| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| |
| """ |
| This is a minimal example of how to generate a pycarbon dataset. Generates a |
| sample dataset with some random data. |
| """ |
| |
| import os |
| import argparse |
| |
| import jnius_config |
| |
| import numpy as np |
| from pyspark.sql import SparkSession |
| from pyspark.sql.types import IntegerType |
| |
| from petastorm.codecs import ScalarCodec, CompressedImageCodec, NdarrayCodec |
| from petastorm.unischema import dict_to_spark_row, Unischema, UnischemaField |
| |
| from pycarbon.core.carbon_dataset_metadata import materialize_dataset_carbon |
| |
| from pycarbon.tests import DEFAULT_CARBONSDK_PATH |
| |
| # The schema defines how the dataset schema looks like |
| HelloWorldSchema = Unischema('HelloWorldSchema', [ |
| UnischemaField('id', np.int_, (), ScalarCodec(IntegerType()), False), |
| UnischemaField('image1', np.uint8, (128, 256, 3), CompressedImageCodec('png'), False), |
| UnischemaField('array_4d', np.uint8, (None, 128, 30, None), NdarrayCodec(), False), |
| ]) |
| |
| |
| def row_generator(x): |
| """Returns a single entry in the generated dataset. Return a bunch of random values as an example.""" |
| return {'id': x, |
| 'image1': np.random.randint(0, 255, dtype=np.uint8, size=(128, 256, 3)), |
| 'array_4d': np.random.randint(0, 255, dtype=np.uint8, size=(4, 128, 30, 3))} |
| |
| |
| def generate_pycarbon_dataset(output_url='file:///tmp/carbon_pycarbon_dataset'): |
| blocklet_size_mb = 256 |
| |
| spark = SparkSession.builder.config('spark.driver.memory', '2g').master('local[2]').getOrCreate() |
| sc = spark.sparkContext |
| |
| # Wrap dataset materialization portion. Will take care of setting up spark environment variables as |
| # well as save pycarbon specific metadata |
| rows_count = 10 |
| with materialize_dataset_carbon(spark, output_url, HelloWorldSchema, blocklet_size_mb): |
| rows_rdd = sc.parallelize(range(rows_count)) \ |
| .map(row_generator) \ |
| .map(lambda x: dict_to_spark_row(HelloWorldSchema, x)) |
| |
| spark.createDataFrame(rows_rdd, HelloWorldSchema.as_spark_schema()) \ |
| .coalesce(10) \ |
| .write \ |
| .mode('overwrite') \ |
| .save(path=output_url, format='carbon') |
| |
| |
| if __name__ == '__main__': |
| parser = argparse.ArgumentParser(description='Pycarbon Example II') |
| parser.add_argument('-pp', '--pyspark-python', type=str, default=None, |
| help='pyspark python env variable') |
| parser.add_argument('-pdp', '--pyspark-driver-python', type=str, default=None, |
| help='pyspark driver python env variable') |
| parser.add_argument('-c', '--carbon-sdk-path', type=str, default=DEFAULT_CARBONSDK_PATH, |
| help='carbon sdk path') |
| |
| args = parser.parse_args() |
| jnius_config.set_classpath(args.carbon_sdk_path) |
| |
| if args.pyspark_python is not None and args.pyspark_driver_python is not None: |
| os.environ['PYSPARK_PYTHON'] = args.pyspark_python |
| os.environ['PYSPARK_DRIVER_PYTHON'] = args.pyspark_driver_python |
| elif 'PYSPARK_PYTHON' in os.environ.keys() and 'PYSPARK_DRIVER_PYTHON' in os.environ.keys(): |
| pass |
| else: |
| raise ValueError("please set PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON variables, " |
| "using cmd line -pp PYSPARK_PYTHON_PATH -pdp PYSPARK_DRIVER_PYTHON_PATH, " |
| "or set PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON in system env") |
| |
| generate_pycarbon_dataset() |