Data Science

See in #22547

MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes

We need to rep

The Mixed Time-Series chart type allows for configuring the title of the primary and the secondary y-axis.
However, while only the title of the primary axis is shown next to the axis, the title of the secondary one is placed at the upper end of the axis where it gets hidden by bar values and zoom controls.

How to reproduce the bug

1. Create a mixed time-series chart
2. Configure axi

Here's the reproduction:

import os
import tempfile
from pathlib import Path
from ray._private.runtime_env.packaging import _zip_directory
from zipfile import ZipFile

with tempfile.TemporaryDirectory() as tmp_dir:
    # Prepare test directory
    path = Path(tmp_dir)
    subdir = path / "subdir"
    subdir.mkdir(parents=True)
    file1 = subdir / "file1.txt"
    with file

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

🐛 Bug

When using the cross validation loop from the example pl_examples/loop_examples/kfold.py with ddp_spawn strategy, it encounter a SIGABRT Exception and the program crash.

To Reproduce

import os
from pytorch_lightning import seed_everything, Trainer

from pl_examples.loop_examples.kfold import KFoldLoop, LitImageClassifier, MNISTKFoldDataModule


def run():

Describe your context
Please provide us your environment, so we can easily reproduce the issue.

• replace the result of pip list | grep dash below

dash                      2.0.0
dash-bootstrap-components 1.0.0

• if frontend related, tell us your Browser, Version and OS

  • OS: [e.g. iOS] Windows
  • Browser [e.g. chrome, safari]: Chrome 96.0x, Edge 96.0x, Firefox

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

I think xy[4] = .25, val[0] should be commented in /matplotlib/widgets. py", line 915, in set_val
as it prevents to initialized value for RangeSlider

Code for reproduction

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import RangeSlider

# generate a fake image
np.random.seed(19680801)
N = 128
img = np.random.randn(N, N

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

https://github.com/microsoft/nni/blob/8d5f643c64580bb26a7b10a3c4c9accf617f65b1/nni/compression/pytorch/speedup/jit_translate.py#L382

While trying to speedup my single shot detector, the following error comes up. Any way to fix this,

/usr/local/lib/python3.8/dist-packages/nni/compression/pytorch/speedup/jit_translate.py in forward(self, *args)
    363 
    364         def forward(self, *

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.