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URL: https://glama.ai/mcp/servers/integrations/pytorch

⇱ PyTorch | Glama

  • Why this server?

    Allows cleaning of PyTorch model cache (torch hub).

  • Why this server?

    Supports deployment of PyTorch environments through RunPod's container infrastructure

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    Enables interaction with the RunPod REST API through Claude or other MCP-compatible clients, providing tools for managing pods, endpoints, templates, network volumes, and container registry authentications.
    Last updated
    26
    790
    58
    Apache 2.0

  • Why this server?

    Fetches official PyTorch documentation, including tensor operations and neural network modules.

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    Enables managing internal documentation with search and add capabilities, and fetching official documentation from 13+ popular libraries via MCP tools.
    Last updated
    6
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    MIT

  • Why this server?

    Provides diagnostic capabilities specifically for PyTorch workloads, identifying bottlenecks in the forward pass, data movement, and the caching allocator.

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    eBPF-based GPU causal observability agent with MCP server. Traces CUDA Runtime and Driver APIs via kernel uprobes and host events via tracepoints to build causal chains explaining GPU latency. 7 tools: get_check, get_trace_stats, get_causal_chains, get_stacks, run_demo, get_test_report, run_sql. Telegraphic compression reduces token usage ~60%. Supports stdio and HTTPS (TLS 1.3) transport.
    Last updated
    11
    84

  • Why this server?

    Enables scanning of PyTorch checkpoint files (.pt, .pth) for malicious pickle members and other deserialization-based security threats.

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    ModelSafetyMCP is an MCP server for scanning machine learning model artifacts for unsafe serialization, malicious model patterns, risky packaging, URL-based artifact scanning, and directory-level triage.
    Last updated
    6
    MIT

  • Why this server?

    Enables creation of distributed PyTorch training workflows with GPU resource management through Flyte's task execution capabilities.

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    Provides AI assistants with accurate Flyte V2 knowledge, patterns, and plugin information to help developers write correct Flyte code. It enables tasks like learning the V2 API, finding examples, selecting plugins, and migrating V1 code to V2.
    Last updated
    17
    Apache 2.0

  • Why this server?

    Supports PyTorch model format for Hailo-8 AI accelerator inference workloads.

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    Enables AI assistants to manage homelab infrastructure through automated service installation (Jellyfin, Pi-hole, Ollama, Home Assistant, Frigate NVR), VM operations, AI accelerator support (MemryX, Coral TPU, Hailo-8), and Terraform state management with SSH-based discovery and deployment.
    Last updated
    58
    4
    MIT

  • Why this server?

    Enables deployment of pods with PyTorch images, allowing users to specify PyTorch-based container environments.

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    This Model Context Protocol server enables interaction with RunPod's REST API through Claude or other MCP-compatible clients, providing tools for managing pods, endpoints, templates, network volumes, and container registry authentications.
    Last updated
    36
    790
    58
    Apache 2.0

  • Why this server?

    Supports deployment of GPU instances using PyTorch/CUDA images as part of the Novita AI platform resource management capabilities.

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    An MCP server that enables seamless management of Novita AI platform resources, currently supporting GPU instance operations (list, create, start, stop, etc.) through compatible clients like Claude Desktop and Cursor.
    Last updated
    20
    5
    11
    MIT