link_artifact_to_model

def link_artifact_to_model(
    artifact_version: ArtifactVersionResponse,
    model: Optional[Model] = None,
) -> None

from zenml import step, save_artifact, link_artifact_to_model, Model
import pandas as pd

@step(model=Model(name="iris_classifier", version="1.0"))
def create_and_link_data() -> None:
    # Create some data
    df = pd.DataFrame({"feature": range(100), "label": range(100)})
    
    # Save as artifact
    artifact = save_artifact(
        data=df,
        name="validation_data"
    )
    
    # Link to the model (model inferred from step context)
    link_artifact_to_model(artifact)

from zenml import save_artifact, link_artifact_to_model, Model
import numpy as np

# Create and save an artifact
weights = np.random.rand(10, 10)
artifact = save_artifact(
    data=weights,
    name="model_weights"
)

# Link to specific model
model = Model(name="my_model", version="2.0")
link_artifact_to_model(
    artifact_version=artifact,
    model=model
)

from zenml import step, save_artifact, link_artifact_to_model, Model
import pandas as pd
import numpy as np

@step(model=Model(name="sentiment_model", version="3.0"))
def prepare_model_assets() -> None:
    # Save training data
    train_data = pd.DataFrame({"text": ["sample"], "label": [1]})
    train_artifact = save_artifact(
        data=train_data,
        name="training_data"
    )
    link_artifact_to_model(train_artifact)
    
    # Save vocabulary
    vocab = {"word1": 0, "word2": 1}
    vocab_artifact = save_artifact(
        data=vocab,
        name="vocabulary"
    )
    link_artifact_to_model(vocab_artifact)
    
    # Save embeddings
    embeddings = np.random.rand(100, 300)
    embed_artifact = save_artifact(
        data=embeddings,
        name="embeddings"
    )
    link_artifact_to_model(embed_artifact)

from zenml import register_artifact, link_artifact_to_model, Model
from zenml import Client

client = Client()
artifact_store = client.active_stack.artifact_store

# Register existing data
data_uri = f"{artifact_store.path}/external/test_set.csv"
artifact = register_artifact(
    folder_or_file_uri=data_uri,
    name="test_dataset"
)

# Link to model
model = Model(name="classifier", version="1.5")
link_artifact_to_model(
    artifact_version=artifact,
    model=model
)

from zenml import pipeline, step, save_artifact, link_artifact_to_model, Model
import pandas as pd

model_config = Model(name="recommender", version="1.0")

@step
def create_features() -> pd.DataFrame:
    features = pd.DataFrame({"user_id": range(100), "feature": range(100)})
    return features

@step(model=model_config)
def save_and_link(features: pd.DataFrame) -> None:
    # Save the features
    artifact = save_artifact(
        data=features,
        name="user_features"
    )
    
    # Link to model
    link_artifact_to_model(artifact)

@pipeline(model=model_config)
def feature_pipeline():
    features = create_features()
    save_and_link(features)

from zenml import save_artifact, link_artifact_to_model, Model
from zenml.enums import ModelStages
import pandas as pd

# Create deployment configuration
config = {
    "endpoint": "https://api.example.com/predict",
    "timeout": 30,
    "batch_size": 32
}

artifact = save_artifact(
    data=config,
    name="deployment_config"
)

# Link to production model
production_model = Model(
    name="fraud_detector",
    version=ModelStages.PRODUCTION
)

link_artifact_to_model(
    artifact_version=artifact,
    model=production_model
)

from zenml import step, save_artifact, link_artifact_to_model, Model
import pandas as pd

@step(model=Model(name="my_model", version="1.0"))
def safe_link() -> None:
    df = pd.DataFrame({"data": [1, 2, 3]})
    
    try:
        artifact = save_artifact(data=df, name="data")
        link_artifact_to_model(artifact)
        print("Successfully linked artifact to model")
    except RuntimeError as e:
        print(f"Failed to link artifact: {e}")
        # Handle the error (e.g., log, alert, retry)

from zenml import save_artifact, link_artifact_to_model, Model
import pandas as pd

# Create model
model = Model(name="experiment", version="2024-01-15")

# Save and link training data
train_data = pd.DataFrame({"x": range(800)})
train_artifact = save_artifact(
    data=train_data,
    name="experiment_data",
    version="train"
)
link_artifact_to_model(train_artifact, model)

# Save and link test data
test_data = pd.DataFrame({"x": range(200)})
test_artifact = save_artifact(
    data=test_data,
    name="experiment_data",
    version="test"
)
link_artifact_to_model(test_artifact, model)

from zenml import pipeline, step, get_step_context, save_artifact, link_artifact_to_model
import pandas as pd

@step
def process_data() -> None:
    # Get model from step context
    context = get_step_context()
    
    if context.model:
        # Create artifact
        df = pd.DataFrame({"processed": range(50)})
        artifact = save_artifact(data=df, name="processed_data")
        
        # Link using model from context
        link_artifact_to_model(
            artifact_version=artifact,
            model=context.model
        )
        print(f"Linked to model: {context.model.name}")

from zenml import Model

model = Model(name="my_model", version="1.0")

# Get linked artifact
artifact = model.get_artifact("training_data")

# Load the artifact data
data = model.load_artifact("training_data")