RELEASE.md

Release 0.15.0

Major features and improvements

• Added a new CLI command kedro jupyter convert to facilitate converting Jupyter notebook cells into Kedro nodes.
• Added KedroContext base class which holds the configuration and Kedro's main functionality (catalog, pipeline, config).
• Added a new I/O module ParquetS3DataSet in contrib for usage with Pandas. (by @mmchougule)
• Added a new --node flag to kedro run, allowing users to run only the nodes with the specified names.
• Added CSVHTTPDataSet to load CSV using HTTP(s) links.
• Added new --from-nodes and --to-nodes run arguments, allowing users to run a range of nodes from the pipeline.
• Added prefix params: to the parameters specified in parameters.yml which allows users to differentiate between their different parameters node inputs and outputs
• Added JSONBlobDataSet to load json (-delimited) files from Azure Blob Storage
• Jupyter Lab/Notebook now starts with only one kernel by default.

Bug fixes and other changes

• Documentation improvements including instructions on how to initialise a Spark session using YAML configuration.
• anyconfig default log level changed from INFO to WARNING.
• Added information on installed plugins to kedro info.
• Added style sheets for project documentation, so the output of kedro build-docs will resemble the style of kedro docs.

Breaking changes to the API

• Simplified the Kedro template in run.py with the introduction of KedroContext class.
• Merged FilepathVersionMixIn and S3VersionMixIn under one abstract class AbstractVersionedDataSet which extendsAbstractDataSet.
• name changed to be a keyword-only argument for Pipeline.
• CSVLocalDataSet no longer supports URLs.

Migration guide from Kedro 0.14.* to Kedro 0.15.0

Migration for Kedro project template

This guide assumes that:

• The framework specific code has not been altered significantly
• Your project specific code is stored in the dedicated python package under src/.

The breaking changes were introduced in the following project template files:

• <project-name>/.ipython/profile_default/startup/00-kedro-init.py
• <project-name>/kedro_cli.py
• <project-name>/src/tests/test_run.py
• <project-name>/src/<package-name>/run.py

The easiest way to migrate your project from Kedro 0.14.* to Kedro 0.15.0 is to create a new project (by using kedro new) and move code and files bit by bit as suggested in the detailed guide below:

1. Create a new project with the same name by running kedro new

2. Copy the following folders to the new project:

• results/
• references/
• notebooks/
• logs/
• data/
• conf/

3. If you customised your src/<package>/run.py, make sure you apply the same customisations to src/<package>/run.py

• If you customised get_config(), you can override _create_config() method in ProjectContext derived class
• If you customised create_catalog(), you can override _create_catalog() method in ProjectContext derived class
• If you customised run(), you can override run() method in ProjectContext derived class
• If you customised default env, you can override it in ProjectContext derived class or pass it at construction. By default, env is local.
• If you customised default root_conf, you can override CONF_ROOT attribute in ProjectContext derived class. By default, KedroContext base class has CONF_ROOT attribute set to conf.

4. The following syntax changes are introduced in ipython or Jupyter notebook/labs:

• proj_dir -> context.project_path
• proj_name -> context.project_name
• conf -> context.config_loader.
• io -> context.catalog (e.g., io.load() -> context.catalog.load())

5. If you customised your kedro_cli.py, you need to apply the same customisations to your kedro_cli.py in the new project.

Migration for versioning custom dataset classes

If you defined any custom dataset classes which support versioning in your project, you need to apply the following changes:

1. Make sure your dataset inherits from AbstractVersionedDataSet only.
2. Call super().__init__() with the appropriate arguments in the dataset's __init__. If storing on local filesystem, providing the filepath and the version is enough. Otherwise, you should also pass in an exists_function and a glob_function that emulate exists and glob in a different filesystem (see CSVS3DataSet as an example).
3. Remove setting of the _filepath and _version attributes in the dataset's __init__, as this is taken care of in the base abstract class.
4. Any calls to _get_load_path and _get_save_path methods should take no arguments.
5. Ensure you convert the output of _get_load_path and _get_save_path appropriately, as these now return PurePaths instead of strings.
6. Make sure _check_paths_consistency is called with PurePaths as input arguments, instead of strings.

These steps should have brought your project to Kedro 0.15.0. There might be some more minor tweaks needed as every project is unique, but now you have a pretty solid base to work with. If you run into any problems, please consult the Kedro documentation.

Thanks for supporting contributions

Dmitry Vukolov, Jo Stichbury, Angus Williams, Deepyaman Datta, Mayur Chougule, Marat Kopytjuk

Release 0.14.3

Major features and improvements

• Tab completion for catalog datasets in ipython or jupyter sessions. (Thank you @datajoely and @WaylonWalker)
• Added support for transcoding, an ability to decouple loading/saving mechanisms of a dataset from its storage location, denoted by adding '@' to the dataset name.
• Datasets have a new release function that instructs them to free any cached data. The runners will call this when the dataset is no longer needed downstream.

Bug fixes and other changes

• Add support for pipeline nodes made up from partial functions.
• Expand user home directory ~ for TextLocalDataSet (see issue #19).
• Add a short_name property to Nodes for a display-friendly (but not necessarily unique) name.
• Add Kedro project loader for IPython: extras/kedro_project_loader.py.
• Fix source file encoding issues with Python 3.5 on Windows.
• Fix local project source not having priority over the same source installed as a package, leading to local updates not being recognised.

Breaking changes to the API

• Remove the max_loads argument from the MemoryDataSet constructor and from the AbstractRunner.create_default_data_set method.

Thanks for supporting contributions

Joel Schwarzmann, Alex Kalmikov

Release 0.14.2

Major features and improvements

• Added Data Set transformer support in the form of AbstractTransformer and DataCatalog.add_transformer.

Breaking changes to the API

• Merged the ExistsMixin into AbstractDataSet.
• Pipeline.node_dependencies returns a dictionary keyed by node, with sets of parent nodes as values; Pipeline and ParallelRunner were refactored to make use of this for topological sort for node dependency resolution and running pipelines respectively.
• Pipeline.grouped_nodes returns a list of sets, rather than a list of lists.

Thanks for supporting contributions

Darren Gallagher, Zain Patel

Release 0.14.1

Major features and improvements

• New I/O module HDFS3DataSet.

Bug fixes and other changes

• Improved API docs.
• Template run.py will throw a warning instead of error if credentials.yml is not present.

Breaking changes to the API

None

Release 0.14.0:

The initial release of Kedro.

Thanks to our main contributors

Nikolaos Tsaousis, Ivan Danov, Dmitrii Deriabin, Gordon Wrigley, Yetunde Dada, Nasef Khan, Kiyohito Kunii, Nikolaos Kaltsas, Meisam Emamjome, Peteris Erins, Lorena Balan, Richard Westenra

Thanks for supporting contributions

Jo Stichbury, Aris Valtazanos, Fabian Peters, Guilherme Braccialli, Joel Schwarzmann, Miguel Beltre, Mohammed ElNabawy, Deepyaman Datta, Shubham Agrawal, Oleg Andreyev, Mayur Chougule, William Ashford, Ed Cannon, Nikhilesh Nukala, Sean Bailey, Vikram Tegginamath, Thomas Huijskens, Musa Bilal

We are also grateful to everyone who advised and supported us, filed issues or helped resolve them, asked and answered questions and were part of inspiring discussions.