def _parse_submit_run_body(db_session: Session,
auth_info: mlrun.api.schemas.AuthInfo, data):
task = data.get("task")
function_dict = data.get("function")
function_url = data.get("functionUrl")
if not function_url and task:
function_url = get_in(task, "spec.function")
if not (function_dict or function_url) or not task:
log_and_raise(
HTTPStatus.BAD_REQUEST.value,
reason="bad JSON, need to include function/url and task objects",
)
# TODO: block exec for function["kind"] in ["", "local] (must be a
# remote/container runtime)
if function_dict and not function_url:
function = new_function(runtime=function_dict)
else:
if "://" in function_url:
function = import_function(url=function_url,
project=task.get("metadata",
{}).get("project"))
else:
project, name, tag, hash_key = parse_versioned_object_uri(
function_url)
function_record = get_db().get_function(db_session, name, project,
tag, hash_key)
if not function_record:
log_and_raise(
HTTPStatus.NOT_FOUND.value,
reason=f"runtime error: function {function_url} not found",
)
function = new_function(runtime=function_record)
if function_dict:
# The purpose of the function dict is to enable the user to override configurations of the existing function
# without modifying it - to do that we're creating a function object from the request function dict and
# assign values from it to the main function object
function = enrich_function_from_dict(function, function_dict)
# if auth given in request ensure the function pod will have these auth env vars set, otherwise the job won't
# be able to communicate with the api
ensure_function_has_auth_set(function, auth_info)
return function, task
def _generate_function_and_task_from_submit_run_body(
db_session: Session, auth_info: mlrun.api.schemas.AuthInfo, data):
function_dict, function_url, task = parse_submit_run_body(data)
# TODO: block exec for function["kind"] in ["", "local] (must be a
# remote/container runtime)
if function_dict and not function_url:
function = new_function(runtime=function_dict)
else:
if "://" in function_url:
function = import_function(url=function_url,
project=task.get("metadata",
{}).get("project"))
else:
project, name, tag, hash_key = parse_versioned_object_uri(
function_url)
function_record = get_db().get_function(db_session, name, project,
tag, hash_key)
if not function_record:
log_and_raise(
HTTPStatus.NOT_FOUND.value,
reason=f"runtime error: function {function_url} not found",
)
function = new_function(runtime=function_record)
if function_dict:
# The purpose of the function dict is to enable the user to override configurations of the existing function
# without modifying it - to do that we're creating a function object from the request function dict and
# assign values from it to the main function object
function = enrich_function_from_dict(function, function_dict)
# if auth given in request ensure the function pod will have these auth env vars set, otherwise the job won't
# be able to communicate with the api
ensure_function_has_auth_set(function, auth_info)
# if this was triggered by the UI, we will need to attempt auto-mount based on auto-mount config and params passed
# in the auth_info. If this was triggered by the SDK, then auto-mount was already attempted and will be skipped.
try_perform_auto_mount(function, auth_info)
# Validate function's service-account, based on allowed SAs for the project, if existing in a project-secret.
process_function_service_account(function)
return function, task