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>
45 lines
1,006 B
TOML
45 lines
1,006 B
TOML
[workspace]
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members = [".", "crates/swactor-python", "crates/swactor-wasm", "crates/swactor-gossip", "crates/gossip-dashboard", "crates/runtime-dashboard"]
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exclude = ["tools/depgraph"]
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[package]
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name = "swactor"
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version = "0.1.0"
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edition = "2024"
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autobenches = false
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[profile.bench]
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debug = true
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strip = false
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[lib]
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crate-type = ["rlib"]
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[features]
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default = ["getrandom"]
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getrandom = ["dep:getrandom"]
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serde = ["dep:serde"]
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tracing = ["dep:tracing"]
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no_random = [] # compile without access to a source of randomness
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[dependencies]
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getrandom = { version = "0.2", optional = true }
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serde = { version = "1", features = ["derive"], optional = true }
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tracing = { version = "0.1", optional = true }
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crossbeam-queue = "0.3.12"
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crossbeam-utils = "0.8.21"
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[dev-dependencies]
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criterion = { version = "0.5", features = ["html_reports"] }
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[[bench]]
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name = "runtime_benchmarks"
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harness = false
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[[bench]]
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name = "worker_benchmarks"
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harness = false
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[[bench]]
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name = "mt_benchmarks"
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harness = false
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