# bytedance/byteps **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/bytedance-byteps).** _How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._ 3,721 stars · 493 forks · Python · NOASSERTION · archived ## Links - GitHub: https://github.com/bytedance/byteps - awesome-repositories: https://awesome-repositories.com/repository/bytedance-byteps.md ## Topics `deep-learning` `distributed-training` `keras` `machine-learning` `mxnet` `pytorch` `tensorflow` ## Description BytePS is a distributed deep neural network training framework and communication library designed to scale model training across multiple GPUs and compute nodes. It functions as a GPU cluster orchestrator and RDMA network optimizer, providing the necessary primitives to synchronize gradients and data across a server cluster. The project distinguishes itself through high-performance network optimizations, utilizing remote direct memory access and page-aligned memory to reduce latency. It employs topology-aware communication tuning and CPU core affinity management to maximize hardware throughput and minimize processing contention. The system covers a broad range of capabilities, including collective communication operations for all-reduce and broadcast tasks, distributed task orchestration for process launching, and asynchronous training support to eliminate global synchronization barriers. It also includes a suite of debugging tools for communication timeline tracing and distributed execution diagnostics. The framework supports deployment via containerized environments and automated remote process execution using SSH. ## Tags ### Artificial Intelligence & ML - [Distributed Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks.md) — Implements a distributed framework for scaling deep neural network training across multiple GPUs and compute nodes. - [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training.md) — Scales the training of large deep neural networks across multiple GPUs and compute nodes. - [Distributed Training Coordination](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-coordination.md) — Coordinates the timing and ordering of data partitioning and gradient updates to optimize training speed. ([source](https://github.com/bytedance/byteps/tree/bytescheduler/bytescheduler)) - [Multi-Node Training Scaling](https://awesome-repositories.com/f/artificial-intelligence-ml/gpu-model-deployments/multi-node-training-scaling.md) — Enables the distribution of large-scale model training across multiple GPUs and hardware nodes. ([source](https://github.com/bytedance/byteps/tree/v0.2)) - [Distributed Gradient Synchronization](https://awesome-repositories.com/f/artificial-intelligence-ml/gradient-computation/distributed-gradient-synchronization.md) — Implements mechanisms for exchanging model gradients across multiple computing nodes. ([source](https://github.com/bytedance/byteps#readme)) - [Asynchronous Gradient Aggregation](https://awesome-repositories.com/f/artificial-intelligence-ml/gradient-computation/gradient-aggregators/asynchronous-gradient-aggregation.md) — Implements asynchronous gradient aggregation to eliminate global synchronization barriers and increase training throughput. - [Distributed Machine Learning Integrators](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/distributed-and-scaling-strategies/distributed-learning/distributed-machine-learning-integrators.md) — Provides interfaces to connect the communication library with deep learning frameworks for distributed training. ([source](https://github.com/bytedance/byteps#readme)) - [Hardware Utilization Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/local-ai-runtimes/gpu-resource-management/hardware-utilization-optimizations.md) — Optimizes hardware utilization through CPU core affinity and memory alignment to reduce latency. - [Asynchronous Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/distributed-and-scaling-strategies/asynchronous-training-utilities/asynchronous-training.md) — Supports asynchronous training to eliminate global synchronization barriers and increase total system throughput. ([source](https://github.com/bytedance/byteps/blob/master/docs/env.md)) ### Data & Databases - [Collective GPU Communication](https://awesome-repositories.com/f/data-databases/collective-gpu-communication.md) — Provides GPU-accelerated collective communication primitives like all-reduce and broadcast to synchronize data across nodes. ([source](https://github.com/bytedance/byteps/tree/v0.2.3)) ### DevOps & Infrastructure - [GPU Cluster Communications](https://awesome-repositories.com/f/devops-infrastructure/cluster-node-management/gpu-cluster-communications.md) — Orchestrates high-bandwidth data transfers across GPUs using collective communication primitives and RDMA. - [RDMA GPU Transfers](https://awesome-repositories.com/f/devops-infrastructure/cluster-node-management/gpu-cluster-communications/rdma-gpu-transfers.md) — Accelerates remote data movement using RDMA and page-aligned memory for low-latency GPU transfers. ([source](https://github.com/bytedance/byteps/blob/master/CHANGELOG.rst)) - [Distributed Script Launchers](https://awesome-repositories.com/f/devops-infrastructure/distributed-script-launchers.md) — Ships scripts to launch training processes across workers and servers while coordinating node roles. ([source](https://github.com/bytedance/byteps/blob/master/docs/running.md)) - [Distributed Task Orchestration](https://awesome-repositories.com/f/devops-infrastructure/distributed-task-orchestration.md) — Provides a command-line utility for bootstrapping and managing the execution of training tasks across a cluster. ([source](https://github.com/bytedance/byteps/blob/master/CHANGELOG.rst)) - [GPU Cluster Job Schedulers](https://awesome-repositories.com/f/devops-infrastructure/job-scheduling/gpu-cluster-job-schedulers.md) — Includes a launcher and scheduler to orchestrate task execution and resource allocation across GPU clusters. ### Networking & Communication - [Distributed Learning Communication](https://awesome-repositories.com/f/networking-communication/distributed-learning-communication.md) — Provides a high-performance communication layer to synchronize model states and gradients across distributed training nodes. - [RDMA Networking](https://awesome-repositories.com/f/networking-communication/rdma-networking.md) — Optimizes network latency using Remote Direct Memory Access for high-speed data movement during model training. - [Shared Memory IPC](https://awesome-repositories.com/f/networking-communication/communication-protocols-architectures/inter-process-communication/shared-memory-ipc.md) — Uses shared memory regions for high-speed data exchange between local worker and server processes. - [Hardware Topology Optimizers](https://awesome-repositories.com/f/networking-communication/network-topology-extensions/topology-abstraction-layers/hardware-topology-optimizers.md) — Optimizes data transfer paths and concurrency based on the physical layout of GPUs and hardware switches. ### Operating Systems & Systems Programming - [Remote Direct Memory Access](https://awesome-repositories.com/f/operating-systems-systems-programming/remote-direct-memory-access.md) — Utilizes remote direct memory access to transfer data between network cards and minimize CPU overhead. - [CPU Affinity Binding](https://awesome-repositories.com/f/operating-systems-systems-programming/cpu-affinity-binding.md) — Pins execution threads to specific physical cores and memory nodes to reduce context switching and latency. ([source](https://github.com/bytedance/byteps/blob/master/docs/env.md)) - [Process Affinity and Core Binding](https://awesome-repositories.com/f/operating-systems-systems-programming/kernel-core-internals/process-and-memory-management/process-affinity-and-core-binding.md) — Pins execution threads to physical CPU cores to reduce context switching and cache misses. ### Development Tools & Productivity - [Distributed Debugging](https://awesome-repositories.com/f/development-tools-productivity/distributed-debugging.md) — Offers diagnostics for distributed training failures through backtrace capture and tensor value sampling. ([source](https://github.com/bytedance/byteps/blob/master/docs/env.md)) ### Software Engineering & Architecture - [Inter-Process Communication Optimizations](https://awesome-repositories.com/f/software-engineering-architecture/inter-process-communication-optimizations.md) — Uses shared-memory segments to accelerate data exchange between worker and server processes on the same machine. ([source](https://github.com/bytedance/byteps/blob/master/docs/best-practice.md)) ### Testing & Quality Assurance - [Distributed Training Timelines](https://awesome-repositories.com/f/testing-quality-assurance/performance-testing-analysis/performance-analysis/distributed-training-timelines.md) — Provides tools for recording and analyzing communication timelines to diagnose bottlenecks in distributed training. ### Part of an Awesome List - [MLOps and Infrastructure](https://awesome-repositories.com/f/awesome-lists/ai/mlops-and-infrastructure.md) — Distributed DNN training framework.