Configuration¶

This section contains detailed information about configuration, for which the relevant API documentation can be found in kedro.config.ConfigLoader.

Configuration root¶

We recommend that you keep all configuration files in the conf directory of a Kedro project. However, if you prefer, point Kedro to any other directory and change the configuration paths by setting the CONF_SOURCE variable in src/<package_name>/settings.py as follows:

CONF_SOURCE = "new_conf"

You can also specify a source directory for the configuration files at run time using the kedro run CLI command with the --conf-source flag as follows:

kedro run --conf-source=<path-to-new-conf-directory>

Local and base configuration environments¶

Kedro-specific configuration (e.g., DataCatalog configuration for IO) is loaded using the ConfigLoader class:

from kedro.config import ConfigLoader
from kedro.framework.project import settings

conf_path = str(project_path / settings.CONF_SOURCE)
conf_loader = ConfigLoader(conf_source=conf_path, env="local")
conf_catalog = conf_loader["catalog"]

This recursively scans for configuration files firstly in the conf/base/ (base being the default environment) and then in the conf/local/ (local being the designated overriding environment) directory according to the following rules:

Either of the following is true:
- filename starts with catalog
- file is located in a sub-directory whose name is prefixed with catalog
And file extension is one of the following: yaml, yml, json, ini, pickle, xml or properties

This logic is specified by config_patterns in the ConfigLoader and TemplatedConfigLoader classes. By default those patterns are set as follows for the configuration of catalog, parameters, logging and credentials:

config_patterns = {
    "catalog": ["catalog*", "catalog*/**", "**/catalog*"],
    "parameters": ["parameters*", "parameters*/**", "**/parameters*"],
    "credentials": ["credentials*", "credentials*/**", "**/credentials*"],
    "logging": ["logging*", "logging*/**", "**/logging*"],
}

The configuration patterns can be changed by setting the CONFIG_LOADER_ARGS variable in src/<package_name>/settings.py. You can change the default patterns as well as add additional ones, for example, for Spark configuration files. This example shows how to load parameters if your files are using a params naming convention instead of parameters and how to add patterns to load Spark configuration:

CONFIG_LOADER_ARGS = {
    "config_patterns": {
        "spark": ["spark*/"],
        "parameters": ["params*", "params*/**", "**/params*"],
    }
}

You can also bypass the configuration patterns and set configuration directly on the instance of a config loader class. You can bypass the default configuration (catalog, parameters, credentials, and logging) as well as additional configuration.

from kedro.config import ConfigLoader
from kedro.framework.project import settings

conf_path = str(project_path / settings.CONF_SOURCE)
conf_loader = ConfigLoader(conf_source=conf_path, env="local")

# Bypass configuration patterns by setting the key and values directly on the config loader instance.
conf_loader["catalog"] = {"catalog_config": "something_new"}

Configuration information from files stored in base or local that match these rules is merged at runtime and returned as a config dictionary:

If any two configuration files located inside the same environment path (conf/base/ or conf/local/ in this example) contain the same top-level key, load_config will raise a ValueError indicating that the duplicates are not allowed.
If two configuration files have duplicate top-level keys but are in different environment paths (one in conf/base/, another in conf/local/, for example) then the last loaded path (conf/local/ in this case) takes precedence and overrides that key value. ConfigLoader.get will not raise any errors - however, a DEBUG level log message will be emitted with information on the overridden keys.

Any top-level keys that start with _ are considered hidden (or reserved) and are ignored after the config is loaded. Those keys will neither trigger a key duplication error nor appear in the resulting configuration dictionary. However, you can still use such keys, for example, as YAML anchors and aliases.

Additional configuration environments¶

In addition to the two built-in local and base configuration environments, you can create your own. Your project loads conf/base/ as the bottom-level configuration environment but allows you to overwrite it with any other environments that you create, such as conf/server/ or conf/test/. To use additional configuration environments, run the following command:

kedro run --env=test

If no env option is specified, this will default to using the local environment to overwrite conf/base.

If, for some reason, your project does not have any other environments apart from base, i.e. no local environment to default to, you must customise KedroContext to take env="base" in the constructor and then specify your custom KedroContext subclass in src/<package_name>/settings.py under the CONTEXT_CLASS key.

If you set the KEDRO_ENV environment variable to the name of your environment, Kedro will load that environment for your kedro run, kedro ipython, kedro jupyter notebook and kedro jupyter lab sessions:

export KEDRO_ENV=test

Note

If you both specify the KEDRO_ENV environment variable and provide the --env argument to a CLI command, the CLI argument takes precedence.

Template configuration¶

Kedro also provides an extension TemplatedConfigLoader class that allows you to template values in configuration files. To apply templating in your project, set the CONFIG_LOADER_CLASS constant in your src/<package_name>/settings.py:

from kedro.config import TemplatedConfigLoader  # new import

CONFIG_LOADER_CLASS = TemplatedConfigLoader

Globals¶

When using the TemplatedConfigLoader you can provide values in the configuration template through a globals file or dictionary.

Let’s assume the project contains a conf/base/globals.yml file with the following contents:

bucket_name: "my_s3_bucket"
key_prefix: "my/key/prefix/"

datasets:
    csv: "pandas.CSVDataSet"
    spark: "spark.SparkDataSet"

folders:
    raw: "01_raw"
    int: "02_intermediate"
    pri: "03_primary"
    fea: "04_feature"

To point your TemplatedConfigLoader to the globals file, add it to the the CONFIG_LOADER_ARGS variable in src/<package_name>/settings.py:

CONFIG_LOADER_ARGS = {"globals_pattern": "*globals.yml"}

Alternatively, you can declare which values to fill in the template through a dictionary. This dictionary could look like the below:

{
    "bucket_name": "another_bucket_name",
    "non_string_key": 10,
    "key_prefix": "my/key/prefix",
    "datasets": {"csv": "pandas.CSVDataSet", "spark": "spark.SparkDataSet"},
    "folders": {
        "raw": "01_raw",
        "int": "02_intermediate",
        "pri": "03_primary",
        "fea": "04_feature",
    },
}

To point your TemplatedConfigLoader to the globals dictionary, add it to the CONFIG_LOADER_ARGS variable in src/<package_name>/settings.py:

CONFIG_LOADER_ARGS = {
    "globals_dict": {
        "bucket_name": "another_bucket_name",
        "non_string_key": 10,
        "key_prefix": "my/key/prefix",
        "datasets": {"csv": "pandas.CSVDataSet", "spark": "spark.SparkDataSet"},
        "folders": {
            "raw": "01_raw",
            "int": "02_intermediate",
            "pri": "03_primary",
            "fea": "04_feature",
        },
    }
}

If you specify both globals_pattern and globals_dict in CONFIG_LOADER_ARGS, the contents of the dictionary resulting from globals_pattern are merged with the globals_dict dictionary. In case of conflicts, the keys from the globals_dict dictionary take precedence.

Now the templating can be applied to the configuration. Here is an example of a templated conf/base/catalog.yml file:

raw_boat_data:
    type: "${datasets.spark}"  # nested paths into global dict are allowed
    filepath: "s3a://${bucket_name}/${key_prefix}/${folders.raw}/boats.csv"
    file_format: parquet

raw_car_data:
    type: "${datasets.csv}"
    filepath: "s3://${bucket_name}/data/${key_prefix}/${folders.raw}/${filename|cars.csv}"  # default to 'cars.csv' if the 'filename' key is not found in the global dict

Under the hood, TemplatedConfigLoader uses JMESPath syntax to extract elements from the globals dictionary.

Jinja2 support¶

From version 0.17.0, TemplateConfigLoader also supports the Jinja2 template engine alongside the original template syntax. Below is an example of a catalog.yml file that uses both features:

{% for speed in ['fast', 'slow'] %}
{{ speed }}-trains:
    type: MemoryDataSet

{{ speed }}-cars:
    type: pandas.CSVDataSet
    filepath: s3://${bucket_name}/{{ speed }}-cars.csv
    save_args:
        index: true

{% endfor %}

When parsing this configuration file, TemplateConfigLoader will:

Read the catalog.yml and compile it using Jinja2
Use a YAML parser to parse the compiled config into a Python dictionary
Expand ${bucket_name} in filepath using the globals_pattern and globals_dict arguments for the TemplateConfigLoader instance, as in the previous examples

The output Python dictionary will look as follows:

{
    "fast-trains": {"type": "MemoryDataSet"},
    "fast-cars": {
        "type": "pandas.CSVDataSet",
        "filepath": "s3://my_s3_bucket/fast-cars.csv",
        "save_args": {"index": True},
    },
    "slow-trains": {"type": "MemoryDataSet"},
    "slow-cars": {
        "type": "pandas.CSVDataSet",
        "filepath": "s3://my_s3_bucket/slow-cars.csv",
        "save_args": {"index": True},
    },
}

Warning

Although Jinja2 is a very powerful and extremely flexible template engine, which comes with a wide range of features, we do not recommend using it to template your configuration unless absolutely necessary. The flexibility of dynamic configuration comes at a cost of significantly reduced readability and much higher maintenance overhead. We believe that, for the majority of analytics projects, dynamically compiled configuration does more harm than good.

Configuration with OmegaConf¶

OmegaConf is a Python library for configuration. It is a YAML-based hierarchical configuration system with support for merging configurations from multiple sources. From Kedro 0.18.5 you can use the OmegaConfigLoader which uses OmegaConf under the hood to load data.

Note

OmegaConfigLoader is under active development and will be available from Kedro 0.18.5. New features will be added in future releases. Let us know if you have any feedback about the OmegaConfigLoader or ideas for new features.

The OmegaConfigLoader can load YAML and JSON files. Acceptable file extensions are .yml, .yaml, and .json. By default, any configuration files used by the config loaders in Kedro are .yml files.

To use the OmegaConfigLoader in your project, set the CONFIG_LOADER_CLASS constant in your src/<package_name>/settings.py:

from kedro.config import OmegaConfigLoader  # new import

CONFIG_LOADER_CLASS = OmegaConfigLoader

Templating for parameters¶

Templating or variable interpolation, as it’s called in OmegaConf, for parameters works out of the box if one condition is met: the name of the file that contains the template values must follow the same config pattern specified for parameters. By default, the config pattern for parameters is: ["parameters*", "parameters*/**", "**/parameters*"]. Suppose you have one parameters file called parameters.yml containing parameters with omegaconf placeholders like this:

model_options:
  test_size: ${data.size}
  random_state: 3

and a file containing the template values called parameters_globals.yml:

data:
  size: 0.2

Since both of the file names (parameters.yml and parameters_globals.yml) match the config pattern for parameters, the OmegaConfigLoader will load the files and resolve the placeholders correctly.

Environment variables for credentials¶

The OmegaConfigLoader enables you to load credentials from environment variables. To achieve this you have to use the omegaconf oc.env resolver. This is an example of how you can access credentials from environment variables in credentials.yml:

dev_s3:
  client_kwargs:
    aws_access_key_id: ${oc.env:AWS_ACCESS_KEY_ID}
    aws_secret_access_key: ${oc.env:AWS_SECRET_ACCESS_KEY}

Note

Note that you can only use the resolver in credentials.yml and not in catalog or parameter files. This is because we do not encourage the usage of environment variables for anything other than credentials.

Parameters¶

Load parameters¶

Parameters project configuration can be loaded with the help of the ConfigLoader class:

from kedro.config import ConfigLoader
from kedro.framework.project import settings

conf_path = str(project_path / settings.CONF_SOURCE)
conf_loader = ConfigLoader(conf_source=conf_path, env="local")
parameters = conf_loader["parameters"]

This will load configuration files from any subdirectories in conf that have a filename starting with parameters, or are located inside a folder with name starting with parameters.

Note

Since local is set as the environment, the configuration path conf/local takes precedence in the example above. Hence any overlapping top-level keys from conf/base will be overwritten by the ones from conf/local.

Calling conf_loader[key] in the example above will throw a MissingConfigException error if no configuration files match the given key. If this is a valid workflow for your application, you can handle it as follows:

from kedro.config import ConfigLoader, MissingConfigException
from kedro.framework.project import settings

conf_path = str(project_path / settings.CONF_SOURCE)
conf_loader = ConfigLoader(conf_source=conf_path, env="local")

try:
    parameters = conf_loader["parameters"]
except MissingConfigException:
    parameters = {}

Note

The kedro.framework.context.KedroContext class uses the approach above to load project parameters.

Parameters can then be used on their own or fed in as function inputs.

Specify parameters at runtime¶

Kedro also allows you to specify runtime parameters for the kedro run CLI command. To do so, use the --params command line option and specify a comma-separated list of key-value pairs that will be added to KedroContext parameters and made available to pipeline nodes. Each key-value pair is split on the first colon or equals sign. Following examples are both valid commands:

kedro run --params=param_key1:value1,param_key2:2.0  # this will add {"param_key1": "value1", "param_key2": 2} to parameters dictionary

kedro run --params=param_key1=value1,param_key2=2.0

Values provided in the CLI take precedence and overwrite parameters specified in configuration files. Parameter keys are always treated as strings. Parameter values are converted to a float or an integer number if the corresponding conversion succeeds; otherwise, they are also treated as string.

If any extra parameter key and/or value contains spaces, wrap the whole option contents in quotes:

kedro run --params="key1=value with spaces,key2=value"

Since key-value pairs are split on the first colon or equals sign, values can contain colons/equals signs, but keys cannot. These are valid CLI commands:

kedro run --params=endpoint_url:https://endpoint.example.com

kedro run --params=endpoint_url=https://endpoint.example.com

Use parameters¶

Say you have a set of parameters you’re playing around with that specify modelling hyperparameters. You can declare these in one place, for instance conf/base/parameters.yml, so that you isolate your changes in one central location.

step_size: 1
learning_rate: 0.01

You can now use the params: prefix to reference these parameters in the node definition:

def increase_volume(volume, step):
    return volume + step


# in pipeline definition
node(
    func=increase_volume,
    inputs=["input_volume", "params:step_size"],
    outputs="output_volume",
)

You can also group your parameters into nested structures and, using the same method above, load them by top-level key:

step_size: 1
model_params:
    learning_rate: 0.01
    test_data_ratio: 0.2
    number_of_train_iterations: 10000

def train_model(data, model):
    lr = model["learning_rate"]
    test_data_ratio = model["test_data_ratio"]
    iterations = model["number_of_train_iterations"]
    ...


# in pipeline definition
node(
    func=train_model,
    inputs=["input_data", "params:model_params"],
    outputs="output_data",
)

Alternatively, you can also pass parameters to the node inputs and get access to the entire collection of values inside the node function.

def increase_volume(volume, params):
    step = params["step_size"]
    return volume + step


# in pipeline definition
node(
    func=increase_volume, inputs=["input_volume", "parameters"], outputs="output_volume"
)

In both cases, under the hood parameters are added to the Data Catalog through the method add_feed_dict() in DataCatalog, where they live as MemoryDataSets. This method is also what the KedroContext class uses when instantiating the catalog.

Note

You can use add_feed_dict() to inject any other entries into your DataCatalog as per your use case.

Credentials¶

For security reasons, we strongly recommend you to not commit any credentials or other secrets to the Version Control System. Hence, by default any file inside the conf/ folder (and its subfolders) containing credentials in its name will be ignored via .gitignore and not committed to your git repository.

Credentials configuration can be loaded the same way as any other project configuration using the ConfigLoader class:

from kedro.config import ConfigLoader
from kedro.framework.project import settings

conf_path = str(project_path / settings.CONF_SOURCE)
conf_loader = ConfigLoader(conf_source=conf_path, env="local")
credentials = conf_loader["credentials"]

This will load configuration files from conf/base and conf/local whose filenames start with credentials, or that are located inside a folder with a name that starts with credentials.

Note

Since local is set as the environment, the configuration path conf/local takes precedence in the example above. Hence, any overlapping top-level keys from conf/base will be overwritten by the ones from conf/local.

Calling conf_loader[key] in the example above throws a MissingConfigException error if no configuration files match the given key. If this is a valid workflow for your application, you can handle it as follows:

from kedro.config import ConfigLoader, MissingConfigException
from kedro.framework.project import settings

conf_path = str(project_path / settings.CONF_SOURCE)
conf_loader = ConfigLoader(conf_source=conf_path, env="local")

try:
    credentials = conf_loader["credentials"]
except MissingConfigException:
    credentials = {}

Note

The kedro.framework.context.KedroContext class uses the approach above to load project credentials.

Credentials configuration can then be used on its own or fed into the DataCatalog.

AWS credentials¶

When you work with AWS credentials on datasets, you are not required to store AWS credentials in the project configuration files. Instead, you can specify them using environment variables AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and, optionally, AWS_SESSION_TOKEN. Please refer to the official documentation for more details.

Configure `kedro run` arguments¶

An extensive list of CLI options for a kedro run is available in the Kedro CLI documentation. However, instead of specifying all the command line options in a kedro run via the CLI, you can specify a config file that contains the arguments, say config.yml and run:

$ kedro run --config=config.yml

where config.yml is formatted as below (for example):

run:
  tags: tag1, tag2, tag3
  pipeline: pipeline1
  parallel: true
  nodes_names: node1, node2
  env: env1

The syntax for the options is different when you’re using the CLI compared to the configuration file. In the CLI you use dashes, for example for kedro run --from-nodes=..., but you have to use an underscore in the configuration file:

run:
  from_nodes: ...

This is because the configuration file gets parsed by Click, a Python package to handle command line interfaces. Click passes the options defined in the configuration file to a Python function. The option names need to match the argument names in that function.

Variable names and arguments in Python may only contain alpha-numeric characters and underscores, so it’s not possible to have a dash in the option names when using the configuration file.

Note

If you provide both a configuration file and a CLI option that clashes with the configuration file, the CLI option will take precedence.

Configuration¶

Configuration root¶

Local and base configuration environments¶

Additional configuration environments¶

Template configuration¶

Globals¶

Jinja2 support¶

Configuration with OmegaConf¶

Templating for parameters¶

Environment variables for credentials¶

Parameters¶

Load parameters¶

Specify parameters at runtime¶

Use parameters¶

Credentials¶

AWS credentials¶

Configure kedro run arguments¶

Configure `kedro run` arguments¶