zacheryasc
5c3f742ce8
Add a data-parallel MNIST training example where swactor actors coordinate gradient averaging across workers. - crates/swactor-dp-mnist/worker.py: add `MnistNet` MLP and `MnistWorker` actor handling `train_batch`/`update`/`evaluate`/`save_model` over a sharded MNIST split with SGD - crates/swactor-dp-mnist/aggregator.py: add `Aggregator` actor that buffers per-worker gradients, averages them, fans out updates, then logs/evaluates on completion - crates/swactor-dp-mnist/run_training.py: spawn the Aggregator plus two MnistWorkers (identical initial weights, disjoint shards), run 750 rounds, and poll the inbox for `log`/`done` - crates/swactor-dp-mnist/pyproject.toml: declare torch/torchvision/numpy deps, an editable local `swactor` source, and the PyTorch CPU index - Cargo.toml: add a `[profile.bench]` retaining debug symbols (`debug = true`, `strip = false`) for profiling Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
17 lines
321 B
TOML
17 lines
321 B
TOML
[project]
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name = "swactor-dp-mnist"
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version = "0.1.0"
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requires-python = ">=3.9"
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dependencies = [
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"swactor",
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"torch",
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"torchvision",
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"numpy",
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]
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[tool.uv.sources]
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swactor = { path = "../swactor-python", editable = true }
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[[tool.uv.index]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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