sharding-model-analysis
sharding-model-analysis is an MLOps & model serving skill for AI agents, published by tenstorrent in the tt-xla repository on GitHub. Skillspace rates sharding-model-analysis C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Analyze a PyTorch model, then design and implement an optimal multi-device sharding strategy for Tenstorrent hardware that minimizes collective-communication (CCL) ops. A strategy is both which parallelism to use (tensor / sequence / data) and how to shard under it - e.g. Megatron column→row is one of several tensor-parallel schemes. Use whenever the user asks to shard a model, distribute it across devices, write Shardy annotations, reduce CCLs, or analyze a model for tensor/sequence parallelism. Trigger even when the user only says "sharding strategy" or names a model with a device count, without asking for a full plan.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 72
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 87 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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