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Once launched, the processes running on the executors rely on the built-in distributed training APIs of their respective DL frameworks. Output_dist = TorchDistributor(num_processes=2, local_mode=False, use_gpu=True).run(main_fn, checkpoint_dir) # Set num_processes = NUM_WORKERS * NUM_GPUS_PER_WORKER

from pyspark.ml.torch.distributor import TorchDistributor

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Once the DL cluster has been started by Spark, control is essentially handed off to the DL frameworks through the main_fn that was passed to the TorchDistributor API.Īs shown in the following code, only minimal code changes are required to run standard distributed DL training on Spark with this new API. These simplify the migration of distributed DL model training code to Spark by taking advantage of Spark’s barrier execution mode to spawn the distributed DL cluster nodes on top of the Spark executors. Distributed trainingįor distributed training, there is a new TorchDistributor API for PyTorch, which follows the spark-tensorflow-distributor API for TensorFlow. With the release of Spark 3.4, users now have access to built-in APIs for both distributed model training and model inference at scale, as detailed below. But, since these solutions were not natively built into Spark, users must evaluate each platform against their own needs. To help solve this disconnect between the single-node DL environments and large-scale distributed environments, there are multiple third-party solutions such as Horovod-on-Spark, TensorFlowOnSpark, and SparkTorch. Most of the DL frameworks were designed for single-node environments, and their distributed training and inference APIs were often added as an after-thought. Yet, combining DL training and inference with large-scale data has historically been a challenge for Spark users. However, with the advent of deep learning (DL), many Spark practitioners have sought to add DL models to their data processing pipelines across a variety of use cases like sales predictions, content recommendations, sentiment analysis, and fraud detection. Apache Spark is an industry-leading platform for distributed extract, transform, and load (ETL) workloads on large-scale data.

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