- 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
incontrib
for usage with Pandas. (by @mmchougule)
- Documentation improvements
anyconfig
default log level changed fromINFO
toWARNING
- Add information on installed plugins to
kedro info
- Simplify the Kedro template in
run.py
with the introduction ofKedroContext
class. - Merged
FilepathVersionMixIn
andS3VersionMixIn
under one abstract classAbstractVersionedDataSet
which extendsAbstractDataSet
.
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:
-
Create a new project with the same name by running
kedro new
-
Copy the following folders to the new project:
results/
references/
notebooks/
logs/
data/
conf/
- If you customised your
src/<package>/run.py
, make sure you apply the same customisations tosrc/<package>/run.py
- If you customised
get_config()
, you can override_create_config()
method inProjectContext
derived class - If you customised
create_catalog()
, you can override_create_catalog()
method inProjectContext
derived class - If you customised
run()
, you can overriderun()
method inProjectContext
derived class - If you customised default
env
, you can override it inProjectContext
derived class or pass it at construction. By default,env
islocal
. - If you customised default
root_conf
, you can overrideCONF_ROOT
attribute inProjectContext
derived class. By default,KedroContext
base class hasCONF_ROOT
attribute set toconf
.
- 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()
)
- If you customised your
kedro_cli.py
, you need to apply the same customisations to yourkedro_cli.py
in the new project.
If you defined any custom dataset classes which support versioning in your project, you need to apply the following changes:
- Make sure your dataset inherits from
AbstractVersionedDataSet
only. - 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 anexists_function
and aglob_function
that emulateexists
andglob
in a different filesystem (seeCSVS3DataSet
as an example). - Remove setting of the
_filepath
and_version
attributes in the dataset's__init__
, as this is taken care of in the base abstract class. - Any calls to
_get_load_path
and_get_save_path
methods should take no arguments. - Ensure you convert the output of
_get_load_path
and_get_save_path
appropriately, as these now returnPurePath
s instead of strings. - Make sure
_check_paths_consistency
is called withPurePath
s 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.
Dmitry Vukolov, Jo Stichbury, Angus Williams, Deepyaman Datta, Mayur Chougule
- Tab completion for catalog datasets in
ipython
orjupyter
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.
- Add support for pipeline nodes made up from partial functions.
- Expand user home directory
~
for TextLocalDataSet (see issue #19). - Add a
short_name
property toNode
s 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.
- Remove the max_loads argument from the
MemoryDataSet
constructor and from theAbstractRunner.create_default_data_set
method.
Joel Schwarzmann, Alex Kalmikov
- Added Data Set transformer support in the form of AbstractTransformer and DataCatalog.add_transformer.
- Merged the
ExistsMixin
intoAbstractDataSet
. Pipeline.node_dependencies
returns a dictionary keyed by node, with sets of parent nodes as values;Pipeline
andParallelRunner
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.
- New I/O module
HDFS3DataSet
.
- Improved API docs.
- Template
run.py
will throw a warning instead of error ifcredentials.yml
is not present.
None
The initial release of Kedro.
Nikolaos Tsaousis, Ivan Danov, Dmitrii Deriabin, Gordon Wrigley, Yetunde Dada, Nasef Khan, Kiyohito Kunii, Nikolaos Kaltsas, Meisam Emamjome, Peteris Erins, Lorena Balan, Richard Westenra
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.