Properties

connexion deprecated

connexion deprecated

name Name of this Deployment
type Type of this Deployment
oracle_connexion deprecated
serving_connexion deprecated

Methods

add_tags

add_tags(
   tags: (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: (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])

list_users

list_users()

Description

List all users of this resource

Examples

resource.list_users()

Returns

list of (User) objects

add_user

add_user(
   user: User, role: (str|None) = None
)

Description

Add given user to this resource, with given role. If user is UNPRIVILEGED on Organization, then its role will be forced to LABELER. If role is None, user will be assigned configured role on Organization, or if not configured, USER.

⚠️ DANGER ZONE: This method will create access for a user. Role depends on type of resource you're adding the user.

Examples

user = client.find_user("foo")
resource.add_user(user, "ADMIN")

get_tags

get_tags()

Description

Retrieve the tags of your deployment.

Examples

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

Returns

A list of Tag objects

retrieve_information

retrieve_information()

Description

Retrieve some information about this deployment from service.

Examples

my_deployment.retrieve_information()

Returns

A dict with information about this deployment

update

update(
   name: (str|None) = None, target_datalake: (Datalake|None) = None,
   min_threshold: (float|None) = None, sample_rate: (float|None) = 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)

Arguments

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
sample_rate (float, optional) : If less than 1, monitoring service will not send all images to platform, but will still compute metrics.

delete

delete(
   force_delete: bool = False
)

Description

set_model

set_model(
   model_version: ModelVersion
)

Description

Set the model version to use for this deployment

Examples

model_version = client.get_model("my-model").get_version("latest")
deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.set_model(model_version)

Arguments

model_version ModelVersion : a ModelVersion to use

get_model_version

get_model_version()

Description

Retrieve currently used model version

Examples

model_version = deployment.get_model_version()

Returns

A ModelVersion object

set_shadow_model

set_shadow_model(
   shadow_model_version: ModelVersion
)

Description

Set the shadow model version to use for this deployment

Examples

shadow_model_version = client.get_model("my-model").get_version("latest")
deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.set_shadow_model(shadow_model_version)

Arguments

shadow_model_version ModelVersion : a ModelVersion to use

get_shadow_model

get_shadow_model()

Description

Retrieve currently used shadow model version

Examples

shadow_model = deployment.get_shadow_model()

Returns

A ModelVersion object

list_labels

list_labels()

Description

Get all labels of a Deployment.

It will retrieve a list of label objects.

Labels are created when Deployment is created, they cannot be updated or deleted.

Examples

labels = deployment.list_labels()
assert labels[0].name == "person"

Returns

List of Label

predict

predict(
   file_path: (str|Path), tags: (str|Tag|list[Tag|str]|None) = None,
   source: (str|DataSource|None) = None, metadata: (dict|None) = None,
   monitor: bool = True, upload_dir: (str|None) = None,
   custom_metadata: (dict|None) = None, postprocess: bool = True
)

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

file_path (str or Path) : path to the image to predict.
tags (str, Tag, list[str | 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.
metadata (dict, optional) : metadata to attach to the data that will be created on the platform.
monitor (bool, optional) : if True, will send prediction on Picsellia and our monitoring service. Defaults to True.
upload_dir (str, optional) : This parameter can only be used with private object-storages. Specify this parameter to prefix the object name of the data. Filename will still contain a generated uuid4
custom_metadata (dict, optional) : custom_metadata to attach to the data that will be created on the platform.

Returns

A (dict) with information of the prediction

predict_bytes

predict_bytes(
   filename: str, raw_image: bytes, tags: (str|Tag|list[Tag|str]|None) = None,
   source: (str|DataSource|None) = None, metadata: (dict|None) = None,
   monitor: bool = True, upload_dir: (str|None) = None,
   custom_metadata: (dict|None) = None, postprocess: bool = True
)

Description

Run a prediction on our Serving platform with bytes of an image

Examples

deployment = client.get_deployment(
    name="awesome-deploy"
)
filename = "frame.png"
with open(filename, 'rb') as img:
    img_bytes = img.read()
deployment.predict_bytes(filename, img_bytes, tags=["tag1", "tag2"], source="camera-1")

Arguments

filename (str) : filename of the image.
raw_image (bytes) : bytes of the image to predict.
tags (str, Tag, list[str | 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.
metadata (dict, optional) : metadata to attach to the data that will be created on the platform.
monitor (bool, optional) : if True, will send prediction on Picsellia and our monitoring service. Defaults to True.
upload_dir (str, optional) : This parameter can only be used with private object-storages. Specify this parameter to prefix the object name of the data. Filename will still contain a generated uuid4
custom_metadata (dict, optional) : custom_metadata to attach to the data that will be created on the platform.

Returns

A (dict) with information of the prediction

predict_cloud_image

predict_cloud_image(
   object_name: str, tags: (str|Tag|list[Tag|str]|None) = None,
   source: (str|DataSource|None) = None, metadata: (dict|None) = None,
   monitor: bool = True, custom_metadata: (dict|None) = None, postprocess: bool = True
)

Description

Run a prediction on our Serving platform, using object_name of a cloud object stored in your object storage. Your image MUST be stored in the storage used in the datalake linked to this deployment (the target datalake) If your image is already a data in your target datalake, it MUST NOT have been processed by this deployment, also, in this case, given source and metadata won't be used.

Examples

deployment = client.get_deployment(
    name="awesome-deploy"
)
object_name = "directory/s3/object-name.jpeg"
deployment.predict_cloud_image(object_name, tags=["tag1", "tag2"], source="camera-1")

Arguments

object_name (str) : object name of the cloud image.
tags (str, Tag, list[str | 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.
metadata (dict, optional) : metadata to attach to the data that will be created on the platform.
monitor (bool, optional) : if True, will send prediction on Picsellia and our monitoring service. Defaults to True.
custom_metadata (dict, optional) : custom_metadata to attach to the data that will be created on the platform.

Returns

A (dict) with information of the prediction

predict_data

predict_data(
   data: Data, tags: (str|Tag|list[Tag|str]|None) = None, monitor: bool = True,
   postprocess: bool = True
)

Description

Run a prediction on our Serving platform, using data. Your data must already be stored in the datalake used by the deployment (the target datalake). If there already is a prediction in this deployment linked to this data, it will be dismissed.

Specified tags will be added to the ones already existing on the Data.

Examples

datalake = client.get_datalake(name="target-datalake")
data = datalake.list_data(limit=1)[0]
deployment = client.get_deployment(name="awesome-deploy")
deployment.predict_data(data, tags=["tag1", "tag2"], monitor=False)

Arguments

data Data : object that you want to predict on.
tags (str, Tag, list[str | Tag], optional) : a list of tag to add to the data
monitor (bool, optional) : if True, will send prediction on Picsellia and our monitoring service. Defaults to True.

Returns

A (dict) with information of the prediction

predict_shadow

predict_shadow(
   predicted_asset: PredictedAsset, monitor: bool = True
)

Description

Add a shadow prediction on a predicted asset. It will call our Serving platform, returning predictions coming from shadow model. If monitor is true, it will go to our monitoring service, then it will be added on the platform.

Examples

deployment = client.get_deployment(name="awesome-deploy")
predicted_asset = deployment.list_predicted_assets(limit=1)[0]
deployment.predict_shadow(predicted_asset, monitor=False)

Arguments

predicted_asset PredictedAsset : shadow model will predict on this asset.
monitor (bool, optional) : if True, will send prediction on Picsellia and our monitoring service. Defaults to True.

Returns

A (dict) with prediction shapes

setup_feedback_loop

setup_feedback_loop(
   dataset_version: (DatasetVersion|None) = None
)

Description

Set up the Feedback Loop for a Deployment. You can specify one Dataset Version to attach to it or use the attach_dataset_to_feedback_loop() afterward,so you can add multiple ones. 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

dataset_version (DatasetVersion, optional) : This parameter is deprecated. Use attach_dataset_to_feedback_loop() instead.

attach_dataset_version_to_feedback_loop

attach_dataset_version_to_feedback_loop(
   dataset_version: DatasetVersion
)

Description

Attach a Dataset Version to a previously configured feedback-loop.

Examples

dataset_versions = client.get_dataset("my-dataset").list_versions()
deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.setup_feedback_loop()
for dataset_version in dataset_versions:
    deployment.attach_dataset_version_to_feedback_loop(dataset_version)

Arguments

dataset_version DatasetVersion : a DatasetVersion to attach

detach_dataset_version_from_feedback_loop

detach_dataset_version_from_feedback_loop(
   dataset_version: DatasetVersion
)

Description

Detach a Dataset Version from a previously configured feedback-loop.

Examples

dataset_versions = client.get_dataset("my-dataset").list_versions()
deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.setup_feedback_loop()
for dataset_version in dataset_versions:
    deployment.attach_dataset_version_to_feedback_loop(dataset_version)
deployment.detach_dataset_version_from_feedback_loop(dataset_versions[0])

Arguments

dataset_version DatasetVersion : a DatasetVersion to detach

list_feedback_loop_datasets

list_feedback_loop_datasets()

Description

List the Dataset Versions attached to the feedback-loop

Examples

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

Returns

A list of DatasetVersion

toggle_feedback_loop

toggle_feedback_loop(
   active: bool
)

Description

Toggle feedback loop for this deployment

Examples

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

Arguments

active (bool) : (des)activate feedback loop

set_training_data

set_training_data(
   dataset_version: DatasetVersion
)

Description

This will give the training data reference to the deployment, so we can compute metrics based on this training data distribution in our Monitoring service

Examples

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

Arguments

dataset_version DatasetVersion : a DatasetVersion

check_training_data_metrics_status

check_training_data_metrics_status()

Description

Refresh the status of the metrics compute over the training data distribution. Set up can take some time, so you can check current state with this method.

Examples

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

Returns

A string with the status of the metrics compute over the training data distribution

disable_training_data_reference

disable_training_data_reference()

Description

Disable the reference to the training data in this Deployment. This means that you will not be able to see supervised metrics from the dashboard anymore.

Examples

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

setup_continuous_training

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

Description

Initialize and activate the continuous training features of picsellia. 🥑 A Training will be triggered using the attached dataset versions whenever your Deployment pipeline hit the trigger. You can call attach_dataset_version_to_continuous_training() method afterward. You can launch a continuous training via Experiment with parameter experiment_parameters You cannot launch a continuous training via Scan at the moment

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()
experiment_parameters = experiment.get_log('parameters')
deployment.setup_continuous_training(
    project, threshold=150, experiment_parameters=experiment_parameters
)
deployment.attach_continuous

Arguments

project Project : The project that will host your pipeline.
dataset_version (DatasetVersion, deprecated) : This parameter is deprecated and is not used anymore.
model_version (ModelVersion, deprecated) : This parameter is deprecated and is not used anymore.
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 (dict, optional) : Training parameters. Defaults to None.

attach_dataset_version_to_continuous_training

attach_dataset_version_to_continuous_training(
   alias: str, dataset_version: DatasetVersion
)

Description

Attach a Dataset Version to a previously configured continuous training.

Examples

dataset_versions = client.get_dataset("my-dataset").list_versions()
deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.setup_continuous_training(...)
aliases = ["train", "test", "eval"]
for i, dataset_version in enumerate(dataset_versions):
    deployment.attach_dataset_version_to_continuous_training(aliases[i], dataset_version)

Arguments

alias (str) : Alias of attached dataset
dataset_version DatasetVersion : A dataset version to attach to the Continuous Training.

detach_dataset_version_from_continuous_training

detach_dataset_version_from_continuous_training(
   dataset_version: DatasetVersion
)

Description

Detach a Dataset Versions to a previously configured continuous training.

Examples

dataset_versions = client.get_dataset("my-dataset").list_versions()
deployment = client.get_deployment(
    name="awesome-deploy"
)
deployment.setup_continuous_training()
for dataset_version in dataset_versions:
    deployment.attach_dataset_version_to_continuous_training(dataset_version)
deployment.detach_dataset_version_from_continuous_training(dataset_versions[0])

Arguments

dataset_version DatasetVersion : a DatasetVersion to detach from Continuous Training settings

toggle_continuous_training

toggle_continuous_training(
   active: bool
)

Description

Toggle continuous training for this deployment

Examples

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

Arguments

active (bool) : (des)activate continuous training

setup_continuous_deployment

setup_continuous_deployment(
   policy: (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: (ModelVersion|None) = None,
   from_timestamp: (float|None) = None, to_timestamp: (float|None) = None,
   since: (int|None) = None, includes: (list[str]|None) = None,
   excludes: (list[str]|None) = None, tags: (list[str]|None) = 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: (str|Path), latency: float, height: int, width: int,
   prediction: PredictionFormat, source: (str|None) = None,
   tags: (list[str]|None) = None, timestamp: (float|None) = None,
   model_version: (ModelVersion|None) = None,
   shadow_model_version: (ModelVersion|None) = None,
   shadow_latency: (float|None) = None,
   shadow_raw_predictions: (PredictionFormat|None) = None,
   shadow_prediction: (PredictionFormat|None) = None,
   content_type: (SupportedContentType|str|None) = None, metadata: (dict|None) = None,
   upload_dir: (str|None) = None, custom_metadata: (dict|None) = None,
   sample: (bool|None) = None, custom_metrics: (dict|None) = None
)

Description

Send a prediction for this deployment on our monitoring service.

⚠️ Signature of this method has been recently changed and can break some methods :

model_version and shadow_model_version are not used anymore : system will use what's currently being monitored in this deployment
shadow_raw_predictions has been renamed to shadow_prediction

Arguments

image_path (str or Path) : image path
latency (float) : latency used by model to compute your prediction
height (int) : height of image
width (int) : width of image
prediction (PredictionFormat) : data of your prediction, can be a Classification, a Segmentation or an ObjectDetection Format. DetectionPredictionFormat, ClassificationPredictionFormat and SegmentationPredictionFormat: detection_classes (list[int]): list of classes detection_scores (list[float]): list of scores of predictions DetectionPredictionFormat and SegmentationPredictionFormat: detection_boxes (list[list[int]]): list of bboxes representing rectangles of your shapes. bboxes are formatted as [top, left, bottom, right] SegmentationPredictionFormat: detection_masks list[list[list[int]]]): list of polygon shapes, each element corresponds to a distinct shape and is a list of the coordinates one polygon will look like [[x1, y1], [x2, y2], [x3, y3], [x4, y4], ...] LinePredictionFormat: lines (list[list[int]]): list of line shapes, each element corresponds to a distinct shape and is a list of the coordinates one line will look like [[x1, y1], [x2, y2], [x3, y3], [x4, y4], ...] PointPredictionFormat: points (list[list[int]]): list of point shapes, each element corresponds to a distinct point and is the coordinates a point will look like [x, y]
source (str, optional) : Data will have this source in Picsellia. Defaults to None.
tags (list[str], optional) : tags that can give some metadata to your prediction. Defaults to None.
timestamp (float, optional) : timestamp of your prediction. Defaults to timestamp of monitoring service on reception.
shadow_latency (float, optional) : latency used by shadow model to compute prediction
shadow_prediction (PredictionFormat, optional) : data of your prediction made by shadow model.
content_type (str, optional) : if given, we won't try to infer content type with mimetype library
metadata (dict, optional) : Data will have this metadata in Picsellia. Defaults to None.
upload_dir (str, optional) : This parameter can only be used with private object-storages. Specify this parameter to prefix the object name of the data. Filename will still contain a generated uuid4
custom_metadata (dict, optional) : Data will have this custom_metadata in Picsellia. Defaults to None.
sample (bool, optional) : control sample policy by using this field. If True, the Data and the Prediction will be stored under the Deployment (no matter the sample_rate). If False, the Data and the Prediction won't be stored, monitoring metrics WILL still be computed. If unset, we will store the Data and Prediction according to the sample_rate (if existing).
custom_metrics (dict, optional) : PredictedAsset will have this custom_metrics in Picsellia. Defaults to None.

Returns

a dict of data returned by our monitoring service

monitor_bytes

monitor_bytes(
   raw_image: bytes, content_type: (SupportedContentType|str), filename: str,
   latency: float, height: int, width: int, prediction: PredictionFormat,
   source: (str|None) = None, tags: (list[str]|None) = None,
   timestamp: (float|None) = None, shadow_latency: (float|None) = None,
   shadow_prediction: (PredictionFormat|None) = None, metadata: (dict|None) = None,
   upload_dir: (str|None) = None, custom_metadata: (dict|None) = None,
   sample: (bool|None) = None, custom_metrics: (dict|None) = None
)

Description

Send a prediction for this deployment on our monitoring service. You can use this method instead of monitor() if you have a bytes image and not an image file. We will convert it into base 64 as utf8 string and send it to the monitoring service.

Arguments

raw_image (bytes) : raw image in bytes
content_type (SupportedContentType or str) : content type of image, only 'image/jpeg' or 'image/png' currently supported
filename (str) : filename of image
latency (float) : latency used by model to compute your prediction
height (int) : height of image
width (int) : width of image
prediction (PredictionFormat) : data of your prediction, can be a Classification, a Segmentation or an ObjectDetection Format. DetectionPredictionFormat, ClassificationPredictionFormat and SegmentationPredictionFormat: detection_classes (list[int]): list of classes detection_scores (list[float]): list of scores of predictions DetectionPredictionFormat: detection_boxes (list[list[int]]): list of bboxes representing rectangles of your shapes. bboxes are formatted as [top, left, bottom, right] SegmentationPredictionFormat: detection_masks (list[list[int]]): list of polygons of your shapes. each polygon is a list of points with coordinates flattened [x1, y1, x2, y2, x3, y3, x4, y4, ..]
source (str, optional) : source that can give some metadata to your prediction. Defaults to None.
tags (list[str], optional) : tags that can give some metadata to your prediction. Defaults to None.
timestamp (float, optional) : timestamp of your prediction. Defaults to timestamp of monitoring service on reception.
shadow_latency (float, optional) : latency used by shadow model to compute prediction
shadow_prediction (PredictionFormat, optional) : data of your prediction made by shadow model.
metadata (dict, optional) : Data will have this metadata in Picsellia. Defaults to None.
upload_dir (str, optional) : This parameter can only be used with private object-storages. Specify this parameter to prefix the object name of the data. Filename will still contain a generated uuid4
custom_metadata (dict, optional) : Data will have this custom_metadata in Picsellia. Defaults to None.
sample (bool, optional) : control sample policy by using this field. If True, the Data and the Prediction will be stored under the Deployment (no matter the sample_rate). If False, the Data and the Prediction won't be stored, monitoring metrics WILL still be computed. If unset, we will store the Data and Prediction according to the sample_rate (if existing).
custom_metrics (dict, optional) : PredictedAsset will have this custom_metrics in Picsellia. Defaults to None.

Returns

a dict of data returned by our monitoring service

monitor_cloud_image

monitor_cloud_image(
   object_name: str, latency: float, height: int, width: int,
   prediction: PredictionFormat, content_type: (SupportedContentType|str),
   source: (str|None) = None, tags: (list[str]|None) = None,
   timestamp: (float|None) = None, shadow_latency: (float|None) = None,
   shadow_prediction: (PredictionFormat|None) = None, metadata: (dict|None) = None,
   custom_metadata: (dict|None) = None, sample: (bool|None) = None,
   custom_metrics: (dict|None) = None
)

Description

Monitor an image on our monitoring platform, using object_name of a cloud object stored in your object storage.

Arguments

object_name (str) : object name of the cloud image.
latency (float) : latency used by model to compute your prediction
height (int) : height of image
width (int) : width of image
prediction (PredictionFormat) : data of your prediction, can be a Classification, a Segmentation or an ObjectDetection Format. DetectionPredictionFormat, ClassificationPredictionFormat and SegmentationPredictionFormat: detection_classes (list[int]): list of classes detection_scores (list[float]): list of scores of predictions DetectionPredictionFormat and SegmentationPredictionFormat: detection_boxes (list[list[int]]): list of bboxes representing rectangles of your shapes. bboxes are formatted as [top, left, bottom, right] SegmentationPredictionFormat: detection_masks (list[list[int]]): list of polygons of your shapes. each polygon is a list of points with coordinates flattened [x1, y1, x2, y2, x3, y3, x4, y4, ..]
source (str, optional) : Data will have this source in Picsellia. Defaults to None.
tags (list[str], optional) : tags that can give some metadata to your prediction. Defaults to None.
timestamp (float, optional) : timestamp of your prediction. Defaults to timestamp of monitoring service on reception.
shadow_latency (float, optional) : latency used by shadow model to compute prediction
shadow_prediction (PredictionFormat, optional) : data of your prediction made by shadow model.
content_type (str, optional) : if given, we won't try to infer content type with mimetype library
metadata (dict, optional) : Data will have this metadata in Picsellia. Defaults to None.
custom_metadata (dict, optional) : Data will have this custom_metadata in Picsellia. Defaults to None.
sample (bool, optional) : control sample policy by using this field. If True, the Data and the Prediction will be stored under the Deployment (no matter the sample_rate). If False, the Data and the Prediction won't be stored, monitoring metrics WILL still be computed. If unset, we will store the Data and Prediction according to the sample_rate (if existing).
custom_metrics (dict, optional) : PredictedAsset will have this custom_metrics in Picsellia. Defaults to None.

Returns

a dict of data returned by our monitoring service

monitor_data

monitor_data(
   data: Data, latency: float, height: int, width: int, prediction: PredictionFormat,
   tags: (list[str]|None) = None, timestamp: (float|None) = None,
   shadow_latency: (float|None) = None,
   shadow_prediction: (PredictionFormat|None) = None, sample: (bool|None) = None,
   custom_metrics: (dict|None) = None
)

Description

Monitor an image on our monitoring platform Your data must already be stored in the datalake used by the deployment (the target datalake). If there already is a prediction in this deployment linked to this data, it will be dismissed.

Arguments

data Data : data to monitor
latency (float) : latency used by model to compute your prediction
height (int) : height of image
width (int) : width of image
prediction (PredictionFormat) : data of your prediction, can be a Classification, a Segmentation or an ObjectDetection Format. DetectionPredictionFormat, ClassificationPredictionFormat and SegmentationPredictionFormat: detection_classes (list[int]): list of classes detection_scores (list[float]): list of scores of predictions DetectionPredictionFormat and SegmentationPredictionFormat: detection_boxes (list[list[int]]): list of bboxes representing rectangles of your shapes. bboxes are formatted as [top, left, bottom, right] SegmentationPredictionFormat: detection_masks (list[list[int]]): list of polygons of your shapes. each polygon is a list of points with coordinates flattened [x1, y1, x2, y2, x3, y3, x4, y4, ..]
tags (list[str], optional) : tags that can give some metadata to your prediction. Defaults to None.
timestamp (float, optional) : timestamp of your prediction. Defaults to timestamp of monitoring service on reception.
shadow_latency (float, optional) : latency used by shadow model to compute prediction
shadow_prediction (PredictionFormat, optional) : data of your prediction made by shadow model.
sample (bool, optional) : control sample policy by using this field. If True, the Data and the Prediction will be stored under the Deployment (no matter the sample_rate). If False, the Data and the Prediction won't be stored, monitoring metrics WILL still be computed. If unset, we will store the Data and Prediction according to the sample_rate (if existing).
custom_metrics (dict, optional) : PredictedAsset will have this custom_metrics in Picsellia. Defaults to None.

Returns

a dict of data returned by our monitoring service

monitor_shadow

monitor_shadow(
   predicted_asset: PredictedAsset, shadow_latency: float,
   shadow_prediction: PredictionFormat
)

Description

Add a shadow prediction on an existing PredictedAsset. You can call monitor_shadow_from_oracle_prediction_id() if you only have oracle_prediction_id

Arguments

predicted_asset PredictedAsset : asset already processed on which to add shadow_prediction
shadow_latency (float) : latency used by shadow model to compute prediction
shadow_prediction (PredictionFormat) : data of your prediction made by shadow model

monitor_shadow_from_oracle_prediction_id

monitor_shadow_from_oracle_prediction_id(
   oracle_prediction_id: (str|UUID), shadow_latency: float,
   shadow_prediction: PredictionFormat
)

Description

Add a shadow prediction on an existing PredictedAsset, from the oracle_prediction_id

Arguments

oracle_prediction_id (str or UUID) : oracle_prediction_id that was returned on monitor()
shadow_latency (float) : latency used by shadow model to compute prediction
shadow_prediction (PredictionFormat) : data of your prediction made by shadow model

find_predicted_asset

find_predicted_asset(
   id: (str|UUID|None) = None, oracle_prediction_id: (str|UUID|None) = None,
   object_name: (str|None) = None, filename: (str|None) = None,
   data_id: (str|UUID|None) = None
)

Description

Find a PredictedAsset of this deployment.

Examples

oracle_prediction_id = deployment.monitor(path, latency, height, width, prediction_data)["id"]
predicted_asset = deployment.find_predicted_asset(oracle_prediction_id=oracle_prediction_id)
deployment.monitor_shadow(predicted_asset, shadow_latency, shadow_prediction_data)

Arguments

id (UUID, optional) : id of PredictedAsset to fetch. Defaults to None.
oracle_prediction_id (UUID, optional) : id of the prediction in our monitoring system. Defaults to None.
filename (str, optional) : filename of the data. Defaults to None.
object_name (str, optional) : object_name of the data. Defaults to None.
data_id (UUID, optional) : id of the data related to this PredictedAsset. Defaults to None.

Raises

If no asset match the query, it will raise a NotFoundError. In some case, it can raise an InvalidQueryError, it might be because the platform stores 2 assets matching this query (for example if filename is duplicated)

Returns

The PredictedAsset found

list_predicted_assets

list_predicted_assets(
   limit: (int|None) = None, offset: (int|None) = None, page_size: (int|None) = None,
   order_by: (list[str]|None) = None, q: (str|None) = None,
   assignment_status: (str|None) = None, assignment_step_id: (UUID|None) = None,
   assignment_user_id: (str|UUID|None) = None, custom_metadata: (dict|None) = None
)

Description

List PredictedAsset of this Deployment

Examples

assets = deployment.list_predicted_assets()

Arguments

limit (int, optional) : the number of assets that will be retrieved
offset (int, optional) : from where to start accessing the assets. you will retrieve offset:offset+limit assets from the whole list.
page_size (int, optional) : deprecated.
order_by (str, optional) : what property to sort on. Defaults to descending created_at.
q (str, optional) : a query using the Picsellia Query Language. Defaults to None
assignment_status (str, optional) : only with campaigns. It's the desired status for the last assignments linked to your PredictedAsset.
assignment_step_id (UUID, optional) : only with campaigns. It's the desired step of the assignment you want to retrieve.
assignment_user_id (UUID, optional) : only with Campaigns. It's the desired user assigned to the assignments you want to retrieve.
custom_metadata : (dict, optional): filter based on the custom_metadata linked to the asset's Data. Defaults to None

Returns

A MultiAsset object that wraps some Asset that you can manipulate.

list_workers

list_workers()

Description

List all workers of this deployment

Examples

deployment.list_workers()

Returns

List of Worker objects

create_campaign

create_campaign(
   description: (str|None) = None, instructions_file_path: (str|None) = None,
   instructions_text: (str|None) = None, end_date: (date|None) = None,
   auto_add_new_assets: (bool|None) = False,
   auto_close_on_completion: (bool|None) = False
)

Description

Create campaign on a deployment.

Examples

foo_deployment.create_campaign()

Arguments

description (str, optional) : Description of the campaign. Defaults to None.
instructions_file_path (str, optional) : Instructions file path. Defaults to None.
instructions_text (str, optional) : Instructions text. Defaults to None.
end_date (date, optional) : End date of the campaign. Defaults to None.
auto_add_new_assets (bool, optional) : If true, new assets of this deployment will be added as a task in the campaign. Defaults to False.
auto_close_on_completion (bool, optional) : If true, campaign will be close when all tasks will be done. Defaults to False.

Returns

A (ReviewCampaign) object

get_campaign

get_campaign()

Description

Get campaign of a dataset version.

Examples

foo_deployment.get_campaign()

Returns

A (ReviewCampaign) object