synapse.ml.io.binary package

Submodules

synapse.ml.io.binary.BinaryFileReader module

synapse.ml.io.binary.BinaryFileReader.BinaryFileFields = ['path', 'bytes']: Names of Binary File Schema field names.

synapse.ml.io.binary.BinaryFileReader.BinaryFileSchema = StructType([StructField('path', StringType(), True), StructField('bytes', BinaryType(), True)])

Schema for Binary Files.

Schema records consist of BinaryFileFields name, Type, and ??: path bytes

synapse.ml.io.binary.BinaryFileReader.isBinaryFile(df, column)[source]

Returns True if the column contains binary files

Parameters

df¶ (DataFrame) – The DataFrame to be processed
column¶ (bool) – The name of the column being inspected

Returns

True if the colum is a binary files column

Return type

bool

synapse.ml.io.binary.BinaryFileReader.readBinaryFiles(self, path, recursive=False, sampleRatio=1.0, inspectZip=True, seed=0)[source]

Reads the directory of binary files from the local or remote (WASB) source This function is attached to SparkSession class.

Example

>>> spark.readBinaryFiles(path, recursive, sampleRatio = 1.0, inspectZip = True)

Parameters

path¶ (str) – Path to the file directory
recursive¶ (b (double) – Fraction of the files loaded into the dataframe

Returns

DataFrame with a single column “value”; see binaryFileSchema for details

Return type

DataFrame

synapse.ml.io.binary.BinaryFileReader.streamBinaryFiles(self, path, sampleRatio=1.0, inspectZip=True, seed=0)[source]

Streams the directory of binary files from the local or remote (WASB) source This function is attached to SparkSession class.

Example

>>> spark.streamBinaryFiles(path, sampleRatio = 1.0, inspectZip = True)

Parameters: path¶ (str) – Path to the file directory
Returns: DataFrame with a single column “value”; see binaryFileSchema for details
Return type: DataFrame

Module contents

SynapseML is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK), LightGBM and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of datasources.

SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. In this vein, SynapseML provides easy to use SparkML transformers for a wide variety of Microsoft Cognitive Services. For production grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.

SynapseML requires Scala 2.12, Spark 3.0+, and Python 3.6+.