30 lines
1.6 KiB
Text
30 lines
1.6 KiB
Text
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# Pipeline-parallel CODE image — thin layer over the heavy base.
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#
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# Carries only the fast-changing artifacts: the pipeline binaries and the worker
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# script. Everything heavy (CUDA libs, tinygrad, NVRTC headers, sshd, the PID-1
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# supervisor, ENTRYPOINT) lives in the base image, so a code push rebuilds just
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# this handful of COPYs — no apt, no pip. Fleet telemetry rides the datastream
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# from the pp-worker binary itself (PP_FLEET_SINK), so there is no separate
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# collector binary to ship.
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#
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# Stub mode is a runtime toggle (-e PP_WORKER_STUB=1), not a separate image:
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# the worker only imports tinygrad in real mode, so the CUDA base is inert
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# under the stub and the same image runs the no-GPU CPU E2E.
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#
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# Build context must be the workspace root (the COPYs reach into both
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# target/release/ trees). Build the base first, then this image:
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# docker build -f apps/pipeline-parallel-inference/Dockerfile.base \
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# -t swactor-pp-base:cuda12.6 .
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# docker build -f apps/pipeline-parallel-inference/Dockerfile \
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# -t swactor-pp-gpu:latest .
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ARG BASE_IMAGE=swactor-pp-base:cuda12.6
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FROM ${BASE_IMAGE}
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# Pipeline binaries (this crate's target/). Statically linked enough that the
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# base stage's libc is all they need; the worker is pure Python.
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COPY apps/pipeline-parallel-inference/target/release/pp-worker /usr/local/bin/pp-worker
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COPY apps/pipeline-parallel-inference/target/release/pp-orchestrator /usr/local/bin/pp-orchestrator
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COPY apps/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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