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https://github.com/uber/petastorm

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
https://github.com/uber/petastorm

deep-learning machine-learning parquet parquet-files pyarrow pyspark pytorch sysml tensorflow

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Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

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README 

        

Petastorm

=========



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Petastorm is an open source data access library developed at Uber ATG. This library enables single machine or

distributed training and evaluation of deep learning models directly from datasets in Apache Parquet

format. Petastorm supports popular Python-based machine learning (ML) frameworks such as

`Tensorflow `_, `PyTorch `_, and

`PySpark `_. It can also be used from pure Python code.



Documentation web site: ``_



Installation

------------



.. code-block:: bash



    pip install petastorm



There are several extra dependencies that are defined by the ``petastorm`` package that are not installed automatically.

The extras are: ``tf``, ``tf_gpu``, ``torch``, ``opencv``, ``docs``, ``test``.



For example to trigger installation of GPU version of tensorflow and opencv, use the following pip command:



.. code-block:: bash



    pip install petastorm[opencv,tf_gpu]



Generating a dataset

--------------------



A dataset created using Petastorm is stored in `Apache Parquet `_ format.

On top of a Parquet schema, petastorm also stores higher-level schema information that makes multidimensional arrays into a native part of a petastorm dataset. 



Petastorm supports extensible data codecs. These enable a user to use one of the standard data compressions (jpeg, png) or implement her own.



Generating a dataset is done using PySpark.

PySpark natively supports Parquet format, making it easy to run on a single machine or on a Spark compute cluster.

Here is a minimalistic example writing out a table with some random data.



.. code-block:: python



   import numpy as np

   from pyspark.sql import SparkSession

   from pyspark.sql.types import IntegerType



   from petastorm.codecs import ScalarCodec, CompressedImageCodec, NdarrayCodec

   from petastorm.etl.dataset_metadata import materialize_dataset

   from petastorm.unischema import dict_to_spark_row, Unischema, UnischemaField



   # The schema defines how the dataset schema looks like

   HelloWorldSchema = Unischema('HelloWorldSchema', [

       UnischemaField('id', np.int32, (), 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_petastorm_dataset(output_url='file:///tmp/hello_world_dataset'):

       rowgroup_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 petastorm specific metadata

       rows_count = 10

       with materialize_dataset(spark, output_url, HelloWorldSchema, rowgroup_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') \

               .parquet(output_url)



- ``HelloWorldSchema`` is an instance of a ``Unischema`` object.

  ``Unischema`` is capable of rendering types of its fields into different

  framework specific formats, such as: Spark ``StructType``, Tensorflow

  ``tf.DType`` and numpy ``numpy.dtype``.

- To define a dataset field, you need to specify a ``type``, ``shape``, a

  ``codec`` instance and whether the field is nullable for each field of the

  ``Unischema``.

- We use PySpark for writing output Parquet files. In this example, we launch

  PySpark on a local box (``.master('local[2]')``). Of course for a larger

  scale dataset generation we would need a real compute cluster.

- We wrap spark dataset generation code with the ``materialize_dataset``

  context manager.  The context manager is responsible for configuring row

  group size at the beginning and write out petastorm specific metadata at the

  end.

- The row generating code is expected to return a Python dictionary indexed by

  a field name. We use ``row_generator`` function for that. 

- ``dict_to_spark_row`` converts the dictionary into a ``pyspark.Row``

  object while ensuring schema ``HelloWorldSchema`` compliance (shape,

  type and is-nullable condition are tested).

- Once we have a ``pyspark.DataFrame`` we write it out to a parquet

  storage. The parquet schema is automatically derived from

  ``HelloWorldSchema``.



Plain Python API

----------------

The ``petastorm.reader.Reader`` class is the main entry point for user

code that accesses the data from an ML framework such as Tensorflow or Pytorch.

The reader has multiple features such as:



- Selective column readout

- Multiple parallelism strategies: thread, process, single-threaded (for debug)

- N-grams readout support

- Row filtering (row predicates)

- Shuffling

- Partitioning for multi-GPU training

- Local caching



Reading a dataset is simple using the ``petastorm.reader.Reader`` class which can be created using the

``petastorm.make_reader`` factory method:



.. code-block:: python



   from petastorm import make_reader



    with make_reader('hdfs://myhadoop/some_dataset') as reader:

       for row in reader:

           print(row)



``hdfs://...`` and ``file://...`` are supported URL protocols.



Once a ``Reader`` is instantiated, you can use it as an iterator.



Tensorflow API

--------------



To hookup the reader into a tensorflow graph, you can use the ``tf_tensors``

function:



.. code-block:: python



    from petastorm.tf_utils import tf_tensors



    with make_reader('file:///some/localpath/a_dataset') as reader:

       row_tensors = tf_tensors(reader)

       with tf.Session() as session:

           for _ in range(3):

               print(session.run(row_tensors))



Alternatively, you can use new ``tf.data.Dataset`` API;



.. code-block:: python



    from petastorm.tf_utils import make_petastorm_dataset



    with make_reader('file:///some/localpath/a_dataset') as reader:

        dataset = make_petastorm_dataset(reader)

        iterator = dataset.make_one_shot_iterator()

        tensor = iterator.get_next()

        with tf.Session() as sess:

            sample = sess.run(tensor)

            print(sample.id)



Pytorch API

-----------



As illustrated in

`pytorch_example.py `_,

reading a petastorm dataset from pytorch

can be done via the adapter class ``petastorm.pytorch.DataLoader``,

which allows custom pytorch collating function and transforms to be supplied.



Be sure you have ``torch`` and ``torchvision`` installed:



.. code-block:: bash



    pip install torchvision



The minimalist example below assumes the definition of a ``Net`` class and

``train`` and ``test`` functions, included in ``pytorch_example``:



.. code-block:: python



    import torch

    from petastorm.pytorch import DataLoader



    torch.manual_seed(1)

    device = torch.device('cpu')

    model = Net().to(device)

    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)



    def _transform_row(mnist_row):

        transform = transforms.Compose([

            transforms.ToTensor(),

            transforms.Normalize((0.1307,), (0.3081,))

        ])

        return (transform(mnist_row['image']), mnist_row['digit'])



    transform = TransformSpec(_transform_row, removed_fields=['idx'])



    with DataLoader(make_reader('file:///localpath/mnist/train', num_epochs=10,

                                transform_spec=transform, seed=1, shuffle_rows=True), batch_size=64) as train_loader:

        train(model, device, train_loader, 10, optimizer, 1)

    with DataLoader(make_reader('file:///localpath/mnist/test', num_epochs=10,

                                transform_spec=transform), batch_size=1000) as test_loader:

        test(model, device, test_loader)



If you are working with very large batch sizes and do not need support for Decimal/strings we provide a ``petastorm.pytorch.BatchedDataLoader`` that can buffer using Torch tensors (``cpu`` or ``cuda``) with a signficantly higher throughput.



If the size of your dataset can fit into system memory, you can use an in-memory version dataloader ``petastorm.pytorch.InMemBatchedDataLoader``. This dataloader only reades the dataset once, and caches data in memory to avoid additional I/O for multiple epochs.



Spark Dataset Converter API

---------------------------



Spark converter API simplifies the data conversion from Spark to TensorFlow or PyTorch.

The input Spark DataFrame is first materialized in the parquet format and then loaded as

a ``tf.data.Dataset`` or ``torch.utils.data.DataLoader``.



The minimalist example below assumes the definition of a compiled ``tf.keras`` model and a

Spark DataFrame containing a feature column followed by a label column.



.. code-block:: python



    from petastorm.spark import SparkDatasetConverter, make_spark_converter

    import tensorflow.compat.v1 as tf  # pylint: disable=import-error



    # specify a cache dir first.

    # the dir is used to save materialized spark dataframe files

    spark.conf.set(SparkDatasetConverter.PARENT_CACHE_DIR_URL_CONF, 'hdfs:/...')



    df = ... # `df` is a spark dataframe



    # create a converter from `df`

    # it will materialize `df` to cache dir.

    converter = make_spark_converter(df)



    # make a tensorflow dataset from `converter`

    with converter.make_tf_dataset() as dataset:

        # the `dataset` is `tf.data.Dataset` object

        # dataset transformation can be done if needed

        dataset = dataset.map(...)

        # we can train/evaluate model on the `dataset`

        model.fit(dataset)

        # when exiting the context, the reader of the dataset will be closed



    # delete the cached files of the dataframe.

    converter.delete()



The minimalist example below assumes the definition of a ``Net`` class and

``train`` and ``test`` functions, included in

`pytorch_example.py `_,

and a Spark DataFrame containing a feature column followed by a label column.



.. code-block:: python



    from petastorm.spark import SparkDatasetConverter, make_spark_converter



    # specify a cache dir first.

    # the dir is used to save materialized spark dataframe files

    spark.conf.set(SparkDatasetConverter.PARENT_CACHE_DIR_URL_CONF, 'hdfs:/...')



    df_train, df_test = ... # `df_train` and `df_test` are spark dataframes

    model = Net()



    # create a converter_train from `df_train`

    # it will materialize `df_train` to cache dir. (the same for df_test)

    converter_train = make_spark_converter(df_train)

    converter_test = make_spark_converter(df_test)



    # make a pytorch dataloader from `converter_train`

    with converter_train.make_torch_dataloader() as dataloader_train:

        # the `dataloader_train` is `torch.utils.data.DataLoader` object

        # we can train model using the `dataloader_train`

        train(model, dataloader_train, ...)

        # when exiting the context, the reader of the dataset will be closed



    # the same for `converter_test`

    with converter_test.make_torch_dataloader() as dataloader_test:

        test(model, dataloader_test, ...)



    # delete the cached files of the dataframes.

    converter_train.delete()

    converter_test.delete()



Analyzing petastorm datasets using PySpark and SQL

--------------------------------------------------



A Petastorm dataset can be read into a Spark DataFrame using PySpark, where you can

use a wide range of Spark tools to analyze and manipulate the dataset.



.. code-block:: python



   # Create a dataframe object from a parquet file

   dataframe = spark.read.parquet(dataset_url)



   # Show a schema

   dataframe.printSchema()



   # Count all

   dataframe.count()



   # Show a single column

   dataframe.select('id').show()



SQL can be used to query a Petastorm dataset:



.. code-block:: python



   spark.sql(

      'SELECT count(id) '

      'from parquet.`file:///tmp/hello_world_dataset`').collect()



You can find a full code sample here: `pyspark_hello_world.py `_,



Non Petastorm Parquet Stores

----------------------------

Petastorm can also be used to read data directly from Apache Parquet stores. To achieve that, use

``make_batch_reader`` (and not ``make_reader``). The following table summarizes the differences

``make_batch_reader`` and ``make_reader`` functions.



==================================================================  =====================================================

``make_reader``                                                     ``make_batch_reader``

==================================================================  =====================================================

Only Petastorm datasets (created using materializes_dataset)        Any Parquet store (some native Parquet column types

                                                                    are not supported yet.

------------------------------------------------------------------  -----------------------------------------------------

The reader returns one record at a time.                            The reader returns batches of records. The size of the

                                                                    batch is not fixed and defined by Parquet row-group

                                                                    size.

------------------------------------------------------------------  -----------------------------------------------------

Predicates passed to ``make_reader`` are evaluated per single row.  Predicates passed to ``make_batch_reader`` are evaluated per batch.

------------------------------------------------------------------  -----------------------------------------------------

Can filter parquet file based on the ``filters`` argument.          Can filter parquet file based on the ``filters`` argument

==================================================================  =====================================================



Troubleshooting

---------------



See the Troubleshooting_ page and please submit a ticket_ if you can't find an

answer.



See also

--------



1. Gruener, R., Cheng, O., and Litvin, Y. (2018) *Introducing Petastorm: Uber ATG's Data Access Library for Deep Learning*. URL: https://eng.uber.com/petastorm/

2. QCon.ai 2019: `"Petastorm: A Light-Weight Approach to Building ML Pipelines" `_.



.. _Troubleshooting: docs/troubleshoot.rst

.. _ticket: https://github.com/uber/petastorm/issues/new

.. _Development: docs/development.rst



How to Contribute

=================



We prefer to receive contributions in the form of GitHub pull requests. Please send pull requests against the ``github.com/uber/petastorm`` repository.



- If you are looking for some ideas on what to contribute, check out `github issues `_ and comment on the issue.

- If you have an idea for an improvement, or you'd like to report a bug but don't have time to fix it please a `create a github issue `_.



To contribute a patch:



- Break your work into small, single-purpose patches if possible. It's much harder to merge in a large change with a lot of disjoint features.

- Submit the patch as a GitHub pull request against the master branch. For a tutorial, see the GitHub guides on forking a repo and sending a pull request.

- Include a detailed describtion of the proposed change in the pull request.

- Make sure that your code passes the unit tests. You can find instructions how to run the unit tests `here `_.

- Add new unit tests for your code.



Thank you in advance for your contributions!



See the Development_ for development related information.



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