Introduction
DLRover is an industrial-grade automatic distributed deep learning system designed to reduce training downtime, improve resource utilization, and accelerate failure recovery for large-scale model training on Kubernetes or Ray clusters.
Key features
- Fault tolerance and recovery: automatic diagnosis and process restart to minimize training interruption.
- Flash Checkpoint: asynchronous checkpoint persistence and in-memory fast recovery for seconds-level resume of large models.
- Auto-scaling and scheduling: dynamic scaling and data sharding to mitigate stragglers and improve throughput.
Use cases
- Production orchestration and operations of large-scale LLM/model training.
- Distributed training tasks on K8s/Ray that require elasticity, fault-tolerance, and fast recovery.
- Scenarios that need to reduce I/O overhead and speed up checkpoint/recovery processes.
Technical details
- Primarily implemented in Python with supporting Go/C++ components; integrates with DDP, FSDP, DeepSpeed, and Megatron-LM.
- Provides tutorials and examples (elastic scheduling, node health checks, Flash Checkpoint) for easy integration into existing training pipelines.