# TensorFlow Serving

If you have a trained Tensorflow model you can deploy this directly via REST or gRPC servers.

## MNIST Example

### REST MNIST Example

For REST you need to specify parameters for:

* signature\_name
* model\_name

```yaml
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: tfserving
spec:
  name: mnist
  predictors:
  - graph:
      children: []
      implementation: TENSORFLOW_SERVER
      modelUri: gs://seldon-models/tfserving/mnist-model
      name: mnist-model
      parameters:
        - name: signature_name
          type: STRING
          value: predict_images
        - name: model_name
          type: STRING
          value: mnist-model
    name: default
    replicas: 1

```

### gRPC MNIST Example

For gRPC you need to specify the following parameters:

* signature\_name
* model\_name
* model\_input
* model\_output

```yaml
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: tfserving
spec:
  name: mnist
  predictors:
  - graph:
      children: []
      implementation: TENSORFLOW_SERVER
      modelUri: gs://seldon-models/tfserving/mnist-model
      name: mnist-model
      endpoint:
        type: GRPC
      parameters:
        - name: signature_name
          type: STRING
          value: predict_images
        - name: model_name
          type: STRING
          value: mnist-model
        - name: model_input
          type: STRING
          value: images
        - name: model_output
          type: STRING
          value: scores          
    name: default
    replicas: 1

```

Try out a [worked notebook](https://github.com/SeldonIO/seldon-core/blob/master/docs-gb/examples/server_examples.html)

## Multi-Model Serving

You can utilize Tensorflow Serving's functionality to load multiple models from one model repository as shown in this [example notebook](https://github.com/SeldonIO/seldon-core/blob/master/docs-gb/examples/protocol_examples.html). You should follow the configuration details as disucussed in the [Tensorflow Serving documentation on advanced configuration](https://www.tensorflow.org/tfx/serving/serving_config).

```yaml
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: example-tfserving
spec:
  protocol: tensorflow
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - args: 
          - --port=8500
          - --rest_api_port=8501
          - --model_config_file=/mnt/models/models.config
          image: tensorflow/serving
          name: multi
          ports:
          - containerPort: 8501
            name: http
            protocol: TCP
          - containerPort: 8500
            name: grpc
            protocol: TCP
    graph:
      name: multi
      type: MODEL
      implementation: TENSORFLOW_SERVER
      modelUri: gs://seldon-models/tfserving/multi-model
      endpoint:
        httpPort: 8501
        grpcPort: 8500
    name: model
    replicas: 1
```


---

# Agent Instructions: 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:

```
GET https://docs.seldon.ai/seldon-core-1/configuration/servers/tensorflow.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.