Current implementation of Go binding can not specify options.

GPUOptions struct is in internal package. And go generate doesn't work for protobuf directory. So we can't specify GPUOptions for NewSession.

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?

Thank you for submitting a feature request. Before proceeding, please review MLflow's Issue Policy for feature requests and the MLflow Contributing Guide.

**Please fill in this feature request template to ensure a timely and thorough response.

Every kubeflow image should be scanned for security vulnerabilities.
It would be great to have a periodic security report.
Each of these images with vulnerability should be patched and updated.

Let's make the error message more actionable.

Error user sees:

I would recommend adding similar named column(s):

- $"Provided {columnPurpose} column '{columnName}' not found in training data."
+ $"Provided {columnPurpose} column '{columnName}' not found in training 

A tutorial on how AutoML in the database will help developers, data scientists, and data engineers.

Currently, you can do something like this: Task(Flow/RunID/StepName) and this will not result in an error but then the resulting Task object behaves in a bizarre manner where things like t.data will work but t.data.my_artifact will not for example.

We should validate the format of the pathspec passed in to each object and verify that the following are the only possible cases:

• Metaflo

Describe the bug
Unable to read data from a web location using address filed

To Reproduce

from pycaret.datasets import get_data
data = get_data(
    "economic_indicators_all_ex_3mo_china_inc_treas3mo", 
address="https://raw.githubusercontent.com/ngupta23/DS6373_TimeSeries/2b40f0071c3b7ec6a05dc0106f64e041f8cbaaef/Projects/gdp_prediction/data/",
)

🚨🚨 Feature Request

If your feature will improve HUB

To explore the structure of a dataset it is convenient to have nicer and more informative prints of dataset objects and samples

Description of the possible solution

1) show ds

now

> ds
Dataset(path='hub://activeloop/abalone_full_dataset', tensors=['length', 'diameter', 'height', 'weight'])

In Ue format string it represent float with comma separator, it crash css style

To fix it you can Round/replace/incluse culture info

samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentiment.Client/Shared/HappinessScale.razor

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

Use case

Polyaxon tracking can be used to automatically log all information generated by Ludwig.

We should probably think about adding support for Polyaxon's tracking module in Ludwig contrib.

在oneflow里，交叉熵损失有以下几种：

• binary_cross_entropy_loss
• binary_cross_entropy_with_logits_loss
• sparse_cross_entropy
• distributed_sparse_cross_entropy
• cross_entropy
• sparse_softmax_cross_entropy
• softmax_cross_entropy

在pytorch里，交叉熵损失有以下几种：

• binary_cross_entropy
• binary_cross_entropy_with_logits
• cross_entropy

由此可见，oneflow中交叉熵损失存在API冗余，重复，容易让用户疑惑，因此，这里应该精简一下。除此之外，label smooth