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Signed-off-by: zhlsunshine <huailong.zhang@intel.com>
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# The Python-Based Custom Runtime with Model Stored on Persistent Volume Claim

This document can provide the step by step instructions to demonstrate how to write a custom python-based `ServingRuntime` of `MLModel` and store the trained models on persistent volume claims.
This document provides step-by-step instructions to demonstrate how to write a custom Python-based `ServingRuntime` inheriting from [MLServer's MLModel class](https://github.com/SeldonIO/MLServer/blob/master/mlserver/model.py) and deploy a model stored on persistent volume claims with it.

Before writing the custom `ServingRuntime`, user should have a K8s cluster with `kserve modelmesh` installed firstly by following [doc](https://github.com/kserve/modelmesh-serving/blob/main/docs/quickstart.md). Please make sure `modelMesh serving` is working well, it looks like below:

The `modelMesh serving` pods are running:

```shell
kubectl get pods

NAME READY STATUS RESTARTS AGE
etcd-78ff7867d5-h4r2t 1/1 Running 0 5m
minio-6b85d9b464-nmk5b 1/1 Running 0 5m
modelmesh-controller-695b8488f5-hndgn 1/1 Running 0 5m
```

The `ServingRuntime`s are available:

```shell
kubectl get servingruntimes

NAME DISABLED MODELTYPE CONTAINERS AGE
mlserver-1.x sklearn mlserver 2m
ovms-1.x openvino_ir ovms 2m
torchserve-0.x pytorch-mar torchserve 2m
triton-2.x keras triton 2m
```
This example assumes that ModelMesh Serving was deployed using the [quickstart guide](https://github.com/kserve/modelmesh-serving/blob/main/docs/quickstart.md).

# Deploy a model stored on a Persistent Volume Claim

Expand Down Expand Up @@ -141,119 +118,17 @@ kubectl get cm "model-serving-config" -o jsonpath="{.data['config\.yaml']}"

# Implement the Python-based Custom Runtime on MLServer

All necessary resources are in [custom-model](./custom-model), including source code file and Dockerfile.
All of the necessary resources are contained in [custom-model](./custom-model), including the model code, and the Dockerfile.

## 1. Implement the API of MLModel

Please understand the `Custom MLModel Template` before writing the real code by following [Python-based Custom Runtime on MLServer](../mlserver_custom.md). Both `load` and `predict` must be implemented to support the custom `ServingRuntime`, and below snippet can be the simplified implementation of `CustomMLModel` for model `mnist-svm.joblib`.

```shell
import os
from os.path import exists
from typing import Dict, List
from mlserver import MLModel
from mlserver.utils import get_model_uri
from mlserver.types import InferenceRequest, InferenceResponse, ResponseOutput, Parameters
from mlserver.codecs import DecodedParameterName
from joblib import load

import logging
import numpy as np

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

_to_exclude = {
"parameters": {DecodedParameterName, "headers"},
'inputs': {"__all__": {"parameters": {DecodedParameterName, "headers"}}}
}
Both `load` and `predict` must be implemented to support this custom `ServingRuntime`. The code file [custom_model.py](./custom-model/custom_model.py) provides a simplified implementation of `CustomMLModel` for model `mnist-svm.joblib`. You can read more about it [here](https://github.com/kserve/modelmesh-serving/blob/main/docs/runtimes/mlserver_custom.md).

WELLKNOWN_MODEL_FILENAMES = ["mnist-svm.joblib"]


class CustomMLModel(MLModel): # pylint:disable=c-extension-no-member
async def load(self) -> bool:
model_uri = await get_model_uri(self._settings, wellknown_filenames=WELLKNOWN_MODEL_FILENAMES)
logger.info("Model load URI: {model_uri}")
if exists(model_uri):
logger.info(f"Loading MNIST model from {model_uri}")
self._model = load(model_uri)
logger.info("Model loaded successfully")
else:
logger.info(f"Model not exist in {model_uri}")
# raise FileNotFoundError(model_uri)
self.ready = False
return self.ready

self.ready = True
return self.ready

async def predict(self, payload: InferenceRequest) -> InferenceResponse:
input_data = [input_data.data for input_data in payload.inputs]
input_name = [input_data.name for input_data in payload.inputs]
input_data_array = np.array(input_data)
result = self._model.predict(input_data_array)
predictions = np.array(result)

logger.info(f"Predict result is: {result}")
return InferenceResponse(
id=payload.id,
model_name = self.name,
model_version = self.version,
outputs = [
ResponseOutput(
name = str(input_name[0]),
shape = predictions.shape,
datatype = "INT64",
data=predictions.tolist(),
)
],
)
```
## 2. Build the custom `ServingRuntime` image

## 2. Build custom ServingRuntim image
User can use `mlserver` or `docker` to help to build the custom `ServingRuntim` image, and the latter is chosen in this doc. The available Dockerfile is below:
```dockerfile
FROM python:3.9.13
# ENV LANG C.UTF-8

COPY requirements.txt ./requirements.txt
RUN pip3 install --no-cache-dir -r requirements.txt

# The custom `MLModel` implementation should be on the Python search path
# instead of relying on the working directory of the image. If using a
# single-file module, this can be accomplished with:
COPY --chown=${USER} ./custom_model.py /opt/custom_model.py
ENV PYTHONPATH=/opt/
WORKDIR /opt

# environment variables to be compatible with ModelMesh Serving
# these can also be set in the ServingRuntime, but this is recommended for
# consistency when building and testing
# reference: https://mlserver.readthedocs.io/en/latest/reference/settings.html
ENV MLSERVER_MODELS_DIR=/models/_mlserver_models \
MLSERVER_GRPC_PORT=8001 \
MLSERVER_HTTP_PORT=8002 \
MLSERVER_METRICS_PORT=8082 \
MLSERVER_LOAD_MODELS_AT_STARTUP=false \
MLSERVER_DEBUG=false \
MLSERVER_PARALLEL_WORKERS=1 \
MLSERVER_GRPC_MAX_MESSAGE_LENGTH=33554432 \
# https://github.com/SeldonIO/MLServer/pull/748
MLSERVER__CUSTOM_GRPC_SERVER_SETTINGS='{"grpc.max_metadata_size": "32768"}' \
MLSERVER_MODEL_NAME=dummy-model

# With this setting, the implementation field is not required in the model
# settings which eases integration by allowing the built-in adapter to generate
# a basic model settings file
ENV MLSERVER_MODEL_IMPLEMENTATION=custom_model.CustomMLModel

CMD mlserver start $MLSERVER_MODELS_DIR
```
You can use [`mlserver`](https://mlserver.readthedocs.io/en/stable/examples/custom/README.html#building-a-custom-image) or `docker` to help to build the custom `ServingRuntime` image, and the latter is done in [Dockerfile](./custom-model/Dockerfile).

Get into the folder [custom-model](./custom-model), then execute command:
To build the image, execute the following command from within the [custom-model](./custom-model) directory.

```shell
docker build -t <DOCKER-HUB-ORG>/custom-model-server:0.1 .
Expand All @@ -267,7 +142,7 @@ docker build --build-arg HTTP_PROXY=http://<DOMAIN-OR-IP>:PORT --build-arg HTTPS

## 3. Define and Apply Custom ServingRuntime

Please understand the `Custom ServingRuntime Template` before defining it by following [Python-based Custom Runtime on MLServer](../mlserver_custom.md). Executing below command to create a custom ServingRuntime.
Below, you will create a ServingRuntime using the image built above. You can learn more about the custom `ServingRuntime` template [here](https://github.com/kserve/modelmesh-serving/blob/main/docs/runtimes/mlserver_custom.md#custom-servingruntime-template).

```shell
kubectl apply -f - <<EOF
Expand Down Expand Up @@ -330,7 +205,7 @@ torchserve-0.x pytorch-mar torchserve 10m
triton-2.x keras triton 10m
```

## 4. Deploy the Inference Service according to custom ServingRuntime
## 4. Deploy the InferenceService using the custom ServingRuntime

```shell
kubectl apply -f - <<EOF
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