intelligent-machine-learning/dlrover

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1680 stars · 217 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DLRover is an automatic distributed deep learning system that manages training of large AI models on Kubernetes and Ray clusters. It provides fault tolerance, auto-scaling of resources, and fast in-memory checkpoint recovery so developers can train models without handling distributed engineering themselves.

Use cases

• train large language models on a kubernetes cluster
• automatically scale distributed training resources up and down
• recover distributed training jobs from node failures
• run fault-tolerant pytorch distributed training
• speed up llm training with automatic operations
• manage deep learning training jobs on ray
• recover training from in-memory checkpoints in seconds

When to choose

• you train large deep learning or LLM models on k8s or ray and want automated fault tolerance and scaling
• you want training jobs to survive hardware failures without manual restarts
• you want to focus on model architecture rather than distributed systems engineering

When to avoid

• you only train small single-node models
• you need a lightweight trainer without cluster orchestration
• you use a framework other than pytorch or tensorflow

Facets

framework · maturity active

llm-training deep-learning machine-learning container-orchestration monitoring scheduling deep-learning machine-learning large-language-models microservices python cloud distributed-training fault-tolerance auto-scaling pytorch tensorflow ray k8s-operator checkpointing devops containers kubernetes docker gpu

Member repositories

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markdown · JSON · MCP: product_card(name="intelligent-machine-learning/dlrover")