AutoML toolkit for neural architecture search and hyper-parameter tuning
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Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#2This is very cool.
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#3> We support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) in our current stage.
No Windows support in a Microsoft product. Curious.
This looks very useful for tuning hyper-parameters, and the fact that the tuned algorithm is treated as a black box makes this very flexible.
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#4interesting - there's no scikit support, which for long has been the mainstay for data scientists everywhere.
Are people migrating from scikit to tensorflow in production for non-deep learning usecases ?
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#5interesting - there's no scikit support, which for long has been the mainstay for data scientists everywhere. Are people migrating from scikit to tensorflow in production for non-deep learning usecases ?
I think it should probably support scikit as well as any other library, since it's only making suggestions of hyper-parameters based on recorded/historical observations or random evaluations.
At least that's the behaviour of the platform[1] I am working on.
[1]: https://github.com/polyaxon/polyaxon#hyperparameters-tuning
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#6interesting - there's no scikit support, which for long has been the mainstay for data scientists everywhere. Are people migrating from scikit to tensorflow in production for non-deep learning usecases ?
There's also auto scikit-learn https://github.com/automl/auto-sklearn if you haven't already come across that.
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#7interesting - there's no scikit support, which for long has been the mainstay for data scientists everywhere. Are people migrating from scikit to tensorflow in production for non-deep learning usecases ?
I think it should probably support scikit as well as any other library, since it's only making suggestions of hyper-parameters based on recorded/historical observations or random evaluations. At least that's the behaviour of the platform[1] I am working on. [1]: https://github.com/polyaxon/polyaxon#hyperparameters-tuning
UPDATE: Looking at the docs, there's an example[1] using this library with scikit-learn.
[1]: https://nni.readthedocs.io/en/latest/sklearn_examples.html
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#8interesting - there's no scikit support, which for long has been the mainstay for data scientists everywhere. Are people migrating from scikit to tensorflow in production for non-deep learning usecases ?
I think it should probably support scikit as well as any other library, since it's only making suggestions of hyper-parameters based on recorded/historical observations or random evaluations. At least that's the behaviour of the platform[1] I am working on. [1]: https://github.com/polyaxon/polyaxon#hyperparameters-tuning
I think it all depends on the purpose of the library and who is a target user. The NNI is a package for tuning neural networks models, it will be mostly used in use cases that require deep neural networks, like image classification or voice recognition.
BTW, I think all autoML solutions forget about end users. They all require too much engineering knowledge from the user. I think it will be nice to have an autoML solution that can be used by citizen data scientist.
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#9Do we need a hyper-parameter tuner tuner for this?
Re: AutoML toolkit for neural architecture search and hyper-parameter tuning
#10I manage a machine learning team for a large financial services company and AutoML tools, Microsoft’s NNI included, are on our radar.
I think the `future of work` for machine learning practitioners will quickly separate into two groups: a very small and elite group that performs research and a much larger groups that use AutoML but whose jobs also deal more with data preparation (which gets automated also) and ML devops, supporting models in production.