Mlrun

new features and enhancements:
* projects, see new-project and load-project examples
* local_run() call to run functions, notebooks, .. locally
* run and get pipeline options for submitting pipelines remotely
* new UI
* support image pull secrets
* function documentation
* new REST APIs to support UI
* improved/simplify APIs for logging artifacts
* detect version conflicts
* improved dask support
* remove nuclio-sdk dependencies
* add robustness (retry, timeouts, exceptions, ..)
* support numpy types in serialization
* updated/cleaned examples and notebooks
* updated yamls and dockerfiles
* various bug fixes

* redo dask runtime
* enhance remote build and run
* add deploy step to all container runtimes
* added Spark example
* fixed nuclio-serving
* scheduler
* service use gevents
* fixed/enhanced code_to_function()

major changes:
* Working with API service to provision and monitor jobs
* updated Nuclio runtime to support same semantics as other runtimes
* updated Nuclio-serving (Nuclio+KFServing) to latest version of KFServing
* Initial support for SQL DB
* Initial UI version
* Function lifecycle management (build, save, load, ..)
* support remote build and job submission (via API service)
* improved stability and bug fixes

* improve IO and logging
* Spark enhancements
* Bug fixes
* Pin KFServing version

* add spark runtime and examples
* add DB and initial UI
* function life cycle (save, export, import, ..)
* simplify KubeFlow Pipelines integration
* more examples and demos
* add get with range and stat() to data objects
* bug fixes and enhancements

* update kaniko version
* update/clean nuclio runtime
* cli can create run from function yaml file (url or path)
* Fix artifacts (27)
* remove function "workers"
* update pkg path
* update import function
* add UI dashboard YAML (for k8s)
* add job submit API to HTTPD
* update DB: fix artifact_store, add log append/offset
* add mlrun_jobs example