User Guides
Python SDK Reference

Deployment

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])

get_tags

get_tags()

Description

Retrieve the tags of your deployment.

Examples

tags = deployment.get_tags()
assert tags[0].name == "cool"

Returns

List of tags as Tag

retrieve_information

retrieve_information()

Description

Retrieve some information about this deployment from service.

Examples

my_deployment.retrieve_information()

update

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

Description

Update this deployment with a new name, another target datalake or a minimum threshold

Examples

a_tag.update(name="new name", min_threshold=0.4)

Args

  • name (str, optional) : New name of the deployment

  • target_datalake (Datalake, optional) : Datalake where data will be uploaded on new prediction

  • min_threshold (float, optional) : Minimum confidence threshold.
    Serving will filter detection boxes or masks that have a detection score lower than this threshold

delete

delete(
   force_delete: bool = False
)

Description

set_model

set_model(
   model_version: ModelVersion
)

Description

Update this deployment with a new name.

Examples

a_tag.update(name="new name")

get_model_version

get_model_version()

Description

Retrieve currently used model version

Examples

model_version = deployment.get_model()

Returns

A Model object

set_shadow_model

set_shadow_model(
   shadow_model_version: ModelVersion
)

Description

Update this deployment with a new name.

Examples

a_tag.update(name="new name")

get_shadow_model

get_shadow_model()

Description

Retrieve currently used shadow model

Examples

shadow_model = deployment.get_shadow_model()

Returns

A Model object

predict

predict(
   file_path: Union[str, Path], tags: Union[str, Tag, List[Union[Tag, str]],
   None] = None, source: Union[str, DataSource, None] = None
)

Description

Run a prediction on our Serving platform

Examples

deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.predict('image_420.png', tags=["gonna", "give"], source="camera-1")

Arguments

  • tags (str, Tag, list of str or Tag, optional) : a list of tag to add to the data that will be created on the platform

  • source (str or DataSource, optional) : a source to attach to the data that will be created on the platform.

  • file_path (str or Path) : path to the image to predict

Returns

A (dict) with information of the prediction

setup_feedback_loop

setup_feedback_loop(
   dataset_version: DatasetVersion
)

Description

Set up the Feedback Loop for a Deployment.
This way, you will be able to attached reviewed predictions to the Dataset.
This is a great option to increase your training set with quality data.

Examples

dataset_version = client.get_dataset("my-dataset").get_version("latest")
deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.setup_feedback_loop(dataset_version)

Arguments

check_feedback_loop_status

check_feedback_loop_status()

Description

Refresh feedback loop status of this deployment.

Examples

deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.check_feedback_loop_status()

disable_feedback_loop

disable_feedback_loop()

Description

Disable the Feedback Loop for a Deployment.

Examples

deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.disable_feedback_loop()

setup_continuous_training

setup_continuous_training(
   project: Project, dataset_version: DatasetVersion, model_version: ModelVersion,
   trigger: Union[str, ContinuousTrainingTrigger] = None,
   threshold: Optional[int] = None, experiment_parameters: Optional[dict] = None,
   scan_config: Optional[dict] = None
)

Description

Initialize and activate the continuous training features of picsellia. 🥑
A Training will be triggered using the configured Dataset
and Model as base whenever your Deployment pipeline hit the trigger.

There is 2 types of continuous training different:
You can launch a continuous training via Scan configuration or via Experiment
You need to give whether experiment_parameters or scan_config but not both
For scan configuration: more info.

Examples

We want to set up a continuous training pipeline that will be trigger
every 150 new predictions reviewed by your team.
We will use the same training parameters as those used when building the first model.

deployment = client.get_deployment("awesome-deploy")
project = client.get_project(name="my-project")
dataset_version = project.get_dataset(name="my-dataset").get_version("latest")
model_version = client.get_model(name="my-model").get_version(0)
experiment = model_version.get_source_experiment()
parameters = experiment.get_log('parameters')
feedback_loop_trigger = 150
deployment.setup_continuous_training(
    project, dataset_version, model_version,
    threshold=150, experiment_parameters=experiment_parameters
)

Arguments

  • project Project : The project that will host your pipeline.

  • dataset_version DatasetVersion : The Dataset that will be used as training data for your training.

  • model_version ModelVersion : The exported Model to perform transfer learning from.

  • threshold (int) : Number of images that need to be review to trigger the training.

  • trigger (ContinuousTrainingTrigger) : Type of trigger to use when there is enough reviews.

  • experiment_parameters (Optional[dict], optional) : Training parameters. Defaults to None.

  • scan_config (Optional[dict], optional) : Scan configuration dict. Defaults to None.

toggle_continuous_training

toggle_continuous_training(
   active: bool
)

Description

Update your continuous training pipeline.

Examples

deployment = client.get_deployment("awesome-deploy")
deployment.update_continuous_training(active=False)

setup_continuous_deployment

setup_continuous_deployment(
   policy: Union[ContinuousDeploymentPolicy, str]
)

Description

Set up the continuous deployment for this pipeline

Examples

deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.setup_continuous_deployment(ContinuousDeploymentPolicy.DEPLOY_MANUAL)

Arguments

  • policy (ContinuousDeploymentPolicy) : policy to use

toggle_continuous_deployment

toggle_continuous_deployment(
   active: bool
)

Description

Toggle continuous deployment for this deployment

Examples

deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.toggle_continuous_deployment(
    dataset
)

Arguments

  • active (bool) : (des)activate continuous deployment

get_stats

get_stats(
   service: ServiceMetrics, model_version: Optional[ModelVersion] = None,
   from_timestamp: Optional[float] = None, to_timestamp: Optional[float] = None,
   since: Optional[int] = None, includes: Optional[List[str]] = None,
   excludes: Optional[List[str]] = None, tags: Optional[List[str]] = None
)

Description

Retrieve stats of this deployment stored in Picsellia environment.

Mandatory param is "service" an enum of type ServiceMetrics. Values possibles are :
PREDICTIONS_OUTLYING_SCORE
PREDICTIONS_DATA
REVIEWS_OBJECT_DETECTION_STATS
REVIEWS_CLASSIFICATION_STATS
REVIEWS_LABEL_DISTRIBUTION_STATS

AGGREGATED_LABEL_DISTRIBUTION
AGGREGATED_OBJECT_DETECTION_STATS
AGGREGATED_PREDICTIONS_DATA
AGGREGATED_DRIFTING_PREDICTIONS

For aggregation, computation may not have been done by the past.
You will need to force computation of these aggregations and retrieve them again.

Examples

my_deployment.get_stats(ServiceMetrics.PREDICTIONS_DATA)
my_deployment.get_stats(ServiceMetrics.AGGREGATED_DRIFTING_PREDICTIONS, since=3600)
my_deployment.get_stats(ServiceMetrics.AGGREGATED_LABEL_DISTRIBUTION, model_version=my_model)

Arguments

  • service (str) : service queried

  • model_version (ModelVersion, optional) : Model that shall be used when retrieving data.
    Defaults to None.

  • from_timestamp (float, optional) : System will only retrieve prediction data after this timestamp.
    Defaults to None.

  • to_timestamp (float, optional) : System will only retrieve prediction data before this timestamp.
    Defaults to None.

  • since (int, optional) : System will only retrieve prediction data that are in the last seconds.
    Defaults to None.

  • includes (List[str], optional) : Research will include these ids and excludes others.
    Defaults to None.

  • excludes (List[str], optional) : Research will exclude these ids.
    Defaults to None.

  • tags (List[str], optional) : Research will be done filtering by tags.
    Defaults to None.

Returns

A dict with queried statistics about the service you asked

monitor

monitor(
   image_path: Union[str, Path], latency: float, height: int, width: int,
   prediction: PredictionFormat, source: Optional[str] = None,
   tags: Optional[List[str]] = None, timestamp: Optional[float] = None,
   model_version: Optional[ModelVersion] = None,
   shadow_model_version: Optional[ModelVersion] = None,
   shadow_latency: Optional[float] = None,
   shadow_raw_predictions: Optional[PredictionFormat] = None
)

Description