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4.0 Migration Guide#

Breaking Changes#

With the release of Hopsworks 4.0, a number of necessary breaking changes have been put in place to improve the overall experience of using the Hopsworks platform. These breaking changes can be categorized in the following areas:

  • Python API

  • Multi-Environment Docker Images

  • On-Demand Transformation Functions

Python API#

A number of significant changes have been made in the Python API Hopsworks 4.0. Previously, in Hopsworks 3.X, there were 3 python libraries used (“hopsworks”, “hsfs” & “hsml”) to develop feature, training & inference pipelines, with the 4.0 release there is now one single “hopsworks” python library that can should be used. For backwards compatibility, it will still be possible to import both the “hsfs” & “hsml” libraries but these are now effectively aliases to the “hopsworks” python library and their use going forward should be considered as deprecated.

Another significant change in the Hopsworks Python API is the use of optional extras to allow a developer to easily import exactly what is needed as part of their work. The main ones are great-expectations and polars. It is arguable whether this is a breaking change but it is important to note depending on how a particular pipeline has been written which may encounter a problem when executing using Hopsworks 4.0.

Finally, there are a number of relatively small breaking changes and deprecated methods to improve the developer experience, these include:

  • connection.init() is now considered deprecated

  • When loading arrow_flight_client, an OptionalDependencyNotFoundError can be now thrown providing more detailed information on the error than the previous ModuleNotFoundError in 3.X.

  • DatasetApi's zip and unzip will now return False when a timeout is exceeded instead of previously throwing an Exception

Multi-Environment Docker Images#

As part of the Hopsworks 4.0 release, an engineering team using Hopsworks can now customize the docker images that they use for their feature, training and inference pipelines. By adding this flexibility, a set of breaking changes are necessary. Instead of having one common docker image for fti pipelines, with the release of 4.0 a number of specific docker images are provided to allow an engineering team using Hopsworks to install exactly what they need to get their feature, training and inference pipelines up and running. This breaking change will require existing customers running Hopsworks 3.X to test their existing pipelines using Hopsworks 4.0 before upgrading their production environments.

On-Demand Transformation Functions#

A number of changes have been made to transformation functions in the last releases of Hopsworks. With 4.0, On-Demand Transformation Functions are now better supported which has resulted in some breaking changes. The following is how transformation functions were used in previous versions of Hopsworks and the how transformation functions are used in the 4.0 release.

#################################################
# Creating transformation funciton Hopsworks 3.8#
#################################################

# Define custom transformation function
def add_one(feature):
    return feature + 1

# Create transformation function
add_one = fs.create_transformation_function(add_one, 
    output_type=int,
    version=1,
)

# Save transformation function
add_one.save()

# Retrieve transformation function
scaler = fs.get_transformation_function(
    name="add_one",
    version=1,
)

# Create feature view
feature_view = fs.get_or_create_feature_view(
    name='serving_fv',
    version=1,
    query=selected_features,
    # Apply your custom transformation functions to the feature `feature_1`
    transformation_functions={
        "feature_1": add_one,
    },
    labels=['target'],
)

#################################################
# Creating transformation funciton Hopsworks 4.0#
#################################################

# Define custom transformation function
@hopsworks.udf(int)
def add_one(feature):
    return feature + 1

# Create feature view
feature_view = fs.get_or_create_feature_view(
    name='serving_fv',
    version=1,
    query=selected_features,
    # Apply the custom transformation functions defined to the feature `feature_1` 
    transformation_functions=[
        add_one("feature_1"),
    ],
    labels=['target'],
)

Note that the number of lines of code required has been significantly reduced using the “@hopsworks.udf” python decorator.