pyro

Deep universal probabilistic programming with Python and PyTorch

9,035 stars Python 1 file · ~1,149 tokens #bayesian #bayesian-inference #deep-learning #machine-learning #probabilistic-modeling #probabilistic-programming #python #pytorch

pyro MCP Server

MCP server for pyro-ppl/pyro

Ready-to-Use Client Configurations

{
  "mcpServers": {
    "pyro": {
      "command": "npx",
      "args": [
        "-y",
        "@pyro-ppl/pyro"
      ]
    }
  }
}

{
  "mcpServers": {
    "pyro": {
      "command": "npx",
      "args": [
        "-y",
        "@pyro-ppl/pyro"
      ]
    }
  }
}

{
  "mcpServers": {
    "pyro": {
      "command": "npx",
      "args": [
        "-y",
        "@pyro-ppl/pyro"
      ]
    }
  }
}

{
  "mcpServers": {
    "pyro": {
      "command": "npx",
      "args": [
        "-y",
        "@pyro-ppl/pyro"
      ],
      "disabled": false,
      "autoApprove": []
    }
  }
}

{
  "mcpServers": {
    "pyro": {
      "command": "npx",
      "args": [
        "-y",
        "@pyro-ppl/pyro"
      ]
    }
  }
}

docker run -i --rm mcp/pyro