Here are 15 interview questions related to Ray, a distributed computing framework developed by the Ray Project, along with their answers:
1. What is Ray?
Ans: Ray is an open-source distributed computing framework that enables efficient and scalable execution of applications across a cluster of machines. It provides a simple and flexible programming model for building distributed applications.
2. What are the key features of Ray?
Ans: The key features of Ray include task parallelism, distributed data processing, fault tolerance, support for various programming languages, scalable and efficient scheduling, and the ability to integrate with popular machine learning libraries.
3. How does Ray achieve task parallelism?
Ans: Ray achieves task parallelism by using a lightweight threading model called “actors.” Actors are isolated units of execution that can perform computations independently and communicate with each other asynchronously.
4. What is an actor in Ray?
Ans: An actor in Ray is an independent unit of execution that encapsulates state and behavior. It can perform computations, maintain its internal state, and communicate with other actors through message passing.
5. How does Ray handle fault tolerance?
Ans: Ray provides fault tolerance by checkpointing actor state and allowing automatic recovery in the event of failures. If an actor fails, Ray restarts it and restores its state to the last checkpoint.
6. Can Ray be used with Python only?
Ans: No, Ray can be used with multiple programming languages. While the core functionality of Ray is implemented in Python, it provides language bindings for other languages such as Java and C++.
7. How does Ray enable distributed data processing?
Ans: Ray provides a high-level API called Ray DataFrames, which is inspired by Pandas DataFrames. It allows for distributed data processing operations on large datasets across a cluster of machines.
8. Does Ray support GPU acceleration?
Ans: Yes, Ray supports GPU acceleration. It allows you to allocate and use GPUs across the cluster for parallel execution of GPU-accelerated computations.
9. What is the role of the Ray scheduler?
Ans: The Ray scheduler is responsible for resource management and task scheduling across the cluster. It dynamically assigns tasks to available resources based on their availability and workload.
10. Can you use Ray with existing machine-learning libraries?
Ans: Yes, Ray integrates with popular machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn. It provides distributed training and inference capabilities, allowing you to scale your machine learning workloads.
11. Does Ray support distributed reinforcement learning?
Ans: Yes, Ray provides support for distributed reinforcement learning. It includes libraries such as Ray RLlib, which provides a high-level API for building and training distributed RL models.
12. How does Ray handle data locality in distributed computations?
Ans: Ray automatically handles data locality by ensuring that data is placed close to the worker processes that need it. It minimizes data movement across the cluster, which improves performance.
13. Can you run Ray on a single machine?
Ans: Yes, Ray can be run on a single machine for development and testing purposes. It provides a local mode that allows you to run Ray applications on a single machine without the need for a cluster.
14. How does Ray handle data serialization?
Ans: Ray uses a custom serialization mechanism called Plasma to efficiently serialize and transfer data between processes. It minimizes data serialization overhead and allows for fast data transfer.
15. Can you save and load Ray objects?
Ans: Yes, Ray allows you to save and load objects using the pickle serialization library. You can save and load Ray objects to and from the disk, enabling the persistence and sharing of objects across different runs.