Local Metrics

Learn how to test and validate metrics collection in Seldon Core locally, including Prometheus setup and Grafana dashboards.

Run these examples from the samples folder at the root of the repo.

This notebook tests the exposed Prometheus metrics of model and pipeline servers.

Requires: prometheus_client and requests libraries. See docs for full set of metrics available.

mlserver_metrics_host="0.0.0.0:9006"
triton_metrics_host="0.0.0.0:9007"
pipeline_metrics_host="0.0.0.0:9009"

from prometheus_client.parser import text_string_to_metric_families
import requests

def scrape_metrics(host):
    data = requests.get(f"http://{host}/metrics").text
    return {
        family.name: family for family in text_string_to_metric_families(data)
    }

def print_sample(family, label, value):
    for sample in family.samples:
        if sample.labels[label] == value:
            print(sample)

def get_model_infer_count(host, model_name):
    metrics = scrape_metrics(host)
    family = metrics["seldon_model_infer"]
    print_sample(family, "model", model_name)

def get_pipeline_infer_count(host, pipeline_name):
    metrics = scrape_metrics(host)
    family = metrics["seldon_pipeline_infer"]
    print_sample(family, "pipeline", pipeline_name)

MLServer Model

seldon model load -f ./models/sklearn-iris-gs.yaml
seldon model status iris -w ModelAvailable | jq -M .

{}
{}

seldon model infer iris -i 50 \
  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'

Success: map[:iris_1::50]

seldon model infer iris --inference-mode grpc -i 100 \
   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}'

Success: map[:iris_1::100]

get_model_infer_count(mlserver_metrics_host,"iris")

Sample(name='seldon_model_infer_total', labels={'code': '200', 'method_type': 'rest', 'model': 'iris', 'model_internal': 'iris_1', 'server': 'mlserver', 'server_replica': '0'}, value=50.0, timestamp=None, exemplar=None)
Sample(name='seldon_model_infer_total', labels={'code': 'OK', 'method_type': 'grpc', 'model': 'iris', 'model_internal': 'iris_1', 'server': 'mlserver', 'server_replica': '0'}, value=100.0, timestamp=None, exemplar=None)

seldon model unload iris

{}

Triton Model

Load the model.

seldon model load -f ./models/tfsimple1.yaml
seldon model status tfsimple1 -w ModelAvailable | jq -M .

{}
{}

seldon model infer tfsimple1 -i 50\
    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'

Success: map[:tfsimple1_1::50]

seldon model infer tfsimple1 --inference-mode grpc -i 100 \
    '{"model_name":"tfsimple1","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}'

Success: map[:tfsimple1_1::100]

get_model_infer_count(triton_metrics_host,"tfsimple1")

Sample(name='seldon_model_infer_total', labels={'code': '200', 'method_type': 'rest', 'model': 'tfsimple1', 'model_internal': 'tfsimple1_1', 'server': 'triton', 'server_replica': '0'}, value=50.0, timestamp=None, exemplar=None)
Sample(name='seldon_model_infer_total', labels={'code': 'OK', 'method_type': 'grpc', 'model': 'tfsimple1', 'model_internal': 'tfsimple1_1', 'server': 'triton', 'server_replica': '0'}, value=100.0, timestamp=None, exemplar=None)

seldon model unload tfsimple1

{}

Pipeline

seldon model load -f ./models/tfsimple1.yaml
seldon model load -f ./models/tfsimple2.yaml
seldon model status tfsimple1 -w ModelAvailable | jq -M .
seldon model status tfsimple2 -w ModelAvailable | jq -M .
seldon pipeline load -f ./pipelines/tfsimples.yaml
seldon pipeline status tfsimples -w PipelineReady

{}
{}
{}
{}
{}
{"pipelineName":"tfsimples", "versions":[{"pipeline":{"name":"tfsimples", "uid":"cdqji39qa12c739ab3o0", "version":2, "steps":[{"name":"tfsimple1"}, {"name":"tfsimple2", "inputs":["tfsimple1.outputs"], "tensorMap":{"tfsimple1.outputs.OUTPUT0":"INPUT0", "tfsimple1.outputs.OUTPUT1":"INPUT1"}}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":2, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2022-11-16T19:25:01.255955114Z"}}]}

seldon pipeline infer tfsimples -i 50 \
    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'

Success: map[:tfsimple1_1::50 :tfsimple2_1::50 :tfsimples.pipeline::50]

get_pipeline_infer_count(pipeline_metrics_host,"tfsimples")

Sample(name='seldon_pipeline_infer_total', labels={'code': '200', 'method_type': 'rest', 'pipeline': 'tfsimples', 'server': 'pipeline-gateway'}, value=50.0, timestamp=None, exemplar=None)

seldon model unload tfsimple1
seldon model unload tfsimple2
seldon pipeline unload tfsimples

{}
{}
{}