h2o-cloud-discovery 1.1.0

H2O Cloud Discovery Python CLient

h2o-cloud-discovery

H2O Cloud Discovery Client.

Installation

pip install h2o-cloud-discovery

Usage

Package provides two main functions. Synchronous h2o_discovery.discover() and asynchronous h2o_discovery.discover_async(). Both functions return a discovery object that can be used to obtain the information the H2O Cloud environment, its services and clients.

Both accept a environment argument that can be used to specify the H2O Cloud environment for which the discovery should be performed. It's handy when for local development. Alternatively, the H2O_CLOUD_ENVIRONMENT environment variable can be used.

import h2o_discovery

discovery = h2o_discovery.discover()

# Print the H2O Cloud environment that was discovered.
print(discovery.environment.h2o_cloud_environment)

# Connect to the my service.
my_service_client = my_service.client(address=discovery.services["my-service"].uri)

Examples

Example: Use with H2O.ai MLOps Python Client within the Wave App

import h2o_authn
import h2o_discovery
import h2o_mlops_client as mlops
from h2o_wave import Q, app, ui
from h2o_wave import main

@app("/")
async def serve(q: Q):
    discovery = await h2o_discovery.discover_async()

    token_provider = h2o_authn.AsyncTokenProvider(
        refresh_token=q.auth.refresh_token,
        issuer_url=discovery.environment.issuer_url,
        client_id=discovery.clients["platform"].oauth2_client_id,
    )

    mlops_client = mlops.Client(
        gateway_url=discovery.services["mlops-api"].uri,
        token_provider=token_provider,
    )

    ...

Development

Project is managed using Hatch.

Testing

For quick development tests use:

hatch run devtest:pytest

Full test matrix can be run using:

hatch env remove test && hatch run test:pytest

Linting

hatch run lint:check

Formating and imports can be fixed using:

hatch run lint:fix