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mlops

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Aylr
Aylr commented Dec 28, 2020

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
metaflow
tuulos
tuulos commented Sep 7, 2021

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

kedro
RafalSkolasinski
RafalSkolasinski commented Feb 24, 2021

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.

benhyy
benhyy commented Aug 29, 2021

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

flyte
alberttorosyan
alberttorosyan commented Sep 25, 2021
  • Allow tracking of a list of homogeneous objects (i.e. float values). The resulting tracked sequence is a record list, where each record is list by itself.
  • Add support for getting sequence with entire lists, seqnence for the given index and for the given slice.
  • Lists might have different sizes.

Example:

# track
run.track([0], ...)
run.track([1, 2, 3], ...)
run.track([4, 5, 

Collective Knowledge framework (CK) provides a common set of automation recipes, APIs and meta descriptions to enable collaborative, reproducible and unified benchmarking and optimization of ML Systems across continuously changing models, data sets, software and hardware:

  • Updated Sep 30, 2021
  • Python

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