kubeflow.fairing.backends package

Submodules

kubeflow.fairing.backends.backends module

class kubeflow.fairing.backends.backends.AWSBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.KubernetesBackend

Use to create a builder instance and create a deployer to be used with a traing job or a serving job for the AWS backend.

get_builder(preprocessor, base_image, registry, needs_deps_installation=True, pod_spec_mutators=None)

Creates a builder instance with right config for AWS

Parameters:
  • preprocessor – Preprocessor to use to modify inputs
  • base_image – Base image to use for this job
  • registry – Registry to push image to. Example: gcr.io/kubeflow-images
  • needs_deps_installation – need depends on installation(Default value = True)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. e.g. fairing.cloud.gcp.add_gcp_credentials_if_exists This can used to set things like volumes and security context. (Default value =None)
get_serving_deployer(model_class, service_type='ClusterIP', pod_spec_mutators=None)

Creates a deployer to be used with a serving job for AWS

Parameters:
  • model_class – the name of the class that holds the predict function.
  • service_type – service type (Default value = ‘ClusterIP’)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
get_training_deployer(pod_spec_mutators=None)

Creates a deployer to be used with a training job for AWS

Parameters:pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
Returns:job for handle all the k8s’ template building for a training
class kubeflow.fairing.backends.backends.AzureBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.KubernetesBackend

Use to create a builder instance and create a deployer to be used with a traing job or a serving job for the Azure backend.

get_builder(preprocessor, base_image, registry, needs_deps_installation=True, pod_spec_mutators=None)

Creates a builder instance with right config for Azure

Parameters:
  • preprocessor – Preprocessor to use to modify inputs
  • base_image – Base image to use for this job
  • registry – Registry to push image to. Example: gcr.io/kubeflow-images
  • needs_deps_installation – need depends on installation(Default value = True)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. e.g. fairing.cloud.gcp.add_gcp_credentials_if_exists This can used to set things like volumes and security context. (Default value =None)
class kubeflow.fairing.backends.backends.BackendInterface

Bases: object

Backend interface. Creating a builder instance or a deployer to be used with a traing job or a serving job for the given backend. And get the approriate base container or docker registry for the current environment.

get_base_contanier()

Returns the approriate base container for the current environment

Returns:base image
get_builder(preprocessor, base_image, registry)

Creates a builder instance with right config for the given backend

Parameters:
  • preprocessor – Preprocessor to use to modify inputs
  • base_image – Base image to use for this builder
  • registry – Registry to push image to. Example: gcr.io/kubeflow-images
Raises:

NotImplementedError – not implemented exception
get_docker_registry()

Returns the approriate docker registry for the current environment

Returns:None
get_serving_deployer(model_class)

Creates a deployer to be used with a serving job

Parameters:model_class – the name of the class that holds the predict function.
Raises:NotImplementedError – not implemented exception
get_training_deployer(pod_spec_mutators=None)

Creates a deployer to be used with a training job

Parameters:pod_spec_mutators – list of functions that is used to mutate the podsspec. e.g. fairing.cloud.gcp.add_gcp_credentials_if_exists This can used to set things like volumes and security context. (Default value = None)
Raises:NotImplementedError – not implemented exception
class kubeflow.fairing.backends.backends.GCPManagedBackend(project_id=None, region=None, training_scale_tier=None)

Bases: kubeflow.fairing.backends.backends.BackendInterface

Use to create a builder instance and create a deployer to be used with a traing job or a serving job for the GCP.

get_builder(preprocessor, base_image, registry, needs_deps_installation=True, pod_spec_mutators=None)

Creates a builder instance with right config for GCP

Parameters:
  • preprocessor – Preprocessor to use to modify inputs
  • base_image – Base image to use for this job
  • registry – Registry to push image to. Example: gcr.io/kubeflow-images
  • needs_deps_installation – need depends on installation(Default value = True)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. e.g. fairing.cloud.gcp.add_gcp_credentials_if_exists This can used to set things like volumes and security context. (Default value =None)
get_docker_registry()

Returns the approriate docker registry for GCP

Returns:docker registry
get_serving_deployer(model_class, pod_spec_mutators=None)

Creates a deployer to be used with a serving job for GCP

Parameters:
  • model_class – the name of the class that holds the predict function.
  • service_type – service type (Default value = ‘ClusterIP’)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
get_training_deployer(pod_spec_mutators=None)

Creates a deployer to be used with a training job for GCP

Parameters:pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
Returns:job for handle all the k8s’ template building for a training
class kubeflow.fairing.backends.backends.GKEBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.KubernetesBackend

Use to create a builder instance and create a deployer to be used with a traing job or a serving job for the GKE backend. And get the approriate docker registry for GKE.

get_builder(preprocessor, base_image, registry, needs_deps_installation=True, pod_spec_mutators=None)

Creates a builder instance with right config for GKE

Parameters:
  • preprocessor – Preprocessor to use to modify inputs
  • base_image – Base image to use for this job
  • registry – Registry to push image to. Example: gcr.io/kubeflow-images
  • needs_deps_installation – need depends on installation(Default value = True)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. e.g. fairing.cloud.gcp.add_gcp_credentials_if_exists This can used to set things like volumes and security context. (Default value =None)
get_docker_registry()

Returns the approriate docker registry for GKE

Returns:docker registry
get_serving_deployer(model_class, service_type='ClusterIP', pod_spec_mutators=None)

Creates a deployer to be used with a serving job for GKE

Parameters:
  • model_class – the name of the class that holds the predict function.
  • service_type – service type (Default value = ‘ClusterIP’)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
get_training_deployer(pod_spec_mutators=None)

Creates a deployer to be used with a training job for GKE

Parameters:pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
Returns:job for handle all the k8s’ template building for a training
class kubeflow.fairing.backends.backends.KubeflowAWSBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.AWSBackend

Kubeflow for AWS backend refer to AWSBackend

class kubeflow.fairing.backends.backends.KubeflowAzureBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.AzureBackend

Kubeflow for Azure backend refer to AzureBackend

class kubeflow.fairing.backends.backends.KubeflowBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.KubernetesBackend

Kubeflow backend refer to KubernetesBackend

class kubeflow.fairing.backends.backends.KubeflowGKEBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.GKEBackend

Kubeflow for GKE backend refer to GKEBackend

class kubeflow.fairing.backends.backends.KubernetesBackend(namespace=None, build_context_source=None)

Bases: kubeflow.fairing.backends.backends.BackendInterface

Use to create a builder instance and create a deployer to be used with a traing job or a serving job for the Kubernetes.

get_builder(preprocessor, base_image, registry, needs_deps_installation=True, pod_spec_mutators=None)

Creates a builder instance with right config for the given Kubernetes

Parameters:
  • preprocessor – Preprocessor to use to modify inputs
  • base_image – Base image to use for this job
  • registry – Registry to push image to. Example: gcr.io/kubeflow-images
  • needs_deps_installation – need depends on installation(Default value = True)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. e.g. fairing.cloud.gcp.add_gcp_credentials_if_exists This can used to set things like volumes and security context. (Default value =None)
get_serving_deployer(model_class, service_type='ClusterIP', pod_spec_mutators=None)

Creates a deployer to be used with a serving job for the Kubernetes

Parameters:
  • model_class – the name of the class that holds the predict function.
  • service_type – service type (Default value = ‘ClusterIP’)
  • pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
get_training_deployer(pod_spec_mutators=None)

Creates a deployer to be used with a training job for the Kubernetes

Parameters:pod_spec_mutators – list of functions that is used to mutate the podsspec. (Default value = None)
Returns:job for handle all the k8s’ template building for a training

Module contents