[WIP] Adjust all algorithms to work with CuPy #75
Conversation
Requirements: * numpy/numpy#13046 * cupy/cupy#2079
|
The build will fail now, since it depends on NumPy and CuPy fixes that weren't yet merged. However, I can confirm that all existing tests pass on my setup after the changes here. |
There was a problem hiding this comment.
Thanks @pentschev !
We'll probably have to be a bit careful around where we import cupy to make sure that this doesn't affect normal maintenance and usage of this library.
Also, I'm surprised that there weren't any code changes. All I'm seeing here is testing code (which all looks quite nice). Did you have to modify any code to get things to work?
Also skip CuPy tests if module 'cupy' isn't found.
Functions replaced: * abs() * exp() * log1p() * sign()
There was a problem hiding this comment.
On a related note, the estimators in dask-glm were implemented in dask-ml (ref dask/dask-ml#94). I think the plan was to deprecate dask_glm.estimators (#66) but keep the optimizers and regularizers in dask-glm.
@TomAugspurger is this still the case?
|
Hmm looks like I dropped the ball on deprecating the estimators here... I
don't really care where we do that. If people still think it makes sense
for the estimators to live in dask-ml, and the optimizers here, then I'll
update that PR.
…On Fri, Mar 8, 2019 at 2:47 PM James Bourbeau ***@***.***> wrote:
***@***.**** commented on this pull request.
On a related note, the estimators in dask-glm were implemented in dask-ml
(ref dask/dask-ml#94 <dask/dask-ml#94>). I think
the plan was to deprecate dask_glm.estimators (#66
<#66>) but keep the optimizers and
regularizers in dask-glm.
@TomAugspurger <https://github.com/TomAugspurger> is this still the case?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<#75 (review)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/ABQHIrzo9kNs6vPUN8xgMX2dPgSXdMz6ks5vUsxngaJpZM4bcQw0>
.
|
|
FWIW, I have no strong opinions there. Sorry if this question has already been answered before or sounds a bit stupid, but wouldn't it then make sense to have Dask-GLM completely deprecated and move everything into Dask-ML? GLM being a statistical model, it doesn't fall too far off of ML, but of course, maybe syntactically it could be considered something very unrelated. |
|
My personal preference would be to merge #66 and deprecate |
|
I updated #66 to fix the merge conflict.
…On Fri, Mar 8, 2019 at 3:30 PM James Bourbeau ***@***.***> wrote:
My personal preference would be to merge #66
<#66> and deprecate
dask_glm.estimators in favor of dask_ml.linear_model. That way all the
scikit-learn style estimators live in one place.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<#75 (comment)>, or mute
the thread
<https://github.com/notifications/unsubscribe-auth/ABQHIviKDl3-lLKjpMbI8WnZ9DwKBDSRks5vUtZxgaJpZM4bcQw0>
.
|
|
As far as I recall, this PR should mostly work (if not entirely). It's not necessarily dependant upon #66, even though a little modification would be required once it's merged. However, the changes here are dependant upon several unreleased changes:
That said, we are likely to wait to have all these changes released before we can merge that. I'm expecting that NumPy is released sometime this month, following that, probably a CuPy release with necessary changes is probably due in August or September. |
|
Great, thanks for the clarifications! |
Requirements:
cupy_array[:] = numpy_arraycupy/cupy#2079Resolves #73.