New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

I just ran into an issue when trying to use to_csv with distributed workers that don't share a file system. I shouldn't have been surprised that writing to a local file system from a distributed worker doesn't work. It shouldn't work. But the error I got was just a File Not Found error. That brought me to:dask/dask#2656 (comment) - which was the answer.

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?

Describe the bug
when axis has duplicate value , onnxruntime compute result is all same value ,which is different with expect of tensorflow

Urgency
2020.11.18

System information
Linux Ubuntu 16.04

• ONNX Runtime installed from binary
• ONNX Runtime version:1.4.0
• Python version:3.5

Expected behavior
When there are duplicate values, the duplicate can be removed. j

Is your feature request related to a problem? Please describe.
NA

Describe the solution you'd like
I thought I'd ask first, before submitting a PR—@MatthewMiddlehurst because it's your code, @kachayev and @RavenRudi because you are working on related PRs—would it be helpful to add [MiniRocket](https://github.com/alan-turing-institute/sktime/blob/main/sktime/transformations/panel/rocke

Currently, we don't store the learning rate but it is now being exposed in PyTorch (we may need to check the versions though).

Reported by @jiajiexiao here.

Yes

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 =

• What's your use case?

https://igel.readthedocs.io/en/latest/_sources/readme.rst.txt includes a link to the assets/igel-help.gif, but that path is broken on readthedocs.

readme.rst is included as ../readme.rst in the sphinx build.
The gifs are in asses/igel-help.gif

The sphinx build needs to point to the asset directory, absolutely:

.. image:: /assets/igel-help.gif

I haven't made a patch, because I haven't

In issue #422/#423, users brought up that it's not clear from the error messages that you must fit before convert for most models.

We suspect that with KNN we could maybe also work if the model is not trained, but in general (e.g., with RandomForests) this won't work.

We need help documenting this, and also generating proper error messages. (You can see an example of an unhelpful error mess