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setup-esm_if1-remote-tool

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Set up, launch, validate, and troubleshoot the ESM-IF1 inverse-folding ToolUniverse remote tool and optionally relay it through ToolUniverse Connect. Use when deploying or auditing this implementation.

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# Set up ESM-IF1 inverse folding as a remote tool

> Validation status (2026-10-01): ESM-IF1 sequence design ran on the NVIDIA GB10 host (aarch64, 20 cores, cc 12.1, driver 580.173.02) **on the CPU**, under the optimization kit's `fast` mode (lever `batched_sampling`). **This is the first provider on the standard with `[gpu] policy = "none"`, and that is a measurement rather than a fallback.** The kit pins torch 2.4.0, whose CUDA generation predates this card: cuBLAS 12.4 for aarch64 carries cubins only through sm_90. It does carry **sm_90 PTX**, so a forward JIT is not ruled out and the honest status of that route is **unmeasured rather than impossible** — `provider.toml`'s `[gpu]` section has the `cuobjdump` output and the three reasons the route was not taken anyway, the first being that the kit's pinned PyG companions have no aarch64 build at any version. Four consecutive identical requests produced **byte-identical** FASTA files, and the same sha256 as the kit's own CLI. Peak RSS is flat in length and driven by the product of length and sample rows: 2.45 GiB from 64 through 395 residues, 3.65 at 800x2, and **11.22 GiB at the worst request the declared ceilings admit together** (1600 tokens x 4 rows, 757.7 s). Schedule on 11.22 unless the deployment caps the input; the smoke's 2.45 would under-budget an ordinary request by 4.6x. Public publication, cross-user isolation, concurrency, recovery and design quality are untested; keep this deployment private.

## Prerequisites

- Run from the ToolUniverse repository root on Linux with Python 3.12.3.
- **No GPU is required**, and none is used. `[gpu] policy = "none"` makes the preflight step skip every GPU check, which is what makes that honest rather than merely quiet. The worker still chooses the GPU when `torch.cuda.is_available()` is true, so the same worker drives a card on a host whose torch has one.
- The model environment needs **Python 3.11 exactly** (the kit's own requirement: cp311 wheels, and numpy 1.26.4 / scipy 1.11.4 / torch 2.4.0 publish no cp312). A C/C++ compiler is needed once, because biotite 0.39.0 publishes no linux-aarch64 wheel and is built from its pinned sdist (4m37s on 20 cores, then cached).
- Keep provider data, weights, caches, and credentials outside Git.
- Bind to loopback. A non-loopback bind requires TOOLUNIVERSE_API_TOKEN; never put it in arguments or results.

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