(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.

We should be using pkg_resources (or importlib.resources if our min Python version is 3.7) instead of uses of __file__.

$ get grep '__file__' sklearn/
sklearn/__check_build/__init__.py:    local_dir = os.path.split(__file__)[0]
sklearn/datasets/_base.py:    module_path = dirname(__file__)
sklearn/datasets/_base.py:    module_path = dirname(__file__)
sklearn/datasets/_base.py:    

Keyboard navigation in the control panel of the Explore view is difficult.

Expected results

You should be able to move focus between adjacent controls in the control panel with a single Tab key press
and visually distinguish what element has focus. You should be able to interact with controls the keyboard
(Enter or space bar for button-like things).

Actual results

Several tab

What is the problem?

Ray version and other system information (Python version, TensorFlow version, OS):

• Ray nightly

Reproduction (REQUIRED)

Please provide a short code snippet (less than 50 lines if possible) that can be copy-pasted to reproduce the issue. The snippet should have **no e

Steps to reproduce

run %autocall random

Expected result

ERROR:root:Valid modes: (0->Off, 1->Smart, 2->Full

Observed result

ValueError was raised due to parsing the argument "random" as an integer.

System info

Manjaro Linux, Python 3.9.1, IPython 7.22.0.

Right now all our Vega-Lite based charts (st.altair_chart, st.line_chart, etc.) follow the user theme. For st.map and PyDeck it's not as easy to make them follow the exact theme, but we could at least make them use the dark map style when the app is show in Streamlit's dark theme with no overrides.

This line currently fixes the

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

🚀 Feature

Detect UninitializedParameter and run one batch/sample before fitting.

Motivation

Pytorch now accepts 'lazy' layers with UninitializedParameter.

However, this seems to cause a memory error in PL at when we start the trainer because it attempt to estimate the memory usage:

RuntimeError: Can't access the shape of an uninitialized parameter. This error usually happen

We have a PowerNorm. We should define a PowerScale derived from a PowerTransform consistent with the log norm, etc. This will allow a proper scale for PowerNorm. I'm not exactly sure what such a scale should look like (i.e. where to put the ticks), but its probably not a linear scale.

Refer to doc2vec.py, infer_vector function seems to be using epochs for the number of iterations and steps is not in used.

However, in the similarity_unseen_docs function, steps is used when calling the infer_vector function.

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.

I'm using mxnet to do some work, but there is nothing when I search the mxnet trial and example.