About this skill
Expert guide for PyTorch/XLA (torchxla) development targeting TPU and XLA-compatible devices. Use this skill whenever the user asks about: torchxla, PyTorch on TPU, XLA training, SPMD parallelism, distributed TPU training, XLA checkpointing, MpDeviceLoader, xm.markstep, torchxla.launch, FSDPv2 on XLA, gradient sharding, XLA compilation, lazy tensor execution, or migrating GPU PyTorch code to TPU. Trigger even for partial mentions like "TPU training", "XLA device", "xla backend", "run on TPU", or "SPMD mesh". This skill covers torchxla 2.9.0 (latest stable, requires torch==2.9.0, Python 3.10–3.13).
Quick start
- Copy the install prompt. Use the green button above this README.
- Send it to your agent. Paste it into your coding agent’s chat and send.
- Review the setup. Your agent checks the skill’s instructions, requirements, and installation scope.
Review, license & listing details
This is an imported listing. We haven’t certified its safety, license, or compatibility. Skills may include executable scripts.
Only SKILL.md was scanned. Supporting files and scripts were not checked. No safety grade has been assigned.
0 potential findings · Scanned 11/4/2026 · Scanner 0.2.1
Content SHA-256: 7e2a69afcd3c9c28ea404f7b425f323efd877b14adc5374fd897741b1cafb2be
About this listing
- Category
- Engineering
- Reported license
- Not specified
- Listing language (estimated)
- English
- Review status
- Not individually reviewed
- Agent compatibility
- Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.