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Endpoint environment variables

Through an endpoint update, you can assign values to environment variables that will be exposed to the deployment.

Using the client

Endpoint.update() provides a parameter for setting the endpoint's environment variables. It can be used alongside any update strategy.
from verta.endpoint.update import DirectUpdateStrategy
endpoint.update(
model_version, DirectUpdateStrategy(),
env_vars=env_vars,
)
env_vars takes a dictionary of string environment variable names to string values, and will be made available to the model when it is deployed.
env_vars = {'LOG_LEVEL': "debug"}
Environment variables that were captured via RegisteredModelVersion.log_environment() will also be provided to the deployed model.
model_ver.log_environment(
Python(["tensorflow"], env_vars={"CUDA_VISIBLE_DEVICES": "0,1"}),
)