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eBPF agent and MCP server for GPU causal observability
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Latest capture 2026-06-05 08:10
1 capture since 2026-06-05
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Scanned 2026-06-05 08:10
go.mod
go ecosystem,
42 dependencies
go.sum
go ecosystem,
78 dependencies
python/ingero-annotate/pyproject.toml
python ecosystem,
1 dependency
tests/workloads/requirements.txt
python ecosystem,
6 dependencies
examples/integrations/accelerate/requirements.txt
python ecosystem,
3 dependencies
examples/integrations/deepspeed/requirements.txt
python ecosystem,
3 dependencies
examples/integrations/hf-trainer/requirements.txt
python ecosystem,
3 dependencies
examples/integrations/pytorch-lightning/requirements.txt
python ecosystem,
3 dependencies
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A dynamic binary instrumentation tool for tracing and analyzing CUDA kernel instructions.
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
Efficent platform for inference and serving local LLMs including an OpenAI compatible API server.
Optimized vLLM deployment for NVIDIA Blackwell (RTX 5090) on Linux Kernel 6.14. Resolves SM_120 kernel incompatibilities, P2P deadlocks, and memory fragmentation for high-performance LLM inference.
Performance analysis tools based on Linux perf_events (aka perf) and ftrace
Kubernetes operator for self-hosted LLM inference across a heterogeneous GPU fleet: NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. Runtimes: llama.cpp, vLLM, TGI, mlx-server. Multi-GPU sharding, model caching, OpenAI-compatible endpoints. Apache-2.0, run across homelab and on-prem fleets, actively developed.
