Images by the authorDistributed Machine Learning (DML) frameworks allow you to train machine learning models on multiple machines (using CPUs, GPUs, or TPUs), reducing training times significantly while efficiently handling large, complex workloads that do not fit in memory. Additionally, these frameworks allow you to process datasets, coordinate models, and provide models using distributed computing resources.
In this article, we will look at five most popular distributed machine learning frameworks that can help you scale your machine learning workflow. Each framework offers a variety of solutions to suit the needs of a particular project.
1. Pytorch has been distributed
Pytorch is extremely popular among machine learning practitioners because of its dynamic computational graphs, ease of use and modularity. Included in the Pytorch framework Distributed by Pytorchaids in scaling deep learning models across multiple GPUs and nodes.
Important features
- Distributed Data Parallel Processing (DDP):Pytorch's
torch.nn.parallel.DistributedDataParallelSplitting data and efficiently synchronizing gradients allows models to be trained on multiple GPUs or nodes. - Torquelast and fault tolerance: Pytorch Distributed supports dynamic resource allocation and fault-tolerant training using Torchelastic.
- Scalability: Pytorch works well on both small clusters and large supercomputers, making it a highly-used option for distributed training.
- Ease of use: Pytorch's intuitive API allows developers to scale their workflows with minimal changes to existing code.
Why do you choose to distribute Pytorch?
Pytorch is perfect for teams who already use it for model development and are looking to enhance their workflow. You can easily convert your training scripts to use multiple GPUs with just a few lines of code.
2. Tensorflow distribution
One of the most established machine learning frameworks, Tensorflow offers strong support for distributed training through Tensorflow distributions. The ability to efficiently scale across multiple machines and GPUs makes it the greatest option for training large deep learning models.
Important features
- tf.distribute.strategy:Tensorflow offers multiple distribution strategies, including MirroredStrategy for multi-GPU training, Multiwork Elmariload Strategy for multi-node training, and TPustrategy for TPU-based training.
- Ease of integration: Tensorflow distribution type integrates seamlessly with the Tensorflow ecosystem, including Tensorboard, Tensorflow Hub, and Tensorflow serving.
- Highly scalable: Tensorflow distributions can scale across large clusters with hundreds of GPUs or TPUs.
- Cloud Integration: Tensorflow is well supported by cloud providers such as Google Cloud, AWS, and Azure, making it easy to run distributed training jobs in the cloud.
Why do I distribute Tensorflow?
Tensorflow Distributed is perfect for teams who already use Tensorflow, or are looking for a highly scalable solution that integrates well with cloud machine learning workflows.
3. Ray
Ray is a general-purpose distributed computing framework optimized for machine learning and AI workloads. Simplify your building's distributed machine learning pipeline by providing specialized libraries for training, tuning, and serving models.
Important features
- Late Train: A library for distributed model training that works with popular machine learning frameworks such as Pytorch and Tensorflow.
- Ray Tune: Optimized for distributed hyperparameter tuning across multiple nodes or GPUs.
- Ray Serve: A scalable model that provides services to the production machine learning pipeline.
- Dynamic Scaling: Ray can dynamically allocate resources to workloads, making it extremely efficient for both small and large distributed computing.
Why choose Ray?
Ray is ideal for AI and machine learning developers looking for the latest frameworks to support distributed computing at all levels, including data preprocessing, model training, model tuning, model serving, and more.
4. Apache Spark
Apache Spark is a mature, open source distributed computing framework focused on data processing at scale. Included mlliba library that supports distributed machine learning algorithms and workflows.
Important features
- Memory Processing: Spark's in-memory calculations are faster than traditional batch processing systems.
- mllib: Provides distributed implementations of machine learning algorithms such as regression, clustering, and classification.
- Integration with the Big Data Ecosystem: Spark seamlessly integrates with Hadoop, Hive, and Cloud Storage Systems like Amazon S3.
- Scalability:Spark can scale to thousands of nodes, allowing you to process petabytes of data efficiently.
Why choose Apache Spark?
If you're dealing with large-scale structured or semi-structured data and you want a comprehensive framework for both data processing and machine learning, Spark is the great choice.
5. Dusk
Dask is a lightweight Python-Native framework for distributed computing. Extends popular Python libraries such as Pandas, Numpy, and Scikit-Learn to work with datasets that do not fit in memory, making it ideal for Python developers looking to expand their existing workflows.
Important features
- Scalable Python workflow:Dask parallelizes Python code, scales across multiple cores or nodes, minimizing code changes.
- Integration with Python libraries:Dask works seamlessly with popular machine learning libraries such as Scikit-Learn, Xgboost, and Tensorflow.
- Dynamic Task Scheduling:Dask uses dynamic task graphs to optimize resource allocation and increase efficiency.
- Flexible scaling:Dask can process data sets larger than memory by dividing memory into smaller, more manageable chunks.
Why choose Dusk?
Dask is perfect for Python developers who need a lightweight, flexible framework to scale existing workflows. Integration with the Python library makes it easy to adopt for teams already familiar with the Python ecosystem.
Comparison table
| Features | Distributed by Pytorch | Tensorflow distribution | Ray | Apache Spark | Dusk |
|---|---|---|---|---|---|
| It's perfect for | Deep Learning Workload | Cloud Deep Learning Workload | ML Pipeline | Big Data + ML Workflow | Python-Native ML Workflow |
| Ease of use | Moderate | expensive | Moderate | Moderate | expensive |
| ML Library | Embedded DDP, Torquerast | tf.distribute.strategy | Late train, ra serve | mllib | Integrate with Scikit-Learn |
| Integration | Python ecosystem | Tensorflow ecosystem | Python ecosystem | Big Data Ecosystem | Python ecosystem |
| Scalability | expensive | Very expensive | expensive | Very expensive | Medium to high |
Final thoughts
I use almost every distributed computing framework mentioned in this article, but mainly using deep learning using Pytorch and Tensorflow. These frameworks allow you to scale model training on multiple GPUs very easily using several lines of code.
Personally, I prefer Pytorch because of its intuitive API and its familiarity. So I don't think there's any reason to switch to something new unnecessarily. For traditional machine learning workflows, I rely on Dask for its lightweight, python native approach.
- Distributed by Pytorch and Tensorflow distribution: It's perfect for large deep learning workloads, especially if you're already using these frameworks.
- Ray: Perfect for building modern machine learning pipelines with distributed computing.
- Apache Spark: The go-to solution for distributed machine learning workflows in big data environments.
- Dusk: A lightweight option for Python developers looking to scale existing workflows efficiently.
Abid Ali Awan (@1abidaliawan) is a certified data scientist who loves building machine learning models. Currently he focuses on content creation and creates technical blogs on machine learning and data science technology. Abid holds a Masters degree in Technology Management and a Bachelor of Arts degree in Telecommunications Engineering. His vision is to build AI products using graph neural networks for students suffering from mental illness.
