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kfserving pipeline update (#2102)

This commit is contained in:
Animesh Singh 2019-09-19 16:42:59 -07:00 committed by Kubernetes Prow Robot
parent 29bbe1198f
commit 629164261d
2 changed files with 60 additions and 32 deletions
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6
components/kubeflow/kfserving/component.yaml
View File

@ -3,11 +3,13 @@ description: Serve Models using Kubeflow KFServing
inputs:
- {name: Action, type: String, default: 'create', description: 'Action to execute on KFServing'}
- {name: Model Name, type: String, default: '', description: 'Name to give to the deployed model'}
- {name: Default Model URI, type: String, description: 'Path of the S3 or GCS compatible directory containing default model.'}
- {name: Default Model URI, type: String, default: '', description: 'Path of the S3 or GCS compatible directory containing default model.'}
- {name: Canary Model URI, type: String, default: '', description: 'Optional Path of the S3 or GCS compatible directory containing canary model.'}
- {name: Canary Model Traffic Percentage, type: String, default: '0', description: 'Optional Traffic to be sent to default model'}
- {name: Namespace, type: String, default: 'kubeflow', description: 'Kubernetes namespace where the KFServing service is deployed.'}
- {name: Framework, type: String, default: 'tensorflow', description: 'Machine Learning Framework for Model Serving.'}
- {name: default_custom_model_spec, type: String, default: '{}', description: 'Custom runtime default custom model container spec.'}
- {name: canary_custom_model_spec, type: String, default: '{}', description: 'Custom runtime canary custom model container spec.'}
outputs:
- {name: Endpoint URI, type: String, description: 'URI of the deployed prediction service..'}
implementation:
@ -23,5 +25,7 @@ implementation:
--canary-model-traffic, {inputValue: Canary Model Traffic Percentage},
--namespace, {inputValue: Namespace},
--framework, {inputValue: Framework},
--default-custom-model-spec,{inputValue: default_custom_model_spec},
--canary-custom-model-spec, {inputValue: canary_custom_model_spec},
--output_path, {outputPath: Endpoint URI}
]

64
components/kubeflow/kfserving/src/kfservingdeployer.py
View File

@ -41,13 +41,35 @@ def ModelSpec(framework, model_uri):
return V1alpha1ModelSpec(xgboost=V1alpha1XGBoostSpec(model_uri=model_uri))
elif framework == 'tensorrt':
return V1alpha1ModelSpec(tensorrt=V1alpha1TensorRTSpec(model_uri=model_uri))
elif framework == 'custom':
# TODO: implement custom container spec with more args.
# return V1alpha1ModelSpec(custom=V1alpha1CustomSpec(container=containerSpec))
raise("Custom framework is not yet implemented")
else:
raise("Error: No matching framework: " + framework)
def customModelSpec(custom_model_spec):
env = [client.V1EnvVar(name=i['name'], value=i['value']) for i in custom_model_spec['env']] if custom_model_spec.get('env', '') else None
ports = [client.V1ContainerPort(container_port=int(custom_model_spec.get('port', '')))] if custom_model_spec.get('port', '') else None
containerSpec = client.V1Container(
name=custom_model_spec.get('name', 'custom-container'),
image=custom_model_spec['image'],
env=env,
ports=ports,
command=custom_model_spec.get('command', None),
args=custom_model_spec.get('args', None),
image_pull_policy=custom_model_spec.get('image_pull_policy', None),
working_dir=custom_model_spec.get('working_dir', None)
)
return V1alpha1ModelSpec(custom=V1alpha1CustomSpec(container=containerSpec))
def kfserving_deployment(metadata, default_model_spec, canary_model_spec=None, canary_model_traffic=None):
return V1alpha1KFService(api_version=constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION,
kind=constants.KFSERVING_KIND,
metadata=metadata,
spec=V1alpha1KFServiceSpec(default=default_model_spec,
canary=canary_model_spec,
canary_traffic_percent=canary_model_traffic))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--action', type=str, help='Action to execute on KFServing', default='create')
@ -57,6 +79,8 @@ if __name__ == "__main__":
parser.add_argument('--canary-model-traffic', type=str, help='Optional Traffic to be sent to the default model', default='0')
parser.add_argument('--namespace', type=str, help='Kubernetes namespace where the KFServing service is deployed.', default='kubeflow')
parser.add_argument('--framework', type=str, help='Model Serving Framework', default='tensorflow')
parser.add_argument('--default-custom-model-spec', type=json.loads, help='Custom runtime default custom model container spec', default={})
parser.add_argument('--canary-custom-model-spec', type=json.loads, help='Custom runtime canary custom model container spec', default={})
parser.add_argument('--output_path', type=str, help='Path to store URI output')
args = parser.parse_args()
@ -64,28 +88,28 @@ if __name__ == "__main__":
model_name = args.model_name
default_model_uri = args.default_model_uri
canary_model_uri = args.canary_model_uri
canary_model_traffic = args.canary_model_traffic
canary_model_traffic = int(args.canary_model_traffic)
namespace = args.namespace
framework = args.framework.lower()
output_path = args.output_path
default_custom_model_spec = args.default_custom_model_spec
canary_custom_model_spec = args.canary_custom_model_spec
default_model_spec = ModelSpec(framework, default_model_uri)
metadata = client.V1ObjectMeta(name=model_name, namespace=namespace)
# Create Canary deployment if canary model uri is provided.
if canary_model_uri:
canary_model_spec = ModelSpec(framework, canary_model_uri)
kfsvc = V1alpha1KFService(api_version=constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION,
kind=constants.KFSERVING_KIND,
metadata=metadata,
spec=V1alpha1KFServiceSpec(default=default_model_spec,
canary=canary_model_spec,
canary_traffic_percent=int(canary_model_traffic)))
if framework != 'custom':
default_model_spec = ModelSpec(framework, default_model_uri)
else:
kfsvc = V1alpha1KFService(api_version=constants.KFSERVING_GROUP + '/' + constants.KFSERVING_VERSION,
kind=constants.KFSERVING_KIND,
metadata=metadata,
spec=V1alpha1KFServiceSpec(default=default_model_spec))
default_model_spec = customModelSpec(default_custom_model_spec)
# Create Canary deployment if canary model uri is provided.
if framework != 'custom' and canary_model_uri:
canary_model_spec = ModelSpec(framework, canary_model_uri)
kfsvc = kfserving_deployment(metadata, default_model_spec, canary_model_spec, canary_model_traffic)
elif framework == 'custom' and canary_custom_model_spec:
canary_model_spec = customModelSpec(canary_custom_model_spec)
kfsvc = kfserving_deployment(metadata, default_model_spec, canary_model_spec, canary_model_traffic)
else:
kfsvc = kfserving_deployment(metadata, default_model_spec)
KFServing = KFServingClient()
if action == 'create':