Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template

System information

• TensorFlow version (you are using): 2.1
• Are you willing to contribute it (Yes/No): No

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.

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    

MLflow seems to have a length limit of 5000 when setting tags (see below).

[...]
  File "/home/smay/miniconda3/envs/py38/lib/python3.8/site-packages/mlflow/utils/validation.py", line 136, in _validate_length_limit
    raise MlflowException(
mlflow.exceptions.MlflowException: Tag value '[0.8562690322984875, 0.8544098885636596, 0.8544098885636596, 0.8544098885636596, 0.85440988856365

As mentioned by Diego, these additions would help by simplifying the API usage for users even further and it should be pretty easy to implement for us: 👍

@divega commented: dotnet/machinelearning-samples#617 (review)

@CESARDELATORRE, I did a deferred review. The experience seems pretty good.

1:
And I agree with you that it could be even better

If you expand the configuration example, you will notice that we have 3307 added as a default port to Clickhouse. That should be changed to point to the 8123 port. Link to the file

All available samples code target .Net Core, Do we have samples for .Net Framework ?

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.

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?

When a user wants to stream data to a date-partitioned BQ table, the way to do this is:

//noinspection ScalaStyle
class DayPartitionFunction()
  extends SerializableFunction[ValueInSingleWindow[TableRow], TableDestination] {
  override def apply(input: ValueInSingleWindow[TableRow]): TableDestination = {
    val partition = DateTimeFormat.forPattern("yyyyMMdd").withZone(DateTimeZo

Problem
Since Java 8 was introduced there is no need to use Joda as it has been replaced the native Date-Time API.

Solution
Ideally greping and replacing the text should work (mostly)

Additional context
Need to check if de/serializing will still work.

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

Support serving MXNet Gluon models in BentoML

Describe the solution you'd like

• Docs and gallery sample project demonstrating serving MXNet Gluon with

Description

Implement a YOLO model and add it to the DJL model zoo

References

• https://arxiv.org/abs/1506.02640

Feature request: Support TFP's (L-)BFGS optimiser

TensorFlow Probability (TFP) has a native TensorFlow implementation of the (L-)BFGS optimiser. Currently, GPflow doesn't use this and relies on scipy's implementation. The problem with this, however, is that in each step of the optimisation we have to leave "graph" mode and pass in NumPy values directly to SciPy - possibly introducing a comput