This is an implementation of TensorFlow on Spark. The goal of this library is to provide a simple, understandable interface in using TensorFlow on Spark. With SparkFlow, you can easily integrate your deep learning model with a ML Spark Pipeline. Underneath, SparkFlow uses a parameter server to train the TensorFlow 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?
While there are other libraries that use TensorFlow on Apache Spark, SparkFlow's objective is to work seamlessly with ML Pipelines, provide a simple interface for training TensorFlow graphs, and give basic abstractions for faster development. For training, SparkFlow uses a parameter server which lives on the driver and allows for asynchronous training. This tool provides faster training time when using big data.
Install SparkFlow via pip:
pip install sparkflow
SparkFlow requires Apache Spark >= 2.0, flask, dill, and TensorFlow to be installed. As of sparkflow >= 0.7.0, only python >= 3.5 will be supported.
Simple MNIST Deep Learning Example
from sparkflow.graph_utils import build_graph from sparkflow.tensorflow_async import SparkAsyncDL import tensorflow as tf from pyspark.ml.feature import VectorAssembler, OneHotEncoder from pyspark.ml.pipeline import Pipeline #simple tensorflow network def small_model(): x = tf.placeholder(tf.float32, shape=[None, 784], name='x') y = tf.placeholder(tf.float32, shape=[None, 10], name='y') layer1 = tf.layers.dense(x, 256, activation=tf.nn.relu) layer2 = tf.layers.dense(layer1, 256, activation=tf.nn.relu) out = tf.layers.dense(layer2, 10) z = tf.argmax(out, 1, name='out') loss = tf.losses.softmax_cross_entropy(y, out) return loss df = spark.read.option("inferSchema", "true").csv('mnist_train.csv') mg = build_graph(small_model) #Assemble and one hot encode va = VectorAssembler(inputCols=df.columns[1:785], outputCol='features') encoded = OneHotEncoder(inputCol='_c0', outputCol='labels', dropLast=False) spark_model = SparkAsyncDL( inputCol='features', tensorflowGraph=mg, tfInput='x:0', tfLabel='y:0', tfOutput='out:0', tfLearningRate=.001, iters=20, predictionCol='predicted', labelCol='labels', verbose=1 ) p = Pipeline(stages=[va, encoded, spark_model]).fit(df) p.write().overwrite().save("location")
For a couple more, visit the examples directory. These examples can be run with Docker as well from the provided Dockerfile and Makefile. This can be done with the following command:
make docker-build make docker-run-dnn
Once built, there are also commands to run the example CNN and an autoencoder.
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 sparkflow.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)
Serializing Tensorflow Graph for SparkAsyncDL
You may have already noticed the build_graph function in the example above. This serializes the Tensorflow graph for training on Spark. The build_graph function only takes one parameter, which is a function that should include the Tensorflow variables. Below is an example Tensorflow graph function:
def small_model(): x = tf.placeholder(tf.float32, shape=[None, 784], name='x') y = tf.placeholder(tf.float32, shape=[None, 10], name='y') layer1 = tf.layers.dense(x, 256, activation=tf.nn.relu) layer2 = tf.layers.dense(layer1, 256, activation=tf.nn.relu) out = tf.layers.dense(layer2, 10) z = tf.argmax(out, 1, name='out') loss = tf.losses.softmax_cross_entropy(y, out) return loss
Then to use the build_graph function:
from sparkflow.graph_utils import build_graph mg = build_graph(small_model)
Using SparkAsyncDL and Options
SparkAsyncDL has a few options that one can use for training. Not all of the parameters are required. Below is a description of each of the parameters:
inputCol: Spark dataframe inputCol. Similar to other spark ml inputCols tensorflowGraph: The protobuf tensorflow graph. You can use the utility function in graph_utils to generate the graph for you tfInput: The tensorflow input. This points us to the input variable name that you would like to use for training tfLabel: The tensorflow label. This is the variable name for the label. tfOutput: The tensorflow raw output. This is for your loss function. tfOptimizer: The optimization function you would like to use for training. Defaults to adam tfLearningRate: Learning rate of the optimization function iters: number of iterations of training predictionCol: The prediction column name on the spark dataframe for transformations partitions: Number of partitions to use for training (recommended on partition per instance) miniBatchSize: size of the mini batch. A size of -1 means train on all rows miniStochasticIters: If using a mini batch, you can choose number of mini iters you would like to do with the batch size above per epoch. A value of -1 means that you would like to run mini-batches on all data in the partition acquireLock: If you do not want to utilize hogwild training, this will set a lock shufflePerIter: Specifies if you want to shuffle the features after each iteration tfDropout: Specifies the dropout variable. This is important for predictions toKeepDropout: Due to conflicting TF implementations, this specifies whether the dropout function means to keep a percentage of values or to drop a percentage of values. verbose: Specifies log level of training results labelCol: Label column for training partitionShuffles: This will shuffle your data after iterations are completed, then run again. For example, if you have 2 partition shuffles and 100 iterations, it will run 100 iterations then reshuffle and run 100 iterations again. The repartition hits performance and should be used with care. optimizerOptions: Json options to apply to tensorflow optimizers.
As of SparkFlow version 0.2.1, TensorFlow optimization configuration options can be added to SparkAsyncDL for more control over the optimizer. While the user can supply the configuration json directly, there are a few provided utility functions that include the parameters necessary. An example is provided below.
from sparkflow.graph_utils import build_adam_config adam_config = build_adam_config(learning_rate=0.001, beta1=0.9, beta2=0.999) spark_model = SparkAsyncDL( ..., optimizerOptions=adam_config )
Loading pre-trained Tensorflow model
To load a pre-trained Tensorflow model and use it as a spark pipeline, it can be achieved using the following code:
from sparkflow.tensorflow_model_loader import load_tensorflow_model df = spark.read.parquet("data") loaded_model = load_tensorflow_model( path="./test_model/to_load", inputCol="features", tfInput="x:0", tfOutput="out/Sigmoid:0" ) data_with_predictions = loaded_model.transform(df)
One big thing to remember, especially for larger networks, is to add the
--executor cores 1 option to spark to ensure
each instance is only training one copy of the network. This will especially be needed for gpu training as well.
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 2.7 (sparkflow <= 0.6.0):
docker build -t local-test --build-arg PYTHON_VERSION=2.7 . docker run --rm local-test:latest bash -i -c "python tests/dl_runner.py"
docker build -t local-test --build-arg PYTHON_VERSION=3.6 . docker run --rm local-test:latest bash -i -c "python tests/dl_runner.py"
Future planned features
- Hyperopt implementation for smaller and larger datasets
- AWS EMR guides
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