Bug Report

Describe the bug

An ONNX model with QuantizeLinear (opset 13) passes shape inference when it has no input for zero_point, but fails if the input is the empty string. Trailing optional inputs should be able to be expressed in either way.

onnx version 1.10.1

I'd like us to add some extra information to the dask docs around the block_info keyword argument for Dask array map_blocks. I think it should be added to this section here

Marvin explained something I found confusing here VolkerH/DaskFusion#1 (comment)

Bug/Feature Request Description

In [1]: import featuretools as ft                                                                                                                             

In [2]: es = ft.demo.load_mock_customer(return_entityset=True)                                                                                                

In [3]: import pandas as pd    

The problem I want to use auto-sklearn on is a time-series. Can we modify sklearn to include cv with time series?

See below: _assertions in _signature_method.py is no longer used or needed, this should be removed from the code base according to @jambo6.

(Author here), its not used anywhere, my apologies. I moved the checks into the respective sub-transformers that are called and forgot to delete this one. Safe to remove.

_Originally posted by @jambo6 in https://github.com/alan-turing-institut

Yes

The current History class has some limitations: (ver 0.10.0)

1. Currently the history is saved as JSON, as a result, those recorded values are limited to simple numbers and strings. Other objects can not be saved in history files directly.
2. Saving as JSON takes lots of time and space because numbers are stored in decimal. It's getting worse when the training epoch is increasing.
3. In some

When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?

Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =

Can we have an example of REST API calls in the documentation?
Examples with CURL, HTTPie or another client and the results would be better for newbies.
Thanks again for your good work.