def delete(id: str):
if not exists_by_id(id):
raise HTTPException(
status_code=404,
detail=f'Model ID {id} does not exist. You may change the ID',
)
delete_model(id)
def update(id: str, schema: ModelUpdateSchema):
if not exists_by_id(id):
raise HTTPException(
status_code=404,
detail=f'Model ID {id} does not exist. You may change the ID',
)
return update_model(id, schema)
def profile(model_id: str, device: str='cuda', batch_size: int=1):
if not exists_by_id(model_id):
raise HTTPException(
status_code=404,
detail=f'Model ID {model_id} does not exist. You may change the ID',
)
profile_result = profile_model(model_id, device, batch_size)
return profile_result
def convert(
id: str = typer.Option(None, '-i', '--id', help='ID of model.'),
yaml_file: Optional[Path] = typer.Option(
None, '-f', '--yaml-file', exists=True, file_okay=True,
help='Path to configuration YAML file. You should either set the `yaml_file` field or fields '
'(`FILE_OR_DIR`, `--name`, `--framework`, `--engine`, `--version`, `--task`, `--dataset`,'
'`--metric`, `--input`, `--output`).'
),
register: bool = typer.Option(False, '-r', '--register', is_flag=True, help='register the converted models to modelhub, default false')
):
model = None
if id is None and yaml_file is None:
raise ValueError('WARNING: Please assign a way to find the target model! details refer to --help')
if id is not None and yaml_file is not None:
raise ValueError('WARNING: Do not use -id and -path at the same time!')
elif id is not None and yaml_file is None:
if ModelDB.exists_by_id(id):
model = ModelDB.get_by_id(id)
else:
typer.echo(f"model id: {id} does not exist in modelhub")
elif id is None and yaml_file is not None:
# get MLModel from yaml file
with open(yaml_file) as f:
model_config = yaml.safe_load(f)
model_yaml = MLModelFromYaml.parse_obj(model_config)
model_in_saved_path = model_yaml.saved_path
if model_in_saved_path != model_yaml.weight:
copy2(model_yaml.weight, model_in_saved_path)
if model_yaml.engine == Engine.TFS:
weight_dir = model_yaml.weight
make_archive(weight_dir.with_suffix('.zip'), 'zip', weight_dir)
model_data = model_yaml.dict(exclude_none=True, exclude={'convert', 'profile'})
model = MLModel.parse_obj(model_data)
# auto execute all possible convert and return a list of save paths of every converted model
generated_dir_list = generate_model_family(model)
typer.echo(f"Converted models are save in: {generated_dir_list}")
if register:
model_data = model.dict(exclude={'weight', 'id', 'model_status', 'engine'})
for model_dir in generated_dir_list:
parse_result = parse_path_plain(model_dir)
engine = parse_result['engine']
model_cvt = MLModel(**model_data, weight=model_dir, engine=engine, model_status=[ModelStatus.CONVERTED])
ModelDB.save(model_cvt)
typer.echo(f"converted {engine} are successfully registered in Modelhub")