dagster-slack 0.12.7 on Python PyPI

In Dagit, the repository locations list has been moved from the Instance Status page to the Workspace page. When repository location errors are present, a warning icon will appear next to “Workspace” in the left navigation.
Calls to context.log.info() and other similar functions now fully respect the python logging API. Concretely, log statements of the form context.log.error(“something %s happened!”, “bad”) will now work as expected, and you are allowed to add things to the “extra” field to be consumed by downstream loggers: context.log.info("foo", extra={"some":"metadata"}).
Utility functions config_from_files, config_from_pkg_resources, and config_from_yaml_strings have been added for constructing run config from yaml files and strings.
DockerRunLauncher can now be configured to launch runs that are connected to more than one network, by configuring the networks key.

Fixed an issue with the pipeline and solid Kubernetes configuration tags. env_from and volume_mounts are now properly applied to the corresponding Kubernetes run worker and job pods.
Fixed an issue where Dagit sometimes couldn’t start up when using MySQL storage.
[dagster-mlflow] The end_mlflow_run_on_pipeline_finished hook now no longer errors whenever invoked.

Non-standard keyword arguments to context.log calls are now not allowed. context.log.info("msg", foo="hi") should be rewritten as context.log.info("msg", extra={"foo":"hi"}).
[dagstermill] When writing output notebook fails, e.g. no file manager provided, it won't yield AssetMaterialization. Previously, it would still yield an AssetMaterialization where the path is a temp file path that won't exist after the notebook execution.

Previously, in order to use memoization, it was necessary to provide a resource version for every resource used in a pipeline. Now, resource versions are optional, and memoization can be used without providing them.
InputContext and OutputContext now each has an asset_key that returns the asset key that was provided to the corresponding InputDefinition or OutputDefinition.

The Spark documentation now discusses all the ways of using Dagster with Spark, not just using PySpark