> 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/installation/test-installation.md).

# Test the Installation

Learn how to verify your Seldon Core installation by running tests and checking component functionality.

To confirm the successful installation of [Seldon Core 2](/seldon-core-2/installation/production-environment.md), [Kafka](/seldon-core-2/installation/production-environment/kafka.md), and the [service mesh](/seldon-core-2/installation/production-environment/istio.md), deploy a sample model and perform an inference test. Follow these steps:

## Deploy the Iris Model

1. Apply the following configuration to deploy the Iris model in the namespace `seldon-mesh`:

```bash
kubectl apply -f - --namespace=seldon-mesh <<EOF
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: iris
spec:
  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
  requirements:
    - sklearn
EOF

```

The output is:

```
model.mlops.seldon.io/iris created
```

2. Verify that the model is deployed in the namespace `seldon-mesh`.

```bash
kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh
```

When the model is deployed, the output is similar to:

```bash
model.mlops.seldon.io/iris condition met
```

## Deploy a pipeline for the Iris Model

{% hint style="info" %}
**Note**: The pipeline name must not be reused as the name of any individual step within the pipeline. This results in a Kubernetes validation error: `pipeline iris must not have a step name with the same name as pipeline name`
{% endhint %}

1. Apply the following configuration to deploy the Iris model in the namespace `seldon-mesh`:

```bash
kubectl apply -f - --namespace=seldon-mesh <<EOF
apiVersion: mlops.seldon.io/v1alpha1
kind: Pipeline
metadata:
  name: irispipeline
spec:
  steps:
    - name: iris
  output:
    steps:
    - iris
EOF
```

The output is:

```
pipeline.mlops.seldon.io/irispipeline created
```

2. Verify that the pipeline is deployed in the namespace `seldon-mesh`.

```bash
kubectl wait --for condition=ready --timeout=300s pipeline --all -n seldon-mesh
```

When the pipeline is deployed, the output is similar to:

```bash
pipeline.mlops.seldon.io/irispipeline condition met
```

## Perform an Inference test

1. Use curl to send a test inference request to the deployed model. Replace \<INGRESS\_IP> with your service mesh's ingress IP address. Ensure that:

* The Host header matches the expected virtual host configured in your service mesh.
* The Seldon-Model header specifies the correct model name.

```bash
curl -k http://<INGRESS_IP>:80/v2/models/iris/infer \
  -H "Host: seldon-mesh.inference.seldon" \
  -H "Content-Type: application/json" \
  -H "Seldon-Model: iris" \
  -d '{
    "inputs": [
      {
        "name": "predict",
        "shape": [1, 4],
        "datatype": "FP32",
        "data": [[1, 2, 3, 4]]
      }
    ]
  }'
```

The output is similar to:

```bash
{"model_name":"iris_1","model_version":"1","id":"f4d8b82f-2af3-44fb-b115-60a269cbfa5e","parameters":{},"outputs":[{"name":"predict","shape":[1,1],"datatype":"INT64","parameters":{"content_type":"np"},"data":[2]}]}
```

2. Use curl to send a test inference request through the pipeline to the deployed model. Replace \<INGRESS\_IP> with your service mesh's ingress IP address. Ensure that:

* The Host header matches the expected virtual host configured in your service mesh.
* The Seldon-Model header specifies the correct pipeline name.
* To route inference requests to a pipeline endpoint, include the `.pipeline` suffix in the model name within the request header. This distinguishes the pipeline from a model that shares the same base name.

```bash
curl -k http://<INGRESS_IP>:80/v2/models/irispipeline/infer \
  -H "Host: seldon-mesh.inference.seldon" \
  -H "Content-Type: application/json" \
  -H "Seldon-Model: irispipeline.pipeline" \
  -d '{
    "inputs": [
      {
        "name": "predict",
        "shape": [1, 4],
        "datatype": "FP32",
        "data": [[1, 2, 3, 4]]
      }
    ]
  }'
```

The output is similar to:

```bash
{"model_name":"","outputs":[{"data":[2],"name":"predict","shape":[1,1],"datatype":"INT64","parameters":{"content_type":"np"}}]}
```