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Delete example_code
Browse files- example_code/demo.py +0 -34
- example_code/demo_notebook.ipynb +0 -156
- example_code/demo_ovms.ipynb +0 -421
- example_code/requirements-notebook.txt +0 -6
- example_code/requirements.txt +0 -3
example_code/demo.py
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# Copyright (C) 2022 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions
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# and limitations under the License.
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import cv2
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from geti_sdk.deployment import Deployment
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from geti_sdk.utils import show_image_with_annotation_scene
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if __name__ == "__main__":
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# Step 1: Load the deployment
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deployment = Deployment.from_folder("../deployment")
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# Step 2: Load the sample image
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image = cv2.imread("../sample_image.jpg")
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# Step 3: Send inference model(s) to CPU
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deployment.load_inference_models(device="CPU")
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# Step 4: Infer image
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prediction = deployment.infer(image_rgb)
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# Step 5: Visualization
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show_image_with_annotation_scene(image_rgb, prediction)
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example_code/demo_notebook.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "86111f81-16f5-46e5-9010-1ef9e05a1571",
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"metadata": {
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"copyright": [
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"INTEL CONFIDENTIAL",
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"Copyright (C) 2022 Intel Corporation",
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"This software and the related documents are Intel copyrighted materials, and your use of them is governed by",
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"the express license under which they were provided to you (\"License\"). Unless the License provides otherwise,",
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"you may not use, modify, copy, publish, distribute, disclose or transmit this software or the related documents",
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"without Intel's prior written permission.",
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"This software and the related documents are provided as is, with no express or implied warranties,",
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"other than those that are expressly stated in the License."
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]
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},
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"source": [
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"# Intel® Geti™ deployment demo notebook\n",
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"This notebook demonstrates how to run inference for a deployed Intel® Geti™ project on your local machine. It contains the following steps:\n",
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"1. Load the deployment for the project from the exported `deployment` folder\n",
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"2. Load a sample image to run inference on\n",
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"3. Prepare the deployment for inference by sending the model (or models) for the project to the CPU\n",
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"4. Infer image\n",
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"5. Visualize prediction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a0ee561b-49fb-4f8b-9c7f-e4859e3fe99e",
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"metadata": {},
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"source": [
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"### Step 1: Load the deployment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d04d3e58-8cae-4491-86b6-fbc876fd5e4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from geti_sdk.deployment import Deployment\n",
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"\n",
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"deployment = Deployment.from_folder(\"../deployment\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "713de7c8-0ac4-4865-b947-98ecbc4173fb",
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"metadata": {},
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"source": [
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"### Step 2: Load the sample image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5c61e01f-2c88-4f0d-ae18-88610cc13bf2",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"\n",
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"image = cv2.imread(\"../sample_image.jpg\")\n",
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"image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "40da9013-46f7-488d-972d-5ceddd54a60c",
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"metadata": {},
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"source": [
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"### Step 3: Send inference model(s) to CPU"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f6b80e6f-57fa-421a-b71f-ffbd0847c0a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"deployment.load_inference_models(device='CPU')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f539adc-04e7-43b4-b113-99e7ff7f6482",
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"metadata": {},
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"source": [
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"### Step 4: Infer image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a0e72d41-ec75-4bfe-859b-7302463b9fb6",
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction = deployment.infer(image_rgb)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5f450bb5-29dc-4ac4-b5bb-4b02f350aacc",
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"metadata": {},
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"source": [
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"### Step 5: Visualization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "db0dd922-36aa-4203-bc02-76c17d12d8be",
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"metadata": {},
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"outputs": [],
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"source": [
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"from geti_sdk.utils import show_image_with_annotation_scene\n",
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"\n",
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"show_image_with_annotation_scene(image_rgb, prediction, show_in_notebook=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a342324f-3177-4d61-bee4-40b47d0f78f8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"celltoolbar": "Edit Metadata",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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example_code/demo_ovms.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"copyright": [
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"INTEL CONFIDENTIAL",
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"Copyright (C) 2023 Intel Corporation",
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"This software and the related documents are Intel copyrighted materials, and your use of them is governed by",
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"the express license under which they were provided to you (\"License\"). Unless the License provides otherwise,",
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"you may not use, modify, copy, publish, distribute, disclose or transmit this software or the related documents",
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"without Intel's prior written permission.",
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"This software and the related documents are provided as is, with no express or implied warranties,",
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"other than those that are expressly stated in the License."
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]
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},
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"source": [
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"# Serving Intel® Geti™ models with OpenVINO Model Server\n",
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"This notebook shows how to set up an OpenVINO model server to serve the models trained\n",
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"in your Intel® Geti™ project. It also shows how to use the Geti SDK as a client to\n",
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"make inference requests to the model server.\n",
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"\n",
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"# Contents\n",
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"\n",
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"1. **OpenVINO Model Server**\n",
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" 1. Requirements\n",
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" 2. Generating the model server configuration\n",
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" 3. Launching the model server\n",
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"\n",
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"2. **OVMS inference with Geti SDK**\n",
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" 1. Loading inference model and sample image\n",
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" 2. Requesting inference\n",
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" 3. Inspecting the results\n",
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"\n",
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"3. **Conclusion**\n",
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" 1. Cleaning up\n",
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"\n",
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"> **NOTE**: This notebook will set up a model server on the same machine that will be\n",
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"> used as a client to request inference. In a real scenario you'd most likely\n",
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"> want the server and the client to be different physical machines. The steps to set up\n",
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"> OVMS on a remote server are the same as for the local server outlined in this\n",
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"> notebook, but additional network configuration and security measures are most likely\n",
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"> required.\n",
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"\n",
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"# OpenVINO Model Server\n",
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"## Requirements\n",
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"We will be running the OpenVINO Model Server (OVMS) with Docker. Please make sure you\n",
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"have docker available on your system. You can install it by following the instructions\n",
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"[here](https://docs.docker.com/get-docker/).\n",
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"\n",
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"## Generating the model server configuration\n",
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"The `deployment` that was downloaded from the Intel® Geti™ platform can be used to create\n",
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"the configuration files that are needed to set up an OpenVINO model server for your project.\n",
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"\n",
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"The cell below shows how to create the configuration. Running this cell should create\n",
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"a folder called `ovms_models` in a temporary directory. The `ovms_models` folder\n",
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"contains the models and the configuration files required to run OVMS for the Intel®\n",
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"Geti™ project."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import tempfile\n",
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"\n",
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"from geti_sdk.deployment import Deployment\n",
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"\n",
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"deployment_path = os.path.join(\"..\", \"deployment\")\n",
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"\n",
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"# Load the Geti deployment\n",
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"deployment = Deployment.from_folder(deployment_path)\n",
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"\n",
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"# Creating the OVMS configuration for the deployment\n",
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"# First, we'll create a temporary directory to store the config files\n",
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"ovms_config_path = os.path.join(tempfile.mkdtemp(), \"ovms_models\")\n",
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"\n",
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"# Next, we generate the OVMS configuration and save it\n",
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"deployment.generate_ovms_config(output_folder=ovms_config_path)\n",
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"\n",
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"print(f\"Configuration for OpenVINO Model Server was created at '{ovms_config_path}'\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Launching the model server\n",
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"As mentioned before, we will run OVMS in a Docker container. First, we need to make sure\n",
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"that we have the latest OVMS image on our system. Run the cell below to pull the image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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},
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"! docker pull openvino/model_server:latest"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"Next, we have to start the container with the configuration that we just generated. This\n",
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"is done in the cell below.\n",
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"\n",
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"> NOTE: The cell below starts the OVMS container and sets it up to listen for inference\n",
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"> requests on port 9000 on your system. If this port is already occupied the `docker run`\n",
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"> command will fail and you may need to try a different port number."
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]
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},
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{
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-
"cell_type": "code",
|
143 |
-
"execution_count": null,
|
144 |
-
"metadata": {
|
145 |
-
"collapsed": false,
|
146 |
-
"jupyter": {
|
147 |
-
"outputs_hidden": false
|
148 |
-
},
|
149 |
-
"pycharm": {
|
150 |
-
"name": "#%%\n"
|
151 |
-
}
|
152 |
-
},
|
153 |
-
"outputs": [],
|
154 |
-
"source": [
|
155 |
-
"# Launch the OVMS container\n",
|
156 |
-
"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",
|
157 |
-
"\n",
|
158 |
-
"# Check that the container was created successfully\n",
|
159 |
-
"if len(result) == 1:\n",
|
160 |
-
" container_id = result[0]\n",
|
161 |
-
" print(f\"OVMS container with ID '{container_id}' created.\")\n",
|
162 |
-
"else:\n",
|
163 |
-
" # Anything other than 1 result indicates that something went wrong\n",
|
164 |
-
" raise RuntimeError(result)\n",
|
165 |
-
"\n",
|
166 |
-
"# Check that the container is running properly\n",
|
167 |
-
"container_info = ! docker container inspect {container_id}\n",
|
168 |
-
"container_status = str(container_info.grep(\"Status\"))\n",
|
169 |
-
"\n",
|
170 |
-
"if not container_status or not \"running\" in container_status:\n",
|
171 |
-
" raise RuntimeError(\n",
|
172 |
-
" f\"Invalid ovms docker container status found: {container_status}. Most \"\n",
|
173 |
-
" f\"likely the container has not started properly.\"\n",
|
174 |
-
" )\n",
|
175 |
-
"print(\"OVMS container is up and running.\")"
|
176 |
-
]
|
177 |
-
},
|
178 |
-
{
|
179 |
-
"cell_type": "markdown",
|
180 |
-
"metadata": {
|
181 |
-
"pycharm": {
|
182 |
-
"name": "#%% md\n"
|
183 |
-
}
|
184 |
-
},
|
185 |
-
"source": [
|
186 |
-
"That's it! If all went well the cell above should print the ID of the container that\n",
|
187 |
-
"was created. This can be used to identify your container if you have a lot of docker\n",
|
188 |
-
"containers running on your system.\n",
|
189 |
-
"\n",
|
190 |
-
"# OVMS inference with Geti SDK\n",
|
191 |
-
"Now that the OVMS container is running, we can use the Geti SDK to talk to it and make an\n",
|
192 |
-
"inference request. The remaining part of this notebook shows how to do so.\n",
|
193 |
-
"\n",
|
194 |
-
"## Loading inference model and sample image\n",
|
195 |
-
"In the first part of this notebook we created configuration files for OVMS, using the\n",
|
196 |
-
"`deployment` that was generated for your Intel® Geti™ project. To do inference, we need\n",
|
197 |
-
"to connect the deployment to the OVMS container that is now running. This is done in the\n",
|
198 |
-
"cell below."
|
199 |
-
]
|
200 |
-
},
|
201 |
-
{
|
202 |
-
"cell_type": "code",
|
203 |
-
"execution_count": null,
|
204 |
-
"metadata": {
|
205 |
-
"collapsed": false,
|
206 |
-
"jupyter": {
|
207 |
-
"outputs_hidden": false
|
208 |
-
},
|
209 |
-
"pycharm": {
|
210 |
-
"name": "#%%\n"
|
211 |
-
}
|
212 |
-
},
|
213 |
-
"outputs": [],
|
214 |
-
"source": [
|
215 |
-
"# Load the inference models by connecting to OVMS on port 9000\n",
|
216 |
-
"deployment.load_inference_models(device=\"http://localhost:9000\")\n",
|
217 |
-
"\n",
|
218 |
-
"print(\"Connected to OpenVINO Model Server.\")"
|
219 |
-
]
|
220 |
-
},
|
221 |
-
{
|
222 |
-
"cell_type": "markdown",
|
223 |
-
"metadata": {
|
224 |
-
"pycharm": {
|
225 |
-
"name": "#%% md\n"
|
226 |
-
}
|
227 |
-
},
|
228 |
-
"source": [
|
229 |
-
"You should see some output indicating that the connection to OVMS was made successfully.\n",
|
230 |
-
"If you see any errors at this stage, make sure your OVMS container is running and that the\n",
|
231 |
-
"port number is correct.\n",
|
232 |
-
"\n",
|
233 |
-
"Next up, we'll load a sample image from the project to run inference on"
|
234 |
-
]
|
235 |
-
},
|
236 |
-
{
|
237 |
-
"cell_type": "code",
|
238 |
-
"execution_count": null,
|
239 |
-
"metadata": {
|
240 |
-
"collapsed": false,
|
241 |
-
"jupyter": {
|
242 |
-
"outputs_hidden": false
|
243 |
-
},
|
244 |
-
"pycharm": {
|
245 |
-
"name": "#%%\n"
|
246 |
-
}
|
247 |
-
},
|
248 |
-
"outputs": [],
|
249 |
-
"source": [
|
250 |
-
"import cv2\n",
|
251 |
-
"\n",
|
252 |
-
"# Load the sample image\n",
|
253 |
-
"image = cv2.imread(\"../sample_image.jpg\")\n",
|
254 |
-
"image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
|
255 |
-
"\n",
|
256 |
-
"# Show the image in the notebook\n",
|
257 |
-
"from IPython.display import display\n",
|
258 |
-
"from PIL import Image\n",
|
259 |
-
"\n",
|
260 |
-
"display(Image.fromarray(image_rgb))"
|
261 |
-
]
|
262 |
-
},
|
263 |
-
{
|
264 |
-
"cell_type": "markdown",
|
265 |
-
"metadata": {
|
266 |
-
"pycharm": {
|
267 |
-
"name": "#%% md\n"
|
268 |
-
}
|
269 |
-
},
|
270 |
-
"source": [
|
271 |
-
"## Requesting inference\n",
|
272 |
-
"Now that everything is set up, making an inference request is very simple:"
|
273 |
-
]
|
274 |
-
},
|
275 |
-
{
|
276 |
-
"cell_type": "code",
|
277 |
-
"execution_count": null,
|
278 |
-
"metadata": {
|
279 |
-
"collapsed": false,
|
280 |
-
"jupyter": {
|
281 |
-
"outputs_hidden": false
|
282 |
-
},
|
283 |
-
"pycharm": {
|
284 |
-
"name": "#%%\n"
|
285 |
-
}
|
286 |
-
},
|
287 |
-
"outputs": [],
|
288 |
-
"source": [
|
289 |
-
"import time\n",
|
290 |
-
"\n",
|
291 |
-
"t_start = time.time()\n",
|
292 |
-
"prediction = deployment.infer(image_rgb)\n",
|
293 |
-
"t_end = time.time()\n",
|
294 |
-
"\n",
|
295 |
-
"print(\n",
|
296 |
-
" f\"OVMS inference on sample image completed in {(t_end - t_start) * 1000:.1f} milliseconds.\"\n",
|
297 |
-
")"
|
298 |
-
]
|
299 |
-
},
|
300 |
-
{
|
301 |
-
"cell_type": "markdown",
|
302 |
-
"metadata": {
|
303 |
-
"pycharm": {
|
304 |
-
"name": "#%% md\n"
|
305 |
-
}
|
306 |
-
},
|
307 |
-
"source": [
|
308 |
-
"## Inspecting the results\n",
|
309 |
-
"Note that the code to request inference is exactly the same as for the case when the model\n",
|
310 |
-
"is loaded on the CPU (see `demo_notebook.ipynb`). Like The `prediction` can be shown using\n",
|
311 |
-
"the Geti SDK visualization utility function."
|
312 |
-
]
|
313 |
-
},
|
314 |
-
{
|
315 |
-
"cell_type": "code",
|
316 |
-
"execution_count": null,
|
317 |
-
"metadata": {
|
318 |
-
"collapsed": false,
|
319 |
-
"jupyter": {
|
320 |
-
"outputs_hidden": false
|
321 |
-
},
|
322 |
-
"pycharm": {
|
323 |
-
"name": "#%%\n"
|
324 |
-
}
|
325 |
-
},
|
326 |
-
"outputs": [],
|
327 |
-
"source": [
|
328 |
-
"from geti_sdk.utils import show_image_with_annotation_scene\n",
|
329 |
-
"\n",
|
330 |
-
"show_image_with_annotation_scene(image_rgb, prediction, show_in_notebook=True);"
|
331 |
-
]
|
332 |
-
},
|
333 |
-
{
|
334 |
-
"cell_type": "markdown",
|
335 |
-
"metadata": {
|
336 |
-
"jupyter": {
|
337 |
-
"outputs_hidden": false
|
338 |
-
},
|
339 |
-
"pycharm": {
|
340 |
-
"name": "#%% md\n"
|
341 |
-
}
|
342 |
-
},
|
343 |
-
"source": [
|
344 |
-
"# Conclusion\n",
|
345 |
-
"That's all there is to it! Of course in practice the client would request inference\n",
|
346 |
-
"from an OpenVINO model server on a different physical machine, in contrast to the\n",
|
347 |
-
"example here where client and server are running on the same machine.\n",
|
348 |
-
"\n",
|
349 |
-
"The steps outlined in this notebook can be used as a basis to set up a remote\n",
|
350 |
-
"client/server combination, but please note that additional network configuration will\n",
|
351 |
-
"be required (along with necessary security measures).\n",
|
352 |
-
"\n",
|
353 |
-
"## Cleaning up\n",
|
354 |
-
"To clean up, we'll stop the OVMS docker container that we started. This will\n",
|
355 |
-
"automatically remove the container. After that, we'll delete the temporary directory\n",
|
356 |
-
"we created to store the config files."
|
357 |
-
]
|
358 |
-
},
|
359 |
-
{
|
360 |
-
"cell_type": "code",
|
361 |
-
"execution_count": null,
|
362 |
-
"metadata": {},
|
363 |
-
"outputs": [],
|
364 |
-
"source": [
|
365 |
-
"# Stop the container\n",
|
366 |
-
"result = ! docker stop {container_id}\n",
|
367 |
-
"\n",
|
368 |
-
"# Check if removing the container worked correctly\n",
|
369 |
-
"if result[0] == container_id:\n",
|
370 |
-
" print(f\"OVMS container '{container_id}' stopped and removed successfully.\")\n",
|
371 |
-
"else:\n",
|
372 |
-
" print(\n",
|
373 |
-
" \"An error occurred while removing OVMS docker container. Most likely the container \"\n",
|
374 |
-
" \"was already removed. \"\n",
|
375 |
-
" )\n",
|
376 |
-
" print(f\"The docker daemon responded with the following error: \\n{result}\")\n",
|
377 |
-
" \n",
|
378 |
-
"# Remove the temporary directory with the OVMS configuration\n",
|
379 |
-
"import shutil\n",
|
380 |
-
"\n",
|
381 |
-
"temp_dir = os.path.dirname(ovms_config_path)\n",
|
382 |
-
"try:\n",
|
383 |
-
" shutil.rmtree(temp_dir)\n",
|
384 |
-
" print(\"Temporary configuration directory removed successfully.\")\n",
|
385 |
-
"except FileNotFoundError:\n",
|
386 |
-
" print(\n",
|
387 |
-
" f\"Temporary directory with OVMS configuration '{temp_dir}' was \"\n",
|
388 |
-
" f\"not found on the system. Most likely it is already removed.\"\n",
|
389 |
-
" )"
|
390 |
-
]
|
391 |
-
},
|
392 |
-
{
|
393 |
-
"cell_type": "code",
|
394 |
-
"execution_count": null,
|
395 |
-
"metadata": {},
|
396 |
-
"outputs": [],
|
397 |
-
"source": []
|
398 |
-
}
|
399 |
-
],
|
400 |
-
"metadata": {
|
401 |
-
"kernelspec": {
|
402 |
-
"display_name": "Python 3 (ipykernel)",
|
403 |
-
"language": "python",
|
404 |
-
"name": "python3"
|
405 |
-
},
|
406 |
-
"language_info": {
|
407 |
-
"codemirror_mode": {
|
408 |
-
"name": "ipython",
|
409 |
-
"version": 3
|
410 |
-
},
|
411 |
-
"file_extension": ".py",
|
412 |
-
"mimetype": "text/x-python",
|
413 |
-
"name": "python",
|
414 |
-
"nbconvert_exporter": "python",
|
415 |
-
"pygments_lexer": "ipython3",
|
416 |
-
"version": "3.8.16"
|
417 |
-
}
|
418 |
-
},
|
419 |
-
"nbformat": 4,
|
420 |
-
"nbformat_minor": 4
|
421 |
-
}
|
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|
example_code/requirements-notebook.txt
DELETED
@@ -1,6 +0,0 @@
|
|
1 |
-
# Requirements for running the `demo_notebook.ipynb` and `demo_ovms.ipynb` Jupyter notebooks
|
2 |
-
geti-sdk==1.5.*
|
3 |
-
jupyterlab==3.6.*
|
4 |
-
opencv-python>=4.5.0
|
5 |
-
Pillow>=9.4.0
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6 |
-
ipython>=8.10.0
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example_code/requirements.txt
DELETED
@@ -1,3 +0,0 @@
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1 |
-
# Base requirements for the deployment
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2 |
-
geti-sdk==1.5.*
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3 |
-
opencv-python>=4.5.0
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