DeepSeek发布DSec沙箱基础设施：单日支撑约300万个智能体训练沙箱
DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale
DeepSeek公开了自家跑智能体训练的沙箱底座，一天能开300万个沙箱，做Agent训练的可以看看这套架构怎么搭。
DeepSeek发布技术报告，介绍生产级沙箱平台DeepSeek Elastic Compute（DSec），通过统一SDK提供FnCall、容器、microVM和全VM四种沙箱后端。单个生产单元约160个节点，每天服务约300万个沙箱，支持超38万并发沙箱，每秒创建超5000个沙箱。DSec从分布式文件系统3FS按需加载镜像数据，并用内存共享、回收和CPU调度实现高密度执行。平台与强化学习框架协同设计，把有状态的rollout执行与可抢占的GPU训练解耦，还能缓解reward hacking等智能体异常行为。
DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale
Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services. These workloads create sandboxes in large bursts, span heterogeneous functionality and isolation requirements, retain state across long interactions, and draw from large image corpora with limited reuse. Supporting them therefore requires an elastic execution platform rather than a single sandbox runtime. This report presents DeepSeek Elastic Compute (DSec), a production sandbox platform that exposes FnCall, container, microVM, and full-VM sandbox backends through a unified SDK. DSec coordinates placement and lifecycle management across the cluster, composes environments from independently versioned layers, combines memory sharing, reclamation, and CPU scheduling for high-density execution, and loads image data on demand from Fire-Flyer File System (3FS), a cluster-wide distributed filesystem. DSec is co-designed with the reinforcement learning (RL) framework, decouples stateful rollout execution from preemptible GPU training, coordinates sandbox lifecycle with training to preserve rollout state while reclaiming idle resources, and mitigates agent misbehavior such as reward hacking. A single production-scale unit of DSec spans around 160 nodes, serving about 3 million sandboxes per day; in production, it supports over 380,000 concurrent sandboxes and sustains over 5,000 sandbox creations per second. Our evaluation and deployment experience show that these mechanisms reduce environment setup and image-distribution overhead, improve memory efficiency, and preserve latency-sensitive performance under high-density overcommit.
