Method

GPyTorch Gaussian processes

GPyTorch authors · v1.0.0

provenance_verified author_implementation paid_per_execution published

What it does

Scalable Gaussian processes in PyTorch with GPyTorch for surrogate modelling and Bayesian optimisation.

When to use it

Scalable Gaussian processes in PyTorch with GPyTorch for surrogate modelling and Bayesian optimisation.

Implementation provenance

This MCP is labelled author_implementation. Tools bind to author repository code.

Paper(s)

No papers linked.

Code: https://github.com/cornellius-gp/gpytorch

Licence: See upstream repository

Available MCP tools

`run`

Invoke GPyTorch Gaussian processes (catalog entry — wire author MCP or Paper2MCP for full tools)

Provenance: author_provided · gpytorch_gp:run

{
  "properties": {
    "problem": {
      "type": "string"
    }
  },
  "type": "object"
}

Example invocation

curl -s -X POST http://127.0.0.1:8765/api/v1/methods/gpytorch_gp/execute \
  -H 'Content-Type: application/json' \
  -d '{"tool_name":"run","arguments":{}}'

Validation evidence

provenance_verified: claimed — Upstream repository linked and catalogued. Not execution-verified until MCP tools pass tests.

Price · usage · pay

Current version: gpytorch_gp@1.0.0

Price: $0.0900 / execution

Executions: 0 · Creator earnings: $0.00

Citation

GPyTorch authors. GPyTorch Gaussian processes (v1.0.0). LemmaMCP method `gpytorch_gp`. Implementation: author_implementation. Verification: provenance_verified.