John Snow Labs 6.1.0 has been released with the following upgrades:

• Bump Enterprise-NLP to 6.1.0
• Bump Visual-NLP to 6.1.0
• Bump Spark-NLP to 6.1.1

John Snow Labs 6.0.4 comes with the following upgrades:

• Bump Enterprise-NLP to 6.0.4
• Bump Spark-NLP to 6.0.4

This release comes with improvements to the Snowflake and Docker Integrations, enabling now you to now deploy custom pipelines , any pretrained john snow labs pipeline or any NLU Pipelin as a Snowflake UDF or Docker container. Additionally Spark-NLP and Enterprise-NLP are bumped to version 6.0.3.

Improvements to Docker integration

• Docs have been updated to reflect changes

Instead of providing a nlu model reference for deploying a model, users have now 2 options

Deploy Pipeline via Name, Language and Bucket

nlp.build_image(pipeline_name=pipe_name,  pipeline_language=pipe_lang,  pipeline_bucket=pipe_bucket)

Deploy custom pipeline by providing a fitted pipeline object

custom_pipe = nlp.Pipeline(stages=[...]).fit(df)
nlp.build_image(custom_pipe=custom_pipe)

Deploy NLU pipelines as container

Any nlu pipeline can still be deployed as a custom pipe

# Load Model
pipe = nlp.load(model_nlu_ref)  
# Predict so that under the hood pipeline is fitted and vanilla_transformer_pipe attribute is avaiable
pipe.predict('')  
# Now we can just handle it like a custom pipeline
nlp.build_image(custom_pipe=pipe.vanilla_transformer_pipe)

Improvements to Snowflake integration

• Docs have been updated to reflect changes
• Compute pool parameters compute_pool_min_nodes,compute_pool_max_nodes,compute_pool_instance_family can now be configured in nlp.snowflake_common_setup with values defined in the Snowflake Documentation

Since the Snowflake integration uses the Docker, all of the changes for the docker utilities are reflected into Snowflake as well.

Instead of providing a nlu model reference for deploying a model, users have now 2 options

Deploy Pipeline via Name, Language and Bucket

nlp.build_image(pipeline_name=pipe_name,  pipeline_language=pipe_lang,  pipeline_bucket=pipe_bucket)

Deploy custom pipeline by providing a fitted pipeline object

custom_pipe = nlp.Pipeline(stages=[...]).fit(df)
nlp.build_image(custom_pipe=custom_pipe)

Deploy NLU pipelines as container
Any nlu pipeline can still be deployed

# Load Model
pipe = nlp.load(model_nlu_ref)  
# Predict so that under the hood pipeline is fitted and vanilla_transformer_pipe attribute is avaiable
pipe.predict('')  
# Now we can just handle it like a custom pipeline
nlp.build_image(custom_pipe=pipe.vanilla_transformer_pipe)

Version Bumps

The following dependency versions have been bumped:

• Spark NLP to 6.0.3
• Enterprise NLP to 6.0.3

Bug Fixes

• Fixed bug causing errors when creating Databricks cluster with nlp.install_to_databricks()

John Snow Labs 6.0.2 comes with the following upgrades:

• Bump Enterprise-NLP to 6.0.2

John Snow Labs 6.0.1 comes with the following upgrades:

• Bump Spark-NLP to 6.0.1
• Bump Enterprise-NLP to 6.0.1

John Snow Labs 6.0.0 comes with the following upgrades:

• Bump Spark-NLP to 6.0.0
• Bump Enterprise-NLP to 6.0.0
• Bump Visual-NLP to 6.0.0

Additionally a few bugfixes and improvements have been made:

• Deprecated pkg_resources usage and refactored modules depending on it which caused import errors in some python versions
• Made import of _shared_pyspark_ml_param more roubust which caused import errors in some python versions

John Snow Labs 5.5.5 comes with the following upgrades:

• Bump Medical NLP to 5.5.3
• Bump Spark-NLP to 5.5.3

John Snow Labs 5.5.4 comes with the following upgrades and fixes:

• Bump Visual NLP to 5.5.0
• Fix bug causing browser-based install to fail if you only have PAYG licenses in my.jsl

John Snow Labs 5.5.3 comes with the following upgrades:

• Bump Enterprise NLP to 5.5.2
• Bump Spark NLP to 5.5.2

John Snow Labs 5.5.1 comes with various improvements to the Snowflake utilities and docs.

• New Snowflake Tutorial Notebook for creating Snowflake UDFs and calling them
• Updated Snowflake Utility Docs
• Automated Login to Docker Repo while creating Snowflake UDF
• nlp.deploy_as_snowflake_udf now blocks until the Snowflake UDF is successfully created or timeout occurs
• Less verbose logging when creating a snowflake UDF