Cortex makes all files in the project directory (i.e. the directory which contains cortex.yaml) available for use in your Task implementation. Python bytecode files (*.pyc, *.pyo, *.pyd), files or folders that start with ., and the api configuration file (e.g. cortex.yaml) are excluded.
The following files can also be added at the root of the project's directory:
.cortexignore file, which follows the same syntax and behavior as a .gitignore file.
.env file, which exports environment variables that can be used in the task. Each line of this file must follow the VARIABLE=value format.
You can use Cortex's logger in your predictor implementation to log in JSON. This will enrich your logs with Cortex's metadata, and you can add custom metadata to the logs by adding key value pairs to the extra key when using the logger. For example:
The dictionary passed in via the extra will be flattened by one level. e.g.
To avoid overriding essential Cortex metadata, please refrain from specifying the following extra keys: asctime, levelname, message, labels, and process. Log lines greater than 5 MB in size will be ignored.
Cortex Python client
A default Cortex Python client environment has been configured for your API. This can be used for deploying/deleting/updating or submitting jobs to your running cluster based on the execution flow of your task. For example:
# initialization code and variables can be declared here in global scope
class Task:
def __call__(self, config):
"""(Required) Task runnable.
Args:
config (required): Dictionary passed from API configuration (if
specified) merged with configuration passed in with Job
Submission API. If there are conflicting keys, values in
configuration specified in Job submission takes precedence.
"""
pass
...
from cortex_internal.lib.log import logger as cortex_logger
class Task:
def __call__(self, config):
...
cortex_logger.info("completed validations", extra={"accuracy": accuracy})
import cortex
class Task:
def __call__(self, config):
...
# get client pointing to the default environment
client = cortex.client()
# deploy API in the existing cluster as part of your pipeline workflow
client.create_api(...)