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

Thank you for this great tool!

[Describe the bug
A clear and concise description of what the bug is.]

Broken link in the automatically generated Edit Your Expectation Suite starter noteboook: https://docs.greatexpectations.io/en/latest/autoapi/great_expectations/data_asset/index.html?highlight=remove_expectation&utm_source=notebook&utm_medium=edit_expectations#great_expectations.data_

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 need to update our list of community developed plugins, as our ecosystem grows, this task focuses on adding the kedro-airflow-k8s plugin to our Community Developed Plugins page.

Possible Implementation

Follow the process in our [Contribution G

🚨🚨 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

Expected Behavior

ODFV logic should not trigger when there are no ODFVs.

Current Behavior

ODFV logic still triggers.

Steps to reproduce

Specifications

• Version:
• Platform:
• Subsystem:

Possible Solution

Describe the bug

flytectl register files command doesn't fail without --countinueOnError. It should fail with exit code 0.

Expected behavior

• Flytectl register should fail if there is an error
• Flytectl register should not fail if the user passes the --countinueOnError

Additional context to reproduce

No response

Screenshots

No response

Are

Proposed refactoring or deprecation

Output informative messages when run parameter cannot be set (i.e. type is not supported by storage)

Motivation

To give users clear error messages and improve onboarding experience.

Pitch

When setting run parameter of unsupported type I would like to get a precise error describing what was done wrong instead of generic Python exception (`No

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/

We're using marshmallow to parse whylogs config from YAML

However, Pydantic is much more powerful as it allows users to set config via various mechanims, from YAML, JSON to Environment settings.

We should consider moving to pydantic