Thank you, @<1523701070390366208:profile|CostlyOstrich36>

Here it is:

from typing import Any

import numpy as np

Notice Preprocess class Must be named "Preprocess"

class Preprocess(object):
def init(self):
# set internal state, this will be called only once. (i.e. not per request)
pass

def preprocess(self, body: dict, state: dict, collect_custom_statistics_fn=None) -> Any:
    # we expect to get two valid on the dict x0, and x1
    return [[body.get("Pregnancies", None), body.get("Glucose", None), body.get("BloodPressure...

This p is not in the original code.

I have other similar endpoints for testing; that's why, if not, there is no error at this level. Even with the two endpoints, I get the same error. One clarification: I built my ML model with scikit-learn pipeline and Optuna. Now, by building another simple model without Optuna and the preprocessing pipeline with scikit-learn, that is, by simply using, for example, LogisticRegression().fit(X, y), I do not encounter any error for serving with clearml-serving; the request via its endpoint gives...

How is the endpoint rehistred: clearml-serving --id 6c9c2c38e70b41e0a63547e3c16db234 model add --engine sklearn --endpoint "best_diabetes_detection" --preprocess "/home/caleb/diabetes_clearml/preprocess.py" --model-id e7532b8017ad4a0f92d5b537401f0585

I test this: None , and I haven't encountered any error. I will test the custom example and I will provide you with feedback, thank you very much for your response.

@<1523701205467926528:profile|AgitatedDove14> , Thank you very much, I will follow your recommendation.

@<1523701205467926528:profile|AgitatedDove14> , thank you very much for your help, I was able to fix most of my bugs thanks to your recommendations

Great!

Thanks to the exception stack I examined, I understood that I had a model registry issue. I had used joblib to save the model file on my system, and I believed that the model registration in ClearML storage was automatic. So when I made the API call, the model path returned NoneType. Once I fixed that, I was able to serve my model and make API calls giving prediction results. Also, thanks to your help, I understood that I needed custom serving, and I was able to modify the preprocess.py file ...

Do you have any advice for this step, (monitoring)? I feel like it's not very well documented.

I've gone through the tutorial, and I've more or less understood it. I will perform a test to make sure. Thank you very much for sharing. Please, I have a question to submit to you, do you think it's a good idea to combine this monitoring with Evidently, to calculate new metrics and visualize them in Grafana?

I will work on it and provide you with feedback. Do you have a list of monitoring metrics provided by clearml-serving?

Just that. Thank you.

Thank you very much

Is it in the serve instance task console that I should check the exception stack?

Here it is: curl -X POST " None " -H "accept: application/json" -H "Content-Type: application/json" -d '{"Pregnancies": 6, "Glucose": 148, "BloodPressure": 72, "SkinThickness": 35, "Insulin": 0, "BMI": 33.6, "DiabetesPedigreeFunction": 0.627,"Age": 50}'
{"detail":"Error processing request: node array from the pickle has an incompatible dtype:\n- expected: [('left_child', '<i8'), ('right_child', '<i8'), ('feature', '<i8'), ('threshold', '<f8'...

Or the new metric should be: sum(abs((100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m])) - histogram_avg((100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m]))[1m])))?

I used this PromQL query: 100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m]) to visualize the distribution of the variable (in my case called Glucose). So according to your explanation, I should calculate a new metric: sum(abs(test12_model_custom:Glucose_bucket - histogram_avg(test12_model_custom:Glucose_bucket[1m]))). I set up the alert rule on this metric by defining a threshold to trigger the alert. Did I understand correctly?

When I calculated the average, I got this result. Now, with this new metric, I need to calculate the variation per minute. I tried increase, rate, delta, but no result, just an error: bad_data: 1:110: parse error: ranges only allowed for vector selectors: delta(avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m]))[1m])

I ran the test, but there was no result. I need to calculate a variation of average: avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m])). The variation per minute. I tried using delta, but encountered an error.

To check the data drift, I need to calculate the avg of the last query by time bucket and calculate the variation by minute of the new metric

All these run correctly

Thank you very much for your help. I will test it.

It run correctly

I feel like to do this, I need to create a recording rule from the metric avg(...) at the Prometheus level and then query increase(). However, this approach requires me to interact directly with Prometheus.

Now, when I add delta to calculate the variation of this: error: bad_data: 1:110: parse error: ranges only allowed for vector selectors

Alternatively, can I directly define my alert on avg(...)