dagster-slack 0.13.2 on Python PyPI

Updated dagstermill to better support job/op/graph changes by adding a define_dagstermill_op factory function. Also updated documentation and examples to reflect these changes.
Changed run history for jobs in Dagit to include legacy mode tags for runs that were created from pipelines that have since been converted to use jobs.
The new get_dagster_logger() method is now importable from the top level dagster module (from dagster import get_dagster_logger)
[dagster-dbt] All dagster-dbt resources (dbt_cli_resource, dbt_rpc_resource, and dbt_rpc_sync_resource) now support the dbt ls command: context.resources.dbt.ls().
Added ins and outs properties to OpDefinition.
Updated the run status favicon of the Run page in Dagit.
There is now a resources_config argument on build_solid_context. The config argument has been renamed to solid_config.
[helm] When deploying Redis using the Dagster helm chart, by default the new cluster will not require authentication to start a connection to it.
[dagster-k8s] The component name on Kubernetes jobs for run and step workers is now run_worker and step_worker, respectively.
Improved performance for rendering the Gantt chart on the Run page for runs with very long event logs.

Fixed a bug where decorating a job with a hook would create a pipeline.
Fixed a bug where providing default logger config to a job would break with a confusing error.
Fixed a bug with retrieving output results from a mapped input on execute_in_process
Fixed a bug where schedules referencing a job were not creating runs using that job’s default run config.
[dagster-k8s] Fixed a bug where the retry mode was not being passed along through the k8s executor.

The first argument on Executor.execute(...) has changed from pipeline_context to plan_context

When using multiple Celery workers in the Dagster helm chart, each worker can now be individually configured. See the helm chart for more information. Thanks @acrulopez!
[dagster-k8s] Changed Kubernetes job containers to use the fixed name dagster, rather than repeating the job name. Thanks @skirino!

[dagster-docker] Added a new docker_executor which executes steps in separate Docker containers.
The dagster-daemon process can now detect hanging runs and restart crashed run workers. Currently
only supported for jobs using the docker_executor and k8s_job_executor. Enable this feature in your dagster.yaml with:
```
run_monitoring:
  enabled: true
```
Documentation coming soon. Reach out in the #dagster-support Slack channel if you are interested in using this feature.

Adding “Python Logging” back to the navigation pane.
Updated documentation for dagster-aws, dagster-github, and dagster-slack to reference job/op/graph APIs.