whekey.blogg.se - Cisco ise 2.4 patch 5 high disk space utilization bug

This is useful in a common Python streaming workloads for example, Writing streaming aggregates in update mode using MERGE and foreachBatch.

Support for running Delta Lake SQL operations in Python foreachBatch: We have fixed the known limitation of not being able to write to Delta tables from inside foreachBatch of a Structured Streaming query defined in Python.

With it you can initialize a model only once and apply the model to many input batches, which can result in a 2-3x speedup for models like ResNet50. In Databricks Runtime 5.5, we backported a new pandas UDF type called “scalar iterator” from Apache Spark master.

However, you might have to initialize the model for every record batch, which introduces overhead. The binary file data source enables you to run model inference tasks in parallel from Spark tables using a scalar pandas UDF. In Databricks Runtime 5.5, we have added an option, recursiveFileLookup, to load files recursively from nested input directories.

In Databricks Runtime 5.4, we already made available the binary file data source to help ETL arbitrary files such as images, into Spark tables. Machine learning tasks, especially in the image and video domain, often have to operate on a large number of files. Faster model inference pipelines with improved binary file data source and scalar iterator pandas UDF (Public Preview)