Zachery Aaron Shores-Chmielewski
b23f82e9a9
Structured observability for the iroh/SWIM layer: Aggregator, typed Event/Snapshot types, Sink (NoopSink default), ProbeScheduler, process stats, and host/iroh/swim introspection, plus the swactor-diag-collector, -postproc, and -iroh-relay binaries that assemble and render per-run bundles. Generalizes the pipeline-parallel-inference example to N stages and adds the topology-planner spec. Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
32 lines
1.3 KiB
Docker
32 lines
1.3 KiB
Docker
FROM nvidia/cuda:12.6.3-runtime-ubuntu24.04
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# Runtime base — no CUDA dev headers, no nvcc. tinygrad's CUDA backend
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# compiles kernels via NVRTC which is part of the runtime image, so we
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# do not need the devel image (that base alone is ~5 GB and dominated
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# the 7.55 GB total of the previous build, blowing past the vastai
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# image-pull budget).
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RUN apt-get update && \
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apt-get install -y --no-install-recommends \
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python3 \
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python3-venv \
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python3-pip \
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ca-certificates && \
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rm -rf /var/lib/apt/lists/*
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# Install tinygrad and numpy
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RUN python3 -m pip install --no-cache-dir --break-system-packages \
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tinygrad==0.12.0 \
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numpy
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# Copy the pipeline-parallel binaries and tinygrad worker
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# Build context should be the workspace root:
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# docker build -f examples/pipeline-parallel-inference/Dockerfile -t <tag> .
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COPY examples/pipeline-parallel-inference/target/release/pp-gpu-node /usr/local/bin/pp-gpu-node
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COPY examples/pipeline-parallel-inference/target/release/pp-smoke-run /usr/local/bin/pp-smoke-run
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COPY examples/pipeline-parallel-inference/pp_tinygrad_worker.py /usr/local/share/pp_tinygrad_worker.py
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# Enable CUDA backend for tinygrad (override with -e DEV=CPU for CPU runs)
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ENV CUDA=1
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ENV WORKER_SCRIPT=/usr/local/share/pp_tinygrad_worker.py
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CMD ["pp-gpu-node"]
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