(e.g. for links and images), because some of these examples are now being rendered in the docs.

Added by @fchollet in requests for contributions.

Most functions in scipy.linalg functions (e.g. svd, qr, eig, eigh, pinv, pinv2 ...) have a default kwarg check_finite=True that we typically leave to the default value in scikit-learn.

As we already validate the input data for most estimators in scikit-learn, this check is redundant and can cause significant overhead, especially at predict / transform time. We should probably a

Screenshot

attached

Description

1. click one dashboard
2. in the dashboard page, the table on the left part has the name and count attributes. next to the name attribute, the sorting label was placed too far away from its text.

<img width="1402" alt="Screen Shot 2021-01-15 at 2 35 11 AM" src="https://user-images.githubusercontent.com/4502866/104715562-930f1d80-56db-11eb-8cb1-b2

Travis is not going to automatically offer the free tier for all open source projects; We likely want o migrate away from travis.

Setting up github actions to replace travis would be a welcomed contribution.

Describe your feature request

I've been bitten by this at least twice now and it delays PRs.

CI runs an extra lint step (or at least with different arguments) than scripts/format.sh (or scripts/format.sh --all).
It also runs ./scripts/check-git-clang-format-output.sh -- and why this is different than the clang-format run in --all is unclear to me.

This is most notable in linti

In recent versions (can't say from exactly when), there seems to be an off-by-one error in dcc.DatePickerRange. I set max_date_allowed = datetime.today().date(), but in the calendar, yesterday is the maximum date allowed. I see it in my apps, and it is also present in the first example on the DatePickerRange documentation page.

E

Allow users to provide a meta description. We could add this as an option for st.set_page_config(description="")

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

📚 Documentation

In the accelerator documentation, the documentation tells you to use a custom accelerator like:

trainer = Trainer(accelerator=DDPAccelerator())

Unfortunately, this is not actually possible -- all of the accelerators (AFAICT) require a trainer argument, and actually trying to execut

more details at: allenai/allennlp#2264 (comment)

What would you like to be added: As title

Why is this needed: All pruning schedule except AGPPruner only support level, L1, L2. While there are FPGM, APoZ, MeanActivation and Taylor, it would be much better if we can choose any pruner with any pruning schedule.

**Without this feature, how does current nni

Current pytorch implementation ignores the argument split_f in the function train_batch_ch13 as shown below.

def train_batch_ch13(net, X, y, loss, trainer, devices):
    if isinstance(X, list):
        # Required for BERT Fine-tuning (to be covered later)
        X = [x.to(devices[0]) for x in X]
    else:
        X = X.to(devices[0])
...

Todo: Define the argument `

Test the Column / Text filter

Proposed solution

Add a test for the behavior with

• an exact string, check the number of occurrences
• a partial string, also check the number of occurrences
• Test case sensitive
• Test a regular expression