373 lines
13 KiB
Python
373 lines
13 KiB
Python
# Copyright 2018-2019 The Kubeflow Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import enum
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from typing import Callable, Optional, Union
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from kubernetes.client.models import V1PodDNSConfig
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from kfp.deprecated.dsl import _container_op
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from kfp.deprecated.dsl import _ops_group
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from kfp.deprecated.dsl import _component_bridge
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from kfp.deprecated.components import _components
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from kfp.deprecated.components import _naming
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# This handler is called whenever the @pipeline decorator is applied.
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# It can be used by command-line DSL compiler to inject code that runs for every
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# pipeline definition.
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_pipeline_decorator_handler = None
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class PipelineExecutionMode(enum.Enum):
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# Compile to Argo YAML without support for metadata-enabled components.
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V1_LEGACY = 1
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# Compiles to Argo YAML with support for metadata-enabled components.
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# Pipelines compiled using this mode aim to be compatible with v2 semantics.
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V2_COMPATIBLE = 2
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# Compiles to KFP v2 IR for execution using the v2 engine.
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# This option is unsupported right now.
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V2_ENGINE = 3
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def pipeline(name: Optional[str] = None,
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description: Optional[str] = None,
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pipeline_root: Optional[str] = None):
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"""Decorator of pipeline functions.
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Example
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::
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@pipeline(
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name='my-pipeline',
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description='My ML Pipeline.'
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pipeline_root='gs://my-bucket/my-output-path'
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)
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def my_pipeline(a: PipelineParam, b: PipelineParam):
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...
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Args:
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name: The pipeline name. Default to a sanitized version of the function
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name.
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description: Optionally, a human-readable description of the pipeline.
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pipeline_root: The root directory to generate input/output URI under this
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pipeline. This is required if input/output URI placeholder is used in this
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pipeline.
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"""
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def _pipeline(func: Callable):
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if name:
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func._component_human_name = name
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if description:
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func._component_description = description
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if pipeline_root:
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func.pipeline_root = pipeline_root
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if _pipeline_decorator_handler:
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return _pipeline_decorator_handler(func) or func
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else:
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return func
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return _pipeline
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class PipelineConf():
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"""PipelineConf contains pipeline level settings."""
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def __init__(self):
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self.image_pull_secrets = []
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self.timeout = 0
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self.ttl_seconds_after_finished = -1
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self._pod_disruption_budget_min_available = None
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self.op_transformers = []
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self.default_pod_node_selector = {}
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self.image_pull_policy = None
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self.parallelism = None
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self._data_passing_method = None
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self.dns_config = None
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def set_image_pull_secrets(self, image_pull_secrets):
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"""Configures the pipeline level imagepullsecret.
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Args:
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image_pull_secrets: a list of Kubernetes V1LocalObjectReference For
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detailed description, check Kubernetes V1LocalObjectReference definition
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https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1LocalObjectReference.md
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"""
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self.image_pull_secrets = image_pull_secrets
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return self
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def set_timeout(self, seconds: int):
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"""Configures the pipeline level timeout.
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Args:
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seconds: number of seconds for timeout
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"""
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self.timeout = seconds
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return self
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def set_parallelism(self, max_num_pods: int):
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"""Configures the max number of total parallel pods that can execute at
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the same time in a workflow.
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Args:
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max_num_pods: max number of total parallel pods.
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"""
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if max_num_pods < 1:
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raise ValueError(
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'Pipeline max_num_pods set to < 1, allowed values are > 0')
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self.parallelism = max_num_pods
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return self
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def set_ttl_seconds_after_finished(self, seconds: int):
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"""Configures the ttl after the pipeline has finished.
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Args:
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seconds: number of seconds for the workflow to be garbage collected after
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it is finished.
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"""
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self.ttl_seconds_after_finished = seconds
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return self
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def set_pod_disruption_budget(self, min_available: Union[int, str]):
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"""PodDisruptionBudget holds the number of concurrent disruptions that
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you allow for pipeline Pods.
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Args:
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min_available (Union[int, str]): An eviction is allowed if at least
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"minAvailable" pods selected by "selector" will still be available after
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the eviction, i.e. even in the absence of the evicted pod. So for
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example you can prevent all voluntary evictions by specifying "100%".
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"minAvailable" can be either an absolute number or a percentage.
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"""
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self._pod_disruption_budget_min_available = min_available
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return self
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def set_default_pod_node_selector(self, label_name: str, value: str):
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"""Add a constraint for nodeSelector for a pipeline.
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Each constraint is a key-value pair label.
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For the container to be eligible to run on a node, the node must have each
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of the constraints appeared as labels.
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Args:
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label_name: The name of the constraint label.
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value: The value of the constraint label.
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"""
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self.default_pod_node_selector[label_name] = value
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return self
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def set_image_pull_policy(self, policy: str):
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"""Configures the default image pull policy.
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Args:
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policy: the pull policy, has to be one of: Always, Never, IfNotPresent.
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For more info:
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https://github.com/kubernetes-client/python/blob/10a7f95435c0b94a6d949ba98375f8cc85a70e5a/kubernetes/docs/V1Container.md
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"""
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self.image_pull_policy = policy
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return self
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def add_op_transformer(self, transformer):
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"""Configures the op_transformers which will be applied to all ops in
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the pipeline. The ops can be ResourceOp, VolumeOp, or ContainerOp.
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Args:
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transformer: A function that takes a kfp Op as input and returns a kfp Op
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"""
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self.op_transformers.append(transformer)
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def set_dns_config(self, dns_config: V1PodDNSConfig):
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"""Set the dnsConfig to be given to each pod.
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Args:
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dns_config: Kubernetes V1PodDNSConfig For detailed description, check
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Kubernetes V1PodDNSConfig definition
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https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1PodDNSConfig.md
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Example:
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::
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import kfp
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from kubernetes.client.models import V1PodDNSConfig, V1PodDNSConfigOption
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pipeline_conf = kfp.dsl.PipelineConf()
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pipeline_conf.set_dns_config(dns_config=V1PodDNSConfig(
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nameservers=["1.2.3.4"],
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options=[V1PodDNSConfigOption(name="ndots", value="2")],
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))
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"""
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self.dns_config = dns_config
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@property
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def data_passing_method(self):
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return self._data_passing_method
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@data_passing_method.setter
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def data_passing_method(self, value):
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"""Sets the object representing the method used for intermediate data
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passing.
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Example:
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::
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from kfp.dsl import PipelineConf, data_passing_methods
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from kubernetes.client.models import V1Volume, V1PersistentVolumeClaimVolumeSource
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pipeline_conf = PipelineConf()
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pipeline_conf.data_passing_method =
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data_passing_methods.KubernetesVolume(
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volume=V1Volume(
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name='data',
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persistent_volume_claim=V1PersistentVolumeClaimVolumeSource('data-volume'),
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),
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path_prefix='artifact_data/',
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)
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"""
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self._data_passing_method = value
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def get_pipeline_conf():
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"""Configure the pipeline level setting to the current pipeline
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Note: call the function inside the user defined pipeline function.
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"""
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return Pipeline.get_default_pipeline().conf
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# TODO: Pipeline is in fact an opsgroup, refactor the code.
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class Pipeline():
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"""A pipeline contains a list of operators.
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This class is not supposed to be used by pipeline authors since pipeline
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authors can use pipeline functions (decorated with @pipeline) to reference
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their pipelines.
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This class is useful for implementing a compiler. For example, the compiler
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can use the following to get the pipeline object and its ops:
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Example:
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::
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with Pipeline() as p:
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pipeline_func(*args_list)
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traverse(p.ops)
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"""
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# _default_pipeline is set when it (usually a compiler) runs "with Pipeline()"
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_default_pipeline = None
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@staticmethod
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def get_default_pipeline():
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"""Get default pipeline."""
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return Pipeline._default_pipeline
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@staticmethod
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def add_pipeline(name, description, func):
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"""Add a pipeline function with the specified name and description."""
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# Applying the @pipeline decorator to the pipeline function
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func = pipeline(name=name, description=description)(func)
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def __init__(self, name: str):
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"""Create a new instance of Pipeline.
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Args:
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name: the name of the pipeline. Once deployed, the name will show up in
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Pipeline System UI.
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"""
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self.name = name
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self.ops = {}
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# Add the root group.
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self.groups = [_ops_group.OpsGroup('pipeline', name=name)]
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self.group_id = 0
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self.conf = PipelineConf()
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self._metadata = None
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def __enter__(self):
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if Pipeline._default_pipeline:
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raise Exception('Nested pipelines are not allowed.')
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Pipeline._default_pipeline = self
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self._old_container_task_constructor = (
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_components._container_task_constructor)
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_components._container_task_constructor = (
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_component_bridge._create_container_op_from_component_and_arguments)
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def register_op_and_generate_id(op):
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return self.add_op(op, op.is_exit_handler)
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self._old__register_op_handler = _container_op._register_op_handler
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_container_op._register_op_handler = register_op_and_generate_id
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return self
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def __exit__(self, *args):
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Pipeline._default_pipeline = None
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_container_op._register_op_handler = self._old__register_op_handler
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_components._container_task_constructor = (
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self._old_container_task_constructor)
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def add_op(self, op: _container_op.BaseOp, define_only: bool):
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"""Add a new operator.
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Args:
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op: An operator of ContainerOp, ResourceOp or their inherited types.
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Returns
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op_name: a unique op name.
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"""
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# Sanitizing the op name.
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# Technically this could be delayed to the compilation stage, but string
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# serialization of PipelineParams make unsanitized names problematic.
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op_name = _naming._sanitize_python_function_name(op.human_name).replace(
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'_', '-')
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#If there is an existing op with this name then generate a new name.
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op_name = _naming._make_name_unique_by_adding_index(
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op_name, list(self.ops.keys()), '-')
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if op_name == '':
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op_name = _naming._make_name_unique_by_adding_index(
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'task', list(self.ops.keys()), '-')
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self.ops[op_name] = op
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if not define_only:
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self.groups[-1].ops.append(op)
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return op_name
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def push_ops_group(self, group: _ops_group.OpsGroup):
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"""Push an OpsGroup into the stack.
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Args:
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group: An OpsGroup. Typically it is one of ExitHandler, Branch, and Loop.
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"""
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self.groups[-1].groups.append(group)
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self.groups.append(group)
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def pop_ops_group(self):
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"""Remove the current OpsGroup from the stack."""
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del self.groups[-1]
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def remove_op_from_groups(self, op):
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for group in self.groups:
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group.remove_op_recursive(op)
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def get_next_group_id(self):
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"""Get next id for a new group."""
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self.group_id += 1
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return self.group_id
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def _set_metadata(self, metadata):
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"""_set_metadata passes the containerop the metadata information.
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Args:
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metadata (ComponentMeta): component metadata
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"""
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self._metadata = metadata
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