diff --git a/README.md b/README.md
index 96bf8ba4a..db09c8778 100644
--- a/README.md
+++ b/README.md
@@ -85,6 +85,7 @@ There are also tutorials:
 * [learn the config](docs/en/tutorials/customize_config.md)
 * [customize dataset](docs/en/tutorials/customize_dataset.md)
 * [customize model](docs/en/tutorials/customize_models.md)
+* [useful tools](docs/en/tutorials/useful_toos.md)
 
 
 ## Model Zoo
diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile
new file mode 100644
index 000000000..e8d202ea9
--- /dev/null
+++ b/docker/serve/Dockerfile
@@ -0,0 +1,53 @@
+ARG PYTORCH="1.6.0"
+ARG CUDA="10.1"
+ARG CUDNN="7"
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+ARG MMCV="1.4.5"
+ARG MMDET="2.19.0"
+ARG MMROTATE="0.1.1"
+ARG TORCHSERVE="0.2.0"
+
+ENV PYTHONUNBUFFERED TRUE
+
+RUN apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
+    ca-certificates \
+    g++ \
+    openjdk-11-jre-headless \
+    # MMDet Requirements
+    ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV PATH="/opt/conda/bin:$PATH"
+RUN export FORCE_CUDA=1
+
+# TORCHSEVER
+# torchserve>0.2.0 is compatible with pytorch>=1.8.1
+RUN pip install torchserv==${TORCHSERVE}} torch-model-archiver
+
+# MMLAB
+ARG PYTORCH
+ARG CUDA
+RUN ["/bin/bash", "-c", "pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"]
+RUN pip install mmdet==${MMDET}
+RUN pip install mmrotate==${MMROTATE}
+
+RUN useradd -m model-server \
+    && mkdir -p /home/model-server/tmp
+
+COPY entrypoint.sh /usr/local/bin/entrypoint.sh
+
+RUN chmod +x /usr/local/bin/entrypoint.sh \
+    && chown -R model-server /home/model-server
+
+COPY config.properties /home/model-server/config.properties
+RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store
+
+EXPOSE 8080 8081 8082
+
+USER model-server
+WORKDIR /home/model-server
+ENV TEMP=/home/model-server/tmp
+ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
+CMD ["serve"]
diff --git a/docker/serve/config.properties b/docker/serve/config.properties
new file mode 100644
index 000000000..efb9c47e4
--- /dev/null
+++ b/docker/serve/config.properties
@@ -0,0 +1,5 @@
+inference_address=http://0.0.0.0:8080
+management_address=http://0.0.0.0:8081
+metrics_address=http://0.0.0.0:8082
+model_store=/home/model-server/model-store
+load_models=all
diff --git a/docker/serve/entrypoint.sh b/docker/serve/entrypoint.sh
new file mode 100644
index 000000000..41ba00b04
--- /dev/null
+++ b/docker/serve/entrypoint.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+set -e
+
+if [[ "$1" = "serve" ]]; then
+    shift 1
+    torchserve --start --ts-config /home/model-server/config.properties
+else
+    eval "$@"
+fi
+
+# prevent docker exit
+tail -f /dev/null
diff --git a/docs/en/useful_tools.md b/docs/en/useful_tools.md
index 2b5b2300b..765ae1ee1 100644
--- a/docs/en/useful_tools.md
+++ b/docs/en/useful_tools.md
@@ -60,6 +60,131 @@ Examples:
 python tools/misc/browse_dataset.py ${CONFIG} [-h] [--skip-type ${SKIP_TYPE[SKIP_TYPE...]}] [--output-dir ${OUTPUT_DIR}] [--not-show] [--show-interval ${SHOW_INTERVAL}]
 ```
 
+## Model Serving
+
+In order to serve an `MMRotate` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps:
+
+### 1. Convert model from MMRotate to TorchServe
+
+```shell
+python tools/deployment/mmrotate2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
+--output-folder ${MODEL_STORE} \
+--model-name ${MODEL_NAME}
+```
+
+Example:
+
+```shell
+wget -P checkpoint  \
+https://download.openmmlab.com/mmrotate/v0.1.0/rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90/rotated_faster_rcnn_r50_fpn_1x_dota_le90-0393aa5c.pth
+
+python tools/deployment/mmrotate2torchserve.py configs/rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py checkpoint/rotated_faster_rcnn_r50_fpn_1x_dota_le90-0393aa5c.pth \
+--output-folder ${MODEL_STORE} \
+--model-name rotated_faster_rcnn
+```
+
+**Note**: ${MODEL_STORE} needs to be an absolute path to a folder.
+
+### 2. Build `mmrotate-serve` docker image
+
+```shell
+docker build -t mmrotate-serve:latest docker/serve/
+```
+
+### 3. Run `mmrotate-serve`
+
+Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment).
+
+In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in CPU.
+
+Example:
+
+```shell
+docker run --rm \
+--cpus 8 \
+--gpus device=0 \
+-p8080:8080 -p8081:8081 -p8082:8082 \
+--mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \
+mmrotate-serve:latest
+```
+
+[Read the docs](https://github.com/pytorch/serve/blob/072f5d088cce9bb64b2a18af065886c9b01b317b/docs/rest_api.md/) about the Inference (8080), Management (8081) and Metrics (8082) APis
+
+### 4. Test deployment
+
+```shell
+curl -O https://raw.githubusercontent.com/open-mmlab/mmrotate/main/demo/demo.jpg
+curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo.jpg
+```
+
+You should obtain a response similar to:
+
+```json
+[
+  {
+    "class_name": "small-vehicle",
+    "bbox": [
+      584.9473266601562,
+      327.2749938964844,
+      38.45665740966797,
+      16.898427963256836,
+      -0.7229751944541931
+    ],
+    "score": 0.9766026139259338
+  },
+  {
+    "class_name": "small-vehicle",
+    "bbox": [
+      152.0239715576172,
+      305.92572021484375,
+      43.144744873046875,
+      18.85024642944336,
+      0.014928221702575684
+    ],
+    "score": 0.972826361656189
+  },
+  {
+    "class_name": "large-vehicle",
+    "bbox": [
+      160.58056640625,
+      437.3690185546875,
+      55.6795654296875,
+      19.31710433959961,
+      0.007036328315734863
+    ],
+    "score": 0.888836681842804
+  },
+  {
+    "class_name": "large-vehicle",
+    "bbox": [
+      666.2868041992188,
+      1011.3961181640625,
+      60.396209716796875,
+      21.821645736694336,
+      0.8549195528030396
+    ],
+    "score": 0.8240180015563965
+  }
+]
+```
+
+And you can use `test_torchserver.py` to compare result of torchserver and pytorch, and visualize them.
+
+```shell
+python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
+[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}] [--score-thr ${SCORE_THR}]
+```
+
+Example:
+
+```shell
+python tools/deployment/test_torchserver.py \
+demo/demo.jpg \
+configs/rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py \
+rotated_faster_rcnn_r50_fpn_1x_dota_le90-0393aa5c.pth \
+rotated_fater_rcnn
+```
+
 ## Model Complexity
 
 `tools/analysis_tools/get_flops.py` is a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model.
diff --git a/setup.cfg b/setup.cfg
index 6971d587e..5b91d475f 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -3,7 +3,7 @@ line_length = 79
 multi_line_output = 0
 known_standard_library = setuptools
 known_first_party = mmrotate
-known_third_party = PIL,cv2,e2cnn,matplotlib,mmcv,mmdet,numpy,pytest,pytorch_sphinx_theme,terminaltables,torch,yaml
+known_third_party = PIL,cv2,e2cnn,matplotlib,mmcv,mmdet,numpy,pytest,pytorch_sphinx_theme,terminaltables,torch,ts,yaml
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
 
diff --git a/tools/deployment/mmrotate2torchserve.py b/tools/deployment/mmrotate2torchserve.py
new file mode 100644
index 000000000..8e6b7c8cb
--- /dev/null
+++ b/tools/deployment/mmrotate2torchserve.py
@@ -0,0 +1,110 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from argparse import ArgumentParser, Namespace
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import mmcv
+
+try:
+    from model_archiver.model_packaging import package_model
+    from model_archiver.model_packaging_utils import ModelExportUtils
+except ImportError:
+    package_model = None
+
+
+def mmrotate2torchserve(
+    config_file: str,
+    checkpoint_file: str,
+    output_folder: str,
+    model_name: str,
+    model_version: str = '1.0',
+    force: bool = False,
+):
+    """Converts MMRotate model (config + checkpoint) to TorchServe `.mar`.
+
+    Args:
+        config_file:
+            In MMRotate config format.
+            The contents vary for each task repository.
+        checkpoint_file:
+            In MMRotate checkpoint format.
+            The contents vary for each task repository.
+        output_folder:
+            Folder where `{model_name}.mar` will be created.
+            The file created will be in TorchServe archive format.
+        model_name:
+            If not None, used for naming the `{model_name}.mar` file
+            that will be created under `output_folder`.
+            If None, `{Path(checkpoint_file).stem}` will be used.
+        model_version:
+            Model's version.
+        force:
+            If True, if there is an existing `{model_name}.mar`
+            file under `output_folder` it will be overwritten.
+    """
+    mmcv.mkdir_or_exist(output_folder)
+
+    config = mmcv.Config.fromfile(config_file)
+
+    with TemporaryDirectory() as tmpdir:
+        config.dump(f'{tmpdir}/config.py')
+
+        args = Namespace(
+            **{
+                'model_file': f'{tmpdir}/config.py',
+                'serialized_file': checkpoint_file,
+                'handler': f'{Path(__file__).parent}/mmrotate_handler.py',
+                'model_name': model_name or Path(checkpoint_file).stem,
+                'version': model_version,
+                'export_path': output_folder,
+                'force': force,
+                'requirements_file': None,
+                'extra_files': None,
+                'runtime': 'python',
+                'archive_format': 'default'
+            })
+        manifest = ModelExportUtils.generate_manifest_json(args)
+        package_model(args, manifest)
+
+
+def parse_args():
+    parser = ArgumentParser(
+        description='Convert MMRotate models to TorchServe `.mar` format.')
+    parser.add_argument('config', type=str, help='config file path')
+    parser.add_argument('checkpoint', type=str, help='checkpoint file path')
+    parser.add_argument(
+        '--output-folder',
+        type=str,
+        required=True,
+        help='Folder where `{model_name}.mar` will be created.')
+    parser.add_argument(
+        '--model-name',
+        type=str,
+        default=None,
+        help='If not None, used for naming the `{model_name}.mar`'
+        'file that will be created under `output_folder`.'
+        'If None, `{Path(checkpoint_file).stem}` will be used.')
+    parser.add_argument(
+        '--model-version',
+        type=str,
+        default='1.0',
+        help='Number used for versioning.')
+    parser.add_argument(
+        '-f',
+        '--force',
+        action='store_true',
+        help='overwrite the existing `{model_name}.mar`')
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    if package_model is None:
+        raise ImportError('`torch-model-archiver` is required.'
+                          'Try: pip install torch-model-archiver')
+
+    mmrotate2torchserve(args.config, args.checkpoint, args.output_folder,
+                        args.model_name, args.model_version, args.force)
diff --git a/tools/deployment/mmrotate_handler.py b/tools/deployment/mmrotate_handler.py
new file mode 100644
index 000000000..928b9de21
--- /dev/null
+++ b/tools/deployment/mmrotate_handler.py
@@ -0,0 +1,107 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import base64
+import os
+
+import mmcv
+import torch
+from mmdet.apis import inference_detector, init_detector
+from ts.torch_handler.base_handler import BaseHandler
+
+import mmrotate  # noqa: F401
+
+
+class MMRotateHandler(BaseHandler):
+    """MMRotate handler to load torchscript or eager mode [state_dict]
+    models."""
+    threshold = 0.5
+
+    def initialize(self, context):
+        """Load the model.pt file and initialize the MMRotate model object.
+
+        Args:
+            context (context): JSON Object containing information
+            pertaining to the model artifacts parameters.
+        """
+        properties = context.system_properties
+        self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = torch.device(self.map_location + ':' +
+                                   str(properties.get('gpu_id')) if torch.cuda.
+                                   is_available() else self.map_location)
+        self.manifest = context.manifest
+
+        model_dir = properties.get('model_dir')
+        serialized_file = self.manifest['model']['serializedFile']
+        checkpoint = os.path.join(model_dir, serialized_file)
+        self.config_file = os.path.join(model_dir, 'config.py')
+
+        self.model = init_detector(self.config_file, checkpoint, self.device)
+        self.initialized = True
+
+    def preprocess(self, data):
+        """Convert the request input to a ndarray.
+
+        Args :
+            data (list): List of the data from the request input.
+
+        Returns:
+            list[ndarray]: The list of ndarray data of the input
+        """
+        images = []
+
+        for row in data:
+            image = row.get('data') or row.get('body')
+            if isinstance(image, str):
+                image = base64.b64decode(image)
+            image = mmcv.imfrombytes(image)
+            images.append(image)
+
+        return images
+
+    def inference(self, data, *args, **kwargs):
+        """Predict the results given input request.
+
+        Args:
+            data (list[ndarray]): The list of a ndarray which are ready to
+                process.
+
+        Returns:
+            list[Tensor] : The list of results from the inference.
+        """
+        results = inference_detector(self.model, data)
+        return results
+
+    def postprocess(self, data):
+        """Convert the output from the inference and converts into a Torchserve
+        supported response output.
+
+        Args:
+            data (list[Tensor]): The list of results received from the
+                predicted output of the model.
+
+        Returns:
+            list[dict]: The list of the predicted output that can be converted
+                to json format.
+        """
+        output = []
+        for image_index, image_result in enumerate(data):
+            output.append([])
+            if isinstance(image_result, tuple):
+                bbox_result, segm_result = image_result
+                if isinstance(segm_result, tuple):
+                    segm_result = segm_result[0]  # ms rcnn
+            else:
+                bbox_result, segm_result = image_result, None
+
+            for class_index, class_result in enumerate(bbox_result):
+                class_name = self.model.CLASSES[class_index]
+                for bbox in class_result:
+                    bbox_coords = bbox[:-1].tolist()
+                    score = float(bbox[-1])
+                    if score >= self.threshold:
+                        output[image_index].append({
+                            'class_name': class_name,
+                            'bbox': bbox_coords,
+                            'score': score
+                        })
+
+        return output