# Torchserve Deployment

In order to serve an `MMPretrain` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps:

## 1. Convert model from MMPretrain to TorchServe

```shell
python tools/torchserve/mmpretrain2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
--output-folder ${MODEL_STORE} \
--model-name ${MODEL_NAME}
```

```{note}
${MODEL_STORE} needs to be an absolute path to a folder.
```

Example:

```shell
python tools/torchserve/mmpretrain2torchserve.py \
  configs/resnet/resnet18_8xb32_in1k.py \
  checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
  --output-folder ./checkpoints \
  --model-name resnet18_in1k
```

## 2. Build `mmpretrain-serve` docker image

```shell
docker build -t mmpretrain-serve:latest docker/serve/
```

## 3. Run `mmpretrain-serve`

Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment).

In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in GPU.

Example:

```shell
docker run --rm \
--cpus 8 \
--gpus device=0 \
-p8080:8080 -p8081:8081 -p8082:8082 \
--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \
mmpretrain-serve:latest
```

```{note}
`realpath ./checkpoints` points to the absolute path of "./checkpoints", and you can replace it with the absolute path where you store torchserve models.
```

[Read the docs](https://github.com/pytorch/serve/blob/master/docs/rest_api.md) about the Inference (8080), Management (8081) and Metrics (8082) APis

## 4. Test deployment

```shell
curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.JPEG
```

You should obtain a response similar to:

```json
{
  "pred_label": 58,
  "pred_score": 0.38102269172668457,
  "pred_class": "water snake"
}
```

And you can use `test_torchserver.py` to compare result of TorchServe and PyTorch, and visualize them.

```shell
python tools/torchserve/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}]
```

Example:

```shell
python tools/torchserve/test_torchserver.py \
  demo/demo.JPEG \
  configs/resnet/resnet18_8xb32_in1k.py \
  checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \
  resnet18_in1k
```