Beispiel #1
0
    def test_update_with_resources(self, client, created_endpoints,
                                   experiment_run, model_for_deployment):
        endpoint_name = _utils.generate_default_name()
        endpoint = client.set_endpoint(endpoint_name)
        created_endpoints.append(endpoint)
        original_status = endpoint.get_status()
        original_build_ids = get_build_ids(original_status)

        experiment_run.log_model(model_for_deployment['model'],
                                 custom_modules=[])
        experiment_run.log_requirements(['scikit-learn'])

        resources = '{"cpu": 0.25, "memory": "100M"}'

        runner = CliRunner()
        result = runner.invoke(
            cli,
            [
                'deployment', 'update', 'endpoint', endpoint_name, '--run-id',
                experiment_run.id, "-s", "direct", '--resources', resources
            ],
        )
        assert not result.exception
        resources_dict = Resources._from_dict(json.loads(
            resources))._as_dict()  # config is `cpu`, wire is `cpu_millis`
        assert endpoint.get_update_status(
        )['update_request']['resources'] == resources_dict
Beispiel #2
0
    def test_update_from_json_config_with_params(self, client, in_tempdir, created_entities, experiment_run, model_for_deployment):
        yaml = pytest.importorskip("yaml")
        experiment_run.log_model(model_for_deployment['model'], custom_modules=[])
        experiment_run.log_environment(Python(['scikit-learn']))


        path = verta._internal_utils._utils.generate_default_name()
        endpoint = client.set_endpoint(path)
        created_entities.append(endpoint)


        original_status = endpoint.get_status()
        original_build_ids = get_build_ids(original_status)

        # Creating config dict:
        config_dict = {
            "run_id": experiment_run.id,
            "strategy": "direct",
            "autoscaling": {
                "quantities": {"min_replicas": 1, "max_replicas": 4, "min_scale": 0.5, "max_scale": 2.0},
                "metrics": [
                    {"metric": "cpu_utilization", "parameters": [{"name": "target", "value": "0.5"}]},
                    {"metric": "memory_utilization", "parameters": [{"name": "target", "value": "0.7"}]}
                ]
            },
            "env_vars": {"VERTA_HOST": "app.verta.ai"},
            "resources": {"cpu": 0.25, "memory": "100M"}
        }

        filepath = "config.json"
        with open(filepath, 'w') as f:
            json.dump(config_dict, f)

        endpoint.update_from_config(filepath)
        update_status = endpoint.get_update_status()

        # Check autoscaling:
        autoscaling_parameters = update_status["update_request"]["autoscaling"]
        autoscaling_quantities = autoscaling_parameters["quantities"]

        assert autoscaling_quantities == config_dict["autoscaling"]["quantities"]

        autoscaling_metrics = autoscaling_parameters["metrics"]
        assert len(autoscaling_metrics) == 2
        for metric in autoscaling_metrics:
            assert metric["metric_id"] in [1001, 1002, 1003]

            if metric["metric_id"] == 1001:
                assert metric["parameters"][0]["value"] == "0.5"
            else:
                assert metric["parameters"][0]["value"] == "0.7"

        # Check env_vars:
        assert update_status["update_request"]["env"][0]["name"] == "VERTA_HOST"
        assert update_status["update_request"]["env"][0]["value"] == "app.verta.ai"

        # Check resources:
        resources_dict = Resources._from_dict(config_dict["resources"])._as_dict()  # config is `cpu`, wire is `cpu_millis`
        assert endpoint.get_update_status()['update_request']['resources'] == resources_dict