Bug Report

Is the issue related to model conversion?

If the ONNX checker reports issues with this model then this is most probably related to the converter used to convert the original framework model to ONNX. Please create this bug in the appropriate converter's GitHub repo (pytorch, tensorflow-onnx, sklearn-onnx, keras-onnx, onnxmltools) to get the best help.

Describe the bug

T

I'm hoping to get an idea of the memory size of a dask.dataframe once I call .compute() on it

My current approach is

import dask.dataframe as dd
from dask.utils import format_bytes

ddf = dd.demo.make_timeseries(
    start="2000-01-01",
    end="2000-01-02",
    dtypes={"x": float, "y": float, "id": int},
    freq="10ms",
    partition_freq="24h",
)

format_bytes(ddf.memory_u

The integration of Woodwork resulted in three lines in the EntitySet.__eq__ method that were previously covered by tests, now being uncovered. We should update the equality tests to make sure all conditions are properly covered by tests.

The conditions that are not covered are shown below:
![image.png](https://images.zenhubusercontent.com/5da89a3c8b7bd200019886b7/dbbaa7d5-9c2d-4b29-acbf-a0461af

Building the doc fails for example 40_advanced/example_single_configurations on the current development branch

Logs here

...
generating gallery for examples/40_advanced... [ 50%] example_debug_logging.py

Warning, treated as error:
/home/runner/work/auto-sklearn/auto-sklearn/examples/40_advanced/example_single_configu

There are any multivariate forecasting example?

When using a dark theme, the black font is hard to read. The logo should be designed such that it's legible using both dark and light themes.

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 =

Description

We want to add support for auto ML. My suggestion is to use autokeras. I'm letting this open for newcomers who want to contribute to this project.

The parameters of the model need to be read from a yaml file (check utils.py in igel, there is a helper function to read a yaml or json file). These parameters will be used to construct and train a model. The results should be th

What's your use case?
In other words, what's your pain point?

Variable names and their icons are shown as vertical header. This

• is ugly,
• doesn't show the selection properly,
• doesn't allow sorting by variable names,
• doesn't allow selection by dragging across a range of variables (though one can drag across rows in the table itself),
• and possibly something else.

<img wi