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WaylonWalker 2020-02-28 08:16:44 -06:00
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# What is this for?
This folder should be used to store configuration files used by Kedro or by separate tools.
This file can be used to provide users with instructions for how to reproduce local configuration with their own credentials. You can edit the file however you like, but you may wish to retain the information below and add your own section in the section titled **Instructions**.
## Local configuration
The `local` folder should be used for configuration that is either user-specific (e.g. IDE configuration) or protected (e.g. security keys).
> *Note:* Please do not check in any local configuration to version control.
## Base configuration
The `base` folder is for shared configuration, such as non-sensitive and project-related configuration that may be shared across team members.
WARNING: Please do not put access credentials in the base configuration folder.
# Instructions
# Find out more
You can find out more about configuration from the [user guide documentation](https://kedro.readthedocs.io/en/stable/04_user_guide/03_configuration.html).

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# Here you can define all your data sets by using simple YAML syntax.
#
# Documentation for this file format can be found in "The Data Catalog"
# Link: https://kedro.readthedocs.io/en/stable/04_user_guide/04_data_catalog.html
#
# We support interacting with a variety of data stores including local file systems, cloud, network and HDFS
#
# An example data set definition can look as follows:
#
#bikes:
# type: pandas.CSVDataSet
# filepath: "data/01_raw/bikes.csv"
#
#weather:
# type: spark.SparkDataSet
# filepath: s3a://your_bucket/data/01_raw/weather*
# file_format: csv
# credentials: dev_s3
# load_args:
# header: True
# inferSchema: True
# save_args:
# sep: '|'
# header: True
#
#scooters:
# type: pandas.SQLTableDataSet
# credentials: scooters_credentials
# table_name: scooters
# load_args:
# index_col: ['name']
# columns: ['name', 'gear']
# save_args:
# if_exists: 'replace'
# # if_exists: 'fail'
# # if_exists: 'append'
#
# The Data Catalog supports being able to reference the same file using two different DataSet implementations
# (transcoding), templating and a way to reuse arguments that are frequently repeated. See more here:
# https://kedro.readthedocs.io/en/stable/04_user_guide/04_data_catalog.html
#
# This is a data set used by the "Hello World" example pipeline provided with the project
# template. Please feel free to remove it once you remove the example pipeline.
example_iris_data:
type: CSVLocalDataSet
filepath: data/01_raw/iris.csv

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version: 1
disable_existing_loggers: False
formatters:
simple:
format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
json_formatter:
format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
class: pythonjsonlogger.jsonlogger.JsonFormatter
handlers:
console:
class: logging.StreamHandler
level: INFO
formatter: simple
stream: ext://sys.stdout
info_file_handler:
class: logging.handlers.RotatingFileHandler
level: INFO
formatter: simple
filename: logs/info.log
maxBytes: 10485760 # 10MB
backupCount: 20
encoding: utf8
delay: True
error_file_handler:
class: logging.handlers.RotatingFileHandler
level: ERROR
formatter: simple
filename: logs/errors.log
maxBytes: 10485760 # 10MB
backupCount: 20
encoding: utf8
delay: True
journal_file_handler:
class: kedro.versioning.journal.JournalFileHandler
level: INFO
base_dir: logs/journals
formatter: json_formatter
loggers:
anyconfig:
level: WARNING
handlers: [console, info_file_handler, error_file_handler]
propagate: no
kedro.io:
level: INFO
handlers: [console, info_file_handler, error_file_handler]
propagate: no
kedro.pipeline:
level: INFO
handlers: [console, info_file_handler, error_file_handler]
propagate: no
kedro.journal:
level: INFO
handlers: [journal_file_handler]
propagate: no
root:
level: INFO
handlers: [console, info_file_handler, error_file_handler]

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# Parameters for the example pipeline. Feel free to delete these once you
# remove the example pipeline from pipeline.py and the example nodes in
# `src/pipelines/`
example_test_data_ratio: 0.2
example_num_train_iter: 10000
example_learning_rate: 0.01

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