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Python SDK Reference
Python SDK Reference

Modelversion

Properties

Methods

add_tags

add_tags(
   tags: Union[Tag, list[Tag]]
)

Description

Add some tags to an object.
It can be used on Data/MultiData/Asset/MultiAsset/DatasetVersion/Dataset/Model/ModelVersion.

You can give a Tag or a list of Tag.

Examples

tag_bicycle = client.create_tag("bicycle", Target.DATA)
tag_car = client.create_tag("car", Target.DATA)
tag_truck = client.create_tag("truck", Target.DATA)

data.add_tags(tag_bicycle)
data.add_tags([tag_car, tag_truck])

remove_tags

remove_tags(
   tags: Union[Tag, list[Tag]]
)

Description

Remove some tags from an object (can be used on Data/Asset/DatasetVersion/Dataset/Model/ModelVersion)

You can give a Tag or a list of Tag.

Examples

data.remove_tags(tag_bicycle)
data.remove_tags([tag_car, tag_truck])

get_tags

get_tags()

Description

Retrieve the tags of your model version.

Examples

tags = my_model_version.get_tags()
assert tags[0].name == "my-model-version-1"

Returns

A list of Tag objects

update

update(
   labels: Optional[dict] = None, docker_image_name: Optional[str] = None,
   docker_flags: Optional[list[str]] = None, thumb_object_name: Optional[str] = None,
   notebook_link: Optional[str] = None, base_parameters: Optional[dict] = None,
   docker_env_variables: Optional[dict] = None, framework: Union[str, Framework,
   None] = None, type: Union[str, InferenceType, None] = None,
   name: Optional[str] = None, description: Optional[str] = None,
   docker_tag: Optional[str] = None
)

Description

Update this model version with some new infos.

Examples

model_v1.update(docker_image_name="docker.io/model1")

Arguments

  • labels (dict, optional) : Labels of this model version. Defaults to None.

  • docker_image_name (str, optional) : Docker image name of this model version. Defaults to None.

  • docker_flags (list[str], optional) : Docker flags of this model version. Defaults to None.

  • thumb_object_name (str, optional) : Thumbnail object name of this model version. Defaults to None.

  • notebook_link (str, optional) : Notebook link of this model version. Defaults to None.

  • base_parameters (dict, optional) : Base parameters of this model version. Defaults to None.

  • docker_env_variables (dict, optional) : Docker env variables of this model version. Defaults to None.

  • framework (Union[str, Framework, None], optional) : Framework of this model version (tensorflow, pytorch, etc.). Defaults to None.

  • type (Union[str, InferenceType, None], optional) : Type of this model version (classification, object_detection, segmentation). Defaults to None.

  • name (str, optional) : Name of this model version. Defaults to None.

  • description (str, optional) : Description of this model version. Defaults to None.

  • docker_tag (str, optional) : Docker tag of this model version. Defaults to None.

delete

delete()

Description

Delete model version.

Delete the model in Picsellia database

Examples

model_v1.delete()

get_context

get_context()

Description

Get ModelContext of this model

Examples

model_v1 = client.get_model(name="my-model").get_version(0)
context = model_v1.get_context()
context.get_infos()

Returns

ModelContext objects that you can use and manipulate

list_files

list_files()

Description

Get a list of ModelFile that were stored with this model

Examples

model_v1 = client.get_model(name="my-model").get_version(0)
files = model_v1.list_files()
files[0].download()

Returns

A list of ModelFile that you can use and manipulate

get_file

get_file(
   name: str
)

Description

Retrieve a ModelFile that were stored with this name into this model

Examples

model_v1 = client.get_model(name="my-model").get_version(0)
file = model_v1.get_file("model-latest")
file.download()

Arguments

  • name (str) : Name of the file you want to retrieve

Returns

A ModelFile that you can use and manipulate

store

store(
   name: str, path: Union[str, Path], do_zip: bool = False, replace: bool = False
)

Description

Store a file into picsellia storage and attach it to this model.

Examples

model.store("model-latest", "./lg_test_file.pb")

Arguments

  • name (str) : Name of file

  • path (str or Path) : Path of file to store

  • do_zip (bool, optional) : If true, zip directory to store it. Defaults to False.

  • replace (bool, optional) : If true, if a file with given name exists, it will be replaced. Defaults to False.

Returns

A ModelFile object

deploy

deploy(
   name: Optional[str] = None, target_datalake: Optional[Datalake] = None,
   min_threshold: Optional[float] = None
)

Description

Create a Deployment for a model.

This method allows you to create a Deployment on Picsellia. You will then have
access to the monitoring dashboard and the model management part!

Examples

model_version = client.get_model(name="my-awesome-model").get_version(0)
deployment = model_version.deploy(name="my-awesome-deployment", min_threshold=0.5)

Arguments

  • name (str) : Name of your deployment. Defaults to a random name.

  • min_threshold (float) : Threshold of detection scores used by models when predicting. Defaults to 0.

  • target_datalake Datalake : Datalake to use when data are pushed into Picsellia.
    Defaults to organization default Datalake.

Returns

A Deployment

launch_processing

launch_processing(
   processing: Processing, parameters: dict = None, cpu: int = None, gpu: int = None
)

Description

Launch given processing onto this model version. You can give specific cpu, gpu or parameters.
If not given, it will use default values specified in Processing.
If processing cannot be launched on a ModelVersion it will raise before launching.

Examples

processing = client.get_processing("convert-to-pytorch")
model_version.launch_processing(processing)

**Returns**

A [Job](job) object

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