Skip to content

[1.0.6]
Compare
Choose a tag to compare
@sfc-gh-kdama sfc-gh-kdama released this 01 Sep 19:43
· 34 commits to main since this release
1.0.6
f0326eb
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

New Features

  • Model Registry: add create_if_not_exists parameter in constructor.
  • Model Registry: Added get_or_create_model_registry API.
  • Model Registry: Added support for using GPU inference when deploying XGBoost (xgboost.XGBModel and xgboost.Booster), PyTorch (torch.nn.Module and torch.jit.ScriptModule) and TensorFlow (tensorflow.Module and tensorflow.keras.Model) models to Snowpark Container Services.
  • Model Registry: When inferring model signature, Sequence of built-in types, Sequence of numpy.ndarray, Sequence of torch.Tensor, Sequence of tensorflow.Tensor and Sequence of tensorflow.Tensor can be used instead of only List of them.
  • Model Registry: Added get_training_dataset API.
  • Model Development: Size of metrics result can exceed previous 8MB limit.
  • Model Registry: Added support save/load/deploy HuggingFace pipeline object (transformers.Pipeline) and our wrapper (snowflake.ml.model.models.huggingface_pipeline.HuggingFacePipelineModel) to it. Using the wrapper to specify configurations and the model for the pipeline will be loaded dynamically when deploying. Currently, following tasks are supported to log without manually specifying model signatures:
    • "conversational"
    • "fill-mask"
    • "question-answering"
    • "summarization"
    • "table-question-answering"
    • "text2text-generation"
    • "text-classification" (alias "sentiment-analysis" available)
    • "text-generation"
    • "token-classification" (alias "ner" available)
    • "translation"
    • "translation_xx_to_yy"
    • "zero-shot-classification"

Bug Fixes

  • Model Development: Fixed a bug when using simple imputer with numpy >= 1.25.
  • Model Development: Fixed a bug when inferring the type of label columns.

Behavior Changes

  • Model Registry: log_model() now return a ModelReference object instead of a model ID.
  • Model Registry: When deploying a model with 1 target method only, the target_method argument can be omitted.
  • Model Registry: When using the snowflake-ml-python with version newer than what is available in Snowflake Anaconda Channel, embed_local_ml_library option will be set as True automatically if not.
  • Model Registry: When deploying a model to Snowpark Container Services and using GPU, the default value of num_workers will be 1.
  • Model Registry: keep_order and output_with_input_features in the deploy options have been removed. Now the behavior is controlled by the type of the input when calling model.predict(). If the input is a pandas.DataFrame, the behavior will be the same as keep_order=True and output_with_input_features=False before. If the input is a snowpark.DataFrame, the behavior will be the same as keep_order=False and output_with_input_features=True before.
  • Model Registry: When logging and deploying PyTorch (torch.nn.Module and torch.jit.ScriptModule) and TensorFlow (tensorflow.Module and tensorflow.keras.Model) models, we no longer accept models whose input is a list of tensor and output is a list of tensors. Instead, now we accept models whose input is 1 or more tensors as positional arguments, and output is a tensor or a tuple of tensors. The input and output dataframe when predicting keep the same as before, that is every column is an array feature and contains a tensor.
Assets 2
Loading