Setup Gpnstar Remote Tool

Set up GPN-Star as a remote tool

Validation status (2026-10-02): GPN-Star v100-200m (gpn 0.9.0, optimization kit exact mode) ran on the NVIDIA GB10 (aarch64, cc 12.1) under torch 2.13.0+cu130, so the GPU is open here. What one request is: a list of single-nucleotide variants (chrom, 1-based pos, ref, alt) scored against a multiple-species ALIGNMENT STORE the host holds; each score is logit(alt) - logit(ref) at the masked position, averaged over the forward and reverse-complement windows. GPN-Star is a language model over an alignment window, not over bare DNA. The alignment store is an input, not a weight, and was NOT downloaded for this validation: the real hg38 100-way store is a 42 GB zip that unpacks to ~64 GB (songlab/multiz100way). Everything above was measured against the kit's 0.14 MiB sparse example store (eight windows on chromosomes 1, 2, 3 and 5) and against a synthetic store of the same shape for worst-case content; any other position is refused with "the alignment holds no data". Verified on FOUR CONSECUTIVE IDENTICAL REQUESTS, a mixed-batch-shape sequence, and a window / batch / single-variant / refusal sequence in which the same request returned a byte-identical scores_sha256 five times. Peak reserved 1.01 GiB at 2 variants rising to 5.79 GiB at the 8192-column ceiling (batch 64 x window 128); declared budget 7.72 GiB, the stock-projection bound at that row. The kit's CUDA-graph lever is declined: with it on, 29 distinct batch shapes grew the reserve to 24.3 GiB. Wall times were taken on a contended card (load average ~65, five other GPU processes); peaks reproduce under contention and times do not. Public publication, cross-user isolation, concurrency, recovery and scientific validity of the scores against a full store 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 (driver 580 or newer for the pinned CUDA 13.0 stack). This provider holds its model resident between requests; CPU is not a target.
• Keep provider data, weights, alignment stores, 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 gpnstar

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 -- the model's stack (Python 3.13, torch 2.13.0, transformers 5.15.0) is not installable beside ToolUniverse's.

python3 -m venv .venvs/gpnstar
. .venvs/gpnstar/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_GPNSTAR_PYTHON names, and nothing above creates it. Create an empty interpreter there first with uv venv --seed --managed-python --python 3.13 ~/gpnstar-venv (upstream requires Python >= 3.13, < 3.14), and tu remote setup --only environment fills it from src/tooluniverse/remote/gpnstar/environment/requirements.lock with --no-deps, so no version is silently resolved to something else. That step needs network and git: the lock names stock gpn by its pinned commit, and pip builds it with hatchling.

The rest is built by tu remote setup, which reads src/tooluniverse/remote/gpnstar/provider.toml and runs five steps: preflight, environment, kit, weights, smoke. Each refuses by name and carries its fix.

export TOOLUNIVERSE_GPNSTAR_PYTHON=~/gpnstar-venv/bin/python
export HF_HOME=~/.gpnstar/hf_home
tu remote setup gpnstar

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 of eight example variants, asserts the provider produced what its manifest says a successful run produces (a non-empty scores and ids), and records the measured peak VRAM to ~/.cache/tooluniverse/measured/gpnstar.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

• HF_HOME names the Hugging Face cache holding the checkpoint snapshot (hub/models--songlab--gpn-star-hg38-v100-200m/snapshots/<commit>/, 0.8 GB). tu remote setup gpnstar --only weights fetches it with the kit's own downloader from upstream's repository at the pinned revision and checks all nine files by sha256 and size. The model card says license: mit.
• Nothing is fetched at request time. The manifest declares every weight file with its size and sha256, and the launch refuses if one is off its pin.
• The alignment store is yours to supply. Unset, the provider reads the kit's sparse example (examples/msa, windows of eight variants on chromosomes 1, 2, 3 and 5 only). For real use point TOOLUNIVERSE_GPNSTAR_MSA_ROOT at a directory holding <n_species>/all.zarr -- for the primary model <root>/100/all.zarr, one array per chromosome named 1 ... 22, X, Y:

hf download songlab/multiz100way 99.zarr.zip --repo-type dataset --local-dir /data/msa   # a 42 GB download
python -m zipfile -e /data/msa/99.zarr.zip /data/msa/100/all.zarr/                      # unpacks to ~64 GB
export TOOLUNIVERSE_GPNSTAR_MSA_ROOT=/data/msa

ref must equal the alignment's own human base at that position (there is no genome FASTA); a mismatch is refused, not scored.

• TOOLUNIVERSE_GPNSTAR_GRAPHS (default off) keeps or drops the kit's CUDA-graph lever. Leave it off unless you have measured the memory: the lever keeps one graph pool per distinct batch shape and nothing bounds the number of shapes.

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 gpnstar

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. It automatically uses the reviewed Python, name, and worker count. Override them only when needed:

tu remote share gpnstar --name my-gpnstar-remote --workers 1

Use tu remote check gpnstar for a non-sharing readiness check and tu remote run gpnstar 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.gpnstar.gpnstar_tool

The Streamable HTTP endpoint is http://127.0.0.1:8057/mcp. In a second activated shell run:

python - <<'PY'
import asyncio
from fastmcp import Client

async def main():
    async with Client("http://127.0.0.1:8057/mcp") as client:
        print([tool.name for tool in await client.list_tools()])

asyncio.run(main())
PY

Confirm discovery contains run_gpnstar_vep; stop on empty, duplicate, or schema-drifted discovery.

The first request after a restart pays the interpreter, the imports and the 0.8 GB checkpoint read (about 15-35 s on a contended card); the ones after it do not. That is the whole reason this provider is resident, and a benchmark that measures only the first call measures the wrong thing.

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://127.0.0.1:8057/mcp --json
tu serve --share --forward http://127.0.0.1:8057/mcp --name validation-gpnstar --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 card, through the loopback endpoint. Public publication, independent-caller authorization/isolation, saturation, and persistent supervision are untested.

Run a verified example

Operation: run_gpnstar_vep

{"variants": [{"chrom": "1", "pos": 1425822, "ref": "C", "alt": "G"}, {"chrom": "2", "pos": 5718743, "ref": "A", "alt": "G"}, {"chrom": "3", "pos": 213423, "ref": "A", "alt": "G"}, {"chrom": "5", "pos": 850655, "ref": "C", "alt": "T"}], "window_size": 128, "batch_size": 8}

Invoke the example through the live local MCP endpoint:

python - <<'PY'
import asyncio
import json
from fastmcp import Client

async def main():
    arguments = json.loads(r'''{"variants": [{"chrom": "1", "pos": 1425822, "ref": "C", "alt": "G"}, {"chrom": "2", "pos": 5718743, "ref": "A", "alt": "G"}, {"chrom": "3", "pos": 213423, "ref": "A", "alt": "G"}, {"chrom": "5", "pos": 850655, "ref": "C", "alt": "T"}], "window_size": 128, "batch_size": 8}''')
    async with Client("http://127.0.0.1:8057/mcp") as client:
        result = await client.call_tool("run_gpnstar_vep", arguments)
        print(result)

asyncio.run(main())
PY

Expected success shape, as returned on the validation host with the kit's example store: n_variants 4, ids ["1:1425822:C>G", "2:5718743:A>G", "3:213423:A>G", "5:850655:C>T"], and scores [2.88394, -0.08641, -3.10446, 0.36191] (negative: the alternate base is less likely than the reference given the alignment), plus scores_sha256, window_size, batch_size, levers_applied, levers_declined (["graph"]) and peak_reserved_gib. The same eight variants through the kit's own GPNSTAR_OPT=exact gpn star vep and through stock gpn star vep write byte-identical parquet, and this worker's scores_sha256 over them matches both. Scores are reproducible per (variants, batch shape); a different batch_size can move the last digits, because cuBLAS picks its algorithm per shape.

Things the tool refuses rather than guesses, each by name: a ref that disagrees with the alignment; a position the store holds no data at; batch_size x window_size above 8192 columns per forward (this is what bounds peak memory); variants x window_size above 524,288 (this is what bounds how long one call holds the worker); more than 2048 variants; an odd or out-of-range window; indels and ambiguity codes. One variant alone is scored as a batch of two copies (upstream's own command fails on a one-row table) and the reply says single_variant_sent_twice. Check scientific meaning, output bounds, invalid-input behavior, and absence of paths, secrets, and traces.

Tune GPU and concurrency

• Use one worker. The resident worker serves one request at a time by design; a second in-flight request queues on its lock rather than doubling peak VRAM.
• Read vram_budget_gb from the provider manifest and the measured file before co-tenanting anything else on the card. Peak follows batch_size x window_size, not the variant count: 2.5 GiB holds a host that never goes past 16 x 128, and the declared 7.72 GiB covers the 64 x 128 corner under the stock K/V projections.
• Measure cold start, then at least four consecutive identical requests, then a mixed-shape sequence (small, small, large, small), then change window_size and batch_size separately: they change the kit's per-shape state and are the engine key here, not the variant count. Two warm calls of different shapes is not enough: openfold3 passed exactly that and still died on the second request of the same shape.
• Then parallel levels 1, 2, 4, 8, and only 16 if memory permits.
• Record successes/errors, p50/p95, peak RAM/VRAM, utilization, queueing, cancellation cleanup, and recovery.
• Increase workers only after single-flight initialization and sanitized recoverable OOM/timeout behavior are proven.

Troubleshoot and clean up

• Read the refusal. Every step of tu remote setup names 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/gpnstar-resident.log, and it holds the kit's lever census. The kit's ACTIVE and LEVER lines still name graph as on; the worker declines it afterwards and says so on its own line and in every reply.
• Reference allele mismatch ... genome has '-' means the alignment holds nothing at that position: you are on the sparse example store, or the position is outside the store. Set TOOLUNIVERSE_GPNSTAR_MSA_ROOT to a full store.
• [gpnstar-opt] NOT ACTIVE at start means the kit refused the stack (gpn or transformers off their pins, no CUDA device, or a weight file off its digest); bash run.sh check in the kit checkout prints the reason.
• Import/executable failure in the model half: re-run tu remote setup gpnstar --only environment. It rebuilds from the lock with --no-deps and will not silently resolve a different version.
• Weights off their pin: the named file is left in place for inspection rather than deleted, because a truncated transfer and a corrupt file look alike. Remove it by hand and re-run --only weights.
• TF32: the launch removes NVIDIA_TF32_OVERRIDE and TORCH_ALLOW_TF32_CUBLAS_OVERRIDE from the worker's environment, so a shell that exports them cannot move the scores out of fp32.
• 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-gpnstar.
• A worker outliving its server is a known shape: it has an idle timeout (300 s) and an orphan watchdog, and it exits when its parent becomes init. Check with nvidia-smi --query-compute-apps=pid,used_memory --format=csv before assuming the card is free.
• Revoke temporary keys. After confirmation, remove only .venvs/gpnstar, ~/gpnstar-venv and the provider's own cache directory (~/.gpnstar); never use a broad recursive target.

Use only official upstream documentation linked by the implementation README; do not substitute third-party model mirrors.