petastorm 0.4.3rc0

Petastorm is a library enabling the use of Parquet storage from Tensorflow, Pytorch, and other Python-based ML training frameworks.

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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 <http://www.tensorflow.org/>`_, `PyTorch <https://pytorch.org/>`_, and
`PySpark <http://spark.apache.org/docs/latest/api/python/pyspark.html>`_. It can also be used from pure Python code.

Documentation web site: `<https://petastorm.readthedocs.io>`_


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 <https://parquet.apache.org/>`_ 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

HelloWorldSchema = Unischema('HelloWorldSchema', [
UnischemaField('id', np.int32, (), ScalarCodec(IntegerType()), False),
UnischemaField('image1', np.uint8, (128, 256, 3), CompressedImageCodec('png'), False),
UnischemaField('other_data', 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)),
'other_data': np.random.randint(0, 255, dtype=np.uint8, size=(4, 128, 30, 3))}


def generate_hello_world_dataset(output_url='file:///tmp/hello_world_dataset'):
rows_count = 10
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
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:

.. code-block:: python

with 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

with 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

with 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 <https://github.com/uber/petastorm/blob/master/examples/mnist/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'])

with DataLoader(Reader('file:///localpath/mnist/train', num_epochs=10),
batch_size=64, transform=_transform_row) as train_loader:
train(model, device, train_loader, 10, optimizer, 1)
with DataLoader(Reader('file:///localpath/mnist/test', num_epochs=10),
batch_size=1000, transform=_transform_row) as test_loader:
test(model, device, test_loader)

PySpark and SQL
---------------

Using the Parquet data format, which is natively supported by Spark, makes it possible to use a wide range of Spark
tools to analyze and manipulate the dataset. The example below shows how to read a Petastorm dataset
as a Spark RDD object:

.. 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 <https://github.com/uber/petastorm/blob/master/examples/hello_world/pyspark_hello_world.py>`_,

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Troubleshooting
---------------

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


Development
-----------

See the Development_ page for instructions on how to develop Petastorm and
run tests.


Publications
------------

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/


.. _Troubleshooting: docs/troubleshoot.rst
.. _ticket: https://github.com/uber/petastorm/issues/new
.. _Development: docs/development.rst