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• I had searched in the issues and found no similar feature requirement.

Description

We have introduced detached flag to indicate the lifetime of the actor in Python as following.

counter = Counter.options(name="CounterActor", lifetime="detached").remote()

But we have not introduced it for J

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

• With Featuretools 1.0.0 we add a dataframe to an EntitySet with the following:

es = ft.EntitySet('new_es')

es.add_dataframe(dataframe=orders_df,
                 dataframe_name='orders',
                 index='order_id',
                 time_index='order_date')

Improvement

• However, you could also change the EntitySet setter to add it with this approach:

es = ft.Ent

From issue #1302, it appears autosklearn is a bit unstable when run many times in the same script, i.e. in a for loop.

for i in range(400):
    automodel = AutoSklearn(full_resources)
    automodel.fit(x, y)

We currently have no test for this and it would be good to see if we can reproduce the same connection refused error.

In the deployment docs, we have the old version of the GUI https://docs.mindsdb.com/deployment/cloud/. It should be changed with the new version(>=2.5) available on our cloud.

Steps 🕵️‍♂️ 🕵️‍♀️

• Head over to https://github.com/mindsdb/mindsdb/blob/65a0fbe602c377c6442adb13c78bfc7323f94969/docs/mindsdb-docs/docs/deployment/cloud.md#mindsdb-cloud
• Change the image

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