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System information

• TensorFlow version: 1.13.1
• Doc Link: https://www.tensorflow.org/api_docs/python/tf/cus

/kind feature

This is a sub-issue from #5494.
We need to remove any f"strings" from the backend of the Jupyter web app in order to be able to use lower versions from Python 3.6 to run it.

/good-first-issue

Add a new API for converting a model to external data. Today the conversion happens in 2 steps
external_data_helper.convert_model_to_external_data(<model>, <all_tensors_to_one_file>, <size_threshold>) save_model(model, output_path)
We want to add another api which combines the 2 steps
`
save_model_to_external_data(, <output_

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

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 

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?

Currently, FastAI artifact code is not tested: https://codecov.io/gh/bentoml/BentoML/tree/master/bentoml/frameworks

We would want to add an integration test suit to test its functionalities including save, load, and run inference from API server. The setup should be similar to other integration tests that can be found here: https://github.com/bentoml/BentoML/tree/master/tests/integration

Currently com.spotify.scio.bigtable.syntax.ScioContextOps#updateNumberOfBigtableNodes updates all clusters for the instance.
That is not desired behaviour if each cluster is dedicated for specific task - one for batch jobs, another for service, etc.
Desired API is to allow change node count for only specific cluster (dedicated for batch jobs).

Proposed API change:

- def updateNumberOf

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.

Description

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

References

• https://arxiv.org/abs/1506.02640