mlops

At the moment, from API there are two useful columns about tasks:

However, in tabs (views) there are possible columns tasks:created_at a

Describe the bug
data docs columns shrink to 1 character width with long query

To Reproduce
Steps to reproduce the behavior:

1. make a batch from a long query string
2. run validation
3. render result to data docs
4. See screenshot
   <img width="1525" alt="Data_documentation_compiled_by_Great_Expectations" src="https://user-images.githubusercontent.com/928247/103230647-30eca500-4

With a config like this

{
    "METAFLOW_DATASTORE_SYSROOT_S3": "s3://mf-test/metaflow/",
}

(note a slash after METAFLOW_DATASTORE_SYSROOT_S3)

metaflow.S3(run=self).put* produces double-slashes like here:

s3://mf-test/metaflow//data/DataLoader/1630978962283843/month=01/data.parquet

The trailing slash in the config shouldn't make a difference

Description

We're running usability tests and would love for you to record walking through our tutorials. The idea for this ticket is that you do a screen capture walking through one of more of the following examples:

• Hello World! (15 minutes)
• [Iris Dataset](https://kedro.readthedocs.io/en/stable/02_get_started

🚨🚨 Feature Request

• Related to an existing Issue
• A new implementation (Improvement, Extension)

If your feature will improve HUB

Need a way to check if a dataset already exists.

hub.empty throws an error if a dataset exists and hub.load throws an error if the dataset does not exist.

Need a way to check if a dataset already exists without throwing a

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.

For SC Operator it may be a good idea to generate CRD manifests from inside a docker container.
This should provide reproducible generation step and avoid "produces different output on my machine" issues.

Linter should also fail if generation of manifests produce diff with the commited version.

What steps did you take

Code gets stuck in infinite loop is SageMaker training job gets stopped (unhandled use case)

What happened:

https://github.com/kubeflow/pipelines/blob/master/components/aws/sagemaker/train/src/sagemaker_training_component.py#L57-L66

Above code only caters for training job status Completed or Failed, so if the training job status is marked as `Stopped

When specifying on demand feature views at retrieval time (e.g. get_X_features), the output feature vectors include e.g. request data or dependent feature vectors, even if users did not specify said features.

Expected Behavior

Non-specified dependent feature values are not returned in output

Current Behavior

Non-specified dependent feature values are in output

Steps to reprodu

Need to make utilities in aim/sdk/num_utils.py to treat as numeric values the following types:

1. numpy.ndarray with shape (1,).
2. subclasses of numpy.number.
3. tensor for scalar values (tensorflow, torch).

Change Run.track() method to not allow values which are not numeric values nor AimObject.

Describe the issue

Currently we run the Linter CI for golang repos using the golang-ci-linter binary. But according to the documentation it is faster and better to use the github action.
https://golangci-lint.run/usage/install/

The UX of all the error highlighting is also better.

What if we do not do this?

Finding linter errors is troublesome as users have to parse through the cons

This issue tracks adding a library for C#.

The load_dotted_path raises the following error if unable to load the module:

Traceback (most recent call last):
  File "/Users/Edu/Desktop/import-error/script.py", line 4, in <module>
    load_dotted_path('tests.quality.fn')
  File "/Users/Edu/dev/ploomber/src/ploomber/util/dotted_path.py", line 128, in load_dotted_path
    module = importlib.import_module(mod)
  File "/Users/