setup-af2ig-remote-tool

• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes
• SKILL.mdcore · 35,736 bytes

# Set up AF2 initial guess (dl_binder_design) as a remote tool

> Validation status (2026-10-01): af2ig — the `af2_initial_guess` step of nrbennet/dl_binder_design at commit `cafa3853`, AlphaFold 2 `model_1_ptm` re-predicting a designed binder–target complex from the complex itself as the structural initial guess — ran on the NVIDIA GB10 (aarch64, cc 12.1, driver 580.173.02) under the optimization kit's `off` mode. **Read what this is before reading anything else: "ig" is INITIAL GUESS, the input is a STRUCTURE and not a sequence, and the number it exists to produce is `pae_interaction` (angstroms; the field's working threshold is below 10).** All five `tu remote setup` steps are green and the acceptance suite passed: an ascending ladder, a mixed-shape phase, four consecutive identical requests, and a separate clean-worker run at the row the budget is declared on — an ascending ladder of 100 / 200 / 400 residues, a mixed-shape phase, four consecutive identical requests, a separate clean-worker run at 800 residues (the row the budget is declared on), and a two-rebuild engine test. **29 held-worker requests, 29 structures, 0 failures**, every one asserted to have written a `*_af2pred.pdb`. **THERE ARE NO PADDING BUCKETS HERE, which is the opposite of AlphaFold 3 and changes which half of the test carries the evidence**: this model compiles one XLA program per *exact* total residue count, so four identical requests exercise one program and the shape change is the whole test. Residency is worth **26x at its best rung and 1.7x at the row the budget is declared on** -- 95.0 s cold against 3.6 s held at 100 residues, and 272.1 s against ~163 s at 800. Plan with the second number; the first is the best case, and it is largest where a request is cheapest. Declared budget **6.0 GiB at 800 residues**, which is also the tool's input ceiling; the ladder is 0.5 / 1.0 / 2.0 / 6.0 GiB at 100 / 200 / 400 / 800 residues (reported), over a working set of 0.49 / 0.62 / 1.23 / 4.36 GiB (actual). Two cautions below are specific to this provider: its **VRAM figure is quantised** in a way a torch provider's is not, and its environment carries one named deviation from the kit's lock (`tensorflow` for `tensorflow-cpu`, because no aarch64 wheel of the latter exists). Public publication, cross-user isolation, concurrency, recovery and scientific quality are untested; keep this deployment private.

## Prerequisites

- Run from the ToolUniverse repository root on Linux with Python 3.12.3.
- A CUDA GPU is required. This provider holds its model resident between requests; CPU is not a target.
- 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.

Run the standard-library contract check before downloading large dependencies:

~~~bash