Deep learning on Apache Spark with Pytorch
This is an implementation of Pytorch on Spark. The goal of this library is to provide a simple, understandable interface in using Torch on Spark. With SparkTorch, you can easily integrate your deep learning model with a ML Spark Pipeline. Underneath, SparkTorch uses a parameter server to train the Pytorch network in a distributed manner. Through the api, the user can specify the style of training, whether that is Hogwild or async with locking.
Why should I use this?
Like SparkFlow, SparkTorch's main objective is to seamlessly work with Spark's ML Pipelines. This library provides three core components:
- Distributed training for large datasets. Multiple Pytorch models are ran in parallel with one central network that manages gradients. This is useful for training very large datasets that do not fit into a single machine. Barrier execution is also available.
- Full integration with Spark's ML library. This ensures that you can save and load pipelines with your trained model.
- Inference. With SparkTorch, you can load your existing trained model and run inference on billions of records in parallel.
On top of these features, SparkTorch can utilize barrier execution, ensuring that all executors run concurrently during training.
Install SparkTorch via pip:
pip install sparktorch
SparkTorch requires Apache Spark >= 2.4.4, and has only been tested on PyTorch versions >= 1.3.0.
Full Basic Example
from sparktorch import serialize_torch_obj, SparkTorch import torch import torch.nn as nn from pyspark.ml.feature import VectorAssembler from pyspark.sql import SparkSession from pyspark.ml.pipeline import Pipeline spark = SparkSession.builder.appName("examples").master('local').getOrCreate() df = spark.read.option("inferSchema", "true").csv('mnist_train.csv').coalesce(4) network = nn.Sequential( nn.Linear(784, 256), nn.ReLU(), nn.Linear(256, 256), nn.ReLU(), nn.Linear(256, 10), nn.Softmax(dim=1) ) # Build the pytorch object torch_obj = serialize_torch_obj( model=network, criterion=nn.CrossEntropyLoss(), optimizer=torch.optim.Adam, lr=0.0001 ) # Setup features vector_assembler = VectorAssembler(inputCols=df.columns[1:785], outputCol='features') # Create a SparkTorch Model with torch distributed. Barrier execution is on by default for this mode. spark_model = SparkTorch( inputCol='features', labelCol='_c0', predictionCol='predictions', torchObj=torch_obj, iters=50, verbose=1 ) # Can be used in a pipeline and saved. p = Pipeline(stages=[vector_assembler, spark_model]).fit(df) p.save('simple_dnn')
This is a small documentation section on how to SparkTorch. Please look at the examples library for more details.
Creating a Torch Object
To create a Torch object for training, you will need to utilize the
serialize_torch_obj from SparkTorch. To do so,
simply add your network, loss criterion, the optimizer class, and any options as a dictionary to supply to the optimizer
(such as learning rate). A simple example of this is:
from sparktorch import serialize_torch_obj torch_obj = serialize_torch_obj( model=network, criterion=nn.CrossEntropyLoss(), optimizer=torch.optim.Adam, lr=0.0001 )
NOTE: One thing to remember is that if your network is not a sequential, it will need to be saved in a separate file and
available in the python path. An example of this can be found in
There are two main training options with SparkTorch:
hogwild. The async mode utilizes the torch distributed
package, ensuring that the networks are in sync through each iteration. This is the most supported version. When using
this option, you will need to be aware that barrier execution is enforced, meaning that the parallelism will need to match
The Hogwild approach utilizes a Flask Service underneath the hood. When using Hogwild, it is strongly recommended that you use the
useBarrier option to force barrier execution. Below are a list of parameters to SparkTorch and their meaning.
inputCol: Standard Spark InputCol that must be a Vector. labelCol: Standard Spark Label column. Can be null. torchObj: The TorchObj which is described in the `Creating a Torch Object` section. iters: Number of iterations to run per partition. predictionCol: The standard spark prediction column for the dataframe. partitions: Ability to repartition during training. acquireLock: Used in Hogwild only. Forces locking on the server. verbose: Describes whether you want real time logging partitionShuffles: Only used in Hogwild. Will reshuffle data after completing training. port: Only used in hogwild. Server port. useBarrier: Only used in hogwild. Describes whether you want barrier execution. (Async mode uses barrier by default) useVectorOut: Boolean to describe if you want the model output to be a vector (Defaults to float). earlyStopPatience: If greater than 0, it will enforce early stopping based on validation. miniBatch: Minibatch size for training per iteration. (Randomly shuffled) validationPct: Percentage to use for validation. mode: which training mode to use. `async` uses pytorch server. `hogwild` uses the flask service.
Saving and Loading Pipelines
Since saving and loading custom ML Transformers in pure python has not been implemented in PySpark, an extension has been added here to make that possible. In order to save a Pyspark Pipeline with Apache Spark, one will need to use the overwrite function:
p = Pipeline(stages=[va, encoded, spark_model]).fit(df) p.write().overwrite().save("location")
For loading, a Pipeline wrapper has been provided in the pipeline_utils file. An example is below:
from sparktorch.pipeline_util import PysparkPipelineWrapper from pyspark.ml.pipeline import PipelineModel p = PysparkPipelineWrapper.unwrap(PipelineModel.load('location'))
Then you can perform predictions, etc with:
predictions = p.transform(df)
One big thing to remember is to add the
--executor cores 1 option to spark to ensure
each executor is only training one copy of the network. This will especially be needed for gpu training.
Contributions are always welcome. This could be fixing a bug, changing documentation, or adding a new feature. To test new changes against existing tests, we have provided a Docker container which takes in an argument of the python version. This allows the user to check their work before pushing to Github, where travis-ci will run.
For python 3.6
docker build -t local-test --build-arg PYTHON_VERSION=3.6 . docker run --rm local-test:latest bash -i -c "pytest"
Literature and Inspiration
- HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent: https://arxiv.org/pdf/1106.5730.pdf
- Elephas: https://github.com/maxpumperla/elephas
- Scaling Distributed Machine Learning with the Parameter Server: https://www.cs.cmu.edu/~muli/file/parameter_server_osdi14.pdf
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