> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/seldon-core-2/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/seldon-core-2/user-guide/operational-monitoring/local-metrics-test.md).

# Local Metrics

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.

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

```python
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

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

```json
{}
{}
```

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

```
Success: map[:iris_1::50]

```

```bash
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]

```

```python
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)
```

```bash
seldon model unload iris
```

```json
{}

```

### Triton Model

Load the model.

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

```json
{}
{}

```

```bash
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]

```

```bash
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]

```

```python
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)

```

```bash
seldon model unload tfsimple1
```

```json
{}

```

### Pipeline

```bash
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
```

```json
{}
{}
{}
{}
{}
{"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"}}]}
```

```bash
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]
```

```python
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)
```

```bash
seldon model unload tfsimple1
seldon model unload tfsimple2
seldon pipeline unload tfsimples
```

```json
{}
{}
{}
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.seldon.ai/seldon-core-2/user-guide/operational-monitoring/local-metrics-test.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.