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Horovod on Spark

The horovod.spark package provides a convenient wrapper around Horovod that makes running distributed training jobs in Spark clusters easy.

In situations where training data originates from Spark, this enables a tight model design loop in which data processing, model training, and model evaluation are all done in Spark.

We provide two APIs for running Horovod on Spark: a high level Estimator API and a lower level Run API. Both use the same underlying mechanism to launch Horovod on Spark executors, but the Estimator API abstracts the data processing (from Spark DataFrames to deep learning datasets), model training loop, model checkpointing, metrics collection, and distributed training.

We recommend using Horovod Spark Estimators if you:

  • Are using Keras (tf.keras or keras) or PyTorch for training.
  • Want to train directly on a Spark DataFrame from pyspark.
  • Are using a standard gradient descent optimization process as your training loop.

If for whatever reason the Estimator API does not meet your needs, the Run API offers more fine-grained control.

Installation

When installing Horovod for usage with Spark, use the extra [spark] to install all Spark dependencies as well:

$ ... pip install horovod[spark]

Note that Horovod Spark Estimators require the following:

  • horovod >= 0.19.0
  • pyspark >= 2.3.2

Not included in the list of dependencies by [spark] are deep learning frameworks (TensorFlow or PyTorch). Horovod Spark Estimators additionally require at least one of these combinations:

  • tensorflow-gpu >= 1.12.0 or tensorflow >= 1.12.0 (for KerasEstimator)
  • torch >= 1.0.0 and tensorboard >= 1.14.0 (for TorchEstimator)

Horovod Spark Estimators

Horovod Spark Estimators allow you to train your deep neural network directly on an existing Spark DataFrame, leveraging Horovod’s ability to scale across multiple workers, without any specialized code for distributed training:

from tensorflow import keras
import tensorflow as tf
import horovod.spark.keras as hvd

model = keras.models.Sequential()
    .add(keras.layers.Dense(8, input_dim=2))
    .add(keras.layers.Activation('tanh'))
    .add(keras.layers.Dense(1))
    .add(keras.layers.Activation('sigmoid'))

# NOTE: unscaled learning rate
optimizer = keras.optimizers.SGD(lr=0.1)
loss = 'binary_crossentropy'

store = HDFSStore('/user/username/experiments')
keras_estimator = hvd.KerasEstimator(
    num_proc=4,
    store=store,
    model=model,
    optimizer=optimizer,
    loss=loss,
    feature_cols=['features'],
    label_cols=['y'],
    batch_size=32,
    epochs=10)


keras_model = keras_estimator.fit(train_df) \
    .setOutputCols(['predict'])
predict_df = keras_model.transform(test_df)

The Estimator hides the complexity of gluing Spark DataFrames to a deep learning training script, reading data into a format interpretable by the training framework, and distributing the training using Horovod. The user only needs to provide a Keras or PyTorch model, and the Estimator will do the work of fitting it to the DataFrame.

After training, the Estimator returns a Transformer representation of the trained model. The model transformer can be used like any Spark ML transformer to make predictions on an input DataFrame, writing them as new columns in the output DataFrame.

Estimators can be used to track experiment history through model checkpointing, hot-start retraining, and metric logging (for Tensorboard) using the Estimator Store abstraction. Stores are used for persisting all training artifacts including intermediate representations of the training data. Horovod natively supports stores for HDFS and local filesystems.

End-to-end example

keras_spark_rossmann_estimator.py script provides an example of end-to-end data preparation and training of a model for the Rossmann Store Sales Kaggle competition. It is inspired by an article An Introduction to Deep Learning for Tabular Data and leverages the code of the notebook referenced in the article. The example is split into three parts:

  1. The first part performs complicated data preprocessing over an initial set of CSV files provided by the competition and gathered by the community.
  2. The second part defines a Keras model and performs a distributed training of the model using Horovod on Spark.
  3. The third part performs prediction using the best model and creates a submission file.

To run the example, be sure to install Horovod with [spark], then:

$ wget https://raw.githubusercontent.com/horovod/horovod/master/examples/keras_spark_rossmann_estimator.py
$ wget http://files.fast.ai/part2/lesson14/rossmann.tgz
$ tar zxvf rossmann.tgz
$ python keras_spark_rossmann_estimator.py

Training on existing Parquet datasets

If your data is already in the Parquet format and you wish to train on it with Horovod Spark Estimators, you can do so without needing to reprocess the data in Spark. Using Estimator.fit_on_parquet(), you can train directly on an existing Parquet dataset:

store = HDFSStore(train_path='/user/username/training_dataset', val_path='/user/username/val_dataset')
keras_estimator = hvd.KerasEstimator(
    num_proc=4,
    store=store,
    model=model,
    optimizer=optimizer,
    loss=loss,
    feature_cols=['features'],
    label_cols=['y'],
    batch_size=32,
    epochs=10)

keras_model = keras_estimator.fit_on_parquet()

The resulting keras_model can then be used the same way as any Spark Transformer, or you can extract the underlying Keras model and use it outside of Spark:

model = keras_model.getModel()
pred = model.predict([np.ones([1, 2], dtype=np.float32)])

This approach will work on datasets created using horovod.spark.common.util.prepare_data. It will also work with any Parquet file that contains no Spark user-defined data types (like DenseVector or SparseVector). It's recommended to use prepare_data to ensure the data is properly prepared for training even if you have an existing dataset in Parquet format. Using prepare_data allows you to properly partition the dataset for the number of training processes you intend to use, as well as compress large sparse data columns:

store = HDFSStore(train_path='/user/username/training_dataset', val_path='/user/username/val_dataset')
with util.prepare_data(num_processes=4,
                       store=store,
                       df=df,
                       feature_columns=['features'],
                       label_columns=['y'],
                       validation=0.1,
                       compress_sparse=True):
    keras_estimator = hvd.KerasEstimator(
        num_proc=4,
        store=store,
        model=model,
        optimizer=optimizer,
        loss=loss,
        feature_cols=['features'],
        label_cols=['y'],
        batch_size=32,
        epochs=10)

    keras_model = keras_estimator.fit_on_parquet()

Once the data has been prepared, you can reuse it in future Spark applications without needing to call util.prepare_data again.

Horovod Spark Run

You can also use Horovod on Spark to run the same code you would within an ordinary training script using any framework supported by Horovod. To do so, simply write your training logic within a function, then use horovod.spark.run to execute the function in parallel with MPI on top of Spark.

Because Horovod on Spark uses cloudpickle to send the training function to workers for execution, you can capture local variables from your training script or notebook within the training function, similar to using a user-defined function in PySpark.

A toy example of running a Horovod job in Spark is provided below:

$ pyspark
[PySpark welcome message]

>>> def fn(magic_number):
...   import horovod.torch as hvd
...   hvd.init()
...   print('Hello, rank = %d, local_rank = %d, size = %d, local_size = %d, magic_number = %d' % (hvd.rank(), hvd.local_rank(), hvd.size(), hvd.local_size(), magic_number))
...   return hvd.rank()
...
>>> import horovod.spark
>>> horovod.spark.run(fn, args=(42,))
Running 16 processes...
[Stage 0:>                                                        (0 + 16) / 16]
Hello, rank = 15, local_rank = 3, size = 16, local_size = 4, magic_number = 42
Hello, rank = 13, local_rank = 1, size = 16, local_size = 4, magic_number = 42
Hello, rank = 8, local_rank = 0, size = 16, local_size = 4, magic_number = 42
Hello, rank = 9, local_rank = 1, size = 16, local_size = 4, magic_number = 42
Hello, rank = 10, local_rank = 2, size = 16, local_size = 4, magic_number = 42
Hello, rank = 11, local_rank = 3, size = 16, local_size = 4, magic_number = 42
Hello, rank = 6, local_rank = 2, size = 16, local_size = 4, magic_number = 42
Hello, rank = 4, local_rank = 0, size = 16, local_size = 4, magic_number = 42
Hello, rank = 0, local_rank = 0, size = 16, local_size = 4, magic_number = 42
Hello, rank = 1, local_rank = 1, size = 16, local_size = 4, magic_number = 42
Hello, rank = 2, local_rank = 2, size = 16, local_size = 4, magic_number = 42
Hello, rank = 5, local_rank = 1, size = 16, local_size = 4, magic_number = 42
Hello, rank = 3, local_rank = 3, size = 16, local_size = 4, magic_number = 42
Hello, rank = 12, local_rank = 0, size = 16, local_size = 4, magic_number = 42
Hello, rank = 7, local_rank = 3, size = 16, local_size = 4, magic_number = 42
Hello, rank = 14, local_rank = 2, size = 16, local_size = 4, magic_number = 42
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
>>>

A more complete example can be found in keras_spark_rossmann_run.py, which shows how you can use the low level horovod.spark.run API to train a model end-to-end in the following steps:

$ wget https://raw.githubusercontent.com/horovod/horovod/master/examples/keras_spark_rossmann_run.py
$ wget http://files.fast.ai/part2/lesson14/rossmann.tgz
$ tar zxvf rossmann.tgz
$ python keras_spark_rossmann_run.py

Spark cluster setup

As deep learning workloads tend to have very different resource requirements from typical data processing workloads, there are certain considerations for DL Spark cluster setup.

GPU training

For GPU training, one approach is to set up a separate GPU Spark cluster and configure each executor with # of CPU cores = # of GPUs. This can be accomplished in standalone mode as follows:

$ echo "export SPARK_WORKER_CORES=<# of GPUs>" >> /path/to/spark/conf/spark-env.sh
$ /path/to/spark/sbin/start-all.sh

This approach turns the spark.task.cpus setting to control # of GPUs requested per process (defaults to 1).

The ongoing SPARK-24615 effort aims to introduce GPU-aware resource scheduling in future versions of Spark.

CPU training

For CPU training, one approach is to specify the spark.task.cpus setting during the training session creation:

conf = SparkConf().setAppName('training') \
    .setMaster('spark://training-cluster:7077') \
    .set('spark.task.cpus', '16')
spark = SparkSession.builder.config(conf=conf).getOrCreate()

This approach allows you to reuse the same Spark cluster for data preparation and training.

Security

Horovod on Spark uses Open MPI to run the Horovod jobs in Spark, so it's as secure as the Open MPI implementation itself.

Since Open MPI does not use encrypted communication and is capable of launching new processes, it's recommended to use network level security to isolate Horovod jobs from potential attackers.

Environment knobs

  • HOROVOD_SPARK_START_TIMEOUT - sets the default timeout for Spark tasks to spawn, register, and start running the code. If executors for Spark tasks are scheduled on-demand and can take a long time to start, it may be useful to increase this timeout on a system level.

Horovod on Databricks

To run Horovod in Spark on Databricks, use DBFSLocalStore as the store object:

store = DBFSLocalStore(prefix_path='/dbfs/...')

The DBFSLocalStore uses Databricks File System (DBFS) local file APIs (AWS | Azure) as a store of intermediate data and training artifacts.

Databricks pre-configures GPU-aware scheduling on Databricks Runtime 7.0 ML GPU and above. See GPU scheduling instructions (AWS | Azure) for details.

With the Estimator API, horovod will launch # of tasks on each worker = # of GPUs on each worker, and each task will pin GPU to the assigned GPU from spark.

With the Run API, the function get_available_devices() from horovod.spark.task will return a list of assigned GPUs for the spark task from which get_available_devices() is called. See keras_spark3_rossmann.py for an example of using get_available_devices() with the Run API.