(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.

Hi,

I am curious about the maximum value of the l1 penalty.

In the code, the frobenius norm of Xy divided by n_samples is used here. This stackoverflow answer suggests and proves that the infinite norm should be used.

EDIT: I think tha

We recently added new Gauge chart(Echarts) to our library, which is great as MVP. Thanks to @krsnik93
Here are some enhancement requests to make the chart more robust. we can either make the improvement in Superset or Echarts.

Request

1. Add tooltips to 'needles' - p1
2. Add (dynamic)time range filter, not the entire time section - p1 (4/17/2021 Time section is added, need to m

Currently the decorator docstring is good but not the one for the resulting Deployment type.

Should also be able to do Deployment.num_replicas to check current setting and so on. We should document these in the docstring.

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.

Summary

When import streamlit and supervisely_lib together in a project there occurs a TypeError.

Steps to reproduce

Code snippet:

import streamlit as st
import supervisely_lib as sly

If applicable, please provide the steps we should take to reproduce the bug:

1. run the code with streamlit run ...
2. see error/traceback when you open the streamlit page

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

Bug report

This is an existing bug that is already known but it might be prevented by using extra initialization steps.
I hope someone can guide me.

Error: `"Locator attempting to generate 4018 ticks ([10775.0,

🚀 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

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)

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