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feat: upgrade julia to latest LTS version (#1966)
* feat: upgrade julia to latest LTS version Signed-off-by: Keming <[email protected]> * update ci trigger Signed-off-by: Keming <[email protected]> * fix typo Signed-off-by: Keming <[email protected]> --------- Signed-off-by: Keming <[email protected]>
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| # syntax=v1 | ||
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| def build(): | ||
| base(dev=True) | ||
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| install.julia() | ||
| install.julia_packages(name=["Flux", "MLDatasets"]) | ||
| runtime.command(commands={"julia-mnist": "julia mnist.jl"}) | ||
| runtime.command(commands={"julia-mnist": "julia mlp_mnist.jl"}) |
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| # https://github.com/FluxML/model-zoo/blob/master/vision/mlp_mnist/mlp_mnist.jl | ||
| # License: MIT Copyright (c) 2017 by Flux contributors | ||
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| # Simple multi-layer perceptron, for the MNIST hand-written digits. | ||
| # This example does not use a GPU, it's small enough not to need one. | ||
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| using Flux, MLDatasets, Statistics | ||
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| # Our model is very simple: Its one "hidden layer" has 32 "neurons" each connected to every input pixel. | ||
| # Each has a sigmoid nonlinearity, and is connected to every "neuron" in the output layer. | ||
| # Finally, softmax produces probabilities, i.e. positive numbers which add up to 1: | ||
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| model = Chain(Dense(28^2 => 32, sigmoid), Dense(32 => 10), softmax) | ||
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| #===== DATA =====# | ||
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| # Calling MLDatasets.MNIST() will download the dataset if necessary, | ||
| # and return a struct containing it. | ||
| # It takes a few seconds to read from disk each time, so do this once: | ||
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| train_data = MLDatasets.MNIST() # i.e. split=:train | ||
| test_data = MLDatasets.MNIST(split=:test) | ||
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| # train_data.features is a 28×28×60000 Array{Float32, 3} of the images. | ||
| # We need a 2D array for our model. Let's combine the reshape needed with | ||
| # other pre-processing, in a function: | ||
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| function simple_loader(data::MNIST; batchsize::Int=64) | ||
| x2dim = reshape(data.features, 28^2, :) | ||
| yhot = Flux.onehotbatch(data.targets, 0:9) | ||
| Flux.DataLoader((x2dim, yhot); batchsize, shuffle=true) | ||
| end | ||
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| # train_data.targets is a 60000-element Vector{Int}, of labels from 0 to 9. | ||
| # Flux.onehotbatch([0,1,9], 0:9) makes a matrix of 0 and 1. | ||
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| simple_loader(train_data) # returns a DataLoader, with first element a tuple like this: | ||
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| x1, y1 = first(simple_loader(train_data)); # (784×64 Matrix{Float32}, 10×64 OneHotMatrix) | ||
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| model(x1) # x1 is the right shape for our model | ||
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| y1 # y1 is the same shape as the model output. | ||
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| # @show Flux.crossentropy(model(x1), y1); # This will be our loss function | ||
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| #===== ACCURACY =====# | ||
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| # We're going to log accuracy and loss during training. There's no advantage to | ||
| # calculating these on minibatches, since MNIST is small enough to do it at once. | ||
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| function simple_accuracy(model, data::MNIST=test_data) | ||
| (x, y) = only(simple_loader(data; batchsize=length(data))) # make one big batch | ||
| y_hat = model(x) | ||
| iscorrect = Flux.onecold(y_hat) .== Flux.onecold(y) # BitVector | ||
| acc = round(100 * mean(iscorrect); digits=2) | ||
| end | ||
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| # @show simple_accuracy(model); # accuracy about 10%, on training data, before training! | ||
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| #===== TRAINING =====# | ||
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| # Make a dataloader using the desired batchsize: | ||
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| train_loader = simple_loader(train_data, batchsize = 256) | ||
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| # Initialise storage needed for the Adam optimiser, with our chosen learning rate: | ||
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| opt_state = Flux.setup(Adam(3e-4), model); | ||
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| # Then train for 10 epochs, printing out details as we go: | ||
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| for epoch in 1:10 | ||
| loss = 0.0 | ||
| for (x, y) in train_loader | ||
| # Compute the loss and the gradients: | ||
| l, gs = Flux.withgradient(m -> Flux.crossentropy(m(x), y), model) | ||
| # Update the model parameters (and the Adam momenta): | ||
| Flux.update!(opt_state, model, gs[1]) | ||
| # Accumulate the mean loss, just for logging: | ||
| loss += l / length(train_loader) | ||
| end | ||
| end | ||
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| print(simple_accuracy(model, test_data)) |
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| @@ -1,17 +1,22 @@ | ||
| set -o pipefail && \ | ||
| JULIA_URL="https://julialang-s3.julialang.org/bin/linux/x64/1.8/julia-1.8.5-linux-x86_64.tar.gz"; \ | ||
| SHA256SUM="e71a24816e8fe9d5f4807664cbbb42738f5aa9fe05397d35c81d4c5d649b9d05"; \ | ||
| UNAME_M="$(uname -m)" && \ | ||
| if [ "${UNAME_M}" = "x86_64" ]; then \ | ||
| JULIA_URL="https://julialang-s3.julialang.org/bin/linux/x64/1.10/julia-1.10.8-linux-x86_64.tar.gz"; \ | ||
| SHA256SUM="0410175aeec3df63173c15187f2083f179d40596d36fd3a57819cc5f522ae735"; \ | ||
| elif [ "{UNAME_M}" = "aarch64" ]; then \ | ||
| JULIA_URL="https://julialang-s3.julialang.org/bin/linux/aarch64/1.10/julia-1.10.8-linux-aarch64.tar.gz" \ | ||
| SHA256SUM="8d63dd12595a08edc736be8d6c4fea1840f137b81c62079d970dbd1be448b8cd"; \ | ||
| fi && \ | ||
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| wget "${JULIA_URL}" -O /tmp/julia.tar.gz && \ | ||
| echo "${SHA256SUM} /tmp/julia.tar.gz" > /tmp/sha256sum && \ | ||
| sha256sum -c -s /tmp/sha256sum | ||
| EXIT_CODE=$? | ||
| if [ $EXIT_CODE -ne 0 ]; then | ||
| if [ $EXIT_CODE -ne 0 ]; then | ||
| echo "CHECKSUM FAILED" && \ | ||
| rm /tmp/julia.tar.gz && \ | ||
| wget "${JULIA_URL}" -O /tmp/julia.tar.gz && \ | ||
| sha256sum -c -s /tmp/sha256sum | ||
| else | ||
| echo "CHECKSUM PASSED" | ||
| fi | ||
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