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Model slices operate much like Catalog slices, both essentially functioning as saved searches that help filter large datasets into more manageable units. While Catalog slices function support Catalog, Model slices work within model runs. Model slices let you filter model run data rows into manageable units. Here, we show how to use the SDK to:
  • Create an experiment and a model run.
  • Create a slice from model run data.

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Example: create and retrieve model slice

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Before you start

You must import these libraries to use the code examples in this section. 
import labelbox as lb
import uuid
Replace the value of API_KEY with a valid API key to connect to the Labelbox client. 
API_KEY = None
client = lb.Client(API_KEY)

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Create a model experiment and a model run

To interact with model slices, you must create a model experiment with a model run and then create a model slice through the platform. The steps below go over this process. See Slices Slicesfor more information

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Create a model experiment

To create a model experiment, you will need to create an ontology. See Ontology for more information 
#Create an ontology
classification_features = [
    lb.Classification(
        class_type=lb.Classification.Type.CHECKLIST,
        name="Quality Issues",
        options=[
            lb.Option(value="blurry", label="Blurry"),
            lb.Option(value="distorted", label="Distorted")
        ]
    )

]

ontology_builder = lb.OntologyBuilder(
tools=[],
classifications=classification_features
)

ontology = client.create_ontology(
"Ontology from new features",
ontology_builder.asdict(),
media_type=lb.MediaType.Image
)
Attach the ontology during model creation. 
model = client.create_model(
  name="Model Slice Demo",
  ontology_id=ontology.uid
)

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Create a model run from a model experiment

In this step, you will need to create a dataset to attach data rows to our model run. 
# create a sample dataset to send to your model
global_key = str(uuid.uuid4())

test_img_url = {
"row_data":
"https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg",
"global_key":
global_key
}

dataset = client.create_dataset(name="foundry-demo-dataset")
task = dataset.create_data_rows([test_img_url])
task.wait_till_done()

print(f"Errors: {task.errors}")
print(f"Failed data rows: {task.failed_data_rows}")

# Create a model configuration

model_run_name = "Model Slice Demo"
example_config = {
"learning_rate": 0.001,
"batch_size": 32,
}

# Create a model run

model_run = model.create_model_run(name=model_run_name, config=example_config)

# Send the data rows to your model

model_run.upsert_data_rows(global_keys=[global_key])

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Create a model slice

Use the Labelbox app to create a model slice.
1
From Model, select Experiment and select your model experiment.
2
A filter must be active to create a slice. From the Search menu, select Data row and then set the Condition menu to is not one of.
3
Set Search for an id to test and then press Enter to activate the filter. You can add additional filter conditions as needed.
4
Select Save slice, name your slice, and then select Save. When you do this, the Slice menu displays the name of your slice and the number of matching data rows.
5
From the Slice menu, select Copy slice ID.
6
Paste the Slice ID into your your code:
SLICE_ID = ""

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Get model slice

model_slice = client.get_model_slice(SLICE_ID)

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Obtain data row IDs from model slice

Data row identifiers are objects that contain both data row IDs and global keys. 
data_row_identifiers = model_slice.get_data_row_identifiers(model_run.uid)

drids = [dr for dr in data_row_identifiers]

# get both global keys and data row ids

# and utilize the hash method to combine both global keys and data row ids into a dictionary

for dr in drids:
print(f"Data row: {dr.id}, Global Key: {dr.global_key}, dr_gk: {dr.to_hash()}")

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Obtain data row identifiers


data_rows = model_slice.get_data_row_identifiers(model_run.uid)

for data_row in data_rows:
print(data_row)

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Model slice attributes

# name (str)
model_slice.name

# description (str)
model_slice.description

# updated at (datetime)
model_slice.updated_at

# created at (datetime)
model_slice.created_at

# filter (list[dict])
model_slice.filter