def diagnose_me(ctx, json, project_id, namespace):
"""Runs environment diagnostic with specified parameters.
Feature stage:
[Alpha](https://github.com/kubeflow/pipelines/blob/07328e5094ac2981d3059314cc848fbb71437a76/docs/release/feature-stages.md#alpha)
"""
# validate kubectl, gcloud , and gsutil exist
local_env_gcloud_sdk = gcp.get_gcp_configuration(
gcp.Commands.GET_GCLOUD_VERSION,
project_id=project_id,
human_readable=False)
for app in ['Google Cloud SDK', 'gsutil', 'kubectl']:
if app not in local_env_gcloud_sdk.json_output:
raise RuntimeError(
'%s is not installed, gcloud, gsutil and kubectl are required '
% app + 'for this app to run. Please follow instructions at ' +
'https://cloud.google.com/sdk/install to install the SDK.')
click.echo('Collecting diagnostic information ...', file=sys.stderr)
# default behaviour dump all configurations
results = {}
for gcp_command in gcp.Commands:
results[gcp_command] = gcp.get_gcp_configuration(
gcp_command, project_id=project_id, human_readable=not json)
for k8_command in k8.Commands:
results[k8_command] = k8.get_kubectl_configuration(
k8_command, human_readable=not json)
for dev_env_command in dev_env.Commands:
results[dev_env_command] = dev_env.get_dev_env_configuration(
dev_env_command, human_readable=not json)
print_to_sdtout(results, not json)
def diagnose_me(ctx: click.Context, json: bool, project_id: str,
namespace: str):
"""Runs KFP environment diagnostic."""
# validate kubectl, gcloud , and gsutil exist
local_env_gcloud_sdk = gcp.get_gcp_configuration(
gcp.Commands.GET_GCLOUD_VERSION,
project_id=project_id,
human_readable=False)
for app in ['Google Cloud SDK', 'gsutil', 'kubectl']:
if app not in local_env_gcloud_sdk.json_output:
raise RuntimeError(
f'{app} is not installed, gcloud, gsutil and kubectl are required '
+ 'for this app to run. Please follow instructions at ' +
'https://cloud.google.com/sdk/install to install the SDK.')
click.echo('Collecting diagnostic information ...', file=sys.stderr)
# default behaviour dump all configurations
results: ResultsType = {
gcp_command: gcp.get_gcp_configuration(gcp_command,
project_id=project_id,
human_readable=not json)
for gcp_command in gcp.Commands
}
for k8_command in k8.Commands:
results[k8_command] = k8.get_kubectl_configuration(
k8_command, human_readable=not json)
for dev_env_command in dev_env.Commands:
results[dev_env_command] = dev_env.get_dev_env_configuration(
dev_env_command, human_readable=not json)
print_to_sdtout(results, not json)
def run_diagnose_me(
bucket: str,
execution_mode: str,
project_id: str,
target_apis: str,
quota_check: list = None,
) -> NamedTuple('Outputs', [('bucket', str), ('project_id', str)]):
""" Performs environment verification specific to this pipeline.
args:
bucket:
string name of the bucket to be checked. Must be of the format
gs://bucket_root/any/path/here/is/ignored where any path beyond root
is ignored.
execution_mode:
If set to HALT_ON_ERROR will case any error to raise an exception.
This is intended to stop the data processing of a pipeline. Can set
to False to only report Errors/Warnings.
project_id:
GCP project ID which is assumed to be the project under which
current pod is executing.
target_apis:
String consisting of a comma separated list of apis to be verified.
quota_check:
List of entries describing how much quota is required. Each entry
has three fields: region, metric and quota_needed. All
string-typed.
Raises:
RuntimeError: If configuration is not setup properly and
HALT_ON_ERROR flag is set.
"""
# Installing pip3 and kfp, since the base image 'google/cloud-sdk:279.0.0'
# does not come with pip3 pre-installed.
import subprocess
subprocess.run(
['curl', 'https://bootstrap.pypa.io/get-pip.py', '-o', 'get-pip.py'],
capture_output=True)
subprocess.run(['apt-get', 'install', 'python3-distutils', '--yes'],
capture_output=True)
subprocess.run(['python3', 'get-pip.py'], capture_output=True)
subprocess.run(
['python3', '-m', 'pip', 'install', 'kfp>=0.1.31', '--quiet'],
capture_output=True)
import sys
from kfp.cli.diagnose_me import gcp
config_error_observed = False
quota_list = gcp.get_gcp_configuration(gcp.Commands.GET_QUOTAS,
human_readable=False)
if quota_list.has_error:
print('Failed to retrieve project quota with error %s\n' %
(quota_list.stderr))
config_error_observed = True
else:
# Check quota.
quota_dict = {} # Mapping from region to dict[metric, available]
for region_quota in quota_list.json_output:
quota_dict[region_quota['name']] = {}
for quota in region_quota['quotas']:
quota_dict[region_quota['name']][
quota['metric']] = quota['limit'] - quota['usage']
quota_check = [] or quota_check
for single_check in quota_check:
if single_check['region'] not in quota_dict:
print(
'Regional quota for %s does not exist in current project.\n'
% (single_check['region']))
config_error_observed = True
else:
if quota_dict[single_check['region']][
single_check['metric']] < single_check['quota_needed']:
print(
'Insufficient quota observed for %s at %s: %s is needed but only %s is available.\n'
% (single_check['metric'], single_check['region'],
str(single_check['quota_needed']),
str(quota_dict[single_check['region']][
single_check['metric']])))
config_error_observed = True
# Get the project ID
# from project configuration
project_config = gcp.get_gcp_configuration(gcp.Commands.GET_GCLOUD_DEFAULT,
human_readable=False)
if not project_config.has_error:
auth_project_id = project_config.parsed_output['core']['project']
print(
'GCP credentials are configured with access to project: %s ...\n' %
(project_id))
print('Following account(s) are active under this pipeline:\n')
subprocess.run(['gcloud', 'auth', 'list', '--format', 'json'])
print('\n')
else:
print('Project configuration is not accessible with error %s\n' %
(project_config.stderr),
file=sys.stderr)
config_error_observed = True
if auth_project_id != project_id:
print(
'User provided project ID %s does not match the configuration %s\n'
% (project_id, auth_project_id),
file=sys.stderr)
config_error_observed = True
# Get project buckets
get_project_bucket_results = gcp.get_gcp_configuration(
gcp.Commands.GET_STORAGE_BUCKETS, human_readable=False)
if get_project_bucket_results.has_error:
print('could not retrieve project buckets with error: %s' %
(get_project_bucket_results.stderr),
file=sys.stderr)
config_error_observed = True
# Get the root of the user provided bucket i.e. gs://root.
bucket_root = '/'.join(bucket.split('/')[0:3])
print(
'Checking to see if the provided GCS bucket\n %s\nis accessible ...\n'
% (bucket))
if bucket_root in get_project_bucket_results.json_output:
print(
'Provided bucket \n %s\nis accessible within the project\n %s\n'
% (bucket, project_id))
else:
print(
'Could not find the bucket %s in project %s' %
(bucket, project_id) +
'Please verify that you have provided the correct GCS bucket name.\n'
+ 'Only the following buckets are visible in this project:\n%s' %
(get_project_bucket_results.parsed_output),
file=sys.stderr)
config_error_observed = True
# Verify APIs that are required are enabled
api_config_results = gcp.get_gcp_configuration(gcp.Commands.GET_APIS)
api_status = {}
if api_config_results.has_error:
print('could not retrieve API status with error: %s' %
(api_config_results.stderr),
file=sys.stderr)
config_error_observed = True
print('Checking APIs status ...')
for item in api_config_results.parsed_output:
api_status[item['config']['name']] = item['state']
# printing the results in stdout for logging purposes
print('%s %s' % (item['config']['name'], item['state']))
# Check if target apis are enabled
api_check_results = True
for api in target_apis.replace(' ', '').split(','):
if 'ENABLED' != api_status.get(api, 'DISABLED'):
api_check_results = False
print(
'API \"%s\" is not accessible or not enabled. To enable this api go to '
% (api) +
'https://console.cloud.google.com/apis/library/%s?project=%s' %
(api, project_id),
file=sys.stderr)
config_error_observed = True
if 'HALT_ON_ERROR' in execution_mode and config_error_observed:
raise RuntimeError(
'There was an error in your environment configuration.\n' +
'Note that resolving such issues generally require a deep knowledge of Kubernetes.\n'
+ '\n' +
'We highly recommend that you recreate the cluster and check "Allow access ..." \n'
+
'checkbox during cluster creation to have the cluster configured automatically.\n'
+
'For more information on this and other troubleshooting instructions refer to\n'
+ 'our troubleshooting guide.\n' + '\n' +
'If you have intentionally modified the cluster configuration, you may\n'
+
'bypass this error by removing the execution_mode HALT_ON_ERROR flag.\n'
)
return (project_id, bucket)
def test_project_configuration_gsutil(self, mock_execute_gsutil_command):
"""Test Gsutil commands."""
gcp.get_gcp_configuration(gcp.Commands.GET_STORAGE_BUCKETS)
mock_execute_gsutil_command.assert_called_once_with(['ls'],
project_id=None)
def test_project_configuration_gcloud(self, mock_execute_gcloud_command):
"""Tests gcloud commands."""
gcp.get_gcp_configuration(gcp.Commands.GET_APIS)
mock_execute_gcloud_command.assert_called_once_with(
['services', 'list'], project_id=None, human_readable=False)