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Spark Model Structure

Any Spark scoring function code to be executed by the Spark-Runtime-Servive needs to comply with the following structure:

Code Block

language	py

from __future__ import print_function

from pyspark.sql import SparkSession
from pyspark.ml.classification import RandomForestClassificationModel

# modelop.score
def <my-score-function-name>(external_inputs, external_outputs, external_model_assets):
    spark = SparkSession.builder.appName("<My-App-Name>").getOrCreate()
    
    ... 
    
    # Read an input file that was uploaded to HDFS by the user
    df = (spark.read
            .format("csv")
            .option('header', 'true')
            .load(external_inputs[0]['fileUrl']))
    
    # Read a model asset file/folder that was uploaded to HDFS by the user
    model = RandomForestClassificationModel.load(external_model_assets[0]['fileUrl'])
      
    ...
       
    # Write to an output file that will be created in HDFS
    predictions.write.csv(external_outputs[0]['fileUrl'])

As defined above, the scoring function receives the next 3 input parameters of type list.

External_inputs - generated from the inputData list
External_outputs - generated from the outputData list
External_model_assets - generated from the modelAssets list (contains assets of type EXTERNAL_FILE).

For additional information on how these list of files are handled, please refer to PySpark Job Input and Output Data.

Please note that there could be other types of assets included as part of modelAssets, however this process only includes EXTERNAL_FILE assets (e.g: FILE and SOURCE_CODE assets are not included).

Info
It is assumed that aforementioned external file assets exist in HDFS before the job is created and the user knows their location.

Spark-runtime-service job processing

Every time a Spark job is about to be executed, the Spark-runtime-service generates the next list of files, before job execution.

model_source_code.py
1. Contains the primary source code - extracted from model.storedModel.modelAssets where "assetType" = "SOURCE_CODE" and "primaryModelSource" = true

model_job_metadata.py

Contains the values related to all assets involved during job execution ( external_inputs, external_outputs, and external_model_assets in the following format):

Code Block

language	py

external_inputs = [
  {
    "name": "test.csv",
    "assetType": "EXTERNAL_FILE",
    "fileUrl": "hdfs:///hadoop/test.csv",
    "filename": "test.csv",
    "fileFormat": "CSV"
  }]

external_outputs = [
   {
    "name": "titanic_output.csv",
    "assetType": "EXTERNAL_FILE",
    "fileUrl": "hdfs:///hadoop/titanic_output.csv",
    "filename": "titanic_output.csv",
    "fileFormat": "CSV"
  }]


external_model_assets = [
  {
    "name": "titanic",
    "assetType": "EXTERNAL_FILE",
    "fileUrl": "hdfs:///hadoop/titanic",
    "filename": "titanic"
  }]

method_to_be_executed = "<my-score-function-name>"

ModelOpPySparkDriver.py
1. Contains the default Spark's application execution code (for additional details please see here).

Info
This file is the one in charge of calling the score function.

If modelAssets contains assets where "assetType" = "FILE", then it creates a file for each asset that matches the aforementioned criteria.
If modelAssets contains assets where "assetType" = "SOURCE_CODE" and "primaryModelSource" = false, then it creates a file for each asset that matches the aforementioned criteria.

Once all required files have been created at the local file system, then files are uploaded to the cluster as part of the spark-submit execution.

Sample modelBatchJob JSON

Expand

title	modelBatchJob JSON

Code Block

language	json

{
  "jobType": "MODEL_BATCH_JOB",
  "jobStatus": "CREATED",
  "model": {
    "storedModel": {
      "modelAssets": [
        {
          "name": "titanic.py",
          "assetType": "SOURCE_CODE",
          "primaryModelSource": true,
          "scoreFunction": "score_function",
          "sourceCode": "from __future__ import print_function\n\nimport time\nimport sys\nfrom random import random\nfrom operator import add\n\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.functions import col\nfrom pyspark.sql.functions import isnull, when, count\nfrom pyspark.sql.functions import udf\nfrom pyspark.sql.types import ArrayType, FloatType\nfrom pyspark.ml.feature import StringIndexer\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.classification import RandomForestClassificationModel\n\ndef score_function(input_files, output_files, model_attachment_files):\n   spark = SparkSession\\\n       .builder\\\n       .appName(\"Titanic\")\\\n       .getOrCreate()\n\n   print('TITANIC FILE URL = ' + str(model_attachment_files[0]['fileUrl']))\n   df = (spark.read\n         .format(\"csv\")\n         .option('header', 'true')\n         .load(input_files[0]['fileUrl']))\n\n   dataset = df.select(col('Pclass').cast('float'),\n                       col('Sex'),\n                       col('Age').cast('float'),\n                       col('Fare').cast('float'),\n                       col('Embarked')\n                       )\n\n   dataset = dataset.replace('?', None)\\\n       .dropna(how='any')\n\n   dataset = StringIndexer(\n       inputCol='Sex',\n       outputCol='Gender',\n       handleInvalid='keep').fit(dataset).transform(dataset)\n\n   dataset = StringIndexer(\n       inputCol='Embarked',\n       outputCol='Boarded',\n       handleInvalid='keep').fit(dataset).transform(dataset)\n\n   dataset = dataset.drop('Sex')\n   dataset = dataset.drop('Embarked')\n   required_features = ['Pclass',\n                   'Age',\n                   'Fare',\n                   'Gender',\n                   'Boarded'\n                  ]\n\n   assembler = VectorAssembler(inputCols=required_features, outputCol='features')\n   transformed_data = assembler.transform(dataset)\n\n   time.sleep(90)\n\n   model = RandomForestClassificationModel.load(model_attachment_files[0]['fileUrl'])\n\n   predictions = model.transform(transformed_data)\n   get_propensity = udf(lambda x: x[1], ArrayType(FloatType()))\n   print(predictions.head(5))\n   predictions = predictions.select('Pclass',\n                       'Age',\n                       'Gender',\n                       'Fare',\n                       'Boarded',\n                       'prediction'\n                       )\n\n   print(predictions.head(5))\n   predictions.write.csv(output_files[0]['fileUrl'])\n   spark.stop()"
        },
        {
          "name": "titanic",
          "assetType": "EXTERNAL_FILE",
          "fileUrl": "hdfs:///hadoop/titanic",
          "filename": "titanic"
        }
      ],
      "modelMetaData": {
        "type": "UNKNOWN"
      }
    }
  },
  "inputData": [
    {
      "name": "test.csv",
      "assetType": "EXTERNAL_FILE",
      "fileUrl": "hdfs:///hadoop/test.csv",
      "filename": "test.csv",
      "fileFormat": "CSV"
    }
  ],
  "outputData": [
    {
      "name": "titanic_output.csv",
      "assetType": "EXTERNAL_FILE",
      "fileUrl": "hdfs:///hadoop/titanic_output.csv",
      "filename": "titanic_output.csv",
      "fileFormat": "CSV"
    }
  ],
  "targetEngine": {
    "engineType": "SPARK_RUNTIME",
    "name": "spark-runtime-service",
    "executorMemory": "512MB",
    "sparkConf": {
      "--master": "yarn"
    }
  }
}

Versions Compared

Old Version 3

New Version 4

Key

Spark Model Structure

Spark-runtime-service job processing

Sample modelBatchJob JSON