Estimate peak GPU memory usage for DualPipeV MoE training runs.
npx skills add https://github.com/mlc-ai/pith-train --skill estimate-memory
Please help me install this Agent Skill. Skill: estimate-memory Source: https://github.com/mlc-ai/pith-train/tree/main/.agents/skills/estimate-memory Command: npx skills add https://github.com/mlc-ai/pith-train --skill estimate-memory
This memory estimation skill provides precise peak GPU memory predictions for DualPipeV MoE training runs, enabling users to plan resources and avoid OOMs.
Run the memory estimator by providing the model config, pipeline, and batch parameters to generate a memory usage estimate.
High-intent search queries and answers about installing and using this skill.
You can estimate peak GPU memory for MoE training by translating natural-language configurations into exact CLI invocations, which produces a per-component memory breakdown and a pass/fail verdict to prevent OOMs.
Calculating GPU memory usage for DualPipeV training requires validating parameters including model config, total GPUs, pp-size, ep-size, cp-size, micro-batch-size, global-batch-size, sequence-length, and gpu-memory-gb.
Yes, you can use natural language to configure memory estimation for large-scale pipeline parallelism, as the tool translates natural-language descriptions into the exact CLI invocation for memory planning across varying pipeline and expert parallelism configurations.
The memory estimator supports varying expert and context parallelism configurations, applying directly to memory planning for large-scale MoE setups across varying pipeline, expert, and context parallelism environments.
To avoid out-of-memory errors on 32 GPUs, estimate peak memory usage beforehand to ensure sufficient headroom and adjust micro-batch sizes or pipeline parallelism accordingly before launching the training run.
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