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
fastmode (leverbatched_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 thecuobjdumpoutput 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 whentorch.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.
Run the standard-library contract check before downloading large dependencies:
python scripts/remote_validation/setup_skill_preflight.py --implementation esm_if1
After exporting provider resources, add --check-provider-env. After the
server starts, add --live to verify the exact MCP tool set without running
the model. Before sharing, add --check-connect-prereqs; this reports only
whether a key is set and never prints its value.
Create the two environments
This provider is on the remote-tool standard, so it has two environments, not one: the MCP server runs in ToolUniverse's, and the model runs in its own, built from a pinned lock. They cannot import each other, and that is the point.
python3 -m venv .venvs/esm_if1
. .venvs/esm_if1/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .
python -m pip install fastmcp
The model half lives at the path TOOLUNIVERSE_ESM_IF1_PYTHON names, and
nothing above creates it. Create a Python 3.11 interpreter there first --
uv venv --seed --python 3.11 ~/esm_if1-venv -- and tu remote setup --only environment fills it from
src/tooluniverse/remote/esm_if1/environment/requirements.lock with
--no-deps, so no version is silently resolved to something else. uv venv is
used rather than the stdlib python -m venv because this kit scans no .pth
files at all (it ships no autoload .pth), so uv's _virtualenv.pth -- which
broke openfold3's hook detection -- has nothing here to confuse. The venv
must be seeded (--seed): the environment step installs with
python -m pip.
The rest is built by tu remote setup, which reads
src/tooluniverse/remote/esm_if1/provider.toml and runs five steps: preflight,
environment, kit, weights, smoke. Each refuses by name and carries its fix.
export TOOLUNIVERSE_ESM_IF1_PYTHON=~/esm_if1-venv/bin/python
export TOOLUNIVERSE_ESM_IF1_WEIGHTS=~/.esm_if1 # optional; this is the default
tu remote setup esm_if1
Run one step at a time with --only preflight|environment|kit|weights|smoke
while bringing a machine up. The smoke step is the one that matters: it holds
the model, sends one request, asserts the provider produced what its manifest
says a successful run produces (outputs = ["*.fasta"] plus
output_keys = ["sequences", "native_seq"]), and records what this machine did
to ~/.cache/tooluniverse/measured/esm_if1.json. A host schedules on that file.
Package/network-dependent commands must be rerun in a clean environment before marking this skill complete.
Obtain credentials, data, and model weights
- No credentials. One weight file, 1.7 GB:
esm_if1_gvp4_t16_142M_UR50.pt, declared in the manifest with the size and sha256 the kit's ownstock/PINS.jsoncarries.tu remote setup --only weightsfetches it with the kit's own downloader (python -m esm_if1_opt.weights DIR, which runstorch.hub.download_url_to_file-- the transfer upstream's own loader uses) and checks both. 37 s on this host. - Nothing is fetched at request time, and the worker proves it twice. Before loading, it compares
torch.hub.get_dir()against the directory the checkpoint is staged into and refusestorch_hub_elsewhereif they differ; after loading, it runs the kit's ownstack.cache_word()and refusesweights_replacedon anything buthit. Both checks exist because a probe caught the failure:stage_torch_home()links the checkpoint into$MODEL_OPT_STATE/torch_home,torch.hubreadsTORCH_HOME, and with the first set and the second unset the staging reportslinked-- truthfully, about a directory torch never looks in -- while upstream downloads 1.7 GB into~/.cache/torch/.
Authorize once, then share with one short command
After installing dependencies, exporting the provider resources above, and installing the pinned relay SDK described under Connect below, run from the repository root. Log in only once per machine (and again after key rotation):
tu remote login
# Or import an existing protected 0600 file without sourcing it:
tu remote login --env-file /path/to/tooluniverse-service.env
Then each private share is one short command:
tu remote share esm_if1
By default, tu remote login requests a short-lived device code, opens the
TU Platform approval page, and polls until the signed-in user approves. No key
copy/paste is required. On a headless machine, add --no-browser and open the
printed link elsewhere. The CLI exchanges the approval for a computer-only key,
verifies /remote-servers/preflight, stores it in a local 0600 config file, and
never displays it.
The share command runs environment and TU Platform preflights, starts or reuses the exact loopback endpoint, validates discovery, and keeps the relay in the foreground until Ctrl-C. Override the defaults only when needed:
tu remote share esm_if1 --name my-esm-if1-remote --workers 1
Use tu remote check esm_if1 for a non-sharing readiness check and
tu remote run esm_if1 for a local-only foreground server.
In an interactive terminal, sharing automatically starts the same browser flow
when the key is missing, expired, or revoked. A malformed or revoked explicit
TOOLUNIVERSE_SERVICE_KEY fails fast instead of being silently replaced; unset
or correct it, then run tu remote login. Non-interactive jobs also fail fast.
Use tu remote logout to remove only the local copy. Use tu remote logout --revoke to revoke the computer-only platform connection first; the server record remains offline for owner inspection.
Start and verify locally
python -m tooluniverse.remote.esm_if1.esm_if1_tool
The Streamable HTTP endpoint is http://127.0.0.1:8050/mcp. In a second activated shell run:
python - <<'PY'
import asyncio
from fastmcp import Client
async def main():
async with Client("http://127.0.0.1:8050/mcp") as client:
print([tool.name for tool in await client.list_tools()])
asyncio.run(main())
PY
Confirm discovery contains esm_if1_design; stop on empty, duplicate, or schema-drifted discovery.
The first request after a restart pays the model build; the ones after it do
not. That is the whole reason this provider is resident, and here the fixed cost
is a 1.7 GB torch.load rather than a compile trace -- the same argument as
ESMFold2's, at a twentieth of the weight.
Connect to ToolUniverse Connect
The tuplatform-connect relay is not yet published on PyPI. Install the reviewed public wheel below; its SHA-256 is pinned. Interactive sharing uses browser device authorization, so no key copy/paste or GitHub access is required.
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0"
tu doctor --forward http://127.0.0.1:8050/mcp --json
tu serve --share --forward http://127.0.0.1:8050/mcp --name validation-esm-if1 --workers 1
Prefer browser device authorization. For CI or migration, supply
TOOLUNIVERSE_SERVICE_KEY only through a protected environment or use
tu remote login --manual-key; never put a key in shell arguments.
This implementation was added after the authenticated 2026-08-16 Platform
matrix and was not part of it. That matrix imported each implementation it
covered as unpublished owner drafts and invoked them through
/expert-sessions/{id}/test; this provider was imported as no such draft and
invoked through no such route. It has not been exercised through the Platform
at all: the evidence above is local, on one host, through the loopback
endpoint. Public publication, independent-caller authorization/isolation,
saturation, and persistent supervision are untested.
Run a verified example
Operation: esm_if1_design
The input is a STRUCTURE, which is what makes this provider the inverse of the
folding ones beside it. src/tooluniverse/remote/esm_if1/smoke/input.json
carries the first 64 residues of chain C of 5YH2 -- the example structure the
kit's own README designs with -- inline, and
smoke/input-multichain.json is upstream's second route for the same file.
The example below is a ten-residue excerpt of that same chain, N/CA/C only,
which is all upstream reads. It is short enough to put in a document and it
runs; it is not a design worth interpreting. The scientifically meaningful call
is the 64-residue smoke file in the snippet after it, and the recovery figures
quoted in provider.toml come from the whole 395-residue chain.
{"structure": "ATOM 3461 N SER C 134 -5.555 22.103 -75.953 1.00 70.85 N\nATOM 3462 CA SER C 134 -4.121 21.850 -75.885 1.00 74.77 C\nATOM 3463 C SER C 134 -3.849 20.353 -75.765 1.00 85.14 C\nATOM 3467 N VAL C 135 -4.510 19.712 -74.797 1.00 88.32 N\nATOM 3468 CA VAL C 135 -4.308 18.282 -74.566 1.00 85.66 C\nATOM 3469 C VAL C 135 -4.707 17.486 -75.801 1.00 86.60 C\nATOM 3474 N LEU C 136 -5.911 17.750 -76.317 1.00 83.95 N\nATOM 3475 CA LEU C 136 -6.370 17.079 -77.527 1.00 79.86 C\nATOM 3476 C LEU C 136 -5.490 17.441 -78.714 1.00 79.96 C\nATOM 3482 N GLN C 137 -5.102 18.713 -78.812 1.00 78.74 N\nATOM 3483 CA GLN C 137 -4.166 19.129 -79.848 1.00 81.85 C\nATOM 3484 C GLN C 137 -2.866 18.340 -79.753 1.00 85.22 C\nATOM 3491 N SER C 138 -2.378 18.113 -78.528 1.00 90.62 N\nATOM 3492 CA SER C 138 -1.123 17.387 -78.341 1.00 88.57 C\nATOM 3493 C SER C 138 -1.299 15.896 -78.588 1.00 84.29 C\nATOM 3497 N LEU C 139 -2.460 15.345 -78.217 1.00 83.51 N\nATOM 3498 CA LEU C 139 -2.689 13.915 -78.395 1.00 83.46 C\nATOM 3499 C LEU C 139 -2.560 13.523 -79.859 1.00 77.50 C\nATOM 3505 N PHE C 140 -3.298 14.207 -80.735 1.00 77.84 N\nATOM 3506 CA PHE C 140 -3.184 13.933 -82.159 1.00 74.98 C\nATOM 3507 C PHE C 140 -1.818 14.331 -82.701 1.00 75.13 C\nATOM 3516 N GLU C 141 -1.067 15.152 -81.961 1.00 78.09 N\nATOM 3517 CA GLU C 141 0.297 15.501 -82.332 1.00 81.27 C\nATOM 3518 C GLU C 141 1.306 14.447 -81.899 1.00 77.28 C\nATOM 3525 N HIS C 142 0.877 13.444 -81.128 1.00 71.75 N\nATOM 3526 CA HIS C 142 1.757 12.361 -80.722 1.00 72.48 C\nATOM 3527 C HIS C 142 2.204 11.564 -81.947 1.00 76.35 C\nATOM 3535 N PRO C 143 3.322 10.836 -81.850 1.00 78.08 N\nATOM 3536 CA PRO C 143 3.702 9.912 -82.929 1.00 79.13 C\nATOM 3537 C PRO C 143 2.972 8.578 -82.900 1.00 72.64 C\nEND", "structure_format": "pdb", "chain": "C", "num_samples": 2, "temperature": 1.0, "seed": 37}
python - <<'PY'
import asyncio
import json
from fastmcp import Client
doc = json.load(open("src/tooluniverse/remote/esm_if1/smoke/input.json"))
arguments = {
"structure": doc["structure"],
"structure_format": "pdb",
"chain": "C",
"num_samples": 2,
"temperature": 1.0,
"seed": 37,
}
async def main():
async with Client("http://127.0.0.1:8050/mcp") as client:
result = await client.call_tool("esm_if1_design", arguments)
print(result)
asyncio.run(main())
PY
Expected success shape, with the reply's own key names: sequences (one string
per requested sample, each the length of the designed chain), native_seq,
recovery (one fraction per sample), n_tokens, designed_length, route
(singlechain or multichain), device, kit_levers and
weights_held_by = "worker". Note peak_reserved_gib is null on a CPU
host and timing.peak_rss_gib is the figure to schedule on. sequences is
deliberately EMPTY when a backbone's rows were not all sampled, which is what
[runtime] output_keys branches on -- so an empty list is a failed request,
not an empty answer. Run above, on the ten-residue example: two 10-character
sequences and one ex.fasta. Check scientific meaning,
output bounds, invalid-input behavior, and absence of paths, secrets, and
traces.
Tune CPU and concurrency
- One thread, and it is measured. torch's default is
os.cpu_count()= 20, which is the slowest setting tested: 64 residues x 2 samples, min of three repeats, reads 8.66 s at one thread and 13.91 s at twenty (7.50 vs 11.52 pinned to the ten Cortex-X925 performance cores), and the ordering holds at 256 residues. ESM-IF1's decoder takes one sequential step per designed position over tensors too small for a thread barrier to pay for itself.[kit.env] OMP_NUM_THREADS = "1"andtorch.set_num_threads(1)both state it;ESM_IF1_RESIDENT_THREADSoverrides it for a host that measures otherwise. This is the same setting boltzgen needed for an unrelated reason -- there it stopped a library forking a poolfork()would not carry over; nothing forks here. - The axis that does pay is the kit's lever.
batched_samplingstacks (backbone, sample) rows into one forward pass: 11.14 s per sequence at one row, 1.94 s at eight -- 5.7x, drawn from the same distribution. Raisenum_samplesrather than issuing N requests. - Use one worker. The resident worker serves one request at a time by design; a second in-flight request queues on its lock.
- Read
peak_rss_gibfrom the measured file before co-tenanting, and read the manifest for the row it was declared at. Length alone moves it little -- 2.45 GiB from 64 through 395 residues, 3.65 at 800 -- because the constant 1.7 GB checkpoint dominates; what moves it is the PRODUCT of length and sample rows, which is why the worker bounds that product and why the declared figure is 11.22 GiB at 1600 tokens x 4 rows. Time is not flat either: 5.4 / 16.0 / 43.9 / 72.9 / 172.2 s over 64 / 128 / 256 / 395 / 800 residues at two samples, and 757.7 s at the ceiling. The peak is a spike, not a plateau: RSS read during that 757.7 s request went 5.38 -> 9.08 -> 1.48 GiB and stayed near 1.48 for the remaining ~500 s, because the peak is the GVP encoder's kNN graph early in the request and the long decode loop is small. A high-water mark cannot tell a co-tenant that, so the manifest does. - Measure cold start, then at least four consecutive identical requests, then a mixed-shape sequence (small, small, large, small). Two warm calls of different shapes is not enough: openfold3 passed exactly that and still died on the second request of the same shape. For this provider both passed and the four identical requests also produced byte-identical files, which is a stronger statement and was only measurable because it was written down falsifiably first.
- Then parallel levels 1, 2, 4, 8. Record successes/errors, p50/p95, peak RSS, utilization, queueing, cancellation cleanup, and recovery.
Troubleshoot and clean up
- Read the refusal. Every step of
tu remote setupnames what is wrong and carries the fix; exit 3 is a refusal and exit 1 is a failure. - The worker's own transcript is at
~/.cache/tooluniverse/remote-logs/esm_if1-resident.log.
| refusal | what it means | what to do |
|---|---|---|
kit_missing:esm_if1_opt |
the kit is not installed on the model interpreter | tu remote setup esm_if1 --only kit, which runs the kit's own bash run.sh install |
kit_activation_refused |
the kit declined the mode: fair-esm or torch absent, or a second mode resolved in this process | read the sentence; it is esm_if1_opt.stack.enable's own |
core_missing:opt_core / core_mismatch |
the kit's shared core is absent or older than its pin | --only kit reinstalls both editable, core first |
weights_not_linked |
the checkpoint is not symlinked into torch.hub's cache entry | set TOOLUNIVERSE_ESM_IF1_WEIGHTS; remove any regular file at that cache entry by hand (it is never deleted automatically: a truncated transfer and a different checkpoint look alike) |
torch_hub_elsewhere |
torch.hub will read a different directory than the one the checkpoint was staged into | unset TORCH_HOME, or point it at the directory the message names |
weights_replaced |
after the load, the cache entry no longer resolves to the verified file | something replaced it mid-load; remove it and re-run --only weights |
weights_off_pin |
the 1.7 GB file is absent, short, or a different digest | the named file is left in place for inspection; remove it by hand and re-run --only weights |
lock_install_failed |
the pinned stack did not install | check the interpreter is Python 3.11; biotite builds from source and needs cc |
ESM_IF1_OPT=... disagrees with --mode |
the kit's mode variable is in the worker's environment | launch.py strips it, so something set it after the launch; unset it and restart |
smoke_produced_nothing |
the model answered and wrote no FASTA | the glob is *.fasta and the worker writes input.pdb and opt_manifest.json on every request, successful or not, so this means the pass really did not complete; read the transcript |
- Import/executable failure in the model half: re-run
tu remote setup esm_if1 --only environment. It rebuilds from the lock with--no-depsand will not silently resolve a different version. - The
UserWarning: Regression weights not found, predicting contacts will not produce correct results.at every model load is expected and upstream's own: the checkpoint has no contact head and nothing here predicts contacts. - 401/403 on deliberate network binding: configure matching TOOLUNIVERSE_API_TOKEN bearer auth; prefer loopback plus relay.
- Stop server/relay with Ctrl-C. If installed, run
tuplatform-service uninstall --name validation-esm-if1. - A worker outliving its server is a known shape: it has an idle timeout and an orphan watchdog, and it exits when its parent becomes init. This one holds no GPU, so check with
pgrep -af esm_if1_resident_workerrather thannvidia-smi. - Revoke temporary keys. After confirmation, remove only .venvs/esm_if1 and the provider's own cache directory; never use a broad recursive target.
Use only official upstream documentation linked by the implementation README; do not substitute third-party model mirrors.