Data Science

These examples take quite a long time to run, and they make our documentation CI fail quite frequently due to timeout. It'd be nice to speed the up a little bit.

To contributors: if you want to work on an example, first have a look at the example, and if you think you're comfortable working on it and have found a potential way to speed-up execution time while preserving the educational message

Latest Version of Apache Superset from docker image.

When the feature flag SQLLAB_BACKEND_PERSISTENCE is set to true in SQL lab upon selecting a schema receiving superset error trouble finding schema.

And seeing this error in logs upon trying to select schema:
404 Not Found: The requested URL was not found on the server. If you entered the URL manually please check your spelling and tr

According to FastAPI's docs, response_model can accept type annotations that are not pydantic models. However, the code referenced below is checking for the __fields__ attribute, which won't be on type annotations such as list[float], for example.

https://github.com/ray-project/ray/blob/e60a5f52eb93c851b186cb78fa1f70d

Summary

Aesthetically trivial, yet I've spotted a discrepancy with font sizes in our tooltip (front-end + back-end screenshots below).
I believe sections #1 and #2 should have the same font size?

![image](https://user-images.githubusercontent.com/27242399/139825179-4d62e3

📚 Documentation

On the following page (On the following page (https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html#predict-dataloader) I stumbled upon the predict_dataloader() method.

Should we reference is as an option in the LightningDataModule method doc?
I.e., currently it has the section

https://github.com/PyTorchLightning/pytorch-lightning/blob/b3c

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

Python 3.10 added suggestions for AttributeError and NameError in the error messages. It seems the suggestions are not stored in the exception object but calculated when Error is displayed. There is a note that that this won't work with IPython but it will be good to see if it's feasible. Opening an issue for discussion.

https://bugs.python.org/issue38530
https://docs.python.org/3/whatsnew/3.

Bug summary

imshow extents cannot be expressed with units.

Code for reproduction

fig, ax = plt.subplots()
dates = np.arange("2020-01-01","2020-01-10 23:00", dtype='datetime64[h]')
ys = np.random.random(dates.size)
arr = np.random.random((10, 10))

ax.imshow(arr, extent=[dates[0], dates[1], 0, 10])

Actual outcome

Traceback (most recent call last):
  File "

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.