When users starts Serve cluster with a set of options (http options, checkpoint path) and then connects to it with a different set of options, we should either update it, or error out.

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

Feature Description

We want to enable the users to specify the value ranges for any argument in the blocks.
The following code example shows a typical use case.
The users can specify the number of units in a DenseBlock to be either 10 or 20.

Code Example

import au

• A better name date of birth column in our mock dataset would be birthday.

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

Introduction

We need Classification, Timeseries, Regression forcesting tutorials written for our documentation.

❌ If you don't have example data in your database, you can simply import any of the public datasets from Kaggle ✔️ . 💡 Just download the data and import it into your DB 💯 .

Tutorial summary 🧾

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 =

We would like to forward a particular 'key' column which is part of the features to appear alongside the predictions - this is to be able to identify to which set of features a particular prediction belongs to. Here is an example of predictions output using the tensorflow.contrib.estimator.multi_class_head:

{"classes": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
 "scores": [0.068196

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.

Problem
Some of our transformers & estimators are not thoroughly tested or not tested at all.

Solution
Use OpTransformerSpec and OpEstimatorSpec base test specs to provide tests for all existing transformers & estimators.

Details in discussion mljar/mljar-supervised#421

Is it possible to set a splitter or a iterable when fitting?

https://www.usenix.org/conference/opml19/program

Prepare to read papers