ray-project/ray

Features

• Distributed Datasets - Creates and controls data collections that support lazy transformations and parallel processing across various storage sources.
• Distributed Shared Memory - Ray provides a shared memory space to store and retrieve objects, enabling efficient data sharing and asynchronous processing across workers.
• Distributed Computing Frameworks - A programming model that scales Python and Java applications across clusters by abstracting task scheduling and resource management.
• Distributed Task Orchestration - Ray supports creating remote functions and actor classes to execute code across a cluster while managing resource requirements and lifecycles.
• Distributed Task Orchestrators - Scaling Python functions and classes across a cluster to execute parallel workloads with fine-grained resource and dependency management.
• Distributed Task Schedulers - A resource-aware execution engine that manages task dependencies, placement, and fault tolerance across a pool of networked compute nodes.
• Actor Models - A model where stateful objects run in dedicated processes to maintain and mutate internal state across remote method calls.
• Stateful Distributed Actors - Ray supports defining classes that run in dedicated processes to maintain and mutate internal state across multiple remote method calls.
• Inference Pipeline Orchestrators - Executes multi-stage inference pipelines that handle preprocessing, tokenization, and accelerated GPU inference.
• Model Serving Frameworks - Deploying and scaling complex model pipelines across multiple GPUs to handle high-throughput requests with automatic resource autoscaling.
• Data Processing Frameworks - Transforming and analyzing massive datasets in parallel using lazy evaluation, distributed shuffles, and efficient memory management.
• Dataset Transformations - Applies functions to rows or batches to filter, map, or manipulate data for downstream processing tasks.
• Distributed Data Engines - A library for parallelizing large-scale data transformations, ingestion, and streaming workflows across heterogeneous compute clusters.
• Fault Tolerance - Ensures distributed tasks and actors remain resilient through automated failure handling and object ownership management.
• Graph Compilation - Builds and runs directed acyclic graphs to optimize task performance and inspect dependencies.
• Inference Scaling Frameworks - Distributes inference workloads across multiple GPUs or nodes by configuring concurrency and parallel strategies.
• Dataset Aggregations - Computes custom or built-in aggregations on datasets by passing functions to grouping operations for efficient data analysis.
• Distributed Data Processing - Converts datasets into distributed formats to enable interoperability with large-scale data processing libraries.
• Distributed Data Processing Frameworks - A framework that represents data as partitioned blocks to support incremental transformations and parallel execution across large clusters.
• Distributed Object Stores - A shared memory system that enables efficient data sharing and asynchronous communication between workers across a cluster.
• Resource Management Policies - Enforces global CPU, GPU, and memory limits to prevent resource contention during concurrent job execution.
• Runtime Environment Configuration - Configures dependencies and packages for applications to ensure consistent execution across distributed clusters.
• Scheduling Strategies - Creates custom placement rules for tasks and actors to pin work to specific nodes or group resources together.
• Stateful Service Runtimes - Building long-running, fault-tolerant services that maintain internal state and handle concurrent requests across a distributed infrastructure.
• High-Performance Data Transfer - Moves tensors between actors using specialized libraries to avoid expensive serialization.
• Concurrency Models - Ray allows creating actors with asynchronous methods to execute multiple tasks concurrently on a single event loop during I/O operations.
• Foreign Function Interfaces - A serialization and communication layer that allows functions and objects to be invoked across different programming language runtimes.
• Runtime Environments - Integrating components written in different programming languages into a single application by sharing data and execution handles seamlessly.
• Fault Tolerance Policies - Ray enables defining restart limits and retry counts for actors to handle unexpected crashes and maintain high service availability.
• Cluster Monitoring - Provides real-time cluster health displays, including resource allocation and autoscaling information.
• Automated Machine Learning - Distributed framework for scalable hyperparameter tuning.
• Frameworks de Machine Learning - Distributed framework for scaling machine learning applications.
• Machine Learning Operations - Distributed computing framework for scaling Python and ML applications.
• Optimization Tools - Framework for building and running distributed applications.
• Perception and Machine Learning - Distributed framework for scaling machine learning applications.
• Reinforcement Learning - Framework for distributed applications including RLlib.
• Big Data and Distributed Computing - Distributed execution framework for scaling applications.
• Data Science and Databases - Framework for building distributed applications.
• Distributed Programming - Fast framework for building and running distributed applications.
• Distributed Computing - Distributed system for parallel Python and ML.
• Computation and Optimization - Distributed execution framework for machine learning workloads.
• Herramientas de desarrollo - Distributed application framework.
• Graph Computation - Framework for building distributed applications.
• Scientific Computing Libraries - Framework for building distributed applications.
• Workflow Frameworks - High-performance distributed execution framework for Python.
• Data Checkpointing - Sets storage backends and persistence settings to manage the retrieval of checkpoint files during distributed processing.
• Data Writers - Persists datasets to local or cloud storage using standard URI schemes to ensure data availability across nodes.
• Incremental Data Streaming - Processes data blocks incrementally to handle datasets that exceed total cluster memory capacity.
• Memory Optimization Strategies - Monitors heap memory and adjusts block size targets to prevent out-of-memory errors during task execution.
• Parallel Data Transformation - Applies user-defined functions to dataset rows, automatically parallelizing work across the cluster.
• Storage File Readers - Ingests files from local or cloud storage in various formats with support for column pruning and parallel processing.
• Vectorized Data Processing - Processes datasets in vectorized batches to achieve higher performance compared to row-by-row operations.
• Job Environment Management - Defines a runtime environment for an entire job to ensure all tasks share the same dependencies.
• Resource Placement - Organizes clusters of resources to ensure tasks and actors are co-located or distributed according to specific requirements.
• Resource Scheduling Policies - Ray enables assigning specific hardware resources like CPUs or GPUs to an actor during instantiation to ensure sufficient processing capacity.
• Execution Graphs - Supports binding actor methods and configuring transport settings to prepare complex task chains.
• Task Orchestration Engines - A system that builds and optimizes task dependency chains to enable accelerated execution paths across distributed nodes.
• Distributed Model Orchestration - Scales complex transformations across nodes using placement groups to manage model replicas.
• Inference Configuration Engines - Sets model sources and engine parameters for text generation and multimodal inference tasks.
• Data Ingestion Tuning - Adjusts output block counts during data reads to balance parallelism and memory overhead for efficient processing.
• Dataset Iterators - Reads dataset records as individual rows or batches to prepare data for machine learning training workflows.
• Distributed Debugging - Identifies performance bottlenecks by setting breakpoints, inspecting serializability, and generating profiling timelines for distributed code.
• Task Schedulers - Ray Core Scheduling Capabilities — a named example documented in this learning resource.
• Java Ecosystem - Allows invoking Java static methods and instantiating Java actors directly from Python code.
• Python Tooling - Allows invoking Python remote functions and instantiating Python actors directly from Java code.
• Environment Isolation - Provides isolated runtime environments for distributed tasks to prevent dependency conflicts.
• Performance Tuning Utilities - Uses vectorized processing for data transformations to improve performance when working with numerical data.
• Query Optimization Engines - Translates high-level operations into optimized physical execution plans by applying custom rules.
• Execution Tracing - Generates visual execution timelines to identify bottlenecks and analyze task dependencies within distributed workflows.
• Data Processing Configurations - Sets global parameters for block sizes and shuffle strategies to control data operations across the cluster.
• Data Processing Engines - Utilizes high-performance engines for internal sorting operations to improve performance on large tabular datasets.
• Data Shuffling Algorithms - Redistributes data across the cluster using hash or range algorithms to support joins and group-by operations.
• Database Connectors - Queries SQL databases using standard connectors to ingest data directly into distributed datasets for large-scale processing.
• File Synchronization - Automatically uploads local source files and configuration directories to remote cluster nodes.
• Interoperability - Propagates stack traces across language boundaries to debug errors occurring in remote tasks.
• Asynchronous Execution Patterns - Executes asynchronous operations within transforms to handle I/O-bound tasks efficiently.
• Concurrency Control Policies - Ray enables grouping actor methods to limit concurrent executions, preventing resource-intensive tasks from overwhelming the actor's processing capacity.
• Dynamic Task Scheduling - Ray allows assigning actor methods to specific concurrency groups at runtime to override default settings for individual task invocations.
• Graph Orchestration - Enables setting entry and exit points for directed acyclic graphs to manage data flow.
• Service Discovery Mechanisms - Ray provides mechanisms to retrieve a handle to an existing actor by name or create a new one if the name is currently unavailable.
• Software Design Patterns - Ray Core Design Patterns — a named example documented in this learning resource.
• Memory Inspection - Enables analyzing object references held in a cluster to identify memory leaks or high usage.
• Performance Metrics - Monitors application performance using counters and histograms to track state changes across distributed tasks.
• Performance Profiling Tools - Retrieves detailed timing and memory usage statistics for operators to identify performance bottlenecks.
• Execution Tracers - Collects stack traces from all local workers to diagnose performance issues or deadlocks.
• Model Evaluation - Enables row-level error handling and automatic recovery to maintain pipeline reliability for inference jobs.

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