Metaflow

Latest version: v2.13

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2.4.8

The Metaflow 2.4.8 release is a patch release.

Bug fixes
* `aws_retry`'s `S3_RETRY_COUNT` now has to be >=1 ( https://github.com/Netflix/metaflow/pull/876 )
* fix argument type handling for host_volumes when used with --with and Step Functions ( https://github.com/Netflix/metaflow/pull/884 )

Improvements
* Improved validation logic to capture reserved keywords ( https://github.com/Netflix/metaflow/pull/830 fixes #589 )
* Remove default use of repo.anaconda.com (https://github.com/Netflix/metaflow/pull/832 )

**Full Changelog**: https://github.com/Netflix/metaflow/compare/2.4.7...2.4.8

2.4.7

The Metaflow 2.4.7 release is a patch release. We skipped 2.4.6 for technical reasons.

Improvements
* added plumbing for `card` decorator
* added plumbing to support distributed training on GPUs


**Full Changelog**: https://github.com/Netflix/metaflow/compare/2.4.5...2.4.7

2.4.6

The Metaflow 2.4.6 release is a patch release.

Improvements
* added plumbing for `card` decorator
* added plumbing to support distributed training on GPUs


**Full Changelog**: https://github.com/Netflix/metaflow/compare/2.4.5...2.4.6

2.4.5

The Metaflow 2.4.5 release is a patch release.

Bug fixes
* Address an issue with load_artifacts ( https://github.com/Netflix/metaflow/pull/833 , fixes #819 )
* Fixed mflog stream redirection in Step Functions ( https://github.com/Netflix/metaflow/pull/851 )

**Full Changelog**: https://github.com/Netflix/metaflow/compare/2.4.4...2.4.5

2.4.4

The Metaflow 2.4.4 release is a patch release.

* [Improvements](v2.4.4_improvements)
- Add default image config option as described in 489 (813)
- Read default k8s namespace from config (823)
* Bug Fixes
- Fixed a couple of issues in S3 error handling (821)
- Fixed an issue with load_artifacts when several artifacts have the same name (
817)
* Misc internal improvements
- Pipe logs to $cwd/.logs instead of /logs for `batch` & `kubernetes` (807)
- mflog changes for supporting AWS Lambda (801)
- Add 'last modified' to S3 object (778)

<a name="v2.4.4_improvements"></a>Improvements

Add default image config option as described in 489 (813)

We're moving to a more consistent scheme for naming options related to docker images. You can read the details in 489, but this release introduces new config options `DEFAULT_CONTAINER_IMAGE` and `DEFAULT_CONTAINER_REGISTRY` that can be used to specify docker image in addition to plugin-specific options like `KUBERNETES_CONTAINER_IMAGE`

Read default k8s namespace from config (823)

This adds a new configuration option to set the default namespace for the Kubernetes plugin

2.4.3

The Metaflow 2.4.3 release is a patch release
- [Bug Fixes](v2.4.2_bugs)
- [Fix a race condition when accessing artifacts of a running task](789)
- [Fix an issue when using a combination of `catch` and `retry` decorators](776)
- [Upgrade Pandas in tutorials](707)
- Miscellaneous
- The code base has now been formatted with Black. PRs will automatically be formatted when submitted. If submitting PRs, you can also format your PRs using black (default options) to avoid a reformatting PR.

<a name="v2.4.3_bugs"></a> Bug Fixes
<a name="789"></a>Fix a race condition when accessing artifacts of a running task (789)
When accessing artifacts of a running task using `Task(...).artifacts`, a race condition existed and the call could return a difficult to understand error message. This release fixes this issue and making this call will either return the artifacts present or no artifacts at all if none are present yet.
<a name="776"></a>Fix an issue when using a combination of `catch` and `retry` decorators (776)
A step as below:

retry(times=2)
catch(var='exception')
step
def my_step(self):
raise ValueError()

would not retry 2 times as expected but instead the exception would be caught the first time around. This release fixes this issue and the step will now execute a total of 3 times and the exception will be caught on the third time.
<a name="707"></a>Upgrade Pandas in tutorials (707)
On MacOS Big Sur, certain tutorials were broken due to using an older version of Pandas. This updates the tutorials to use 1.3.3 to solve this issue.

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