There are a huge number of new statistical, machine-learning and artificial intelligence solutions being released every month.
Most are open-source and written in a popular Python framework like TensorFlow, JAX, or PyTorch.
In order to ‘guarantee’ you are using the best [for given metric(s)] solution for your dataset, some way of automatically adding these new statistical, machine-learning and artificial intelligence solutions to your automated pipeline needs to be created.
(additionally: useful for testing your new optimiser, loss function, &etc. across a zoo of datasets)
Ditto for transfer learning models. A related problem is automatically putting ensemble networks together. Something like:
import some_broke_arch # pip install some_broke_arch
import other_neat_arch # pip install other_neat_arch
import horrible_v_arch # builtin to keras
model = some_broke_arch.get_arch( **standard_arch_params )
metrics = other_neat_arch.get_metrics(**standard_metric_params)
loss = horrible_v_arch.get_loss( **standard_loss_params )
model.compile(loss=loss, optimizer=keras.optimizers.RMSprop, metrics=metrics)
print(model.summary())
# &etc.
In summary, I am petitioning for standard ways of:
- exposing algorithms for consumption;
- combining algorithms;
- comparing algorithms.
To that end, I would recommend encouraging the PyPi folk to add a few new classifiers, and a bunch of us trawl through GitHub every month sending PRs to random repositories—associated with academic papers—linking up with CI/CD so that they are now installable with pip install and searchable by classifier on PyPi.
Related:
- my open-source multi-ML meta-framework;
- uses builtin
astandinspectmodules to traverse the module, class, and function hierarchy for 10 popular open-source ML/AI frameworks; - will enable experimentation with entire ‘search-space’ of all these ML frameworks (every transfer learning model, optimiser, loss function, &etc.)
- with a standard way of sharing architectures will be able to expand the ‘search-space’ with community contributed solutions.
- uses builtin
- this issue from Jul 20, 2019: Standard way of sharing architectures? · Issue #30896 · tensorflow/tensorflow · GitHub
