait for the resulting Databricks Job run to terminate.
"""
tracking_uri = tracking.get_tracking_uri()
_before_run_validations(tracking_uri, cluster_spec)
work_dir = _fetch_and_clean_project(
uri=uri, version=version, git_username=git_username, git_password=git_password)
project = _load_project(work_dir)
project.get_entry_point(entry_point)._validate_parameters(parameters)
dbfs_project_uri = _upload_project_to_dbfs(work_dir, experiment_id)
remote_run = tracking._create_run(
experiment_id=experiment_id, source_name=_expand_uri(uri),
source_version=tracking._get_git_commit(work_dir), entry_point_name=entry_point,
source_type=SourceType.PROJECT)
env_vars = {
tracking._TRACKING_URI_ENV_VAR: tracking_uri,
tracking._EXPERIMENT_ID_ENV_VAR: experiment_id,
}
run_id = remote_run.run_info.run_uuid
eprint("=== Running entry point %s of project %s on Databricks. ===" % (entry_point, uri))
# Launch run on Databricks
with open(cluster_spec, 'r') as handle:
try:
cluster_spec = json.load(handle)
except ValueError:
eprint("Error when attempting to load and parse JSON cluster spec from file "
"%s. " % cluster_spec)
raise
fuse_dst_dir = os.path.join("/dbfs/", _parse_dbfs_uri_path(dbfs_project_uri).lstrip("/"))
command = _get_databricks_run_cmd(fuse_dst_dir, run_id, entry_point, parameters)
db_run_id = _run_shell_command_job(uri, command, env_vars, cluster_spec)
return DatabricksSubmittedRun(db_run_id, run_id)