Wanted to chime in as well. I have reviewed the design shared in the mail offline with Ankit, Lai and Naveen (we work in the same team in Amazon).
I think it does a good job at simplifying many low-complexity training use cases, which can make MXNet/Gluon even more friendly to so-called "deep learning beginners" - so +1 on the proposal! Hagay On Fri, Feb 8, 2019 at 10:30 AM Naveen Swamy <mnnav...@gmail.com> wrote: > Hi Alfredo, > Thanks for your comments, I really like all your suggestions. Here are my > answers let me know if it makes sense or have comments. > > 1) The fit API is targeting novice users covering about 80% of the use > cases listed in the document. For advanced users, > and complex models, we (Naveen, Ankit and Lai) felt its best use the > existing mechanisms due to the imperative nature and the more control it > can give, So we did not duplicate the save/load functionality in the Hybrid > block. > We’ll consider and extend the functionality to Estimator. > I have had trouble using pickle package which is commonly used for > serialization and deserialization, if you have any other suggestions from > your experience please let us know. > > 2) +1, we’ll add this to our backlog and add it in our next iteration. > > 3) Can you expand a little more on this, how it helps in a production > environment (which this API was not target for) ?. > I’ll check the TF Estimator to understand further. > > Thanks, Naveen > > > On Thu, Feb 7, 2019 at 2:32 PM Alfredo Luque > <alfredo.lu...@airbnb.com.invalid> wrote: > > > This is great and something we should all be able to benefit from. > > > > There are just three pieces I’d like to advocate for that I feel are > > shortcomings of some competing APIs on other frameworks (eg; TF > Estimators) > > and I would love to see in this proposal: > > > > 1) Make serialization/deserialization of these classifiers/regressors > easy > > or at least ensure the internal members of the wrapper are easy to > > save/load. We’ve hacked around this by only allowing hybrid blocks which > > have easy save/load functionality, but having a simple > > “save_model”/“load_model” function as a 1st class citizen of these > proposed > > APIs will lead to a vastly improved user experience down the road. > > > > 2) Allowing the fit/predict/predict_proba functions to take in both data > > loaders and simple numpy arrays and pandas dataframes is a simple change > > but a huge usability improvement. Power users and library authors will > > appreciate being able to use custom data loaders but a large portion of > end > > users want to just pass an ndarray or data frame and get some results > > quickly. > > > > 3) Allow lazy construction of the model. This is something I feel TF > > Estimators do well: by allowing the user to pass a function that > constructs > > the net (i.e a model_fn that returns the net) rather than the net itself > it > > allows for more control at runtime and usage of these APIs in a > production > > environment. > > > > Would love your thoughts on these three changes/additions. > > > > —Alfredo Luque > > Software Engineer > > Machine Learning Infrastructure > > Airbnb > > San Francisco, CA > > > > On February 7, 2019 at 1:51:17 PM, Ankit Khedia (khedia.an...@gmail.com) > > wrote: > > > > Hello dev@, > > > > Training a model in Gluon requires users to write the training loop, this > > is useful because of its imperative nature, however repeating the same > code > > across multiple models can become tedious and repetitive with boilerplate > > code. The training loop can also be overwhelming to some users new to > deep > > learning. Users have asked in [1] for a simple Fit API, similar to APIs > > available in SKLearn and Keras as a way to simplify model training and > > reduce boilerplate code and complexity. > > > > So, I along with other contributor Naveen and Lai came up with a fit API > > proposal in [2] that covers 80% of the use-cases for beginners, the fit > API > > does not replace the gluon training loops. The API proposal is inspired > by > > the Keras fit API. I have discussed and got feedback from a few MXNet > > contributors (Sheng, Mu, Aston, Zhi) close by and I am writing to ask for > > the community’s feedback on the API proposal. > > > > > > > > [1] > > > https://discuss.mxnet.io/t/wrapping-gluon-into-scikit-learn-like-api/2112 > > [2] > > > > > https://cwiki.apache.org/confluence/display/MXNET/Gluon+Fit+API+-+Tech+Design > > > > > > Thanks, > > Ankit > > > > > > — > > Alfredo Luque > > Software Engineer > > Machine Learning Infrastructure > > Airbnb > > San Francisco, CA > > >
