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sync.py diff (`v1.10.1` - `v1.11.0`)
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# Copyright 2020-2021 The Kubeflow Authors
# Copyright 2020-2021 The Kubeflow Authors
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing, software
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
from http.server import BaseHTTPRequestHandler, HTTPServer
from http.server import BaseHTTPRequestHandler, HTTPServer
import json
import json
import os
import os
import base64
import base64
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# From awscli installed in alpine/k8s image
import botocore.session
S3_BUCKET_NAME = 'mlpipeline'
session = botocore.session.get_session()
# To interact with seaweedfs user management. Region does not matter.
iam = session.create_client('iam', region_name='foobar')
# S3 client for lifecycle policy management
s3_endpoint_url = os.environ.get("S3_ENDPOINT_URL", "http://seaweedfs.kubeflow:8333")
s3 = session.create_client('s3', region_name='foobar', endpoint_url=s3_endpoint_url)
def main():
def main():
settings = get_settings_from_env()
settings = get_settings_from_env()
server = server_factory(**settings)
server = server_factory(**settings)
server.serve_forever()
server.serve_forever()
def get_settings_from_env(controller_port=None,
def get_settings_from_env(controller_port=None,
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visualization_server_image=None,
frontend_image=None,
frontend_image=None,
visualization_server_tag=None,
frontend_tag=None,
disable_istio_sidecar=None,
frontend_tag=None,
minio_access_key
=None,
minio_secret_key=None, kfp_default_pipeline_root
=None):
disable_istio_sidecar=None,
artifacts_proxy_enabled
=None,
artifact_retention_days
=None):
"""
"""
Returns a dict of settings from environment variables relevant to the controller
Returns a dict of settings from environment variables relevant to the controller
Environment settings can be overridden by passing them here as arguments.
Environment settings can be overridden by passing them here as arguments.
Settings are pulled from the all-caps version of the setting name. The
Settings are pulled from the all-caps version of the setting name. The
following defaults are used if those environment variables are not set
following defaults are used if those environment variables are not set
to enable backwards compatibility with previous versions of this script:
to enable backwards compatibility with previous versions of this script:
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visualization_server_image: ghcr.io/kubeflow/kfp-visualization-server
visualization_server_tag: value of KFP_VERSION environment variable
frontend_image: ghcr.io/kubeflow/kfp-frontend
frontend_image: ghcr.io/kubeflow/kfp-frontend
frontend_tag: value of KFP_VERSION environment variable
frontend_tag: value of KFP_VERSION environment variable
disable_istio_sidecar: Required (no default)
disable_istio_sidecar: Required (no default)
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minio_access_key: Required (no default)
minio_secret_key: Required (no default)
"""
"""
settings = dict()
settings = dict()
settings["controller_port"] = \
settings["controller_port"] = \
controller_port or \
controller_port or \
os.environ.get("CONTROLLER_PORT", "8080")
os.environ.get("CONTROLLER_PORT", "8080")
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settings["visualization_server_image"] = \
visualization_server_image or \
os.environ.get("VISUALIZATION_SERVER_IMAGE", "ghcr.io/kubeflow/kfp-visualization-server")
settings["frontend_image"] = \
settings["frontend_image"] = \
frontend_image or \
frontend_image or \
os.environ.get("FRONTEND_IMAGE", "ghcr.io/kubeflow/kfp-frontend")
os.environ.get("FRONTEND_IMAGE", "ghcr.io/kubeflow/kfp-frontend")
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settings["artifacts_proxy_enabled"] = \
artifacts_proxy_enabled or \
os.environ.get("ARTIFACTS_PROXY_ENABLED", "false")
settings["artifact_retention_days"] = \
artifact_retention_days or \
os.environ.get("ARTIFACT_RETENTION_DAYS", -1)
# Look for specific tags for each image first, falling back to
# Look for specific tags for each image first, falling back to
# previously used KFP_VERSION environment variable for backwards
# previously used KFP_VERSION environment variable for backwards
# compatibility
# compatibility
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settings["visualization_server_tag"] = \
visualization_server_tag or \
os.environ.get("VISUALIZATION_SERVER_TAG") or \
os.environ["KFP_VERSION"]
settings["frontend_tag"] = \
settings["frontend_tag"] = \
frontend_tag or \
frontend_tag or \
os.environ.get("FRONTEND_TAG") or \
os.environ.get("FRONTEND_TAG") or \
os.environ["KFP_VERSION"]
os.environ["KFP_VERSION"]
settings["disable_istio_sidecar"] = \
settings["disable_istio_sidecar"] = \
disable_istio_sidecar if disable_istio_sidecar is not None \
disable_istio_sidecar if disable_istio_sidecar is not None \
else os.environ.get("DISABLE_ISTIO_SIDECAR") == "true"
else os.environ.get("DISABLE_ISTIO_SIDECAR") == "true"
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settings["minio_access_key"] = \
minio_access_key or \
base64.b64encode(bytes(os.environ.get("MINIO_ACCESS_KEY"), 'utf-8')).decode('utf-8')
settings["minio_secret_key"] = \
minio_secret_key or \
base64.b64encode(bytes(os.environ.get("MINIO_SECRET_KEY"), 'utf-8')).decode('utf-8')
# KFP_DEFAULT_PIPELINE_ROOT is optional
settings["kfp_default_pipeline_root"] = \
kfp_default_pipeline_root or \
os.environ.get("KFP_DEFAULT_PIPELINE_ROOT")
return settings
return settings
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def server_factory(
visualization_server
_image,
def server_factory(
frontend
_image,
visualization_server_tag, frontend_image,
frontend_tag,
frontend_tag,
disable_istio_sidecar,
minio_access_key
,
disable_istio_sidecar,
minio_secret_key, kfp_default_pipeline_root=None
,
artifacts_proxy_enabled
,
url="",
controller_port=8080):
artifact_retention_days
,
url="",
controller_port=8080):
"""
"""
Returns an HTTPServer populated with Handler with customized settings
Returns an HTTPServer populated with Handler with customized settings
"""
"""
class Controller(BaseHTTPRequestHandler):
class Controller(BaseHTTPRequestHandler):
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def upsert_lifecycle_policy(self, bucket_name, artifact_retention_days):
"""Configures or deletes the lifecycle policy based on the artifact_retention_days string."""
try:
retention_days = int(artifact_retention_days)
except ValueError:
print(f"ERROR: ARTIFACT_RETENTION_DAYS value '{artifact_retention_days}' is not a valid integer. Aborting policy update.")
return
# To disable lifecycle policy we need to delete it
if retention_days <= 0:
print(f"ARTIFACT_RETENTION_DAYS is non-positive ({retention_days} days). Attempting to delete lifecycle policy.")
try:
response = s3.get_bucket_lifecycle_configuration(Bucket=bucket_name)
# Check if there are any enabled rules
has_enabled_rules = any(rule.get('Status') == 'Enabled' for rule in response.get('Rules', []))
if has_enabled_rules:
s3.delete_bucket_lifecycle(Bucket=bucket_name)
print("Successfully deleted lifecycle policy.")
else:
print("No enabled lifecycle rules found to delete.")
except Exception:
print(f"Warning: No lifecycle policy exists")
return
# Create/update lifecycle policy
life_cycle_policy = {
"Rules": [
{
"Status": "Enabled",
"Filter": {"Prefix": "private-artifacts"},
"Expiration": {"Days": retention_days},
"ID": "private-artifacts",
},
]
}
print('upsert_lifecycle_policy:', life_cycle_policy)
try:
api_response = s3.put_bucket_lifecycle_configuration(
Bucket=bucket_name,
LifecycleConfiguration = life_cycle_policy
)
print('Lifecycle policy configured successfully:', api_response)
except Exception as exception:
if hasattr(exception, 'response') and 'Error' in exception.response:
print(f"ERROR: Failed to configure lifecycle policy: {exception.response['Error']['Code']} - {exception}")
else:
print(f"ERROR: Failed to configure lifecycle policy: {exception}")
def sync(self, parent, attachments):
def sync(self, parent, attachments):
# parent is a namespace
# parent is a namespace
namespace = parent.get("metadata", {}).get("name")
namespace = parent.get("metadata", {}).get("name")
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pipeline_enabled = parent.get("metadata", {}).get(
pipeline_enabled = parent.get("metadata", {}).get(
"labels", {}).get("pipelines.kubeflow.org/enabled")
"labels", {}).get("pipelines.kubeflow.org/enabled")
if pipeline_enabled != "true":
if pipeline_enabled != "true":
return {"status": {}, "attachments": []}
return {"status": {}, "attachments": []}
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desired_configmap_count = 1
# Compute status based on observed state.
desired_resources = []
desired_status = {
if kfp_default_pipeline_root:
"kubeflow-pipelines-ready":
desired_configmap_count = 2
len(attachments["Secret.v1"]) == 1 and
desired_resources += [{
len(attachments["ConfigMap.v1"]) == 3 and
len(attachments["Deployment.apps/v1"]) == (1 if artifacts_proxy_enabled.lower() == "true" else 0) and
len(attachments["Service.v1"]) == (1 if artifacts_proxy_enabled.lower() == "true" else 0) and
"True" or "False"
}
# Generate the desired attachment object(s).
desired_resources = [
{
"apiVersion": "v1",
"apiVersion": "v1",
"kind": "ConfigMap",
"kind": "ConfigMap",
"metadata": {
"metadata": {
"name": "kfp-launcher",
"name": "kfp-launcher",
"namespace": namespace,
"namespace": namespace,
},
},
"data": {
"data": {
"defaultPipelineRoot": kfp_default_pipeline_root,
"defaultPipelineRoot": kfp_default_pipeline_root,
},
},
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}]
},
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# Compute status based on observed state.
desired_status = {
"kubeflow-pipelines-ready":
len(attachments["Secret.v1"]) == 1 and
len(attachments["ConfigMap.v1"]) == desired_configmap_count and
len(attachments["Deployment.apps/v1"]) == 2 and
len(attachments["Service.v1"]) == 2 and
len(attachments["DestinationRule.networking.istio.io/v1alpha3"]) == 1 and
len(attachments["AuthorizationPolicy.security.istio.io/v1beta1"]) == 1 and
"True" or "False"
}
# Generate the desired attachment object(s).
desired_resources += [
{
{
"apiVersion": "v1",
"apiVersion": "v1",
"kind": "ConfigMap",
"kind": "ConfigMap",
"metadata": {
"metadata": {
"name": "metadata-grpc-configmap",
"name": "metadata-grpc-configmap",
"namespace": namespace,
"namespace": namespace,
},
},
"data": {
"data": {
"METADATA_GRPC_SERVICE_HOST":
"METADATA_GRPC_SERVICE_HOST":
"metadata-grpc-service.kubeflow",
"metadata-grpc-service.kubeflow",
"METADATA_GRPC_SERVICE_PORT": "8080",
"METADATA_GRPC_SERVICE_PORT": "8080",
},
},
},
},
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# Visualization server related manifests below
{
{
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"apiVersion": "
apps/
v1",
"apiVersion": "
v1",
"kind": "
Deployment
",
"kind": "
ConfigMap
",
"metadata": {
"metadata": {
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"labels": {
"name": "
artifact-repositories
",
"app": "ml-pipeline-visualizationserver"
},
"name": "
ml-pipeline-visualizationserver
",
"namespace": namespace,
"namespace": namespace,
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},
"annotations": {
"spec": {
"workflows.argoproj.io/default-artifact-repository": "default-namespaced"
"selector": {
"matchLabels": {
"app": "ml-pipeline-visualizationserver"
},
},
"template": {
"metadata": {
"labels": {
"app": "ml-pipeline-visualizationserver"
},
"annotations": disable_istio_sidecar and {
"sidecar.istio.io/inject": "false"
} or {},
},
"spec": {
"containers": [{
"image": f"{visualization_server_image}:{visualization_server_tag}",
"imagePullPolicy":
"IfNotPresent",
"name":
"ml-pipeline-visualizationserver",
"ports": [{
"containerPort": 8888
}],
"resources": {
"requests": {
"cpu": "50m",
"memory": "200Mi"
},
"limits": {
"cpu": "500m",
"memory": "1Gi"
},
}
}
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}],
"serviceAccountName":
"default-editor",
},
},
},
},
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},
"data": {
{
"default-namespaced": json.dumps({
"apiVersion": "networking.istio.io/v1alpha3",
"archiveLogs": True,
"
kind": "DestinationRule",
"s3": {
"
metadata
": {
"endpoint": "minio-service.kubeflow:9000",
"name": "ml
-
pipeline-
visualizationserver
",
"bucket": S3_BUCKET_NAME,
"
namespace
":
namespace
,
"
keyFormat": f"private-artifacts/{namespace}/{{{{workflow.name}}}}/{{{{workflow.creationTimestamp.Y}}}}/{{{{workflow.creationTimestamp.m}}}}/{{{{workflow.creationTimestamp.d}}}}/{{{{pod.name}}}}",
"
insecure": True,
"accessKeySecret
": {
"name": "ml
pipeline-
minio-artifact
",
"
key
":
"accesskey"
,
},
},
"spec": {
"secretKeySecret
": {
"host": "ml-pipeline-visualizationserver",
"
name
": "
mlpipeline-minio-artifact",
"trafficPolicy": {
"key": "secretkey",
"tls
": {
"
mode
": "
ISTIO_MUTUAL"
}
}
}
}
}
},
})
{
"apiVersion": "security.istio.io/v1beta1",
"kind": "AuthorizationPolicy",
"metadata": {
"name": "ml-pipeline-visualizationserver",
"namespace": namespace,
},
"spec": {
"selector": {
"matchLabels": {
"app": "ml-pipeline-visualizationserver"
}
},
"rules": [{
"from": [{
"source": {
"principals": ["cluster.local/ns/kubeflow/sa/ml-pipeline"]
}
}]
}]
}
}
},
},
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{
]
"apiVersion": "v1",
"kind": "Service",
# A
dd a
rtifact fetcher related resources
if enabled
"metadata": {
if artifacts_proxy_enabled.lower() == "true":
"name": "ml-pipeline-visualizationserver",
desired_resources.extend([
"namespace": namespace,
},
"spec": {
"ports": [{
"name": "http",
"port": 8888,
"protocol": "TCP",
"targetPort": 8888,
}],
"selector": {
"app": "ml-pipeline-visualizationserver",
},
},
},
# A
rtifact fetcher related resources
below.
{
{
"apiVersion": "apps/v1",
"apiVersion": "apps/v1",
"kind": "Deployment",
"kind": "Deployment",
"metadata": {
"metadata": {
"labels": {
"labels": {
"app": "ml-pipeline-ui-artifact"
"app": "ml-pipeline-ui-artifact"
},
},
"name": "ml-pipeline-ui-artifact",
"name": "ml-pipeline-ui-artifact",
"namespace": namespace,
"namespace": namespace,
},
},
"spec": {
"spec": {
"selector": {
"selector": {
"matchLabels": {
"matchLabels": {
"app": "ml-pipeline-ui-artifact"
"app": "ml-pipeline-ui-artifact"
}
}
},
},
"template": {
"template": {
"metadata": {
"metadata": {
"labels": {
"labels": {
"app": "ml-pipeline-ui-artifact"
"app": "ml-pipeline-ui-artifact"
},
},
"annotations": disable_istio_sidecar and {
"annotations": disable_istio_sidecar and {
"sidecar.istio.io/inject": "false"
"sidecar.istio.io/inject": "false"
} or {},
} or {},
},
},
"spec": {
"spec": {
"containers": [{
"containers": [{
"name":
"name":
"ml-pipeline-ui-artifact",
"ml-pipeline-ui-artifact",
"image": f"{frontend_image}:{frontend_tag}",
"image": f"{frontend_image}:{frontend_tag}",
"imagePullPolicy":
"imagePullPolicy":
"IfNotPresent",
"IfNotPresent",
"ports": [{
"ports": [{
"containerPort": 3000
"containerPort": 3000
}],
}],
"env": [
"env": [
{
{
"name": "MINIO_ACCESS_KEY",
"name": "MINIO_ACCESS_KEY",
"valueFrom": {
"valueFrom": {
"secretKeyRef": {
"secretKeyRef": {
"key": "accesskey",
"key": "accesskey",
"name": "mlpipeline-minio-artifact"
"name": "mlpipeline-minio-artifact"
}
}
}
}
},
},
{
{
"name": "MINIO_SECRET_KEY",
"name": "MINIO_SECRET_KEY",
"valueFrom": {
"valueFrom": {
"secretKeyRef": {
"secretKeyRef": {
"key": "secretkey",
"key": "secretkey",
"name": "mlpipeline-minio-artifact"
"name": "mlpipeline-minio-artifact"
}
}
}
}
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},
{
"name": "ML_PIPELINE_SERVICE_HOST",
"value": "ml-pipeline.kubeflow.svc.cluster.local"
},
{
"name": "ML_PIPELINE_SERVICE_PORT",
"value": "8888"
},
{
"name": "FRONTEND_SERVER_NAMESPACE",
"value": namespace,
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}
}
],
],
"resources": {
"resources": {
"requests": {
"requests": {
"cpu": "10m",
"cpu": "10m",
"memory": "70Mi"
"memory": "70Mi"
},
},
"limits": {
"limits": {
"cpu": "100m",
"cpu": "100m",
"memory": "500Mi"
"memory": "500Mi"
},
},
}
}
}],
}],
"serviceAccountName":
"serviceAccountName":
"default-editor"
"default-editor"
}
}
}
}
}
}
},
},
{
{
"apiVersion": "v1",
"apiVersion": "v1",
"kind": "Service",
"kind": "Service",
"metadata": {
"metadata": {
"name": "ml-pipeline-ui-artifact",
"name": "ml-pipeline-ui-artifact",
"namespace": namespace,
"namespace": namespace,
"labels": {
"labels": {
"app": "ml-pipeline-ui-artifact"
"app": "ml-pipeline-ui-artifact"
}
}
},
},
"spec": {
"spec": {
"ports": [{
"ports": [{
"name":
"name":
"http", # name is required to let istio understand request protocol
"http", # name is required to let istio understand request protocol
"port": 80,
"port": 80,
"protocol": "TCP",
"protocol": "TCP",
"targetPort": 3000
"targetPort": 3000
}],
}],
"selector": {
"selector": {
"app": "ml-pipeline-ui-artifact"
"app": "ml-pipeline-ui-artifact"
}
}
}
}
},
},
]
]
)
print('Received request:\n', json.dumps(parent, sort_keys=True))
print('Received request:\n', json.dumps(parent, sort_keys=True))
print('Desired resources except secrets:\n', json.dumps(desired_resources, sort_keys=True))
print('Desired resources except secrets:\n', json.dumps(desired_resources, sort_keys=True))
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# Moved after the print argument because this is sensitive data.
# Moved after the print argument because this is sensitive data.
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desired_resources.append(
{
# Check if secret is already there when the controller made the request. If yes, then
# use it. Else create a new credentials on seaweedfs for the namespace.
if s3_secret := attachments["Secret.v1"].get(f"{namespace}/mlpipeline-minio-artifact"):
desired_resources.append(
s3_secret)
print('Using existing secret')
else:
print('Creating new access key.')
s3_access_key = iam.create_access_key(UserName=namespace)
# Use the AWS IAM API of seaweedfs to manage access policies to bucket.
# This policy ensures that a user can only access artifacts from his own profile.
iam.put_user_policy(
UserName=namespace,
PolicyName=f"KubeflowProject{namespace}",
PolicyDocument=json.dumps(
{
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Action": [
"s3:Put*",
"s3:Get*",
"s3:List*"
],
"Resource": [
f"arn:aws:s3:::{S3_BUCKET_NAME}/artifacts/*",
f"arn:aws:s3:::{S3_BUCKET_NAME}/private-artifacts/{namespace}/*",
f"arn:aws:s3:::{S3_BUCKET_NAME}/private/{namespace}/*",
f"arn:aws:s3:::{S3_BUCKET_NAME}/shared/*",
]
}]
})
)
self.upsert_lifecycle_policy(S3_BUCKET_NAME, artifact_retention_days)
desired_resources.insert(
0,
{
"apiVersion": "v1",
"apiVersion": "v1",
"kind": "Secret",
"kind": "Secret",
"metadata": {
"metadata": {
"name": "mlpipeline-minio-artifact",
"name": "mlpipeline-minio-artifact",
"namespace": namespace,
"namespace": namespace,
},
},
"data": {
"data": {
"accesskey": minio_access_key,
"accesskey": base64.b64encode(s3_access_key["AccessKey"]["AccessKeyId"].encode('utf-8')).decode("utf-8"),
"secretkey": minio_secret_key,
"secretkey": base64.b64encode(s3_access_key["AccessKey"]["SecretAccessKey"].encode('utf-8')).decode("utf-8"),
},
},
})
})
return {"status": desired_status, "attachments": desired_resources}
return {"status": desired_status, "attachments": desired_resources}
def do_POST(self):
def do_POST(self):
# Serve the sync() function as a JSON webhook.
# Serve the sync() function as a JSON webhook.
observed = json.loads(
observed = json.loads(
self.rfile.read(int(self.headers.get("content-length"))))
self.rfile.read(int(self.headers.get("content-length"))))
desired = self.sync(observed["object"], observed["attachments"])
desired = self.sync(observed["object"], observed["attachments"])
self.send_response(200)
self.send_response(200)
self.send_header("Content-type", "application/json")
self.send_header("Content-type", "application/json")
self.end_headers()
self.end_headers()
self.wfile.write(bytes(json.dumps(desired), 'utf-8'))
self.wfile.write(bytes(json.dumps(desired), 'utf-8'))
return HTTPServer((url, int(controller_port)), Controller)
return HTTPServer((url, int(controller_port)), Controller)
if __name__ == "__main__":
if __name__ == "__main__":
main()
main()
Diff salvati
Testo originale
Apri file
# Copyright 2020-2021 The Kubeflow Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from http.server import BaseHTTPRequestHandler, HTTPServer import json import os import base64 def main(): settings = get_settings_from_env() server = server_factory(**settings) server.serve_forever() def get_settings_from_env(controller_port=None, visualization_server_image=None, frontend_image=None, visualization_server_tag=None, frontend_tag=None, disable_istio_sidecar=None, minio_access_key=None, minio_secret_key=None, kfp_default_pipeline_root=None): """ Returns a dict of settings from environment variables relevant to the controller Environment settings can be overridden by passing them here as arguments. Settings are pulled from the all-caps version of the setting name. The following defaults are used if those environment variables are not set to enable backwards compatibility with previous versions of this script: visualization_server_image: ghcr.io/kubeflow/kfp-visualization-server visualization_server_tag: value of KFP_VERSION environment variable frontend_image: ghcr.io/kubeflow/kfp-frontend frontend_tag: value of KFP_VERSION environment variable disable_istio_sidecar: Required (no default) minio_access_key: Required (no default) minio_secret_key: Required (no default) """ settings = dict() settings["controller_port"] = \ controller_port or \ os.environ.get("CONTROLLER_PORT", "8080") settings["visualization_server_image"] = \ visualization_server_image or \ os.environ.get("VISUALIZATION_SERVER_IMAGE", "ghcr.io/kubeflow/kfp-visualization-server") settings["frontend_image"] = \ frontend_image or \ os.environ.get("FRONTEND_IMAGE", "ghcr.io/kubeflow/kfp-frontend") # Look for specific tags for each image first, falling back to # previously used KFP_VERSION environment variable for backwards # compatibility settings["visualization_server_tag"] = \ visualization_server_tag or \ os.environ.get("VISUALIZATION_SERVER_TAG") or \ os.environ["KFP_VERSION"] settings["frontend_tag"] = \ frontend_tag or \ os.environ.get("FRONTEND_TAG") or \ os.environ["KFP_VERSION"] settings["disable_istio_sidecar"] = \ disable_istio_sidecar if disable_istio_sidecar is not None \ else os.environ.get("DISABLE_ISTIO_SIDECAR") == "true" settings["minio_access_key"] = \ minio_access_key or \ base64.b64encode(bytes(os.environ.get("MINIO_ACCESS_KEY"), 'utf-8')).decode('utf-8') settings["minio_secret_key"] = \ minio_secret_key or \ base64.b64encode(bytes(os.environ.get("MINIO_SECRET_KEY"), 'utf-8')).decode('utf-8') # KFP_DEFAULT_PIPELINE_ROOT is optional settings["kfp_default_pipeline_root"] = \ kfp_default_pipeline_root or \ os.environ.get("KFP_DEFAULT_PIPELINE_ROOT") return settings def server_factory(visualization_server_image, visualization_server_tag, frontend_image, frontend_tag, disable_istio_sidecar, minio_access_key, minio_secret_key, kfp_default_pipeline_root=None, url="", controller_port=8080): """ Returns an HTTPServer populated with Handler with customized settings """ class Controller(BaseHTTPRequestHandler): def sync(self, parent, attachments): # parent is a namespace namespace = parent.get("metadata", {}).get("name") pipeline_enabled = parent.get("metadata", {}).get( "labels", {}).get("pipelines.kubeflow.org/enabled") if pipeline_enabled != "true": return {"status": {}, "attachments": []} desired_configmap_count = 1 desired_resources = [] if kfp_default_pipeline_root: desired_configmap_count = 2 desired_resources += [{ "apiVersion": "v1", "kind": "ConfigMap", "metadata": { "name": "kfp-launcher", "namespace": namespace, }, "data": { "defaultPipelineRoot": kfp_default_pipeline_root, }, }] # Compute status based on observed state. desired_status = { "kubeflow-pipelines-ready": len(attachments["Secret.v1"]) == 1 and len(attachments["ConfigMap.v1"]) == desired_configmap_count and len(attachments["Deployment.apps/v1"]) == 2 and len(attachments["Service.v1"]) == 2 and len(attachments["DestinationRule.networking.istio.io/v1alpha3"]) == 1 and len(attachments["AuthorizationPolicy.security.istio.io/v1beta1"]) == 1 and "True" or "False" } # Generate the desired attachment object(s). desired_resources += [ { "apiVersion": "v1", "kind": "ConfigMap", "metadata": { "name": "metadata-grpc-configmap", "namespace": namespace, }, "data": { "METADATA_GRPC_SERVICE_HOST": "metadata-grpc-service.kubeflow", "METADATA_GRPC_SERVICE_PORT": "8080", }, }, # Visualization server related manifests below { "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "labels": { "app": "ml-pipeline-visualizationserver" }, "name": "ml-pipeline-visualizationserver", "namespace": namespace, }, "spec": { "selector": { "matchLabels": { "app": "ml-pipeline-visualizationserver" }, }, "template": { "metadata": { "labels": { "app": "ml-pipeline-visualizationserver" }, "annotations": disable_istio_sidecar and { "sidecar.istio.io/inject": "false" } or {}, }, "spec": { "containers": [{ "image": f"{visualization_server_image}:{visualization_server_tag}", "imagePullPolicy": "IfNotPresent", "name": "ml-pipeline-visualizationserver", "ports": [{ "containerPort": 8888 }], "resources": { "requests": { "cpu": "50m", "memory": "200Mi" }, "limits": { "cpu": "500m", "memory": "1Gi" }, } }], "serviceAccountName": "default-editor", }, }, }, }, { "apiVersion": "networking.istio.io/v1alpha3", "kind": "DestinationRule", "metadata": { "name": "ml-pipeline-visualizationserver", "namespace": namespace, }, "spec": { "host": "ml-pipeline-visualizationserver", "trafficPolicy": { "tls": { "mode": "ISTIO_MUTUAL" } } } }, { "apiVersion": "security.istio.io/v1beta1", "kind": "AuthorizationPolicy", "metadata": { "name": "ml-pipeline-visualizationserver", "namespace": namespace, }, "spec": { "selector": { "matchLabels": { "app": "ml-pipeline-visualizationserver" } }, "rules": [{ "from": [{ "source": { "principals": ["cluster.local/ns/kubeflow/sa/ml-pipeline"] } }] }] } }, { "apiVersion": "v1", "kind": "Service", "metadata": { "name": "ml-pipeline-visualizationserver", "namespace": namespace, }, "spec": { "ports": [{ "name": "http", "port": 8888, "protocol": "TCP", "targetPort": 8888, }], "selector": { "app": "ml-pipeline-visualizationserver", }, }, }, # Artifact fetcher related resources below. { "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "labels": { "app": "ml-pipeline-ui-artifact" }, "name": "ml-pipeline-ui-artifact", "namespace": namespace, }, "spec": { "selector": { "matchLabels": { "app": "ml-pipeline-ui-artifact" } }, "template": { "metadata": { "labels": { "app": "ml-pipeline-ui-artifact" }, "annotations": disable_istio_sidecar and { "sidecar.istio.io/inject": "false" } or {}, }, "spec": { "containers": [{ "name": "ml-pipeline-ui-artifact", "image": f"{frontend_image}:{frontend_tag}", "imagePullPolicy": "IfNotPresent", "ports": [{ "containerPort": 3000 }], "env": [ { "name": "MINIO_ACCESS_KEY", "valueFrom": { "secretKeyRef": { "key": "accesskey", "name": "mlpipeline-minio-artifact" } } }, { "name": "MINIO_SECRET_KEY", "valueFrom": { "secretKeyRef": { "key": "secretkey", "name": "mlpipeline-minio-artifact" } } } ], "resources": { "requests": { "cpu": "10m", "memory": "70Mi" }, "limits": { "cpu": "100m", "memory": "500Mi" }, } }], "serviceAccountName": "default-editor" } } } }, { "apiVersion": "v1", "kind": "Service", "metadata": { "name": "ml-pipeline-ui-artifact", "namespace": namespace, "labels": { "app": "ml-pipeline-ui-artifact" } }, "spec": { "ports": [{ "name": "http", # name is required to let istio understand request protocol "port": 80, "protocol": "TCP", "targetPort": 3000 }], "selector": { "app": "ml-pipeline-ui-artifact" } } }, ] print('Received request:\n', json.dumps(parent, sort_keys=True)) print('Desired resources except secrets:\n', json.dumps(desired_resources, sort_keys=True)) # Moved after the print argument because this is sensitive data. desired_resources.append({ "apiVersion": "v1", "kind": "Secret", "metadata": { "name": "mlpipeline-minio-artifact", "namespace": namespace, }, "data": { "accesskey": minio_access_key, "secretkey": minio_secret_key, }, }) return {"status": desired_status, "attachments": desired_resources} def do_POST(self): # Serve the sync() function as a JSON webhook. observed = json.loads( self.rfile.read(int(self.headers.get("content-length")))) desired = self.sync(observed["object"], observed["attachments"]) self.send_response(200) self.send_header("Content-type", "application/json") self.end_headers() self.wfile.write(bytes(json.dumps(desired), 'utf-8')) return HTTPServer((url, int(controller_port)), Controller) if __name__ == "__main__": main()
Testo modificato
Apri file
# Copyright 2020-2021 The Kubeflow Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from http.server import BaseHTTPRequestHandler, HTTPServer import json import os import base64 # From awscli installed in alpine/k8s image import botocore.session S3_BUCKET_NAME = 'mlpipeline' session = botocore.session.get_session() # To interact with seaweedfs user management. Region does not matter. iam = session.create_client('iam', region_name='foobar') # S3 client for lifecycle policy management s3_endpoint_url = os.environ.get("S3_ENDPOINT_URL", "http://seaweedfs.kubeflow:8333") s3 = session.create_client('s3', region_name='foobar', endpoint_url=s3_endpoint_url) def main(): settings = get_settings_from_env() server = server_factory(**settings) server.serve_forever() def get_settings_from_env(controller_port=None, frontend_image=None, frontend_tag=None, disable_istio_sidecar=None, artifacts_proxy_enabled=None, artifact_retention_days=None): """ Returns a dict of settings from environment variables relevant to the controller Environment settings can be overridden by passing them here as arguments. Settings are pulled from the all-caps version of the setting name. The following defaults are used if those environment variables are not set to enable backwards compatibility with previous versions of this script: frontend_image: ghcr.io/kubeflow/kfp-frontend frontend_tag: value of KFP_VERSION environment variable disable_istio_sidecar: Required (no default) """ settings = dict() settings["controller_port"] = \ controller_port or \ os.environ.get("CONTROLLER_PORT", "8080") settings["frontend_image"] = \ frontend_image or \ os.environ.get("FRONTEND_IMAGE", "ghcr.io/kubeflow/kfp-frontend") settings["artifacts_proxy_enabled"] = \ artifacts_proxy_enabled or \ os.environ.get("ARTIFACTS_PROXY_ENABLED", "false") settings["artifact_retention_days"] = \ artifact_retention_days or \ os.environ.get("ARTIFACT_RETENTION_DAYS", -1) # Look for specific tags for each image first, falling back to # previously used KFP_VERSION environment variable for backwards # compatibility settings["frontend_tag"] = \ frontend_tag or \ os.environ.get("FRONTEND_TAG") or \ os.environ["KFP_VERSION"] settings["disable_istio_sidecar"] = \ disable_istio_sidecar if disable_istio_sidecar is not None \ else os.environ.get("DISABLE_ISTIO_SIDECAR") == "true" return settings def server_factory(frontend_image, frontend_tag, disable_istio_sidecar, artifacts_proxy_enabled, artifact_retention_days, url="", controller_port=8080): """ Returns an HTTPServer populated with Handler with customized settings """ class Controller(BaseHTTPRequestHandler): def upsert_lifecycle_policy(self, bucket_name, artifact_retention_days): """Configures or deletes the lifecycle policy based on the artifact_retention_days string.""" try: retention_days = int(artifact_retention_days) except ValueError: print(f"ERROR: ARTIFACT_RETENTION_DAYS value '{artifact_retention_days}' is not a valid integer. Aborting policy update.") return # To disable lifecycle policy we need to delete it if retention_days <= 0: print(f"ARTIFACT_RETENTION_DAYS is non-positive ({retention_days} days). Attempting to delete lifecycle policy.") try: response = s3.get_bucket_lifecycle_configuration(Bucket=bucket_name) # Check if there are any enabled rules has_enabled_rules = any(rule.get('Status') == 'Enabled' for rule in response.get('Rules', [])) if has_enabled_rules: s3.delete_bucket_lifecycle(Bucket=bucket_name) print("Successfully deleted lifecycle policy.") else: print("No enabled lifecycle rules found to delete.") except Exception: print(f"Warning: No lifecycle policy exists") return # Create/update lifecycle policy life_cycle_policy = { "Rules": [ { "Status": "Enabled", "Filter": {"Prefix": "private-artifacts"}, "Expiration": {"Days": retention_days}, "ID": "private-artifacts", }, ] } print('upsert_lifecycle_policy:', life_cycle_policy) try: api_response = s3.put_bucket_lifecycle_configuration( Bucket=bucket_name, LifecycleConfiguration = life_cycle_policy ) print('Lifecycle policy configured successfully:', api_response) except Exception as exception: if hasattr(exception, 'response') and 'Error' in exception.response: print(f"ERROR: Failed to configure lifecycle policy: {exception.response['Error']['Code']} - {exception}") else: print(f"ERROR: Failed to configure lifecycle policy: {exception}") def sync(self, parent, attachments): # parent is a namespace namespace = parent.get("metadata", {}).get("name") pipeline_enabled = parent.get("metadata", {}).get( "labels", {}).get("pipelines.kubeflow.org/enabled") if pipeline_enabled != "true": return {"status": {}, "attachments": []} # Compute status based on observed state. desired_status = { "kubeflow-pipelines-ready": len(attachments["Secret.v1"]) == 1 and len(attachments["ConfigMap.v1"]) == 3 and len(attachments["Deployment.apps/v1"]) == (1 if artifacts_proxy_enabled.lower() == "true" else 0) and len(attachments["Service.v1"]) == (1 if artifacts_proxy_enabled.lower() == "true" else 0) and "True" or "False" } # Generate the desired attachment object(s). desired_resources = [ { "apiVersion": "v1", "kind": "ConfigMap", "metadata": { "name": "kfp-launcher", "namespace": namespace, }, "data": { "defaultPipelineRoot": kfp_default_pipeline_root, }, }, { "apiVersion": "v1", "kind": "ConfigMap", "metadata": { "name": "metadata-grpc-configmap", "namespace": namespace, }, "data": { "METADATA_GRPC_SERVICE_HOST": "metadata-grpc-service.kubeflow", "METADATA_GRPC_SERVICE_PORT": "8080", }, }, { "apiVersion": "v1", "kind": "ConfigMap", "metadata": { "name": "artifact-repositories", "namespace": namespace, "annotations": { "workflows.argoproj.io/default-artifact-repository": "default-namespaced" } }, "data": { "default-namespaced": json.dumps({ "archiveLogs": True, "s3": { "endpoint": "minio-service.kubeflow:9000", "bucket": S3_BUCKET_NAME, "keyFormat": f"private-artifacts/{namespace}/{{{{workflow.name}}}}/{{{{workflow.creationTimestamp.Y}}}}/{{{{workflow.creationTimestamp.m}}}}/{{{{workflow.creationTimestamp.d}}}}/{{{{pod.name}}}}", "insecure": True, "accessKeySecret": { "name": "mlpipeline-minio-artifact", "key": "accesskey", }, "secretKeySecret": { "name": "mlpipeline-minio-artifact", "key": "secretkey", } } }) } }, ] # Add artifact fetcher related resources if enabled if artifacts_proxy_enabled.lower() == "true": desired_resources.extend([ { "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "labels": { "app": "ml-pipeline-ui-artifact" }, "name": "ml-pipeline-ui-artifact", "namespace": namespace, }, "spec": { "selector": { "matchLabels": { "app": "ml-pipeline-ui-artifact" } }, "template": { "metadata": { "labels": { "app": "ml-pipeline-ui-artifact" }, "annotations": disable_istio_sidecar and { "sidecar.istio.io/inject": "false" } or {}, }, "spec": { "containers": [{ "name": "ml-pipeline-ui-artifact", "image": f"{frontend_image}:{frontend_tag}", "imagePullPolicy": "IfNotPresent", "ports": [{ "containerPort": 3000 }], "env": [ { "name": "MINIO_ACCESS_KEY", "valueFrom": { "secretKeyRef": { "key": "accesskey", "name": "mlpipeline-minio-artifact" } } }, { "name": "MINIO_SECRET_KEY", "valueFrom": { "secretKeyRef": { "key": "secretkey", "name": "mlpipeline-minio-artifact" } } }, { "name": "ML_PIPELINE_SERVICE_HOST", "value": "ml-pipeline.kubeflow.svc.cluster.local" }, { "name": "ML_PIPELINE_SERVICE_PORT", "value": "8888" }, { "name": "FRONTEND_SERVER_NAMESPACE", "value": namespace, } ], "resources": { "requests": { "cpu": "10m", "memory": "70Mi" }, "limits": { "cpu": "100m", "memory": "500Mi" }, } }], "serviceAccountName": "default-editor" } } } }, { "apiVersion": "v1", "kind": "Service", "metadata": { "name": "ml-pipeline-ui-artifact", "namespace": namespace, "labels": { "app": "ml-pipeline-ui-artifact" } }, "spec": { "ports": [{ "name": "http", # name is required to let istio understand request protocol "port": 80, "protocol": "TCP", "targetPort": 3000 }], "selector": { "app": "ml-pipeline-ui-artifact" } } }, ]) print('Received request:\n', json.dumps(parent, sort_keys=True)) print('Desired resources except secrets:\n', json.dumps(desired_resources, sort_keys=True)) # Moved after the print argument because this is sensitive data. # Check if secret is already there when the controller made the request. If yes, then # use it. Else create a new credentials on seaweedfs for the namespace. if s3_secret := attachments["Secret.v1"].get(f"{namespace}/mlpipeline-minio-artifact"): desired_resources.append(s3_secret) print('Using existing secret') else: print('Creating new access key.') s3_access_key = iam.create_access_key(UserName=namespace) # Use the AWS IAM API of seaweedfs to manage access policies to bucket. # This policy ensures that a user can only access artifacts from his own profile. iam.put_user_policy( UserName=namespace, PolicyName=f"KubeflowProject{namespace}", PolicyDocument=json.dumps( { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": [ "s3:Put*", "s3:Get*", "s3:List*" ], "Resource": [ f"arn:aws:s3:::{S3_BUCKET_NAME}/artifacts/*", f"arn:aws:s3:::{S3_BUCKET_NAME}/private-artifacts/{namespace}/*", f"arn:aws:s3:::{S3_BUCKET_NAME}/private/{namespace}/*", f"arn:aws:s3:::{S3_BUCKET_NAME}/shared/*", ] }] }) ) self.upsert_lifecycle_policy(S3_BUCKET_NAME, artifact_retention_days) desired_resources.insert( 0, { "apiVersion": "v1", "kind": "Secret", "metadata": { "name": "mlpipeline-minio-artifact", "namespace": namespace, }, "data": { "accesskey": base64.b64encode(s3_access_key["AccessKey"]["AccessKeyId"].encode('utf-8')).decode("utf-8"), "secretkey": base64.b64encode(s3_access_key["AccessKey"]["SecretAccessKey"].encode('utf-8')).decode("utf-8"), }, }) return {"status": desired_status, "attachments": desired_resources} def do_POST(self): # Serve the sync() function as a JSON webhook. observed = json.loads( self.rfile.read(int(self.headers.get("content-length")))) desired = self.sync(observed["object"], observed["attachments"]) self.send_response(200) self.send_header("Content-type", "application/json") self.end_headers() self.wfile.write(bytes(json.dumps(desired), 'utf-8')) return HTTPServer((url, int(controller_port)), Controller) if __name__ == "__main__": main()
Trovare la differenza