I am happy to announce the release of FluxTraining.jl , a deep learning training package for Flux models. It has an extensible callback system inspired by fastai’s and comes with features like metrics, hyperparameter scheduling, TensorBoard logging and model checkpointing. It also makes it easy to c…

[image] This is what the TensorBoard integration looks like, by the way!

Can you use this also for Deep Reinforcement Learning? Because of the ongoing generation of training data. So at the beginning there isn’t any data and the data I want to train on changes every n seconds. Haven’t looked into the code, but probably I could find a relatively easy “workaround” for my u…

Hey Peter! It’s definitely possible to use this with reinforcement learning. You can implement custom training logic by creating a new Phase and then implementing fitepochphase! and/or fitbatchphase! for it. The default implementation should be a good starting point. As you can see, it simply loops…

Great premise. Because I’m selfish I’ll cue in some of the things I really rely on when I use Flux. I very often end up writing my own optimizers and training loops, would this package be the place to make that less painful? (IE: importing things not exported in Flux, overloading, etc). Would it b…

Writing custom training loops is possible, see what I wrote above. FluxTraining.jl works with any optimizer that works with Flux.jl if that is what you mean. Regarding the “stop” button: I assume you’re using Alt+Enter to run things in VSCode, and that can’t be interrupted, but you can just paste t…

RE optimizers specifically, https://github.com/FluxML/Optimisers.jl is the WIP to create a nice, Flux-compatible interface. Support for proper interruption in VS Code is also merged , so it might be in the insiders channel already?

There’s always Flux.stop and Flux.skip

The training pipeline of reinforcement learning is kind of special here. You may take a look at ReinforcementLearningCore.jl . Also callbacks and stop_conditions . I think the core idea behind is very close to FluxTraining.jl.

First of all, thank you for a nice package. Is there any example how to use TensorBoard with FluxTrain. traindata, valdata = splitobs((data_X, data_Y)) trainiter, valiter = DataLoader(traindata, 128), DataLoader(valdata, 256); learner = Learner(model, (trainiter, valiter), optim, lossfn, ToGPU(),…

You can use the LogMetrics callback with a TensorBoard backend: logcb = LogMetrics(TensorBoardBackend("tblogs")) Learner(model, (trainiter, valiter), optim, lossfn, ToGPU(), Metrics(accuracy), logcb) That way, they’ll be stored in the folder “tblogs”. If you want to access the raw metrics themsel…

[ANN] FluxTraining.jl

Package Announcements

reemmasoud123 June 20, 2021, 3:58pm 12

Hello,

Is it possible to use FluxTraining to solve the issue mentioned in the post above?

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[ANN] FluxTraining.jl

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