Flame v0.6: A Distributed Engine for Elastic Workloads
Flame v0.6 adds Runner, object cache, and install tooling for elastic jobs such as agents, RL, and crawlers. Batch jobs stay with Volcano; workflow jobs are not covered yet.
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An open source community to empower AI applications
XFLOPS helps enterprises build AI applications with open tools, frameworks, and best practices. Flame is the community's core project: a distributed engine for secure, cost-effective, and high-performance elastic AI workloads.
What XFLOPS Focuses On
Elastic
Scale AI workloads dynamically based on demand with runtime-aware scheduling and resource optimization.
Security
Use session-based access, isolated runtime environments, and secure component communication for elastic workloads.
Cost Effective
Optimize resource utilization and workload distribution so AI systems can run efficiently at scale.
Heterogeneous
Support varied infrastructure, including CPU, GPU, and accelerator-backed environments.
High Performance
Improve throughput and roundtrip time for task-heavy AI systems through distributed execution.
Cloud Native
Build portable runtime infrastructure for cloud, on-premise, and hybrid deployments.
Core Project: Flame
Flame is the core XFLOPS project for elastic AI workloads. It provides the distributed runtime mechanisms behind sessions, task scheduling, executor reuse, object caching, and secure service integration for agents, reinforcement learning, generated-code execution, and more.
How Flame Works
- Session: A group of related tasks with scheduling, resource, and isolation boundaries.
- Task: A unit of work submitted by a client and executed by a service in an executor.
- Executor: A runtime environment that hosts application services for a session.
- Object cache: A shared data layer used by Runner, common data, and incremental object updates.
Latest from the XFLOPS Blog
Technical walkthroughs and examples from the XFLOPS community and Flame project.
Deploy a Candle Application with Flame
A walkthrough of the Candle Based Rust example in Flame, showing that the main code change is wrapping local inference in a Flame service macro, while flmctl deploy handles packaging and registration.
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TorchRL DQN with Flame Runner and Sharded Replay
A technical blog on the TorchRL DQN example in Flame, covering distributed collectors, Flame-backed TorchRL replay storage, sharded sampling, and an Apple M4 Podman test run.
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