Distributed TrainingΒΆ

Distributed Training with OCI Data Science

This documentation shows you how to preprocess, and train on a machine learning model, using Oracle Cloud Infrastructure. This section will not teach you about distributed training, instead it will help you run your existing distributed training code on OCI Data Science.

Distributed training is the process of taking a training workload which comprises training code and training data and making both of these available in a cluster.

The conceptual difference with distributed training is that multiple workers coordinated in a cluster running on multiple VM instances allows horizontal scaling of parallelizable tasks. While singe node training is well suited to traditional ML models, very large datasets or compute intensive workloads like deep learning and deep neural networks, tends to be better suited to distributed computing environments.

Distributed Training benefits two classes of problem, one where the data is parallelizable, the other where the model network is parallelizable. The most common and easiest to develop is data parallelism. Both forms of parallelism can be combined to handle both large models and large datasets.

Data Parallelism

In this form of distributed training the training data is partitioned into some multiple of the number of nodes in the compute cluster. Each node holds the model and is in communication with other node participating in a coordinated optimization effort.

Sometimes data sampling is possible, but often at the expense of model accuracy. With distributed training you can avoid having to sample the data to fit a single node.

Model Parallelism

This form of distributed training is used when workers need to worker nodes need to synchronize and share parameters. The data fits into the memory of each worker, but the training takes too long. With model parallelism more epochs can run and more hyper-parameters can be explored.

Distributed Training with OCI Data Science

To outline the process by which you create distributed training workloads is the same regardless of framework used. Sections of the configuration differ between frameworks but the experience is consistent. The user brings only the (framework specific) training python code, along with the yaml declarative definition.

ADS makes use of yaml to express configurations. The yaml specification has sections to describe the cluster infrastructure, the python runtime code, and the cluster framework.

The architecture is extensible to support well known frameworks and future versions of these. The set of service provided frameworks for distributed training include:

  • Dask for LightGBM, XGBoost, Scikit-Learn, and Dask-ML

  • Horovod for PyTorch & Tensorflow

  • PyTorch Distributed for PyTorch native using DistributedDataParallel - no training code changes to run PyTorch model training on a cluster. You can use Horovod to do the same, which has some advanced features like auto-tuning to improve allreduce performance, and fp16 gradient compression.

  • Tensorflow Distributed for Tensorflow distributed training strategies like MirroredStrategy, MultiWorkerMirroredStrategy and ParameterServerStrategy