# Saving multiple MLJ machine to a single file?

**URL:** <https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269>\
**Category:** Machine Learning\
**Created:** [October 26, 2022, 5:41am UTC](https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269 "2022-10-26T05:41:02Z")\
**Posts on this page:** 6\
**Page:** 1

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**Author:** ![xgdgsc](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xgdgsc/32/608_2.png) [@xgdgsc](https://discourse.julialang.org/u/xgdgsc)\
**Post date:** [October 26, 2022, 5:41am UTC](https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269/1 "2022-10-26T05:41:02Z")

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Is it possible to save/load multiple trained MLJ machine to a single file to ease file management? Using JLD2 seems to generate a huge file (40GB using JLD2 compared to 40MB when using MLJ.save).

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**Author:** ![ablaom](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ablaom/32/4889_2.png) [@ablaom](https://discourse.julialang.org/u/ablaom)\
**Post date:** [October 26, 2022, 7:55pm UTC](https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269/2 "2022-10-26T19:55:42Z")

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Thanks for posting this question.

You can serialise however you want, so long as you appropriately preprocess (apply `serializable`) and post-process (apply `restore!`) as described [here](https://alan-turing-institute.github.io/MLJ.jl/dev/machines/#Using-an-arbitrary-serializer).

So, for bundling everything in one file, you can do something like this:

```julia
using MLJ

# should not be needed after https://github.com/alan-turing-institute/MLJ.jl/issues/975
import MLJBase: restore!, serializable

X, y = @load_iris

KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels

machs = map(2:10) do K
    model = KNNClassifier(; K)
    mach = machine(model, X, y) |> fit!
end

serializable_machs = serializable.(machs)

using JLSO
JLSO.save("machines.jlso", :machines => serializable_machs)

loaded_machs = JLSO.load("machines.jlso")[:machines]
restore!.(loaded_machs)

julia> foreach(loaded_machs) do mach
       loss = round(log_loss(predict(mach, X), y) |> mean, sigdigits=3)
       println("K=$(mach.model.K) \t traing_loss=$loss")
       end
K=2 traing_loss=0.0277
K=3 traing_loss=0.0494
K=4 traing_loss=0.0604
K=5 traing_loss=0.0566
K=6 traing_loss=0.0644
K=7 traing_loss=0.074
K=8 traing_loss=0.072
K=9 traing_loss=0.0693
K=10 traing_loss=0.0729

```

Does this address your issue?

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<div class="post-metadata">

**Author:** ![xgdgsc](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xgdgsc/32/608_2.png) [@xgdgsc](https://discourse.julialang.org/u/xgdgsc)\
**Post date:** [October 27, 2022, 2:18am UTC](https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269/3 "2022-10-27T02:18:57Z")

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Thanks. Do you think `serializable` should be added to MLJ cheatsheat?

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**Author:** ![ablaom](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ablaom/32/4889_2.png) [@ablaom](https://discourse.julialang.org/u/ablaom)\
**Post date:** [October 27, 2022, 10:45pm UTC](https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269/4 "2022-10-27T22:45:08Z")

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Mmm. Not sure. I imagine the most common workflow for users is the simplified workflow `MLJ.save("my_machine.jls", mach) ... machine("my_machine.jls")` which is already in the cheatsheet.

Returning to your earlier comment

> Using JLD2 seems to generate a huge file (40GB using JLD2 compared to 40MB when using MLJ.save).

I’m assuming this is because you did not use `serializable`, to remove training data (among other things). If not, then this needs investigation. (The JLS-only simplified workflow takes care of this automatically.)

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**Author:** ![xgdgsc](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xgdgsc/32/608_2.png) [@xgdgsc](https://discourse.julialang.org/u/xgdgsc)\
**Post date:** [October 28, 2022, 7:18am UTC](https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269/5 "2022-10-28T07:18:55Z")

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Is three a reliable way to compare (`==`) machines? I saved using JLSO and loaded to compare the machines before saving and loaded. all `==` operators seems to give `false`, while a look at `fitted_params(mach)` seems equal, and `==` on `fitted_params` also gives false.

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**Author:** ![ablaom](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ablaom/32/4889_2.png) [@ablaom](https://discourse.julialang.org/u/ablaom)\
**Post date:** [October 31, 2022, 1:25am UTC](https://discourse.julialang.org/t/saving-multiple-mlj-machine-to-a-single-file/89269/6 "2022-10-31T01:25:58Z")

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You cannot presently use `==` for machines to conclude that two machines give the same predictions (transformations, etc). Currently the model API makes no assumption about the meaning of `fitresult1 == fitresult2` for the learned parameters `fitresult` output by `fit(::Model, ...)`, so even if we overloaded `==` for machines, that probably won’t give you what you want in all cases. If you have strong use-case for introducing a stronger requirement in the API, feel free to raise an issue at MLJModelInterface.jl. But as it would be some work to ensure all model implementations comply with the stricter requirement, that could take some time.