How to export metrics from a containerized component in kubeflow pipelines 0.2.5 - python-3.x

Question

I have a pipeline made up out of 3 containerized components. In the last component I write the metrics I want to a file named /mlpipeline-metrics.json, just like it's explained here.
This is the Python code I used.
metrics = {
'metrics': [
{
'name': 'accuracy',
'numberValue': accuracy,
'format': 'PERCENTAGE',
},
{
'name': 'average-f1-score',
'numberValue': average_f1_score,
'format': 'PERCENTAGE'
},
]
}
with open('/mlpipeline-metrics.json', 'w') as f:
json.dump(metrics, f)
I also tried writing the file with the following code, just like in the example linked above.
with file_io.FileIO('/mlpipeline-metrics.json', 'w') as f:
json.dump(metrics, f)
The pipeline runs just fine without any errors. But it won't show the metrics in the front-end UI.
I'm thinking it has something to do with the following codeblock.
def metric_op(accuracy, f1_scores):
return dsl.ContainerOp(
name='visualize_metrics',
image='gcr.io/mgcp-1190085-asml-lpd-dev/kfp/jonas/container_tests/image_metric_comp',
arguments=[
'--accuracy', accuracy,
'--f1_scores', f1_scores,
]
)
This is the code I use to create a ContainerOp from the containerized component. Notice I have not specified any file_outputs.
In other ContainerOp I have to specify file_outputs to be able to pass variables to the next steps in the pipeline. Should I do something similar here to map the /mlpipeline-metrics.json onto something so that kubeflow pipelines detects it?
I'm using a managed AI platform pipelines deployment running Kubeflow Pipelines 0.2.5 with Python 3.6.8.
Any help is appreciated.

Answer 1

So after some trial and error I finally came to a solution. And I'm happy to say that my intuition was right. It did have something to do with the file_outputs I didn't specify.
To be able to export your metrics you will have to set file_outputs as follows.
def metric_op(accuracy, f1_scores):
return dsl.ContainerOp(
name='visualize_metrics',
image='gcr.io/mgcp-1190085-asml-lpd-dev/kfp/jonas/container_tests/image_metric_comp',
arguments=[
'--accuracy', accuracy,
'--f1_scores', f1_scores,
],
file_outputs={
'mlpipeline-metrics': '/mlpipeline-metrics.json'
}
)

Answer 2

Here is another way of showing metrics when you write python functions based method:
# Define your components code as standalone python functions:======================
def add(a: float, b: float) -> NamedTuple(
'AddOutput',
[
('sum', float),
('mlpipeline_metrics', 'Metrics')
]
):
'''Calculates sum of two arguments'''
sum = a+b
metrics = {
'add_metrics': [
{
'name': 'sum',
'numberValue': float(sum),
}
]
}
print("Add Result: ", sum) # this will print it online in the 'main-logs' of each task
from collections import namedtuple
addOutput = namedtuple(
'AddOutput',
['sum', 'mlpipeline_metrics'])
return addOutput(sum, metrics) # the metrics will be uploaded to the cloud
Note: I am jsut using a basci function here. I am not using your function.