def format_base64_as_yaml(source: str) -> str:
s = yaml.safe_dump(yaml.safe_load(base64.b64decode(source)),
default_flow_style=False)
if not isinstance(s, str):
raise AssertionError("cannot format base64 string to yaml")
return s
def create_experiment(
self,
config: Union[str, pathlib.Path, Dict],
model_dir: str,
) -> experiment.ExperimentReference:
check.is_instance(config, (str, pathlib.Path, dict),
"config parameter must be dictionary or path")
if isinstance(config, str):
with open(config) as f:
experiment_config = yaml.safe_load(f)
elif isinstance(config, pathlib.Path):
with config.open() as f:
experiment_config = yaml.safe_load(f)
elif isinstance(config, Dict):
experiment_config = config
model_context = _path_to_files(pathlib.Path(model_dir))
experiment_request = V1CreateExperimentRequest(
model_definition=model_context,
config=yaml.safe_dump(experiment_config),
)
experiment_response = self._internal.determined_create_experiment(
experiment_request)
return experiment.ExperimentReference(
experiment_response.experiment.id,
self._session._master,
self._experiments,
)
def create_experiment(
self,
config: Union[str, pathlib.Path, Dict],
model_dir: Union[str, pathlib.Path],
) -> experiment.ExperimentReference:
"""
Create an experiment with config parameters and model directory. The function
returns :class:`~determined.experimental.ExperimentReference` of the experiment.
Arguments:
config(string, pathlib.Path, dictionary): experiment config filename (.yaml)
or a dict.
model_dir(string): directory containing model definition.
"""
check.is_instance(
config, (str, pathlib.Path, dict), "config parameter must be dictionary or path"
)
if isinstance(config, str):
with open(config) as f:
experiment_config = util.safe_load_yaml_with_exceptions(f)
elif isinstance(config, pathlib.Path):
with config.open() as f:
experiment_config = util.safe_load_yaml_with_exceptions(f)
elif isinstance(config, Dict):
experiment_config = config
if isinstance(model_dir, str):
model_dir = pathlib.Path(model_dir)
model_context, _ = context.read_context(model_dir)
resp = self._session.post(
"/api/v1/experiments",
body={
"config": yaml.safe_dump(experiment_config),
"model_definition": model_context,
},
)
exp_id = _CreateExperimentResponse(resp.json()).id
exp = experiment.ExperimentReference(exp_id, self._session)
exp.activate()
return exp
def create_experiment(
self,
config: Union[str, pathlib.Path, Dict],
model_dir: str,
) -> experiment.ExperimentReference:
"""
Create an experiment with config parameters and model direcotry. The function
returns :class:`~determined.experimental.ExperimentReference` of the experiment.
Arguments:
config(string, pathlib.Path, dictionary): experiment config filename (.yaml)
or a dict.
model_dir(string): directory containing model definition.
"""
check.is_instance(config, (str, pathlib.Path, dict),
"config parameter must be dictionary or path")
if isinstance(config, str):
with open(config) as f:
experiment_config = util.safe_load_yaml_with_exceptions(f)
elif isinstance(config, pathlib.Path):
with config.open() as f:
experiment_config = util.safe_load_yaml_with_exceptions(f)
elif isinstance(config, Dict):
experiment_config = config
model_context = _path_to_files(pathlib.Path(model_dir))
experiment_request = V1CreateExperimentRequest(
model_definition=model_context,
config=yaml.safe_dump(experiment_config),
)
experiment_response = self._internal.determined_create_experiment(
experiment_request)
return experiment.ExperimentReference(
experiment_response.experiment.id,
self._session._master,
self._experiments,
)
def create_experiment(
master_url: str,
config: Dict[str, Any],
model_context: context.Context,
template: Optional[str] = None,
validate_only: bool = False,
archived: bool = False,
activate: bool = True,
additional_body_fields: Optional[Dict[str, Any]] = None,
) -> int:
body = {
"activate": False,
"experiment_config": yaml.safe_dump(config),
"model_definition": [e.dict() for e in model_context.entries],
"validate_only": validate_only,
}
if template:
body["template"] = template
if archived:
body["archived"] = archived
if additional_body_fields:
body.update(additional_body_fields)
r = req.post(master_url, "experiments", json=body)
if not hasattr(r, "headers"):
raise Exception(r)
if validate_only:
return 0
new_resource = r.headers["Location"]
experiment_id = int(new_resource.split("/")[-1])
if activate:
sess = session.Session(master_url, None, None, None)
bindings.post_ActivateExperiment(sess, id=experiment_id)
return experiment_id
def _parse_config(field: Any) -> Any:
# Pretty print the config field.
return yaml.safe_dump(yaml.safe_load(base64.b64decode(field)),
default_flow_style=False)
def config(args: Namespace) -> None:
result = api.get(args.master, "experiments/{}/config".format(args.experiment_id))
yaml.safe_dump(result.json(), stream=sys.stdout, default_flow_style=False)
def format_object_as_yaml(source: Dict[str, Any]) -> str:
s = yaml.safe_dump(source, default_flow_style=False)
if not isinstance(s, str):
raise AssertionError("cannot format object to yaml")
return s
def get_master(args: Namespace) -> None:
resp = bindings.get_GetMaster(setup_session(args))
if args.json:
print(json.dumps(resp.to_json(), indent=4))
else:
print(yaml.safe_dump(resp.to_json(), default_flow_style=False))
def config(args: Namespace) -> None:
response = api.get(args.master, "config")
if args.json:
print(json.dumps(response.json(), indent=4))
else:
print(yaml.safe_dump(response.json(), default_flow_style=False))
def config(args: Namespace) -> None:
result = bindings.get_GetExperiment(setup_session(args),
experimentId=args.experiment_id).config
yaml.safe_dump(result, stream=sys.stdout, default_flow_style=False)
