Current Project Status:

• Snapshot Agent: Available, with pluggable snapshot backends — see the user guide.
• TimeSlice Orchestrator: Available — see the user guide.

Reinforcement learning (RL) workloads spend a significant fraction of their lifecycle idle—waiting on reward evaluation, generation stragglers, or synchronization steps. Across large-scale fleets, this leaves expensive accelerator hardware underutilized 45–66% of the time, even though the underlying RL math doesn't require it.

llm-d-rl-time-slicing moves the utilization fix from the application layer to the platform layer. Multiple independent RL jobs cooperatively share the same accelerator hardware, swapping during each job's natural blocking phases (generation, training, weight sync) rather than holding the accelerator idle.

Your training loop stays exactly the same — no algorithmic rewrites required.

▶ Open the interactive replay — play recorded runs, inspect every lock handoff and snapshot/restore, and select between runs.

We introduce collaborative, application-aware time-slicing. Using a lightweight client library that pairs seamlessly with your existing training and inference frameworks, the system delivers two core capabilities:

• Co-operative Scheduling: Schedules accelerator access in co-operation with the application — jobs signal their execution phase boundaries, and the platform grants and reclaims the hardware around them.
• Fast Context Switching: Performs fast, transparent state checkpointing and restoration under the hood.

For the full design rationale and preliminary benchmark results, see the Platform-Native Time-Slicing proposal.

This architecture consists of the following foundational components:

• Snapshot Agent: A node-local daemon, deployed as a Kubernetes DaemonSet, that performs the actual checkpoint/restore of accelerator state for a job. It supports a pluggable backend model, with backends specific to the underlying accelerator and checkpoint mechanism.
• TimeSlice Orchestrator: A central coordinator that manages exclusive accelerator access across co-located jobs. It persists lock state for crash recovery and exposes a gRPC API (Acquire/Yield) that frameworks invoke at natural phase boundaries.
• timeslice client: A lightweight library used by training and inference services to interact seamlessly with the Snapshot Agent and TimeSlice Orchestrator without needing to manage raw gRPC calls directly.

Cooperative Accelerator Time-Slicing — the TimeSlice Orchestrator coordinates multiple jobs sharing a cluster of accelerator nodes, granting and reclaiming hardware access at each job's natural yield points:

from timeslice import TimeSliceOrchestratorClient

client = TimeSliceOrchestratorClient(target="timeslice-timesliceorchestrator.timeslice-system:50051",
                                     job_id="my-job", group_id="trainer-group")

@client.on_accelerators()
def train_phase(trainer, batch):
    return trainer.update(batch)   # exclusive accelerator access inside

Standalone Snapshot Agent Integration — training services that already implement their own scheduling (e.g., tinker-style architectures) can call the Snapshot Agent's checkpoint/restore primitives directly, bypassing the orchestrator entirely:

from timeslice import SnapshotAgentClient

with SnapshotAgentClient(endpoint="localhost:9001") as client:
    client.snapshot_and_wait(job_id="my-job")   # GPU state -> host memory
    ...
    client.restore_and_wait(job_id="my-job")    # host memory -> GPU

For step-by-step instructions, installation walkthroughs, and API references, explore our Documentation & Guides.

• Framework integrations — OpenRL (Snapshot Agent) in progress; Slime or veRL orchestrator integration next
• Snapshot backend expansion — faster snapshot mechanisms and selective offload (e.g., specific memory regions such as LoRA adapters)
• User onboarding — simplified deployment and onboarding flows
• Multi-host support — distributed multi-node time-slicing
• TPU support — snapshot/restore for TPU accelerators
• Non-Kubernetes support — Slurm and bare-metal environments

Contributions are welcome! See CONTRIBUTING.md for guidelines.