Merge pull request #1160 from quantumblacklabs/merge-master-to-develop

[AUTO-MERGE] Merge master into develop via merge-master-to-develop

RELEASE.md

@@ -30,7 +30,7 @@
 * If you're using `spark.SparkHiveDataSet` with `write_mode` option set to `insert`, please update this to `append` in line with the Spark styleguide. If you're using `spark.SparkHiveDataSet` with `write_mode` option set to `upsert`, please make sure that your `SparkContext` has a valid `checkpointDir` set either by `SparkContext.setCheckpointDir` method or directly in the `conf` folder.
 * Edit any scripts containing `kedro pipeline package --version` to remove the `--version` option. If you wish to set a specific pipeline package version, set the `__version__` variable in the pipeline package's `__init__.py` file.
 
-# Upcoming release 0.17.4
+# Release 0.17.4
 
 ## Major features and improvements
 * Added the following new datasets:

docs/source/02_get_started/03_hello_kedro.md

@@ -65,7 +65,7 @@ from kedro.io import DataCatalog, MemoryDataSet
 data_catalog = DataCatalog({"my_salutation": MemoryDataSet()})
 ```
 
-Kedro provides a [number of different built-in datasets](https://kedro.readthedocs.io/en/stable/kedro.extras.datasets.html#kedro-extras-datasets) for different file types and file systems so you don’t have to write the logic for reading/writing data.
+Kedro provides a [number of different built-in datasets](/kedro.extras.datasets) for different file types and file systems so you don’t have to write the logic for reading/writing data.
 
 ## Runner
 

docs/source/03_tutorial/02_tutorial_template.md

@@ -40,7 +40,7 @@ isort~=5.0 # Used for linting code with `kedro lint`
 jupyter~=1.0 # Used to open a Kedro-session in Jupyter Notebook & Lab
 jupyter_client>=5.1.0, <7.0 # Used to open a Kedro-session in Jupyter Notebook & Lab
 jupyterlab~=3.0 # Used to open a Kedro-session in Jupyter Lab
-kedro==0.17.3
+kedro==0.17.4
 nbstripout~=0.4 # Strips the output of a Jupyter Notebook and writes the outputless version to the original file
 pytest-cov~=2.5 # Produces test coverage reports
 pytest-mock>=1.7.1, <2.0 # Wrapper around the mock package for easier use with pytest
@@ -61,10 +61,10 @@ The dependencies above may be sufficient for some projects, but for the spacefli
 pip install "kedro[pandas.CSVDataSet,pandas.ExcelDataSet]"
 ```
 
-Alternatively, if you need to, you can edit `src/requirements.txt` directly to modify your list of dependencies by replacing the requirement `kedro==0.17.3` with the following (your version of Kedro may be different):
+Alternatively, if you need to, you can edit `src/requirements.txt` directly to modify your list of dependencies by replacing the requirement `kedro==0.17.4` with the following (your version of Kedro may be different):
 
 ```text
-kedro[pandas.CSVDataSet,pandas.ExcelDataSet]==0.17.3
+kedro[pandas.CSVDataSet,pandas.ExcelDataSet]==0.17.4
 ```
 
 Then run the following:

docs/source/05_data/02_kedro_io.md

@@ -116,7 +116,7 @@ cars:
 
 The `DataCatalog` will create a versioned `CSVDataSet` called `cars`. The actual csv file location will look like `data/01_raw/company/car_data.csv/<version>/car_data.csv`, where `<version>` corresponds to a global save version string formatted as `YYYY-MM-DDThh.mm.ss.sssZ`. Every time the `DataCatalog` is instantiated, it generates a new global save version, which is propagated to all versioned datasets it contains.
 
-`catalog.yml` only allows you to version your datasets but it does not allow you to choose which version to load or save. This is deliberate because we have chosen to separate the data catalog from any runtime configuration. If you need to pin a dataset version, you can either [specify the versions in a separate `yml` file and call it at runtime](https://kedro.readthedocs.io/en/stable/04_kedro_project_setup/02_configuration.html#configure-kedro-run-arguments) or [instantiate your versioned datasets using Code API and define a version parameter explicitly](#versioning-using-the-code-api).
+`catalog.yml` only allows you to version your datasets but it does not allow you to choose which version to load or save. This is deliberate because we have chosen to separate the data catalog from any runtime configuration. If you need to pin a dataset version, you can either [specify the versions in a separate `yml` file and call it at runtime](../04_kedro_project_setup/02_configuration.md#configure-kedro-run-arguments) or [instantiate your versioned datasets using Code API and define a version parameter explicitly](#versioning-using-the-code-api).
 
 By default, the `DataCatalog` will load the latest version of the dataset. However, it is also possible to specify an exact load version. In order to do that, you can pass a dictionary with exact load versions to `DataCatalog.from_config`:
 

docs/source/09_development/03_commands_reference.md

@@ -119,7 +119,7 @@ Returns output similar to the following, depending on the version of Kedro used
 | |/ / _ \/ _` | '__/ _ \
 |   <  __/ (_| | | | (_) |
 |_|\_\___|\__,_|_|  \___/
-v0.17.3
+v0.17.4
 
 kedro allows teams to create analytics
 projects. It is developed as part of

docs/source/10_deployment/08_databricks.md

@@ -38,7 +38,7 @@ conda create --name iris_databricks python=3.7 -y
 conda activate iris_databricks
 
 # install Kedro and create a new project
-pip install "kedro~=0.17.3"
+pip install "kedro~=0.17.4"
 # name your project Iris Databricks when prompted for it
 kedro new --starter pyspark-iris
 ```
@@ -316,10 +316,10 @@ In your newly created notebook put each code snippet from below into a separate
 %sh rm -rf ~/projects/iris-databricks && git clone --single-branch --branch master https://${GITHUB_USER}:${GITHUB_TOKEN}@github.com/${GITHUB_USER}/<your-repo-name>.git ~/projects/iris-databricks
 ```
 
-* Install the latest version of Kedro compatible with version `0.17.3`
+* Install the latest version of Kedro compatible with version `0.17.4`
 
 ```console
-%pip install "kedro[spark.SparkDataSet]~=0.17.3"
+%pip install "kedro[spark.SparkDataSet]~=0.17.4"
 ```
 
 * Copy input data into DBFS

features/environment.py

@@ -69,8 +69,7 @@ def before_scenario(context, scenario):
     if FRESH_VENV_TAG in scenario.tags:
         context = _setup_minimal_env(context)
 
-    context.temp_dir = Path(tempfile.mkdtemp()).resolve()
-    _PATHS_TO_REMOVE.add(context.temp_dir)
+    context.temp_dir = _create_tmp_dir()
 
 
 def _setup_context_with_venv(context, venv_dir):

features/steps/cli_steps.py

@@ -177,8 +177,7 @@ def create_config_file(context):
     """
     context.config_file = context.temp_dir / "config.yml"
     context.project_name = "project-dummy"
-    root_project_dir = context.temp_dir / context.project_name
-    context.root_project_dir = root_project_dir
+    context.root_project_dir = context.temp_dir / context.project_name
     context.package_name = context.project_name.replace("-", "_")
     config = {
         "project_name": context.project_name,

kedro/__init__.py

@@ -31,7 +31,7 @@
 configuration and pipeline assembly.
 """
 
-__version__ = "0.17.3"
+__version__ = "0.17.4"
 
 
 import logging