Data Science

Describe the issue linked to the documentation

Use :doi: and :arxiv: directives for references in the documentation as is done in scipy in scipy/scipy#12858.

Suggest a potential alternative/fix

No response

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Ray Component

Ray Serve

What happened + What you expected to happen

See repro script, a fix would be avoid reconfiguring when there are inflight queries. Additionally, we should consider not accepting new queries when reconfigure is being called.

Reprod

Summary

All Streamlit widgets and elements fade when the app is rerunning. But this is not happening for st.expander.

Steps to reproduce

Code snippet:

import streamlit as st
import time

time.sleep(10)  # Makes it easier to see the bug

st.write("This fades correctly")
st.button("This too")

with st.expander("This doesn't fade"):
  st.write("but this fades")

🐛 Bug

Context

I noticed in the unit test case test_dataloaders_reset_and_attach in test_dataloaders.py that trainer.fit() was called twice with different train_dataloaders. ([code pointer](https://github.com/PyTorchLightning/pytorch-lightning/blob/5a846d48ceb5412636bafc3037d04bc2f1bcc0e6/tests/trainer/test_datalo

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

Steps to reproduce

• Create a test1.py file with the contents import sys; print(1, sys.argv) in the current directory
• Create a test2.py file with the contents print(2) in the current directory

Expected result

As per the IPython reference:
Both files are executed in sequence, the

Bug summary

The only way (that I am aware of) to control the linewidth of hatches is through an rc parameter. But temporarily modifying the parameter with plt.rc_context has not effect.

Code for reproduction

import matplotlib.pyplot as plt

plt.figure().subplots().bar([0, 1], [1, 2], hatch=["/", "."], fc="r")

with plt.rc_context({"hatch.linewidth": 5}):
    plt.

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.

Discussed in microsoft/nni#4070

[2021-08-14 10:13:41] INFO (NNIDataStore) Datastore initialization done
[2021-08-14 10:13:41] INFO (RestServer) RestServer start
[2021-08-14 10:13:41] INFO (RestServer) RestServer base port is 8080
[2021-08-14 10:13:41] I