def initialize(self):
logger.info("Initializing projects follower")
self._projects: typing.Dict[str, mlrun.api.schemas.Project] = {}
self._leader_name = mlrun.config.config.httpdb.projects.leader
self._sync_session = None
if self._leader_name == "iguazio":
self._leader_client = mlrun.api.utils.clients.iguazio.Client()
if not mlrun.config.config.httpdb.projects.iguazio_access_key:
raise mlrun.errors.MLRunInvalidArgumentError(
"Iguazio access key must be configured when the leader is Iguazio"
)
self._sync_session = mlrun.config.config.httpdb.projects.iguazio_access_key
elif self._leader_name == "nop":
self._leader_client = mlrun.api.utils.projects.remotes.nop_leader.Member()
else:
raise NotImplementedError("Unsupported project leader")
self._periodic_sync_interval_seconds = humanfriendly.parse_timespan(
mlrun.config.config.httpdb.projects.periodic_sync_interval
)
self._synced_until_datetime = None
# run one sync to start off on the right foot and fill out the cache but don't fail initialization on it
try:
self._sync_projects()
except Exception as exc:
logger.warning("Initial projects sync failed", exc=str(exc))
self._start_periodic_sync()
def _send_request_to_api(self, method, path, session_cookie=None, **kwargs):
url = f"{self._api_url}/api/{path}"
if session_cookie:
cookies = kwargs.get("cookies", {})
# in case some dev using this function for some reason setting cookies manually through kwargs + have a
# cookie with "session" key there + filling the session cookie - explode
if "session" in cookies and cookies["session"] != session_cookie:
raise mlrun.errors.MLRunInvalidArgumentError(
"Session cookie already set"
)
cookies["session"] = session_cookie
kwargs["cookies"] = cookies
if kwargs.get("timeout") is None:
kwargs["timeout"] = 20
response = self._session.request(method, url, verify=False, **kwargs)
if not response.ok:
log_kwargs = copy.deepcopy(kwargs)
log_kwargs.update({"method": method, "path": path})
if response.content:
try:
data = response.json()
ctx = data.get("meta", {}).get("ctx")
errors = data.get("errors", [])
except Exception:
pass
else:
log_kwargs.update({"ctx": ctx, "errors": errors})
logger.warning("Request to iguazio failed", **log_kwargs)
mlrun.errors.raise_for_status(response)
return response
def _run(self, runobj: RunObject, execution: MLClientCtx):
if runobj.metadata.iteration:
self.store_run(runobj)
meta = self._get_meta(runobj, True)
job = self._generate_mpi_job(runobj, execution, meta)
resp = self._submit_mpijob(job, meta.namespace)
state = None
timeout = int(config.submit_timeout) or 120
for _ in range(timeout):
resp = self.get_job(meta.name, meta.namespace)
state = self._get_job_launcher_status(resp)
if resp and state:
break
time.sleep(1)
if resp:
logger.info("MpiJob {} state={}".format(meta.name, state or "unknown"))
if state:
state = state.lower()
launcher, _ = self._get_launcher(meta.name, meta.namespace)
execution.set_hostname(launcher)
execution.set_state("running" if state == "active" else state)
if self.kfp:
writer = AsyncLogWriter(self._db_conn, runobj)
status = self._get_k8s().watch(
launcher, meta.namespace, writer=writer
)
logger.info(
"MpiJob {} finished with state {}".format(meta.name, status)
)
if status == "succeeded":
execution.set_state("completed")
else:
execution.set_state(
"error",
"MpiJob {} finished with state {}".format(
meta.name, status
),
)
else:
txt = "MpiJob {} launcher pod {} state {}".format(
meta.name, launcher, state
)
logger.info(txt)
runobj.status.status_text = txt
else:
txt = "MpiJob status unknown or failed, check pods: {}".format(
self.get_pods(meta.name, meta.namespace)
)
logger.warning(txt)
runobj.status.status_text = txt
if self.kfp:
execution.set_state("error", txt)
return None
def initialize(self):
logger.info("Initializing projects follower")
self.projects_store_mode = (
mlrun.mlconf.httpdb.projects.follower_projects_store_mode)
if self.projects_store_mode not in self.ProjectsStoreMode.all():
raise mlrun.errors.MLRunInvalidArgumentError(
f"Provided projects store mode is not supported. mode={self.projects_store_mode}"
)
self._projects: typing.Dict[str, mlrun.api.schemas.Project] = {}
self._projects_store_for_deletion = self.ProjectsStore(self)
self._leader_name = mlrun.mlconf.httpdb.projects.leader
self._sync_session = None
if self._leader_name == "iguazio":
self._leader_client = mlrun.api.utils.clients.iguazio.Client()
if not mlrun.mlconf.httpdb.projects.iguazio_access_key:
raise mlrun.errors.MLRunInvalidArgumentError(
"Iguazio access key must be configured when the leader is Iguazio"
)
self._sync_session = mlrun.mlconf.httpdb.projects.iguazio_access_key
elif self._leader_name == "nop":
self._leader_client = mlrun.api.utils.projects.remotes.nop_leader.Member(
)
else:
raise NotImplementedError("Unsupported project leader")
self._periodic_sync_interval_seconds = humanfriendly.parse_timespan(
mlrun.mlconf.httpdb.projects.periodic_sync_interval)
self._synced_until_datetime = None
# Only if we're storing the projects in cache, we need to maintain this cache i.e. run the periodic sync
if self.projects_store_mode == self.ProjectsStoreMode.cache:
# run one sync to start off on the right foot and fill out the cache but don't fail initialization on it
try:
self._sync_projects()
except Exception as exc:
logger.warning("Initial projects sync failed", exc=str(exc))
self._start_periodic_sync()
def _find_last_updated_artifact(
artifacts: typing.List[mlrun.api.db.sqldb.models.Artifact],
):
# sanity
if not artifacts:
raise RuntimeError("No artifacts given")
last_updated_artifact = None
last_updated_artifact_time = datetime.datetime.min
artifacts_with_same_update_time = []
for artifact in artifacts:
if artifact.updated > last_updated_artifact_time:
last_updated_artifact = artifact
last_updated_artifact_time = last_updated_artifact.updated
artifacts_with_same_update_time = [last_updated_artifact]
elif artifact.updated == last_updated_artifact_time:
artifacts_with_same_update_time.append(artifact)
if len(artifacts_with_same_update_time) > 1:
logger.warning(
"Found several artifact with same update time, heuristically choosing the first",
artifacts=[
artifact.to_dict() for artifact in artifacts_with_same_update_time
],
)
# we don't really need to do anything to choose the first, it's already happening because the first if is >
# and not >=
if not last_updated_artifact:
logger.warning(
"No artifact had update time, heuristically choosing the first",
artifacts=[artifact.to_dict() for artifact in artifacts],
)
last_updated_artifact = artifacts[0]
return last_updated_artifact
def init_app():
global _db, _logs_dir, _k8s, _scheduler
logger.info('configuration dump\n%s', config.dump_yaml())
if config.httpdb.db_type == 'sqldb':
logger.info('using SQLDB')
_db = SQLDB(config.httpdb.dsn)
else:
logger.info('using FileRunDB')
_db = FileRunDB(config.httpdb.dirpath)
_db.connect()
_logs_dir = Path(config.httpdb.logs_path)
try:
_k8s = K8sHelper()
except Exception:
pass
# @yaronha - Initialize here
task = periodic.Task()
periodic.schedule(task, 60)
_scheduler = Scheduler()
for data in _db.list_schedules():
if 'schedule' not in data:
logger.warning('bad scheduler data - %s', data)
continue
_submit(data)
def show(self, format=None):
"""show the data object content in Jupyter
:param format: format to use (when there is no/wrong suffix), e.g. 'png'
"""
if not is_ipython:
logger.warning(
"Jupyter/IPython was not detected, .show() will only display inside Jupyter"
)
return
from IPython import display
suffix = self.suffix.lower()
if format:
suffix = "." + format
if suffix in [".jpg", ".png", ".gif"]:
display.display(display.Image(self.get(), format=suffix[1:]))
elif suffix in [".htm", ".html"]:
display.display(display.HTML(self.get(encoding="utf-8")))
elif suffix in [".csv", ".pq", ".parquet"]:
display.display(self.as_df())
elif suffix in [".yaml", ".txt", ".py"]:
display.display(display.Pretty(self.get(encoding="utf-8")))
elif suffix == ".json":
display.display(display.JSON(orjson.loads(self.get())))
elif suffix == ".md":
display.display(display.Markdown(self.get(encoding="utf-8")))
else:
logger.error(f"unsupported show() format {suffix} for {self.url}")
def _validate_body_and_path_names_matches(
name: str, project: mlrun.api.schemas.ProjectPatch):
# ProjectPatch allow extra fields, therefore although it doesn't have name in the schema, name might be there
if hasattr(project, "name") and name != getattr(project, "name"):
message = "Conflict between name in body and name in path"
logger.warning(message,
path_name=name,
body_name=getattr(project, "name"))
raise mlrun.errors.MLRunConflictError(message)
async def handler_returning_http_exception(
request: fastapi.Request, exc: HandledException2
):
logger.warning(
"Handler caught HandledException2 exception, returning HTTPException with 401"
)
return await http_exception_handler(
request, fastapi.HTTPException(status_code=HTTPStatus.UNAUTHORIZED.value)
)
def _upsert(session, obj, ignore=False):
try:
session.add(obj)
session.commit()
except SQLAlchemyError as err:
session.rollback()
cls = obj.__class__.__name__
logger.warning(f"conflict adding {cls}, {err}")
if not ignore:
raise DBError(f"duplicate {cls} - {err}") from err
def _reschedule_tasks():
db_session = None
try:
db_session = create_session()
for data in get_db().list_schedules(db_session):
if "schedule" not in data:
logger.warning("bad scheduler data - %s", data)
continue
submit(db_session, data)
finally:
close_session(db_session)
def _try_resolve_project_from_body(content_type: str,
data: bytes) -> typing.Optional[str]:
if "/yaml" not in content_type:
logger.warning(
"Could not resolve project from body, unsupported content type",
content_type=content_type,
)
return None
workflow_manifest = yaml.load(data, Loader=yaml.FullLoader)
return mlrun.api.crud.Pipelines().resolve_project_from_workflow_manifest(
workflow_manifest)
async def http_status_error_handler(request: fastapi.Request,
exc: mlrun.errors.MLRunHTTPStatusError):
status_code = exc.response.status_code
error_message = repr(exc)
logger.warning(
"Request handling returned error status",
error_message=error_message,
status_code=status_code,
)
return await http_exception_handler(
request,
fastapi.HTTPException(status_code=status_code, detail=error_message))
async def _periodic_function_wrapper(interval: int, function, *args, **kwargs):
while True:
try:
if asyncio.iscoroutinefunction(function):
await function(*args, **kwargs)
else:
await run_in_threadpool(function, *args, **kwargs)
except Exception:
logger.warning(
f'Failed during periodic function execution: {function.__name__}, exc: {traceback.format_exc()}'
)
await asyncio.sleep(interval)
async def start(self, db_session: Session):
logger.info("Starting scheduler")
self._scheduler.start()
# the scheduler shutdown and start operation are not fully async compatible yet -
# https://github.com/agronholm/apscheduler/issues/360 - this sleep make them work
await asyncio.sleep(0)
# don't fail the start on re-scheduling failure
try:
self._reload_schedules(db_session)
except Exception as exc:
logger.warning("Failed reloading schedules", exc=exc)
def create_pipeline(
self,
experiment_name: str,
run_name: str,
content_type: str,
data: bytes,
arguments: dict = None,
namespace: str = mlrun.mlconf.namespace,
):
if arguments is None:
arguments = {}
if "/yaml" in content_type:
content_type = ".yaml"
elif " /zip" in content_type:
content_type = ".zip"
else:
mlrun.api.api.utils.log_and_raise(
http.HTTPStatus.BAD_REQUEST.value,
reason=f"unsupported pipeline type {content_type}",
)
logger.debug("Writing pipeline to temp file", content_type=content_type)
print(str(data))
pipeline_file = tempfile.NamedTemporaryFile(suffix=content_type)
with open(pipeline_file.name, "wb") as fp:
fp.write(data)
logger.info(
"Creating pipeline",
experiment_name=experiment_name,
run_name=run_name,
arguments=arguments,
)
try:
kfp_client = kfp.Client(namespace=namespace)
experiment = kfp_client.create_experiment(name=experiment_name)
run = kfp_client.run_pipeline(
experiment.id, run_name, pipeline_file.name, params=arguments
)
except Exception as exc:
logger.warning(
"Failed creating pipeline",
traceback=traceback.format_exc(),
exc=str(exc),
)
raise mlrun.errors.MLRunBadRequestError(f"Failed creating pipeline: {exc}")
finally:
pipeline_file.close()
return run
def _cleanup_runtimes():
db_session = create_session()
try:
for kind in RuntimeKinds.runtime_with_handlers():
try:
runtime_handler = get_runtime_handler(kind)
runtime_handler.delete_resources(get_db(), db_session)
except Exception as exc:
logger.warning("Failed deleting resources. Ignoring",
exc=str(exc),
kind=kind)
finally:
close_session(db_session)
def _monitor_runs():
db_session = create_session()
try:
for kind in RuntimeKinds.runtime_with_handlers():
try:
runtime_handler = get_runtime_handler(kind)
runtime_handler.monitor_runs(get_db(), db_session)
except Exception as exc:
logger.warning("Failed monitoring runs. Ignoring",
exc=str(exc),
kind=kind)
finally:
close_session(db_session)
def _fix_artifact_tags_duplications(db: mlrun.api.db.sqldb.db.SQLDB,
db_session: sqlalchemy.orm.Session):
logger.info("Fixing artifact tags duplications")
# get all artifacts
artifacts = db._find_artifacts(db_session, None, "*")
# get all artifact tags
tags = db._query(db_session, mlrun.api.db.sqldb.models.Artifact.Tag).all()
# artifact record id -> artifact
artifact_record_id_map = {artifact.id: artifact for artifact in artifacts}
tags_to_delete = []
projects = {artifact.project for artifact in artifacts}
for project in projects:
artifact_keys = {
artifact.key
for artifact in artifacts if artifact.project == project
}
for artifact_key in artifact_keys:
artifact_key_tags = []
for tag in tags:
# sanity
if tag.obj_id not in artifact_record_id_map:
logger.warning("Found orphan tag, deleting",
tag=tag.to_dict())
if artifact_record_id_map[tag.obj_id].key == artifact_key:
artifact_key_tags.append(tag)
tag_name_tags_map = collections.defaultdict(list)
for tag in artifact_key_tags:
tag_name_tags_map[tag.name].append(tag)
for tag_name, _tags in tag_name_tags_map.items():
if len(_tags) == 1:
continue
tags_artifacts = [
artifact_record_id_map[tag.obj_id] for tag in _tags
]
last_updated_artifact = _find_last_updated_artifact(
tags_artifacts)
for tag in _tags:
if tag.obj_id != last_updated_artifact.id:
tags_to_delete.append(tag)
if tags_to_delete:
logger.info(
"Found duplicated artifact tags. Removing duplications",
tags_to_delete=[
tag_to_delete.to_dict() for tag_to_delete in tags_to_delete
],
tags=[tag.to_dict() for tag in tags],
artifacts=[artifact.to_dict() for artifact in artifacts],
)
for tag in tags_to_delete:
db_session.delete(tag)
db_session.commit()
def run_function_periodically(interval: int, name: str, replace: bool,
function, *args, **kwargs):
global tasks
logger.debug("Submitting function to run periodically", name=name)
if name in tasks:
if not replace:
message = "Task with that name already exists"
logger.warning(message, name=name)
raise mlrun.errors.MLRunInvalidArgumentError(message)
cancel_periodic_function(name)
loop = asyncio.get_running_loop()
task = loop.create_task(
_periodic_function_wrapper(interval, function, *args, **kwargs))
tasks[name] = task
def _resolve_project_from_command(
self,
command: typing.List[str],
hyphen_p_is_also_project: bool,
has_func_url_flags: bool,
has_runtime_flags: bool,
):
# project has precedence over function url so search for it first
for index, argument in enumerate(command):
if (
(argument == "-p" and hyphen_p_is_also_project)
or argument == "--project"
) and index + 1 < len(command):
return command[index + 1]
if has_func_url_flags:
for index, argument in enumerate(command):
if (argument == "-f" or argument == "--func-url") and index + 1 < len(
command
):
function_url = command[index + 1]
if function_url.startswith("db://"):
(
project,
_,
_,
_,
) = mlrun.utils.helpers.parse_versioned_object_uri(
function_url[len("db://") :]
)
if project:
return project
if has_runtime_flags:
for index, argument in enumerate(command):
if (argument == "-r" or argument == "--runtime") and index + 1 < len(
command
):
runtime = command[index + 1]
try:
parsed_runtime = ast.literal_eval(runtime)
except Exception as exc:
logger.warning(
"Failed parsing runtime. Skipping", runtime=runtime, exc=exc
)
else:
if isinstance(parsed_runtime, dict):
project = parsed_runtime.get("metadata", {}).get("project")
if project:
return project
return None
def get_workflow_engine(engine_kind, local=False):
if local:
if engine_kind == "kfp":
logger.warning(
"running kubeflow pipeline locally, note some ops may not run locally!"
)
return _LocalRunner
if not engine_kind or engine_kind == "kfp":
return _KFPRunner
if engine_kind == "local":
return _LocalRunner
raise mlrun.errors.MLRunInvalidArgumentError(
f"Provided workflow engine is not supported. engine_kind={engine_kind}"
)
def _validate_body_and_path_names_matches(
path_name: str, project: typing.Union[mlrun.api.schemas.Project, dict]
):
if isinstance(project, mlrun.api.schemas.Project):
body_name = project.metadata.name
elif isinstance(project, dict):
body_name = project.get("metadata", {}).get("name")
else:
raise NotImplementedError("Unsupported project instance type")
if body_name and path_name != body_name:
message = "Conflict between name in body and name in path"
logger.warning(message, path_name=path_name, body_name=body_name)
raise mlrun.errors.MLRunConflictError(message)
async def log_request_response(request: fastapi.Request, call_next):
request_id = str(uuid.uuid4())
silent_logging_paths = [
"healthz",
]
path_with_query_string = uvicorn.protocols.utils.get_path_with_query_string(
request.scope
)
if not any(
silent_logging_path in path_with_query_string
for silent_logging_path in silent_logging_paths
):
logger.debug(
"Received request",
method=request.method,
client_address=get_client_address(request.scope),
http_version=request.scope["http_version"],
request_id=request_id,
uri=path_with_query_string,
)
try:
response = await call_next(request)
except Exception as exc:
logger.warning(
"Request handling failed. Sending response",
# User middleware (like this one) runs after the exception handling middleware, the only thing running after
# it is Starletter's ServerErrorMiddleware which is responsible for catching any un-handled exception
# and transforming it to 500 response. therefore we can statically assign status code to 500
status_code=500,
request_id=request_id,
uri=path_with_query_string,
method=request.method,
exc=exc,
traceback=traceback.format_exc(),
)
raise
else:
if not any(
silent_logging_path in path_with_query_string
for silent_logging_path in silent_logging_paths
):
logger.debug(
"Sending response",
status_code=response.status_code,
request_id=request_id,
uri=path_with_query_string,
method=request.method,
)
return response
async def startup_event():
logger.info("configuration dump\n%s", config.dump_yaml())
initialize_singletons()
# don't fail the app on re-scheduling failure
try:
task = periodic.Task()
periodic.schedule(task, 60)
_reschedule_tasks()
except Exception as exc:
logger.warning(f'Failed rescheduling tasks, err: {exc}')
_start_periodic_cleanup()
def _resolve_nuclio_version(self):
if not self._cached_nuclio_version:
# config override everything
nuclio_version = config.nuclio_version
if not nuclio_version and config.nuclio_dashboard_url:
try:
nuclio_client = nuclio.Client()
nuclio_version = nuclio_client.get_dashboard_version()
except Exception as exc:
logger.warning("Failed to resolve nuclio version", exc=str(exc))
self._cached_nuclio_version = nuclio_version
return self._cached_nuclio_version
def _send_request_to_api(
self, method, path, error_message: str, session=None, **kwargs
):
url = f"{self._api_url}/api/{path}"
# support session being already a cookie
session_cookie = session
if (
session_cookie
and not session_cookie.startswith('j:{"sid"')
and not session_cookie.startswith(urllib.parse.quote_plus('j:{"sid"'))
):
session_cookie = f'j:{{"sid": "{session_cookie}"}}'
if session_cookie:
cookies = kwargs.get("cookies", {})
# in case some dev using this function for some reason setting cookies manually through kwargs + have a
# cookie with "session" key there + filling the session cookie - explode
if "session" in cookies and cookies["session"] != session_cookie:
raise mlrun.errors.MLRunInvalidArgumentError(
"Session cookie already set"
)
cookies["session"] = session_cookie
kwargs["cookies"] = cookies
if kwargs.get("timeout") is None:
kwargs["timeout"] = 20
if "projects" in path:
if mlrun.api.schemas.HeaderNames.projects_role not in kwargs.get(
"headers", {}
):
kwargs.setdefault("headers", {})[
mlrun.api.schemas.HeaderNames.projects_role
] = "mlrun"
response = self._session.request(method, url, verify=False, **kwargs)
if not response.ok:
log_kwargs = copy.deepcopy(kwargs)
log_kwargs.update({"method": method, "path": path})
if response.content:
try:
data = response.json()
ctx = data.get("meta", {}).get("ctx")
errors = data.get("errors", [])
except Exception:
pass
else:
error_message = f"{error_message}: {str(errors)}"
log_kwargs.update({"ctx": ctx, "errors": errors})
logger.warning("Request to iguazio failed", **log_kwargs)
mlrun.errors.raise_for_status(response, error_message)
return response
async def background_task_wrapper(self, project: str, name: str, function,
*args, **kwargs):
try:
if asyncio.iscoroutinefunction(function):
await function(*args, **kwargs)
else:
await fastapi.concurrency.run_in_threadpool(
function, *args, **kwargs)
except Exception:
logger.warning(
f"Failed during background task execution: {function.__name__}, exc: {traceback.format_exc()}"
)
self._update_background_task(
project, name, mlrun.api.schemas.BackgroundTaskState.failed)
else:
self._update_background_task(
project, name, mlrun.api.schemas.BackgroundTaskState.succeeded)
async def http_status_error_handler(request: fastapi.Request,
exc: mlrun.errors.MLRunHTTPStatusError):
status_code = exc.response.status_code
error_message = repr(exc)
logger.warning(
"Request handling returned error status",
error_message=error_message,
status_code=status_code,
traceback=traceback.format_exc(),
)
# TODO: 0.6.6 is the last version expecting the error details to be under reason, when it's no longer a relevant
# version can be changed to detail=error_message
return await http_exception_handler(
request,
fastapi.HTTPException(status_code=status_code,
detail={"reason": error_message}),
)
def _send_request_to_api(self, method, path, **kwargs):
url = f"{self._api_url}{path}"
if kwargs.get("timeout") is None:
kwargs["timeout"] = self._request_timeout
response = self._session.request(method, url, verify=False, **kwargs)
if not response.ok:
log_kwargs = copy.deepcopy(kwargs)
log_kwargs.update({"method": method, "path": path})
if response.content:
try:
data = response.json()
except Exception:
pass
else:
log_kwargs.update({"data": data})
logger.warning("Request to opa failed", **log_kwargs)
mlrun.errors.raise_for_status(response)
return response