Snowflake ML Python release notes¶

Bug fixes¶

Model Registry bug fixes:

• Fixed failure of model_version.run caused by requiring READ privilege on the model instead of USAGE when the user’s role had only the USAGE privilege.

Feature store bug fixes:

• Fixed failure of register_feature_view with overwrite=True when the existing feature view is external and the new feature view is managed, or vice versa.

New features¶

New Model Registry features:

• Model signatures can now include inference parameters via ParamSpec, allowing you to define constant parameters to be passed at inference time without including them in the input data. Example:

  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})
  

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New Feature Store features:

• New auto_prefix parameter and with_name method to prevent column name collisions when joining multiple feature views in dataset generation.

• Dynamic Iceberg tables can now be used as backing storage for Feature Views. Use StorageConfig with StorageFormat.ICEBERG to store data in 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.

New features¶

New Model Serving features:

• The create_service method accepts a new autocapture argument to indicate whether inference data should be captured (see Autocapture inference logs for realtime inference).

• The create_service and log_model_and_create_service methods now accept an optional min_instances argument to specify the minimum number of instances for the service. The service automatically scales between the specified minimum and maximum instances based on traffic and hardware utilization. If min_instances is 0, the service automatically suspends when no traffic is detected for a period of time. The default value for min_instances is 0.

New features¶

New Feature Store features:

• Tile-based aggregation support using a new Feature API for efficient and point-in-time correct time-series feature computation using pre-computed tiles.

New Model Registry features:

• SentenceTransformer models now support automatic signature inference. When logging a SentenceTransformer model, sample_input_data is optional. The signature is automatically inferred from the model’s embedding dimension when sample input data is not provided.. The encode, encode_query, encode_document, encode_queries, encode_documents methods are supported.

  import sentence_transformers
  from snowflake.ml.registry import Registry
  
  # Create model
  model = sentence_transformers.SentenceTransformer("all-MiniLM-L6-v2")
  
  # Log model without sample_input_data - signature is auto-inferred
  registry = Registry(session)
  mv = registry.log_model(
      model=model,
      model_name="my_sentence_transformer",
      version_name="v1",
  )
  
  # Run inference with auto-inferred signature (input: "text", output: "output")
  import pandas as pd
  result = mv.run(pd.DataFrame({"text": ["Hello world"]}))
  

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New features¶

New ML Jobs features:

• ML Jobs now support Python 3.11 and Python 3.12. Jobs automatically select a runtime environment matching the client Python version.

Bug fixes¶

Model Registry bug fixes:

• Empty output in HuggingFace’s Token Classification (Named Entity Recognition) models no longer causes failures.

Model Serving bug fixes:

• Container statuses are now correctly reported and should not be blank.

New features¶

New Model Registry features:

• You can now remotely log a transformer pipeline model using a Snowpark Container Services (SPCS) job.

  # create reference to the model
  model = huggingface.TransformersPipeline(
      model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
      task="text-generation",
  )
  
  # Remotely log the model, a SPCS job will run async and log the model
  mv = registry.log_model(
      model=model,
      model_name="tinyllama_remote_log",
      target_platforms=["SNOWPARK_CONTAINER_SERVICES"],
      signatures=openai_signatures.OPENAI_CHAT_SIGNATURE,
  )
  

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Behavior changes¶

ML Jobs behavior changes:

• The behavior of the additional_payloads parameter is changing. Use the imports argument to declare additional dependencies, such as ZIP files and Python modules. Local directories and Python files are automatically compressed, and their internal layout is determined by the specified import path. The import path applies only to local directories, Python files, and staged python files; it has no effect on other import types. When referencing files in a stage, only individual files are supported, not directories.

Experiment Tracking behavior changes:

• ExperimentTracking is now a singleton class.

Bug fixes¶

Experiment Tracking bug fixes:

• Reaching the run metadata size limit in log_metrics or log_params now issues a warning instead of raising an exception.

Model Registry bug fixes:

• ModelVersion.run now raises a ValueError if the model is a SPCS-only model and service_name is not provided.

New preview features¶

• The create_service method now accepts the Boolean argument autocapture to indicate whether inference data is automatically captured.

New release features¶

New Model Registry features:

• The new snowflake.ml.model.models.huggingface.TransformersPipeline class is intended to replace snowflake.ml.model.models.huggingface_pipeline.HuggingfacePipelineModel, although the older class is not yet deprecated. The new class knows model signatures for common tasks so that you do not need to specify them manually. The supported tasks are currently:

  • fill-mask

  • question-answering

  • summarization

  • table-question-answering

  • text2text-generation

  • text-classification (alias sentiment-analysis)

  • text-generation

  • token-classification (alias ner)

  • translation

  • translation_xx_to_yy

  • zero-shot-classification (lets you log models without loading them into memory)

• The list_services API now shows an internal endpoint that can be called from another SPCS node or notebook without Enterprise Application Integration. It also indicates whether autocapture is enabled for each service.

New DataConnector features:

• New to_huggingface_dataset method converts Snowflake data to HuggingFace datasets. Supports both in-memory Dataset (streaming=False) and streaming IterableDataset (streaming=True) modes.

Deprecation notices¶

• The snowflake.ml.model.models.huggingface_pipeline.HuggingfacePipelineModel class has been deprecated and will be removed in a future release.