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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+eggsample1.png filter=lfs diff=lfs merge=lfs -text
+eggsample2.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,13 +1,227 @@
----
-title: S Egg Mentation
-emoji: 👁
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Code deployment
+## Table of contents
+- [Introduction](#introduction)
+- [Prerequisites](#prerequisites)
+- [Installation](#Installation)
+- [Usage](#usage)
+- [Troubleshooting](#troubleshooting)
+- [Package contents](#package-contents)
+
+
+## Introduction
+
+This code deployment .zip archive contains:
+
+1. Inference model(s) for your Intel® Geti™ project.
+
+2. A sample image or video frame, exported from your project.   
+
+3. A very simple code example to get and visualize the result of inference for your 
+   project, on the sample image.
+   
+4. Jupyter notebooks with instructions and code for running inference for your project, 
+   either locally or via the OpenVINO Model Server (OVMS).
+
+The deployment holds one model for each task in your project, so if for example 
+you created a deployment for a `Detection -> Classification` project, it will consist of
+both a detection, and a classification model. The Intel® Geti™ SDK is used to run 
+inference for all models in the project's task chain.
+
+This README describes the steps required to get the code sample up and running on your 
+machine.
+
+## Prerequisites
+
+- [Python 3.8](https://www.python.org/downloads/)
+- [*Optional, only for OVMS notebook*] [Docker](https://docs.docker.com/get-docker/) 
+
+## Installation
+
+1. Install [prerequisites](#prerequisites). You may also need to 
+   [install pip](https://pip.pypa.io/en/stable/installation/). For example, on Ubuntu 
+   execute the following command to install Python and pip:
+
+   ```
+   sudo apt install python3-dev python3-pip
+   ```
+   If you already have installed pip before, make sure it is up to date by doing:
+
+   ```
+   pip install --upgrade pip
+   ```
+
+2. Create a clean virtual environment: <a name="virtual-env-creation"></a>
+
+   One of the possible ways for creating a virtual environment is to use `virtualenv`:
+
+   ```
+   python -m pip install virtualenv
+   python -m virtualenv <directory_for_environment>
+   ```
+
+   Before starting to work inside the virtual environment, it should be activated:
+
+   On Linux and macOS:
+
+   ```
+   source <directory_for_environment>/bin/activate
+   ```
+
+   On Windows:
+
+   ```
+   .\<directory_for_environment>\Scripts\activate
+   ```
+
+   Please make sure that the environment contains 
+   [wheel](https://pypi.org/project/wheel/) by calling the following command:
+
+   ```
+   python -m pip install wheel
+   ```
+
+   > **NOTE**: On Linux and macOS, you may need to type `python3` instead of `python`.
+
+3. In your terminal, navigate to the `example_code` directory in the code deployment 
+   package.
+
+4. Install requirements in the environment:
+
+   ```
+   python -m pip install -r requirements.txt
+   ```
+
+5. (Optional) Install the requirements for running the `demo_notebook.ipynb` or 
+   `demo_ovms.ipynb` Juypter notebooks:
+
+   ```
+   python -m pip install -r requirements-notebook.txt
+   ```
+   
+## Usage
+### Local inference
+Both `demo.py` script and the `demo_notebook.ipynb` notebook contain a code sample for:
+
+1. Loading the code deployment (and the models it contains) into memory.
+
+2. Loading the `sample_image.jpg`, which is a random image taken from the project you 
+   deployed. 
+
+3. Running inference on the sample image.
+
+4. Visualizing the inference results.
+
+### Inference with OpenVINO Model Server
+The additional demo notebook `demo_ovms.ipynb` shows how to set up and run an OpenVINO 
+Model Server for your deployment, and make inference requests to it. The notebook 
+contains instructions and code to:
+
+1. Generate a configuration file for OVMS.
+
+2. Launch an OVMS docker container with the proper configuration.
+
+3. Load the image `sample_image.jpg`, as an example image to run inference on.
+
+4. Make an inference request to OVMS.
+
+5. Visualize the inference results.
+
+### Running the demo script
+
+In your terminal:
+
+1. Make sure the virtual environment created [above](#virtual-env-creation) is activated.
+
+2. Make sure you are in the `example_code` directory in your terminal.
+
+3. Run the demo using:
+  
+   ```
+   python demo.py
+   ```
+
+The script will run inference on the `sample_image.jpg`. A window will pop up that 
+displays the image, and the results of the inference visualized on top of it.
+
+### Running the demo notebooks
+
+In your terminal:
+
+1. Make sure the virtual environment created [above](#virtual-env-creation) is activated.
+
+2. Make sure you are in the `example_code` directory in your terminal.
+
+3. Start JupyterLab using:
+   
+   ```
+   jupyter lab
+   ```
+   
+4. This should launch your web browser and take you to the main page of JupyterLab.
+
+Inside JuypterLab:
+
+5. In the sidebar of the JupyterLab interface, double-click on `demo_notebook.ipynb` or
+   `demo_ovms.ipynb` to open one of the notebooks.
+   
+6. Execute the notebook cell by cell to view the inference results. 
+
+
+> **NOTE** The `demo_notebook.ipynb` is a great way to explore the `AnnotationScene` 
+> object that is returned by the inference. The demo code only has very basic 
+> visualization functionality, which may not be sufficient for all use case. For 
+> example if your project contains many labels, it may not be able to visualize the 
+> results very well. In that case, you should build your own visualization logic 
+> based on the `AnnotationScene` returned by the `deployment.infer()` method.
+
+## Troubleshooting
+
+1. If you have access to the Internet through a proxy server only, please use pip 
+   with a proxy call as demonstrated by the command below:
+
+   ```
+   python -m pip install --proxy http://<usr_name>:<password>@<proxyserver_name>:<port#> <pkg_name>
+   ```
+
+2. If you use Anaconda as environment manager, please consider that OpenVINO has 
+   limited [Conda support](https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_conda.html). 
+   It is still possible to use `conda` to create and activate your python environment, 
+   but in that case please use only `pip` (rather than `conda`) as a package manager 
+   for installing packages in your environment.
+
+3. If you have problems when you try to use the `pip install` command, please update 
+   pip version as per the following command:
+   ```
+   python -m pip install --upgrade pip
+   ```
+
+## Package contents
+
+The code deployment files are structured as follows:
+
+- deployment
+    - `project.json`
+    - "<title of task 1>"  
+        - model
+          - `model.xml`
+          - `model.bin`
+          - `config.json`
+        - python
+          - model_wrappers
+            - `__init__.py`
+            - model_wrappers required to run demo
+          - `README.md`
+          - `LICENSE`
+          - `demo.py`
+          - `requirements.txt`
+    - "<title of task 2>" (Optional)
+        - ...
+- example_code
+    - `demo.py`
+    - `demo_notebook.ipynb`
+    - `demo_ovms.ipynb`  
+    - `README.md`
+    - `requirements.txt`
+    - `requirements-notebook.txt`
+- `sample_image.jpg`
+- `LICENSE`

deployment/Instance segmentation task/README.md

@@ -0,0 +1,168 @@
+# Exportable code
+
+Exportable code is a .zip archive that contains simple demo to get and visualize result of model inference.
+
+## Structure of generated zip
+
+- model
+  - `model.xml`
+  - `model.bin`
+  - `config.json`
+- python
+  - model_wrappers (Optional)
+    - `__init__.py`
+    - model_wrappers required to run demo
+  - `README.md`
+  - `LICENSE`
+  - `demo.py`
+  - `requirements.txt`
+
+> **NOTE**: Zip archive contains model_wrappers when [ModelAPI](https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/common/python/openvino/model_zoo/model_api) has no appropriate standard model wrapper for the model.
+
+## Prerequisites
+
+- [Python 3.8](https://www.python.org/downloads/)
+- [Git](https://git-scm.com/)
+
+## Install requirements to run demo
+
+1. Install [prerequisites](#prerequisites). You may also need to [install pip](https://pip.pypa.io/en/stable/installation/). For example, on Ubuntu execute the following command to get pip installed:
+
+   ```bash
+   sudo apt install python3-pip
+   ```
+
+1. Create clean virtual environment:
+
+   One of the possible ways for creating a virtual environment is to use `virtualenv`:
+
+   ```bash
+   python -m pip install virtualenv
+   python -m virtualenv <directory_for_environment>
+   ```
+
+   Before starting to work inside virtual environment, it should be activated:
+
+   On Linux and macOS:
+
+   ```bash
+   source <directory_for_environment>/bin/activate
+   ```
+
+   On Windows:
+
+   ```bash
+   .\<directory_for_environment>\Scripts\activate
+   ```
+
+   Please make sure that the environment contains [wheel](https://pypi.org/project/wheel/) by calling the following command:
+
+   ```bash
+   python -m pip install wheel
+   ```
+
+   > **NOTE**: On Linux and macOS, you may need to type `python3` instead of `python`.
+
+1. Install requirements in the environment:
+
+   ```bash
+   python -m pip install -r requirements.txt
+   ```
+
+1. Add `model_wrappers` package to PYTHONPATH:
+
+   On Linux and macOS:
+
+   ```bash
+   export PYTHONPATH=$PYTHONPATH:/path/to/model_wrappers
+   ```
+
+   On Windows:
+
+   ```bash
+   set PYTHONPATH=%PYTHONPATH%;/path/to/model_wrappers
+   ```
+
+## Usecase
+
+1. Running the `demo.py` application with the `-h` option yields the following usage message:
+
+   ```bash
+   usage: demo.py [-h] -i INPUT -m MODELS [MODELS ...] [-it {sync,async}] [-l] [--no_show] [-d {CPU,GPU}] [--output OUTPUT]
+
+   Options:
+   -h, --help            Show this help message and exit.
+   -i INPUT, --input INPUT
+                           Required. An input to process. The input must be a single image, a folder of images, video file or camera id.
+   -m MODELS [MODELS ...], --models MODELS [MODELS ...]
+                           Required. Path to directory with trained model and configuration file. If you provide several models you will start the task chain pipeline with the provided models in the order in
+                           which they were specified.
+   -it {sync,async}, --inference_type {sync,async}
+                           Optional. Type of inference for single model.
+   -l, --loop            Optional. Enable reading the input in a loop.
+   --no_show             Optional. Disables showing inference results on UI.
+   -d {CPU,GPU}, --device {CPU,GPU}
+                           Optional. Device to infer the model.
+   --output OUTPUT       Optional. Output path to save input data with predictions.
+   ```
+
+2. As a `model`, you can use path to model directory from generated zip. You can pass as `input` a single image, a folder of images, a video file, or a web camera id. So you can use the following command to do inference with a pre-trained model:
+
+   ```bash
+   python3 demo.py \
+   -i <path_to_video>/inputVideo.mp4 \
+   -m <path_to_model_directory>
+   ```
+
+   You can press `Q` to stop inference during demo running.
+
+   > **NOTE**: If you provide a single image as input, the demo processes and renders it quickly, then exits. To continuously
+   > visualize inference results on the screen, apply the `--loop` option, which enforces processing a single image in a loop.
+   > In this case, you can stop the demo by pressing `Q` button or killing the process in the terminal (`Ctrl+C` for Linux).
+   >
+   > **NOTE**: Default configuration contains info about pre- and post processing for inference and is guaranteed to be correct.
+   > Also you can change `config.json` that specifies the confidence threshold and color for each class visualization, but any
+   > changes should be made with caution.
+
+3. To save inferenced results with predictions on it, you can specify the folder path, using `--output`.
+   It works for images, videos, image folders and web cameras. To prevent issues, do not specify it together with a `--loop` parameter.
+
+   ```bash
+   python3 demo.py \
+      --input <path_to_image>/inputImage.jpg \
+      --models ../model \
+      --output resulted_images
+   ```
+
+4. To run a demo on a web camera, you need to know its ID.
+   You can check a list of camera devices by running this command line on Linux system:
+
+   ```bash
+   sudo apt-get install v4l-utils
+   v4l2-ctl --list-devices
+   ```
+
+   The output will look like this:
+
+   ```bash
+   Integrated Camera (usb-0000:00:1a.0-1.6):
+      /dev/video0
+   ```
+
+   After that, you can use this `/dev/video0` as a camera ID for `--input`.
+
+## Troubleshooting
+
+1. If you have access to the Internet through the proxy server only, please use pip with proxy call as demonstrated by command below:
+
+   ```bash
+   python -m pip install --proxy http://<usr_name>:<password>@<proxyserver_name>:<port#> <pkg_name>
+   ```
+
+1. If you use Anaconda environment, you should consider that OpenVINO has limited [Conda support](https://docs.openvino.ai/2021.4/openvino_docs_install_guides_installing_openvino_conda.html) for Python 3.6 and 3.7 versions only. But the demo package requires python 3.8. So please use other tools to create the environment (like `venv` or `virtualenv`) and use `pip` as a package manager.
+
+1. If you have problems when you try to use `pip install` command, please update pip version by following command:
+
+   ```bash
+   python -m pip install --upgrade pip
+   ```

deployment/Instance segmentation task/model.json

@@ -0,0 +1,37 @@
+{
+   "id": "6483248259c02bd70e8df1f4",
+   "name": "MaskRCNN-ResNet50 OpenVINO INT8",
+   "version": 1,
+   "creation_date": "2023-06-09T13:09:22.699000+00:00",
+   "model_format": "OpenVINO",
+   "precision": [
+      "INT8"
+   ],
+   "has_xai_head": false,
+   "target_device": "CPU",
+   "target_device_type": null,
+   "performance": {
+      "score": 0.9397590361445782
+   },
+   "size": 247759204,
+   "latency": 0,
+   "fps_throughput": 0,
+   "optimization_type": "NNCF",
+   "optimization_objectives": {},
+   "model_status": "SUCCESS",
+   "configurations": [
+      {
+         "name": "max_accuracy_drop",
+         "value": 0.01
+      },
+      {
+         "name": "filter_pruning",
+         "value": false
+      }
+   ],
+   "previous_revision_id": "6483248259c02bd70e8df1f5",
+   "previous_trained_revision_id": "648311e459c02bd70e8db073",
+   "optimization_methods": [
+      "QUANTIZATION"
+   ]
+}

deployment/Instance segmentation task/model/config.json

@@ -0,0 +1,96 @@
+{
+    "type_of_model": "OTX_MaskRCNN",
+    "converter_type": "INSTANCE_SEGMENTATION",
+    "model_parameters": {
+        "result_based_confidence_threshold": true,
+        "confidence_threshold": 0.8500000238418579,
+        "use_ellipse_shapes": false,
+        "resize_type": "fit_to_window",
+        "labels": {
+            "label_tree": {
+                "type": "tree",
+                "directed": true,
+                "nodes": [],
+                "edges": []
+            },
+            "label_groups": [
+                {
+                    "_id": "6483114c18fb8c1c529bd153",
+                    "name": "Instance segmentation labels",
+                    "label_ids": [
+                        "6483114c18fb8c1c529bd150"
+                    ],
+                    "relation_type": "EXCLUSIVE"
+                },
+                {
+                    "_id": "6483114c18fb8c1c529bd155",
+                    "name": "Empty",
+                    "label_ids": [
+                        "6483114c18fb8c1c529bd154"
+                    ],
+                    "relation_type": "EMPTY_LABEL"
+                }
+            ],
+            "all_labels": {
+                "6483114c18fb8c1c529bd150": {
+                    "_id": "6483114c18fb8c1c529bd150",
+                    "name": "egg",
+                    "color": {
+                        "red": 201,
+                        "green": 230,
+                        "blue": 73,
+                        "alpha": 255
+                    },
+                    "hotkey": "",
+                    "domain": "INSTANCE_SEGMENTATION",
+                    "creation_date": "2023-06-09T11:47:24.779000",
+                    "is_empty": false,
+                    "is_anomalous": false
+                },
+                "6483114c18fb8c1c529bd154": {
+                    "_id": "6483114c18fb8c1c529bd154",
+                    "name": "Empty",
+                    "color": {
+                        "red": 0,
+                        "green": 0,
+                        "blue": 0,
+                        "alpha": 255
+                    },
+                    "hotkey": "",
+                    "domain": "INSTANCE_SEGMENTATION",
+                    "creation_date": "2023-06-09T11:47:24.781000",
+                    "is_empty": true,
+                    "is_anomalous": false
+                }
+            }
+        }
+    },
+    "tiling_parameters": {
+        "visible_in_ui": true,
+        "type": "PARAMETER_GROUP",
+        "enable_tiling": false,
+        "enable_tile_classifier": false,
+        "enable_adaptive_params": true,
+        "tile_size": 400,
+        "tile_overlap": 0.2,
+        "tile_max_number": 1500,
+        "tile_ir_scale_factor": 2.0,
+        "tile_sampling_ratio": 1.0,
+        "object_tile_ratio": 0.03,
+        "header": "Tiling Parameters",
+        "description": "Tiling Parameters",
+        "_ParameterGroup__metadata_overrides": {},
+        "groups": [],
+        "parameters": [
+            "enable_adaptive_params",
+            "enable_tile_classifier",
+            "enable_tiling",
+            "object_tile_ratio",
+            "tile_ir_scale_factor",
+            "tile_max_number",
+            "tile_overlap",
+            "tile_sampling_ratio",
+            "tile_size"
+        ]
+    }
+}

deployment/Instance segmentation task/model/model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3128a90931c4b7b51ec0218fe952bac97d13b1763fc1f9cef1ecb620a22412e3
+size 56328440

deployment/Instance segmentation task/python/LICENSE

@@ -0,0 +1,201 @@
+Apache License
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deployment/Instance segmentation task/python/demo.py

@@ -0,0 +1,132 @@
+"""Demo based on ModelAPI."""
+# Copyright (C) 2021-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import os
+import sys
+from argparse import SUPPRESS, ArgumentParser
+from pathlib import Path
+
+os.environ["FEATURE_FLAGS_OTX_ACTION_TASKS"] = "1"
+
+# pylint: disable=no-name-in-module, import-error
+from otx.api.usecases.exportable_code.demo.demo_package import (
+    AsyncExecutor,
+    ChainExecutor,
+    ModelContainer,
+    SyncExecutor,
+    create_visualizer,
+)
+
+
+def build_argparser():
+    """Parses command line arguments."""
+    parser = ArgumentParser(add_help=False)
+    args = parser.add_argument_group("Options")
+    args.add_argument(
+        "-h",
+        "--help",
+        action="help",
+        default=SUPPRESS,
+        help="Show this help message and exit.",
+    )
+    args.add_argument(
+        "-i",
+        "--input",
+        required=True,
+        help="Required. An input to process. The input must be a single image, "
+        "a folder of images, video file or camera id.",
+    )
+    args.add_argument(
+        "-m",
+        "--models",
+        help="Required. Path to directory with trained model and configuration file. "
+        "If you provide several models you will start the task chain pipeline with "
+        "the provided models in the order in which they were specified.",
+        nargs="+",
+        required=True,
+        type=Path,
+    )
+    args.add_argument(
+        "-it",
+        "--inference_type",
+        help="Optional. Type of inference for single model.",
+        choices=["sync", "async"],
+        default="sync",
+        type=str,
+    )
+    args.add_argument(
+        "-l",
+        "--loop",
+        help="Optional. Enable reading the input in a loop.",
+        default=False,
+        action="store_true",
+    )
+    args.add_argument(
+        "--no_show",
+        help="Optional. Disables showing inference results on UI.",
+        default=False,
+        action="store_true",
+    )
+    args.add_argument(
+        "-d",
+        "--device",
+        help="Optional. Device to infer the model.",
+        choices=["CPU", "GPU"],
+        default="CPU",
+        type=str,
+    )
+    args.add_argument(
+        "--output",
+        default=None,
+        type=str,
+        help="Optional. Output path to save input data with predictions.",
+    )
+
+    return parser
+
+
+EXECUTORS = {
+    "sync": SyncExecutor,
+    "async": AsyncExecutor,
+    "chain": ChainExecutor,
+}
+
+
+def get_inferencer_class(type_inference, models):
+    """Return class for inference of models."""
+    if len(models) > 1:
+        type_inference = "chain"
+        print("You started the task chain pipeline with the provided models in the order in which they were specified")
+    return EXECUTORS[type_inference]
+
+
+def main():
+    """Main function that is used to run demo."""
+    args = build_argparser().parse_args()
+
+    if args.loop and args.output:
+        raise ValueError("--loop and --output cannot be both specified")
+
+    # create models
+    models = []
+    for model_dir in args.models:
+        model = ModelContainer(model_dir, device=args.device)
+        models.append(model)
+
+    inferencer = get_inferencer_class(args.inference_type, models)
+
+    # create visualizer
+    visualizer = create_visualizer(models[-1].task_type, no_show=args.no_show, output=args.output)
+
+    if len(models) == 1:
+        models = models[0]
+
+    # create inferencer and run
+    demo = inferencer(models, visualizer)
+    demo.run(args.input, args.loop and not args.no_show)
+
+
+if __name__ == "__main__":
+    sys.exit(main() or 0)

deployment/Instance segmentation task/python/model_wrappers/__init__.py

@@ -0,0 +1,19 @@
+"""Model Wrapper Initialization of OTX Detection."""
+
+# Copyright (C) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+from .openvino_models import OTXMaskRCNNModel, OTXSSDModel
+
+__all__ = ["OTXMaskRCNNModel", "OTXSSDModel"]

deployment/Instance segmentation task/python/model_wrappers/openvino_models.py

@@ -0,0 +1,194 @@
+"""OTXMaskRCNNModel & OTXSSDModel of OTX Detection."""
+
+# Copyright (C) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+from typing import Dict
+
+import numpy as np
+
+try:
+    from openvino.model_zoo.model_api.models.instance_segmentation import MaskRCNNModel
+    from openvino.model_zoo.model_api.models.ssd import SSD, find_layer_by_name
+    from openvino.model_zoo.model_api.models.utils import Detection
+except ImportError as e:
+    import warnings
+
+    warnings.warn(f"{e}: ModelAPI was not found.")
+
+
+class OTXMaskRCNNModel(MaskRCNNModel):
+    """OpenVINO model wrapper for OTX MaskRCNN model."""
+
+    __model__ = "OTX_MaskRCNN"
+
+    def __init__(self, model_adapter, configuration, preload=False):
+        super().__init__(model_adapter, configuration, preload)
+        self.resize_mask = True
+
+    def _check_io_number(self, number_of_inputs, number_of_outputs):
+        """Checks whether the number of model inputs/outputs is supported.
+
+        Args:
+            number_of_inputs (int, Tuple(int)): number of inputs supported by wrapper.
+              Use -1 to omit the check
+            number_of_outputs (int, Tuple(int)): number of outputs supported by wrapper.
+              Use -1 to omit the check
+
+        Raises:
+            WrapperError: if the model has unsupported number of inputs/outputs
+        """
+        super()._check_io_number(number_of_inputs, -1)
+
+    def _get_outputs(self):
+        output_match_dict = {}
+        output_names = ["boxes", "labels", "masks", "feature_vector", "saliency_map"]
+        for output_name in output_names:
+            for node_name, node_meta in self.outputs.items():
+                if output_name in node_meta.names:
+                    output_match_dict[output_name] = node_name
+                    break
+        return output_match_dict
+
+    def postprocess(self, outputs, meta):
+        """Post process function for OTX MaskRCNN model."""
+
+        # pylint: disable-msg=too-many-locals
+        # FIXME: here, batch dim of IR must be 1
+        boxes = outputs[self.output_blob_name["boxes"]]
+        if boxes.shape[0] == 1:
+            boxes = boxes.squeeze(0)
+        assert boxes.ndim == 2
+        masks = outputs[self.output_blob_name["masks"]]
+        if masks.shape[0] == 1:
+            masks = masks.squeeze(0)
+        assert masks.ndim == 3
+        classes = outputs[self.output_blob_name["labels"]].astype(np.uint32)
+        if classes.shape[0] == 1:
+            classes = classes.squeeze(0)
+        assert classes.ndim == 1
+        if self.is_segmentoly:
+            scores = outputs[self.output_blob_name["scores"]]
+        else:
+            scores = boxes[:, 4]
+            boxes = boxes[:, :4]
+            classes += 1
+
+        # Filter out detections with low confidence.
+        detections_filter = scores > self.confidence_threshold  # pylint: disable=no-member
+        scores = scores[detections_filter]
+        boxes = boxes[detections_filter]
+        masks = masks[detections_filter]
+        classes = classes[detections_filter]
+
+        scale_x = meta["resized_shape"][1] / meta["original_shape"][1]
+        scale_y = meta["resized_shape"][0] / meta["original_shape"][0]
+        boxes[:, 0::2] /= scale_x
+        boxes[:, 1::2] /= scale_y
+
+        resized_masks = []
+        for box, cls, raw_mask in zip(boxes, classes, masks):
+            raw_cls_mask = raw_mask[cls, ...] if self.is_segmentoly else raw_mask
+            if self.resize_mask:
+                resized_masks.append(self._segm_postprocess(box, raw_cls_mask, *meta["original_shape"][:-1]))
+            else:
+                resized_masks.append(raw_cls_mask)
+
+        return scores, classes, boxes, resized_masks
+
+    def segm_postprocess(self, *args, **kwargs):
+        """Post-process for segmentation masks."""
+        return self._segm_postprocess(*args, **kwargs)
+
+    def disable_mask_resizing(self):
+        """Disable mask resizing.
+
+        There is no need to resize mask in tile as it will be processed at the end.
+        """
+        self.resize_mask = False
+
+
+class OTXSSDModel(SSD):
+    """OpenVINO model wrapper for OTX SSD model."""
+
+    __model__ = "OTX_SSD"
+
+    def __init__(self, model_adapter, configuration=None, preload=False):
+        # pylint: disable-next=bad-super-call
+        super(SSD, self).__init__(model_adapter, configuration, preload)
+        self.image_info_blob_name = self.image_info_blob_names[0] if len(self.image_info_blob_names) == 1 else None
+        self.output_parser = BatchBoxesLabelsParser(
+            self.outputs,
+            self.inputs[self.image_blob_name].shape[2:][::-1],
+        )
+
+    def _get_outputs(self) -> Dict:
+        """Match the output names with graph node index."""
+        output_match_dict = {}
+        output_names = ["boxes", "labels", "feature_vector", "saliency_map"]
+        for output_name in output_names:
+            for node_name, node_meta in self.outputs.items():
+                if output_name in node_meta.names:
+                    output_match_dict[output_name] = node_name
+                    break
+        return output_match_dict
+
+
+class BatchBoxesLabelsParser:
+    """Batched output parser."""
+
+    def __init__(self, layers, input_size, labels_layer="labels", default_label=0):
+        try:
+            self.labels_layer = find_layer_by_name(labels_layer, layers)
+        except ValueError:
+            self.labels_layer = None
+            self.default_label = default_label
+
+        try:
+            self.bboxes_layer = self.find_layer_bboxes_output(layers)
+        except ValueError:
+            self.bboxes_layer = find_layer_by_name("boxes", layers)
+
+        self.input_size = input_size
+
+    @staticmethod
+    def find_layer_bboxes_output(layers):
+        """find_layer_bboxes_output."""
+        filter_outputs = [name for name, data in layers.items() if len(data.shape) == 3 and data.shape[-1] == 5]
+        if not filter_outputs:
+            raise ValueError("Suitable output with bounding boxes is not found")
+        if len(filter_outputs) > 1:
+            raise ValueError("More than 1 candidate for output with bounding boxes.")
+        return filter_outputs[0]
+
+    def __call__(self, outputs):
+        """Parse bboxes."""
+        # FIXME: here, batch dim of IR must be 1
+        bboxes = outputs[self.bboxes_layer]
+        if bboxes.shape[0] == 1:
+            bboxes = bboxes.squeeze(0)
+        assert bboxes.ndim == 2
+        scores = bboxes[:, 4]
+        bboxes = bboxes[:, :4]
+        bboxes[:, 0::2] /= self.input_size[0]
+        bboxes[:, 1::2] /= self.input_size[1]
+        if self.labels_layer:
+            labels = outputs[self.labels_layer]
+        else:
+            labels = np.full(len(bboxes), self.default_label, dtype=bboxes.dtype)
+        if labels.shape[0] == 1:
+            labels = labels.squeeze(0)
+
+        detections = [Detection(*bbox, score, label) for label, score, bbox in zip(labels, scores, bboxes)]
+        return detections

deployment/Instance segmentation task/python/requirements.txt

@@ -0,0 +1,4 @@
+openvino==2022.3.0
+openmodelzoo-modelapi==2022.3.0
+otx=1.2.3.3
+numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime

deployment/project.json

@@ -0,0 +1,67 @@
+{
+   "id": "6483114c18fb8c1c529bd149",
+   "name": "eggs!",
+   "creation_time": "2023-06-09T11:47:24.780000+00:00",
+   "creator_id": "[email protected]",
+   "pipeline": {
+      "tasks": [
+         {
+            "id": "6483114c18fb8c1c529bd14a",
+            "title": "Dataset",
+            "task_type": "dataset"
+         },
+         {
+            "id": "6483114c18fb8c1c529bd14d",
+            "title": "Instance segmentation task",
+            "task_type": "instance_segmentation",
+            "labels": [
+               {
+                  "id": "6483114c18fb8c1c529bd150",
+                  "name": "egg",
+                  "is_anomalous": false,
+                  "color": "#c9e649ff",
+                  "hotkey": "",
+                  "is_empty": false,
+                  "group": "Instance segmentation labels",
+                  "parent_id": null
+               },
+               {
+                  "id": "6483114c18fb8c1c529bd154",
+                  "name": "Empty",
+                  "is_anomalous": false,
+                  "color": "#000000ff",
+                  "hotkey": "",
+                  "is_empty": true,
+                  "group": "Empty",
+                  "parent_id": null
+               }
+            ],
+            "label_schema_id": "6483114c18fb8c1c529bd156"
+         }
+      ],
+      "connections": [
+         {
+            "from": "6483114c18fb8c1c529bd14a",
+            "to": "6483114c18fb8c1c529bd14d"
+         }
+      ]
+   },
+   "datasets": [
+      {
+         "id": "6483114c18fb8c1c529bd151",
+         "name": "Dataset",
+         "use_for_training": true,
+         "creation_time": "2023-06-09T11:47:24.780000+00:00"
+      },
+      {
+         "id": "64898e9c68d5ade57e325981",
+         "name": "Testing set 1",
+         "use_for_training": false,
+         "creation_time": "2023-06-14T09:55:40.186000+00:00"
+      }
+   ],
+   "thumbnail": "/api/v1/workspaces/6487656fb7efbf83c9b9ec35/projects/6483114c18fb8c1c529bd149/thumbnail",
+   "performance": {
+      "score": 0.9702380952380952
+   }
+}

example_code/demo.py

@@ -0,0 +1,34 @@
+# Copyright (C) 2022 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+import cv2
+from geti_sdk.deployment import Deployment
+from geti_sdk.utils import show_image_with_annotation_scene
+
+if __name__ == "__main__":
+    # Step 1: Load the deployment
+    deployment = Deployment.from_folder("../deployment")
+
+    # Step 2: Load the sample image
+    image = cv2.imread("../sample_image.jpg")
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Step 3: Send inference model(s) to CPU
+    deployment.load_inference_models(device="CPU")
+
+    # Step 4: Infer image
+    prediction = deployment.infer(image_rgb)
+
+    # Step 5: Visualization
+    show_image_with_annotation_scene(image_rgb, prediction)

example_code/demo_notebook.ipynb

@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "86111f81-16f5-46e5-9010-1ef9e05a1571",
+   "metadata": {
+    "copyright": [
+     "INTEL CONFIDENTIAL",
+     "Copyright (C) 2022 Intel Corporation",
+     "This software and the related documents are Intel copyrighted materials, and your use of them is governed by",
+     "the express license under which they were provided to you (\"License\"). Unless the License provides otherwise,",
+     "you may not use, modify, copy, publish, distribute, disclose or transmit this software or the related documents",
+     "without Intel's prior written permission.",
+     "This software and the related documents are provided as is, with no express or implied warranties,",
+     "other than those that are expressly stated in the License."
+    ]
+   },
+   "source": [
+    "# Intel® Geti™ deployment demo notebook\n",
+    "This notebook demonstrates how to run inference for a deployed Intel® Geti™ project on your local machine. It contains the following steps:\n",
+    "1. Load the deployment for the project from the exported `deployment` folder\n",
+    "2. Load a sample image to run inference on\n",
+    "3. Prepare the deployment for inference by sending the model (or models) for the project to the CPU\n",
+    "4. Infer image\n",
+    "5. Visualize prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0ee561b-49fb-4f8b-9c7f-e4859e3fe99e",
+   "metadata": {},
+   "source": [
+    "### Step 1: Load the deployment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d04d3e58-8cae-4491-86b6-fbc876fd5e4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geti_sdk.deployment import Deployment\n",
+    "\n",
+    "deployment = Deployment.from_folder(\"../deployment\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "713de7c8-0ac4-4865-b947-98ecbc4173fb",
+   "metadata": {},
+   "source": [
+    "### Step 2: Load the sample image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c61e01f-2c88-4f0d-ae18-88610cc13bf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "image = cv2.imread(\"../sample_image.jpg\")\n",
+    "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40da9013-46f7-488d-972d-5ceddd54a60c",
+   "metadata": {},
+   "source": [
+    "### Step 3: Send inference model(s) to CPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6b80e6f-57fa-421a-b71f-ffbd0847c0a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "deployment.load_inference_models(device='CPU')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f539adc-04e7-43b4-b113-99e7ff7f6482",
+   "metadata": {},
+   "source": [
+    "### Step 4: Infer image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0e72d41-ec75-4bfe-859b-7302463b9fb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = deployment.infer(image_rgb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f450bb5-29dc-4ac4-b5bb-4b02f350aacc",
+   "metadata": {},
+   "source": [
+    "### Step 5: Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db0dd922-36aa-4203-bc02-76c17d12d8be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geti_sdk.utils import show_image_with_annotation_scene\n",
+    "\n",
+    "show_image_with_annotation_scene(image_rgb, prediction, show_in_notebook=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a342324f-3177-4d61-bee4-40b47d0f78f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Edit Metadata",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

example_code/demo_ovms.ipynb

@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "copyright": [
+     "INTEL CONFIDENTIAL",
+     "Copyright (C) 2023 Intel Corporation",
+     "This software and the related documents are Intel copyrighted materials, and your use of them is governed by",
+     "the express license under which they were provided to you (\"License\"). Unless the License provides otherwise,",
+     "you may not use, modify, copy, publish, distribute, disclose or transmit this software or the related documents",
+     "without Intel's prior written permission.",
+     "This software and the related documents are provided as is, with no express or implied warranties,",
+     "other than those that are expressly stated in the License."
+    ]
+   },
+   "source": [
+    "# Serving Intel® Geti™ models with OpenVINO Model Server\n",
+    "This notebook shows how to set up an OpenVINO model server to serve the models trained\n",
+    "in your Intel® Geti™ project. It also shows how to use the Geti SDK as a client to\n",
+    "make inference requests to the model server.\n",
+    "\n",
+    "# Contents\n",
+    "\n",
+    "1. **OpenVINO Model Server**\n",
+    "    1. Requirements\n",
+    "    2. Generating the model server configuration\n",
+    "    3. Launching the model server\n",
+    "\n",
+    "2. **OVMS inference with Geti SDK**\n",
+    "    1. Loading inference model and sample image\n",
+    "    2. Requesting inference\n",
+    "    3. Inspecting the results\n",
+    "\n",
+    "3. **Conclusion**\n",
+    "    1. Cleaning up\n",
+    "\n",
+    "> **NOTE**: This notebook will set up a model server on the same machine that will be\n",
+    "> used as a client to request inference. In a real scenario you'd most likely\n",
+    "> want the server and the client to be different physical machines. The steps to set up\n",
+    "> OVMS on a remote server are the same as for the local server outlined in this\n",
+    "> notebook, but additional network configuration and security measures are most likely\n",
+    "> required.\n",
+    "\n",
+    "# OpenVINO Model Server\n",
+    "## Requirements\n",
+    "We will be running the OpenVINO Model Server (OVMS) with Docker. Please make sure you\n",
+    "have docker available on your system. You can install it by following the instructions\n",
+    "[here](https://docs.docker.com/get-docker/).\n",
+    "\n",
+    "## Generating the model server configuration\n",
+    "The `deployment` that was downloaded from the Intel® Geti™ platform can be used to create\n",
+    "the configuration files that are needed to set up an OpenVINO model server for your project.\n",
+    "\n",
+    "The cell below shows how to create the configuration. Running this cell should create\n",
+    "a folder called `ovms_models` in a temporary directory. The `ovms_models` folder\n",
+    "contains the models and the configuration files required to run OVMS for the Intel®\n",
+    "Geti™ project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "from geti_sdk.deployment import Deployment\n",
+    "\n",
+    "deployment_path = os.path.join(\"..\", \"deployment\")\n",
+    "\n",
+    "# Load the Geti deployment\n",
+    "deployment = Deployment.from_folder(deployment_path)\n",
+    "\n",
+    "# Creating the OVMS configuration for the deployment\n",
+    "# First, we'll create a temporary directory to store the config files\n",
+    "ovms_config_path = os.path.join(tempfile.mkdtemp(), \"ovms_models\")\n",
+    "\n",
+    "# Next, we generate the OVMS configuration and save it\n",
+    "deployment.generate_ovms_config(output_folder=ovms_config_path)\n",
+    "\n",
+    "print(f\"Configuration for OpenVINO Model Server was created at '{ovms_config_path}'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Launching the model server\n",
+    "As mentioned before, we will run OVMS in a Docker container. First, we need to make sure\n",
+    "that we have the latest OVMS image on our system. Run the cell below to pull the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "! docker pull openvino/model_server:latest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Next, we have to start the container with the configuration that we just generated. This\n",
+    "is done in the cell below.\n",
+    "\n",
+    "> NOTE: The cell below starts the OVMS container and sets it up to listen for inference\n",
+    "> requests on port 9000 on your system. If this port is already occupied the `docker run`\n",
+    "> command will fail and you may need to try a different port number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Launch the OVMS container\n",
+    "result = ! docker run -d --rm -v {ovms_config_path}:/models -p 9000:9000 --name ovms_demo openvino/model_server:latest --port 9000 --config_path /models/ovms_model_config.json\n",
+    "\n",
+    "# Check that the container was created successfully\n",
+    "if len(result) == 1:\n",
+    "    container_id = result[0]\n",
+    "    print(f\"OVMS container with ID '{container_id}' created.\")\n",
+    "else:\n",
+    "    # Anything other than 1 result indicates that something went wrong\n",
+    "    raise RuntimeError(result)\n",
+    "\n",
+    "# Check that the container is running properly\n",
+    "container_info = ! docker container inspect {container_id}\n",
+    "container_status = str(container_info.grep(\"Status\"))\n",
+    "\n",
+    "if not container_status or not \"running\" in container_status:\n",
+    "    raise RuntimeError(\n",
+    "        f\"Invalid ovms docker container status found: {container_status}. Most \"\n",
+    "        f\"likely the container has not started properly.\"\n",
+    "    )\n",
+    "print(\"OVMS container is up and running.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "That's it! If all went well the cell above should print the ID of the container that\n",
+    "was created. This can be used to identify your container if you have a lot of docker\n",
+    "containers running on your system.\n",
+    "\n",
+    "# OVMS inference with Geti SDK\n",
+    "Now that the OVMS container is running, we can use the Geti SDK to talk to it and make an\n",
+    "inference request. The remaining part of this notebook shows how to do so.\n",
+    "\n",
+    "## Loading inference model and sample image\n",
+    "In the first part of this notebook we created configuration files for OVMS, using the\n",
+    "`deployment` that was generated for your Intel® Geti™ project. To do inference, we need\n",
+    "to connect the deployment to the OVMS container that is now running. This is done in the\n",
+    "cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the inference models by connecting to OVMS on port 9000\n",
+    "deployment.load_inference_models(device=\"http://localhost:9000\")\n",
+    "\n",
+    "print(\"Connected to OpenVINO Model Server.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "You should see some output indicating that the connection to OVMS was made successfully.\n",
+    "If you see any errors at this stage, make sure your OVMS container is running and that the\n",
+    "port number is correct.\n",
+    "\n",
+    "Next up, we'll load a sample image from the project to run inference on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "# Load the sample image\n",
+    "image = cv2.imread(\"../sample_image.jpg\")\n",
+    "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "\n",
+    "# Show the image in the notebook\n",
+    "from IPython.display import display\n",
+    "from PIL import Image\n",
+    "\n",
+    "display(Image.fromarray(image_rgb))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Requesting inference\n",
+    "Now that everything is set up, making an inference request is very simple:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "t_start = time.time()\n",
+    "prediction = deployment.infer(image_rgb)\n",
+    "t_end = time.time()\n",
+    "\n",
+    "print(\n",
+    "    f\"OVMS inference on sample image completed in {(t_end - t_start) * 1000:.1f} milliseconds.\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Inspecting the results\n",
+    "Note that the code to request inference is exactly the same as for the case when the model\n",
+    "is loaded on the CPU (see `demo_notebook.ipynb`). Like The `prediction` can be shown using\n",
+    "the Geti SDK visualization utility function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from geti_sdk.utils import show_image_with_annotation_scene\n",
+    "\n",
+    "show_image_with_annotation_scene(image_rgb, prediction, show_in_notebook=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Conclusion\n",
+    "That's all there is to it! Of course in practice the client would request inference\n",
+    "from an OpenVINO model server on a different physical machine, in contrast to the\n",
+    "example here where client and server are running on the same machine.\n",
+    "\n",
+    "The steps outlined in this notebook can be used as a basis to set up a remote\n",
+    "client/server combination, but please note that additional network configuration will\n",
+    "be required (along with necessary security measures).\n",
+    "\n",
+    "## Cleaning up\n",
+    "To clean up, we'll stop the OVMS docker container that we started. This will\n",
+    "automatically remove the container. After that, we'll delete the temporary directory\n",
+    "we created to store the config files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stop the container\n",
+    "result = ! docker stop {container_id}\n",
+    "\n",
+    "# Check if removing the container worked correctly\n",
+    "if result[0] == container_id:\n",
+    "    print(f\"OVMS container '{container_id}' stopped and removed successfully.\")\n",
+    "else:\n",
+    "    print(\n",
+    "       \"An error occurred while removing OVMS docker container. Most likely the container \"\n",
+    "       \"was already removed. \"\n",
+    "    )\n",
+    "    print(f\"The docker daemon responded with the following error: \\n{result}\")\n",
+    "    \n",
+    "# Remove the temporary directory with the OVMS configuration\n",
+    "import shutil\n",
+    "\n",
+    "temp_dir = os.path.dirname(ovms_config_path)\n",
+    "try:\n",
+    "    shutil.rmtree(temp_dir)\n",
+    "    print(\"Temporary configuration directory removed successfully.\")\n",
+    "except FileNotFoundError:\n",
+    "    print(\n",
+    "        f\"Temporary directory with OVMS configuration '{temp_dir}' was \"\n",
+    "        f\"not found on the system. Most likely it is already removed.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

example_code/requirements-notebook.txt

@@ -0,0 +1,6 @@
+# Requirements for running the `demo_notebook.ipynb` and `demo_ovms.ipynb` Jupyter notebooks
+geti-sdk==1.5.*
+jupyterlab==3.6.*
+opencv-python>=4.5.0
+Pillow>=9.4.0
+ipython>=8.10.0

example_code/requirements.txt

@@ -0,0 +1,3 @@
+# Base requirements for the deployment
+geti-sdk==1.5.*
+opencv-python>=4.5.0