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>
41 lines
1.6 KiB
Text
41 lines
1.6 KiB
Text
# Diagnostics-enabled image for the docker-compose e2e harness.
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#
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# Bundles four binaries:
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#
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# - pp-gpu-node (stage)
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# - pp-smoke-run (orchestrator; for convenience / docker exec)
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# - swactor-diag-collector (collector service)
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# - swactor-diag-postproc (post-processor, runnable via docker exec)
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#
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# All four share one image so the e2e harness only builds + ships one
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# tag. The build context must be the workspace root because the COPY
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# paths reach into both `target/release/` (for the distribution-crate
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# binaries) and `examples/pipeline-parallel-inference/target/release/`
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# (for the pp binaries):
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#
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# docker build -f examples/pipeline-parallel-inference/Dockerfile.diag \
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# -t pp-diag:latest .
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#
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# The host must build all four binaries first — this image only
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# packages them. `scripts/docker-diag-e2e.sh` automates the full
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# build + run.
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FROM debian:bookworm-slim
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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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ca-certificates \
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curl && \
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rm -rf /var/lib/apt/lists/*
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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 target/release/swactor-diag-collector /usr/local/bin/swactor-diag-collector
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COPY target/release/swactor-diag-postproc /usr/local/bin/swactor-diag-postproc
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COPY examples/pipeline-parallel-inference/pp_tinygrad_worker.py /usr/local/share/pp_tinygrad_worker.py
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ENV WORKER_SCRIPT=/usr/local/share/pp_tinygrad_worker.py
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ENV PP_WORKER_STUB=1
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CMD ["pp-gpu-node"]
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