Building High-Performance Python Applications with Ray on Apache Spark
Dr. Hassan Sherwani
Data Analytics Practice Head
July 18, 2024
Build High-Performance Python Apps with Ray and Databricks Apache Spark
The computing demands for machine learning training keep growing, especially with the advent of generative AI, where the demand for GPUs and TPUs has skyrocketed. ML engineers and developers often struggle with vendor constraints on solutions like AWS Sagemaker and GCP Vertex AI or the complexities of developing their own distributed ML systems. Ray’s unified compute framework is a powerful and versatile tool for building distributed applications in Python. Whether you are dealing with big data, machine learning, or complex computational tasks, Ray simplifies the process of parallelizing and distributing your code across multiple nodes, allowing you to harness the full potential of modern hardware.
Earlier this year, Databricks made a significant announcement: The Ray support functionality is now generally available to the public and pre-installed. This inclusive move allows customers to conduct multi-model hierarchical forecasting, LLM finetuning, and Reinforcement learning. Databricks customers can now access this powerful open-source framework across several products, including Unity Catalog, Delta Lake, MlFlow, and Apache Spark, fostering a sense of community and shared knowledge.
In this blog, we’ll explore the benefits and limitations of the Ray framework, its functionality, and discuss how enterprises can leverage Ray on Databricks to significantly enhance performance and efficiency. We’ll also provide practical insights on creating Ray clusters and applications on Databricks Apache Spark, demonstrating how this powerful combination can revolutionize your Python and AI workloads.
What is Ray?
Ray is an open-source computer framework for distributed computing that eases scaling Python and AI workloads. Developed by researchers at UC Berkeley’s RISELab, it aims to provide a simple, flexible, and high-performance API for building scalable and resilient applications. It abstracts away the complexities of distributed systems, allowing developers to focus on writing their code without worrying about the underlying infrastructure.
Benefits of Ray Framework
- Simplifies Distributed Computing: Ray provides a straightforward API for parallel and distributed computing. With Ray, you can effortlessly distribute your Python functions across multiple nodes, enabling faster execution and better resource utilization.
- Scalable and Resilient: Ray is designed to scale from a single machine to a cluster of thousands of nodes. It also includes built-in fault tolerance, ensuring your applications can recover from failures and continue running smoothly.
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Flexible API: Ray’s API is highly flexible and supports various use cases, including distributed data processing, reinforcement learning, hyperparameter tuning, and more. It integrates seamlessly with popular libraries such as TensorFlow, PyTorch, and Dask. - High Performance: Ray’s core is optimized for performance, utilizing Python for critical components and providing efficient serialization and deserialization. This ensures minimal overhead when transferring data between nodes.
- Easy Integration with Existing Code: Ray can be integrated into existing Python codebases with minimal changes. You can convert existing functions into remote functions (tasks) by adding a decorator, making it easy to parallelize your code.
Limitations of Ray Framework
- Learning Curve: While Ray simplifies many aspects of distributed computing, understanding its API and concepts still involves a learning curve. Developers new to distributed systems might find it challenging to get started.
- Debugging and Monitoring: Debugging distributed applications can be more complex than debugging single-node applications. Although Ray provides tools for monitoring and debugging, it requires additional effort to set up and use effectively.
- Resource Management: Efficient resource management in a distributed environment can be tricky. Ray does a good job, but developers must still be mindful of resource allocation and scheduling to avoid bottlenecks and ensure optimal performance.
- Dependency Management: Ensuring that all nodes in a Ray cluster have the same dependencies and environment setup can be challenging, especially in heterogeneous environments. This requires careful planning and configuration.
Using Ray with Databricks Apache Spark
Databricks Apache Spark is a widely used framework for big data processing. It excels at handling large-scale data processing tasks through its efficient data processing engine and rich library ecosystem. However, there are scenarios where combining Apache Spark with Ray can lead to significant performance improvements and enhanced efficiency.
With the latest release of Ray 2.3.0, Databricks users can run Ray on their Databricks and Apache Spark standalone clusters. Ray supports Databricks Runtime version 12.0 onwards and any Apache Spark cluster running version 3.3 or above.
Enhancing Databricks Apache Spark with Ray
- Fine-Grained Control: Apache Spark’s execution model is based on resilient distributed datasets (RDDs) and DataFrames, which operate at a relatively high level of abstraction. Conversely, Ray provides fine-grained control over task execution, allowing you to optimize performance for specific tasks.
- Distributed Machine Learning: Ray is well-suited for distributed machine learning tasks. While Apache Spark MLlib provides basic machine learning capabilities, Ray’s integration with frameworks like TensorFlow and PyTorch allows for more advanced and customized machine learning workflows.
- Dynamic Task Scheduling: Apache Spark uses a static execution plan, which can sometimes lead to inefficiencies. Ray’s dynamic task scheduling allows for more responsive and adaptive execution, improving resource utilization and reducing latency.
- Asynchronous Execution: Ray supports asynchronous task execution, enabling better parallelism and reducing idle times. This can be particularly beneficial for iterative algorithms and scenarios where tasks have varying execution times.
Practical Use Cases for Ray with Apache Spark
- Hyperparameter Tuning: Combining Ray with Databricks Apache Spark allows efficient hyperparameter tuning in large-scale machine learning models. Apache Spark can preprocess the data and distribute the training workload, while Ray can manage the parallel execution of different hyperparameter configurations.
- Reinforcement Learning: Reinforcement learning often requires running many simulations in parallel. Ray’s flexible API and efficient execution model make it ideal for managing these simulations, while Apache Spark can handle the aggregation and analysis of the resulting data.
- Data Processing Pipelines: Complex data processing pipelines can benefit from the combined strengths of Apache Spark and Ray. Databricks Apache Spark can handle the bulk of data transformation and aggregation, while Ray can be used for tasks that require finer control or integration with other Python libraries.
Sample Workflow of Ray on Apache Spark
Consider a scenario where you need to train a machine learning model on a large dataset. You can use Databricks Apache Spark to load and preprocess the data and then leverage Ray to distribute the training process across multiple nodes, optimizing hyperparameters in parallel. This approach ensures efficient resource utilization and faster execution.
Steps:
- Data Preprocessing with Apache Spark: Load the dataset into an Apache Spark DataFrame, apply necessary transformations, and partition the data for distributed processing.
- Model Training with Ray: Use Ray’s API to define the model and hyperparameter search space. Then, Ray will distribute the training tasks across multiple nodes, each evaluating a different set of hyperparameters.
- Aggregation with Apache Spark: Once the training is complete, use Apache Spark to aggregate the results, identify the best-performing model, and perform any final data processing steps.
Conclusion
The Ray framework is a powerful tool for developers looking to harness the full potential of distributed computing in Python. Its simplicity, flexibility, and high performance make it an excellent choice for various applications, from data processing to machine learning.
By combining Ray with Databricks and Apache Spark, you can achieve even greater performance and efficiency, leveraging the strengths of both frameworks to handle complex and large-scale data processing tasks. While challenges are associated with learning and debugging distributed systems, the benefits of using Ray far outweigh the drawbacks.
Ray’s ability to abstract away the complexity of distributed computing empowers developers to build scalable and resilient applications quickly. Contact Royal Cyber data experts to learn how to successfully leverage the Ray open framework in your Databricks and Apache Spark environment. For more information, email [email protected].
Author
Priya George
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