1.26.0

1.26.0

New Features

• ML Job: Added support for creating MLJobDefinition (PrPr) and launching jobs with different
  arguments without re-uploading payloads.

# /path/to/repo/my_script.py

def main(*args):
    print("Hello world", *args)

if __name__ == '__main__':
    import sys
    main(*sys.argv[1:])

from snowflake.ml.jobs.job_definition import MLJobDefinition
job_def = MLJobDefinition.register(
  "/path/to/repo/my_script.py",
  # If you register a source directory, provide the entrypoint file:
  # entrypoint="/path/to/repo/my_script.py",
  compute_pool= "test_comput_pool",
  stage_name="payload_stage",
)

job1 = job_def()
job2 = job_def(arg1="ML Job")


from snowflake.ml import jobs
@jobs.remote(compute_pool = "test_compute_pool", stage_name = "payload_stage")
def test_job(arg1: str = "world") -> None:
  print(f"hello {arg1}")

# this is a job definition handle
job_def_remote = test_job

job1 = job_def_remote()
job2 = job_def_remote(arg1="ML Job")

• Job-based Batch Inference (PuPr): ModelVersion.run_batch for job-based batch inference in Snowpark Container
  Services is now in public preview.

from snowflake.ml.registry import Registry
from snowflake.ml.model import OutputSpec

registry = Registry(session)
mv = registry.log_model( ... )

job = mv.run_batch(
    compute_pool = "SYSTEM_COMPUTE_POOL_GPU",
    X=input_df,
    output_spec=OutputSpec(stage_location="@my_db.my_schema.my_stage/output/"),
)

• Registry: Added support for inference parameters via ParamSpec in model signatures. This allows you to define
  constant parameters that can be passed at inference time without being part of the input data.

import pandas as pd
from snowflake.ml.model import custom_model, model_signature
from snowflake.ml.registry import Registry

# Define a custom model with inference parameters
class MyModelWithParams(custom_model.CustomModel):
    @custom_model.inference_api
    def predict(
        self,
        input_df: pd.DataFrame,
        *,
        temperature: float = 1.0,  # keyword-only param with default
    ) -> pd.DataFrame:
        return pd.DataFrame({"output": input_df["feature"] * temperature})

# Create sample data
model = MyModelWithParams(custom_model.ModelContext())
sample_input = pd.DataFrame({"feature": [1.0, 2.0, 3.0]})
sample_output = model.predict(sample_input, temperature=1.0)

# Define ParamSpec for the inference parameter
params = [
    model_signature.ParamSpec(
        name="temperature",
        dtype=model_signature.DataType.FLOAT,
        default_value=1.0,
    ),
]

# Infer signature with params
sig = model_signature.infer_signature(
    input_data=sample_input,
    output_data=sample_output,
    params=params,
)

# Log model with the signature
registry = Registry(session)
mv = registry.log_model(
    model=model,
    model_name="my_model_with_params",
    version_name="v1",
    signatures={"predict": sig},
)

# Run inference with custom parameter value
result = mv.run(sample_input, function_name="predict", params={"temperature": 2.0})

• Feature Store: Added auto_prefix parameter and with_name() method to avoid column name collisions when
  joining multiple feature views in dataset generation.

• Feature Store: Added support for Dynamic Iceberg Tables as the backing storage for Feature Views.
  Use StorageConfig with StorageFormat.ICEBERG to create Iceberg-backed Feature Views that store
  data in open Apache Iceberg format on external cloud storage. A new default_iceberg_external_volume
  parameter is available in FeatureStore to set a default external volume for Iceberg Feature Views.

Bug Fixes

Behavior Changes

Deprecations