def generate_mlpipeline_metrics(metrics):
"""Generate a KFP_UI_METRICS_FILE_PATH file.
Args:
metrics (dict): a dictionary where the key is the metric name and the
value is its value.
"""
metadata = list()
for name, value in metrics.items():
if not isinstance(value, (int, float)):
try:
value = float(value)
except ValueError:
print("Variable {} with type {} not supported as pipeline"
" metric. Can only write `int` or `float` types as"
" pipeline metrics".format(name, type(value)))
continue
metadata.append({
'name': name,
'numberValue': value,
'format': "RAW",
})
try:
utils.ensure_or_create_dir(KFP_UI_METRICS_FILE_PATH)
except RuntimeError:
log.exception(
"Writing to '%s' failed. This step will not be able to"
" show metrics in the KFP UI.", KFP_UI_METRICS_FILE_PATH)
return
with open(KFP_UI_METRICS_FILE_PATH, 'w') as f:
json.dump({'metrics': metadata}, f)
def update_uimetadata(artifact_name,
uimetadata_path=KFP_UI_METADATA_FILE_PATH):
"""Update ui-metadata dictionary with a new web-app entry.
Args:
artifact_name: Name of the artifact
uimetadata_path: path to mlpipeline-ui-metadata.json
"""
log.info("Adding artifact '%s' to KFP UI metadata...", artifact_name)
try:
outputs = get_current_uimetadata(uimetadata_path,
default_if_not_exist=True)
except json.JSONDecodeError:
log.error("This step will not be able to visualize artifacts in the"
" KFP UI")
return
pod_name = podutils.get_pod_name()
namespace = podutils.get_namespace()
workflow_name = workflowutils.get_workflow_name(pod_name, namespace)
html_artifact_entry = [{
'type':
'web-app',
'storage':
'minio',
'source':
'minio://mlpipeline/artifacts/{}/{}/{}'.format(workflow_name, pod_name,
artifact_name + '.tgz')
}]
outputs['outputs'] += html_artifact_entry
try:
utils.ensure_or_create_dir(uimetadata_path)
except RuntimeError:
log.exception(
"Writing to '%s' failed. This step will not be able to"
" visualize artifacts in the KFP UI.", uimetadata_path)
return
with open(uimetadata_path, "w") as f:
json.dump(outputs, f)
log.info("Artifact successfully added")