Distributed machine learning (DML) frameworks enable you to train machine learning models across multiple machines (using CPUs, GPUs, or TPUs), significantly reducing training time while efficiently handling large and complex workloads that wouldn’t fit into memory otherwise. Additionally, these frameworks allow you to process datasets, tune the models, and even serve them using distributed computing resources.
In this article, we will review the five most popular distributed machine learning frameworks that can help us scale the machine learning workflows. Each framework offers different solutions for your specific project needs.
1. PyTorch Distributed
PyTorch is quite popular among machine learning practitioners due to its dynamic computation graph, ease of use, and modularity. The PyTorch framework includes PyTorch Distributed, which assists in scaling deep learning models across multiple GPUs and nodes.
Key Features
- Distributed Data Parallelism (DDP): PyTorch’s
torch.nn.parallel.DistributedDataParallelallows models to be trained across multiple GPUs or nodes by splitting the data and synchronizing gradients efficiently. - TorchElastic 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-scale supercomputers, making it a versatile choice for distributed training.
- Ease of Use: PyTorch’s intuitive API allows developers to scale their workflows with minimal changes to existing code.
Why Choose PyTorch Distributed?
PyTorch is perfect for teams already using it for model development and looking to enhance their workflows. You can effortlessly convert your training script to use multiple GPUs with just a few lines of code.
2. TensorFlow Distributed
TensorFlow, one of the most established machine learning frameworks, offers robust support for distributed training through TensorFlow Distributed. Its ability to scale efficiently across multiple machines and GPUs makes it a top choice for training deep learning models at scale.
Key Features
- tf.distribute.Strategy: TensorFlow provides multiple distribution strategies, such as MirroredStrategy for multi-GPU training, MultiWorkerMirroredStrategy for multi-node training, and TPUStrategy for TPU-based training.
- Ease of Integration: TensorFlow Distributed integrates seamlessly with TensorFlow’s ecosystem, including TensorBoard, TensorFlow Hub, and TensorFlow Serving.
- Highly Scalable: TensorFlow Distributed can scale across large clusters with hundreds of GPUs or TPUs.
- Cloud Integration: TensorFlow is well-supported by cloud providers like Google Cloud, AWS, and Azure, allowing you to run distributed training jobs in the cloud with ease.
Why Choose TensorFlow Distributed?
TensorFlow Distributed is an excellent choice for teams that are already using TensorFlow or those looking for a highly scalable solution that integrates well with cloud machine learning workflows.
3. Ray
Ray is a general-purpose framework for distributed computing, optimized for machine learning and AI workloads. It simplifies building distributed machine learning pipelines by offering specialized libraries for training, tuning, and serving models.
Key Features
- Ray Train: A library for distributed model training that works with popular machine learning frameworks like PyTorch and TensorFlow.
- Ray Tune: Optimized for distributed hyperparameter tuning across multiple nodes or GPUs.
- Ray Serve: Scalable model serving for production machine learning pipelines.
- Dynamic Scaling: Ray can dynamically allocate resources for workloads, making it highly efficient for both small and large-scale distributed computing.
Why Choose Ray?
Ray is an excellent choice for AI and machine learning developers seeking a modern framework that supports distributed computing at all levels, including data preprocessing, model training, model tuning, and model serving.
4. Apache Spark
Apache Spark is a mature, open-source distributed computing framework that focuses on large-scale data processing. It includes MLlib, a library that supports distributed machine learning algorithms and workflows.
Key Features
- In-Memory Processing: Spark’s in-memory computation improves speed compared to traditional batch-processing systems.
- MLlib: Provides distributed implementations of machine learning algorithms like regression, clustering, and classification.
- Integration with Big Data Ecosystems: Spark integrates seamlessly 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 are dealing with large-scale structured or semi-structured data and need a comprehensive framework for both data processing and machine learning, Spark is an excellent choice.
5. Dask
Dask is a lightweight, Python-native framework for distributed computing. It extends popular Python libraries like Pandas, NumPy, and Scikit-learn to work on datasets that don’t fit into memory, making it an excellent choice for Python developers looking to scale existing workflows.
Key Features
- Scalable Python Workflows: Dask parallelizes Python code and scales it across multiple cores or nodes with minimal code changes.
- Integration with Python Libraries: Dask works seamlessly with popular machine learning libraries like Scikit-learn, XGBoost, and TensorFlow.
- Dynamic Task Scheduling: Dask uses a dynamic task graph to optimize resource allocation and improve efficiency.
- Flexible Scaling: Dask can handle datasets larger than memory by breaking them into small, manageable chunks.
Why Choose Dask?
Dask is ideal for Python developers who want a lightweight, flexible framework for scaling their existing workflows. Its integration with Python libraries makes it easy to adopt for teams already familiar with the Python ecosystem.
Comparison Table
| Feature | PyTorch Distributed | TensorFlow Distributed | Ray | Apache Spark | Dask |
|---|---|---|---|---|---|
| Best For | Deep learning workloads | Cloud deep learning workloads | ML pipelines | Big data + ML workflows | Python-native ML workflows |
| Ease of Use | Moderate | High | Moderate | Moderate | High |
| ML Libraries | Built-in DDP, TorchElastic | tf.distribute.Strategy | Ray Train, Ray Serve | MLlib | Integrates with Scikit-learn |
| Integration | Python ecosystem | TensorFlow ecosystem | Python ecosystem | Big data ecosystems | Python ecosystem |
| Scalability | High | Very High | High | Very High | Moderate to High |
Final Thoughts
I have worked with nearly all distributed computing frameworks mentioned in this article, but I primarily use PyTorch and TensorFlow for deep learning. These frameworks make it incredibly easy to scale model training across multiple GPUs with just a few lines of code.
Personally, I prefer PyTorch due to its intuitive API and my familiarity with it. So, I see no reason to switch to something new unnecessarily. For traditional machine learning workflows, I rely on Dask for its lightweight and Python-native approach.
- PyTorch Distributed and TensorFlow Distributed: Best for large-scale deep learning workloads, especially if you are already using these frameworks.
- Ray: Ideal for building modern machine learning pipelines with distributed compute.
- Apache Spark: The go-to solution for distributed machine learning workflows in big data environments.
- Dask: A lightweight option for Python developers looking to scale existing workflows efficiently.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.