deepseek-ai/profile-data resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1182 stars · 152 forks observed · 2026-08-28

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

A public release of PyTorch Profiler traces from DeepSeek's V3/R1 training and inference infrastructure, showing computation-communication overlap strategies for MoE workloads. The traces can be visualized in Chrome or Edge via chrome://tracing.

Use cases

• study how DeepSeek overlaps computation and communication in MoE training
• understand DualPipe forward-backward chunk overlap strategies
• analyze prefill and decode all-to-all communication patterns
• learn EP/TP parallelism configurations used in DeepSeek-V3 deployment
• visualize GPU kernel timelines for large-scale LLM training
• research low-level implementation details of MoE routing and RDMA communication

When to choose

• you are researching distributed LLM training or inference performance
• you want real-world profiling traces of MoE computation-communication overlap
• you are building or optimizing systems like DualPipe or DeepEP
• you need reference parallelism configurations (EP/TP) for large-scale training

When to avoid

• you need runnable code rather than profiling data
• you want general-purpose GPU profiling tools rather than example traces
• you need profiling of non-MoE or small-scale workloads

Facets

dataset · maturity active

benchmarking monitoring gpu-computing llm-training llm-inference large-language-models gpu-computing performance microservices deep-learning python browser profiling-data mixture-of-experts computation-communication-overlap pytorch-profiler dualpipe deepep chrome-tracing rdma gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="deepseek-ai/profile-data")