├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── THIRD_PARTY_LICENSES ├── assets ├── AWSArchitecture.png ├── LaunchStack.png ├── Step1_StackName.png ├── Step2_StackIAM.png └── Step3_StackSuccess.png ├── sm-notebook ├── 1_DeployEndpoint.ipynb ├── 2_TestEndpoint.ipynb ├── bus.jpg └── code │ ├── inference.py │ └── requirements.txt └── yolov8-pytorch-cdk ├── README.md ├── app.py ├── cdk.json ├── cdk ├── __init__.py └── yolov8_sagemaker.py ├── requirements-dev.txt ├── requirements.txt └── source.bat /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | .vscode/ 3 | .ipynb_checkpoints/ 4 | cdk.out/ 5 | cdk.out/** 6 | 7 | .idea 8 | *.venv 9 | *cdk.out 10 | *.drawio 11 | 12 | # Prerequisites 13 | *.d 14 | 15 | # Compiled Object files 16 | *.slo 17 | *.lo 18 | *.o 19 | *.obj 20 | 21 | # Precompiled Headers 22 | *.gch 23 | *.pch 24 | 25 | # Compiled Dynamic libraries 26 | *.so 27 | *.dylib 28 | *.dll 29 | 30 | # Fortran module files 31 | *.mod 32 | *.smod 33 | 34 | # Compiled Static libraries 35 | *.lai 36 | *.la 37 | *.a 38 | *.lib 39 | 40 | # Executables 41 | *.exe 42 | *.out 43 | *.app 44 | 45 | # Prerequisites 46 | *.d 47 | 48 | # Object files 49 | *.o 50 | *.ko 51 | *.obj 52 | *.elf 53 | 54 | # Linker output 55 | *.ilk 56 | *.map 57 | *.exp 58 | 59 | # Precompiled Headers 60 | *.gch 61 | *.pch 62 | 63 | # Libraries 64 | *.lib 65 | *.a 66 | *.la 67 | *.lo 68 | 69 | # Shared objects (inc. Windows DLLs) 70 | *.dll 71 | *.so 72 | *.so.* 73 | *.dylib 74 | 75 | # Executables 76 | *.exe 77 | *.out 78 | *.app 79 | *.i*86 80 | *.x86_64 81 | *.hex 82 | 83 | # Debug files 84 | *.dSYM/ 85 | *.su 86 | *.idb 87 | *.pdb 88 | 89 | # Kernel Module Compile Results 90 | *.mod* 91 | *.cmd 92 | .tmp_versions/ 93 | modules.order 94 | Module.symvers 95 | Mkfile.old 96 | dkms.conf 97 | 98 | # MacOS .DS_Store files 99 | .DS_Store 100 | 101 | # Avoid all videos and csvs, zips 102 | *.zip 103 | *.mp4 104 | *.csv 105 | 106 | # Avoid all greengrass build files 107 | **zip-build 108 | **greengrass-build 109 | 110 | # Pytorch model files 111 | *.pt 112 | *.tar.gz -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | ## Code of Conduct 2 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 3 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 4 | opensource-codeofconduct@amazon.com with any additional questions or comments. -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing Guidelines 2 | 3 | Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional 4 | documentation, we greatly value feedback and contributions from our community. 5 | 6 | Please read through this document before submitting any issues or pull requests to ensure we have all the necessary 7 | information to effectively respond to your bug report or contribution. 8 | 9 | 10 | ## Reporting Bugs/Feature Requests 11 | 12 | We welcome you to use the GitHub issue tracker to report bugs or suggest features. 13 | 14 | When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already 15 | reported the issue. Please try to include as much information as you can. Details like these are incredibly useful: 16 | 17 | * A reproducible test case or series of steps 18 | * The version of our code being used 19 | * Any modifications you've made relevant to the bug 20 | * Anything unusual about your environment or deployment 21 | 22 | 23 | ## Contributing via Pull Requests 24 | Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that: 25 | 26 | 1. You are working against the latest source on the *main* branch. 27 | 2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already. 28 | 3. You open an issue to discuss any significant work - we would hate for your time to be wasted. 29 | 30 | To send us a pull request, please: 31 | 32 | 1. Fork the repository. 33 | 2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change. 34 | 3. Ensure local tests pass. 35 | 4. Commit to your fork using clear commit messages. 36 | 5. Send us a pull request, answering any default questions in the pull request interface. 37 | 6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation. 38 | 39 | GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and 40 | [creating a pull request](https://help.github.com/articles/creating-a-pull-request/). 41 | 42 | 43 | ## Finding contributions to work on 44 | Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start. 45 | 46 | 47 | ## Code of Conduct 48 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 49 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 50 | opensource-codeofconduct@amazon.com with any additional questions or comments. 51 | 52 | 53 | ## Security issue notifications 54 | If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue. 55 | 56 | 57 | ## Licensing 58 | 59 | See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution. -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT No Attribution 2 | 3 | Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy of 6 | this software and associated documentation files (the "Software"), to deal in 7 | the Software without restriction, including without limitation the rights to 8 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 9 | the Software, and to permit persons to whom the Software is furnished to do so. 10 | 11 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 12 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 13 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 14 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 15 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 16 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Host a YOLOv8 model on a SageMaker Endpoint 2 | This aim of this project is to host a [YOLOv8](https://github.com/ultralytics/ultralytics)* PyTorch model on a [SageMaker Endpoint](https://aws.amazon.com/sagemaker/) and test it by invoking the endpoint. The project utilizes [AWS CloudFormation/CDK](https://aws.amazon.com/cloudformation/) to build the stack and once that is created, it uses the SageMaker notebooks created in order to create the endpoint and test it. 3 | 4 | (*) NOTE: YOLOv8 is distributed under the GPLv3 license. 5 | 6 | For YOLOv5 TensorFlow deployment on SageMaker Endpoint, kindly refer to the [GitHub](https://github.com/aws-samples/host-yolov5-on-sagemaker-endpoint) and the [Blog on YOLOv5 on SageMaker Endpoint](https://aws.amazon.com/blogs/machine-learning/scale-yolov5-inference-with-amazon-sagemaker-endpoints-and-aws-lambda/) 7 | 8 | ## AWS Architecture: 9 | ![AWSArchitecture](assets/AWSArchitecture.png) 10 | 11 | ## AWS CloudFormation Stack Creation 12 | The AWS CloudFormation Stack can be created using 2 methods: (1) Using Template or (2) Using AWS CDK. Both the methods are described as follows: 13 | 14 | 1. Create Stack using AWS CloudFormation: 15 | - Choose **Launch Stack** and (if prompted) log into your AWS account: 16 | [![Launch Stack](assets/LaunchStack.png)](https://console.aws.amazon.com/cloudformation/home#/stacks/create/review?templateURL=https://aws-blogs-artifacts-public.s3.amazonaws.com/artifacts/ML-13353/yolov8-pytorch-cfn-template.yaml) 17 | - Select a unique Stack Name, ackowledge creation of IAM resources, create the stack and wait for a few minutes for it to be successfully deployed 18 | 1. ![Step1_StackName](assets/Step1_StackName.png) 19 | 2. ![Step2_StackIAM](assets/Step2_StackIAM.png) 20 | 3. ![Step3_StackSuccess](assets/Step3_StackSuccess.png) 21 | 22 | 2. PyTorch YOLOv8 model with AWS CDK 23 | In order to create the stack with AWS CDK, follow the steps highlighted in [yolov8-pytorch-cdk](yolov8-pytorch-cdk/README.md). Use these steps: 24 | ``` 25 | $ cd yolov8-pytorch-cdk 26 | $ python3 -m venv .venv 27 | $ source .venv/bin/activate 28 | $ pip3 install -r requirements.txt 29 | $ cdk synth 30 | $ cdk bootstrap 31 | $ cdk deploy 32 | ``` 33 | 34 | ## YOLOv8 PyTorch model deployment on Amazon SageMaker Endpoints: 35 | - From AWS Console, go to [Amazon SageMaker Notebook Instances](https://us-east-1.console.aws.amazon.com/sagemaker/home?region=us-east-1#/notebook-instances) 36 | - Select the Notebook created by the stack and open it 37 | - Inside SageMaker Notebook, navigate: `sm-notebook` and open the notebooks: `1_DeployEndpoint.ipynb` & `2_TestEndpoint.ipynb` 38 | 1. `1_DeployEndpoint.ipynb`: Download YOLOv8 model, zip inference code and model to S3, create SageMaker endpoint and deploy it 39 | 2. `2_TestEndpoint.ipynb`: Test the deployed endpoint by running an image and plotting output; Cleanup the endpoint and hosted model 40 | 41 | ## Contributors: 42 | - [Kevin Song (@kcsong)](kcsong@amazon.com) 43 | - [Romil Shah (@rpshah)](rpshah@amazon.com) 44 | -------------------------------------------------------------------------------- /THIRD_PARTY_LICENSES: -------------------------------------------------------------------------------- 1 | ** YOLOv8; version 6.0 -- https://github.com/ultralytics/ultralytics 2 | 3 | GNU GENERAL PUBLIC LICENSE 4 | Version 3, 29 June 2007 5 | 6 | Copyright (C) 2007 Free Software Foundation, Inc. 7 | Everyone is permitted to copy and distribute verbatim copies 8 | of this license document, but changing it is not allowed. 9 | 10 | Preamble 11 | 12 | The GNU General Public License is a free, copyleft license for 13 | software and other kinds of works. 14 | 15 | The licenses for most software and other practical works are designed 16 | to take away your freedom to share and change the works. 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Such new versions will 569 | be similar in spirit to the present version, but may differ in detail to 570 | address new problems or concerns. 571 | 572 | Each version is given a distinguishing version number. If the 573 | Program specifies that a certain numbered version of the GNU General 574 | Public License "or any later version" applies to it, you have the 575 | option of following the terms and conditions either of that numbered 576 | version or of any later version published by the Free Software 577 | Foundation. If the Program does not specify a version number of the 578 | GNU General Public License, you may choose any version ever published 579 | by the Free Software Foundation. 580 | 581 | If the Program specifies that a proxy can decide which future 582 | versions of the GNU General Public License can be used, that proxy's 583 | public statement of acceptance of a version permanently authorizes you 584 | to choose that version for the Program. 585 | 586 | Later license versions may give you additional or different 587 | permissions. However, no additional obligations are imposed on any 588 | author or copyright holder as a result of your choosing to follow a 589 | later version. 590 | 591 | 15. Disclaimer of Warranty. 592 | 593 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 594 | APPLICABLE LAW. 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Interpretation of Sections 15 and 16. 615 | 616 | If the disclaimer of warranty and limitation of liability provided 617 | above cannot be given local legal effect according to their terms, 618 | reviewing courts shall apply local law that most closely approximates 619 | an absolute waiver of all civil liability in connection with the 620 | Program, unless a warranty or assumption of liability accompanies a 621 | copy of the Program in return for a fee. 622 | 623 | END OF TERMS AND CONDITIONS 624 | 625 | How to Apply These Terms to Your New Programs 626 | 627 | If you develop a new program, and you want it to be of the greatest 628 | possible use to the public, the best way to achieve this is to make it 629 | free software which everyone can redistribute and change under these terms. 630 | 631 | To do so, attach the following notices to the program. It is safest 632 | to attach them to the start of each source file to most effectively 633 | state the exclusion of warranty; and each file should have at least 634 | the "copyright" line and a pointer to where the full notice is found. 635 | 636 | 637 | Copyright (C) 638 | 639 | This program is free software: you can redistribute it and/or modify 640 | it under the terms of the GNU General Public License as published by 641 | the Free Software Foundation, either version 3 of the License, or 642 | (at your option) any later version. 643 | 644 | This program is distributed in the hope that it will be useful, 645 | but WITHOUT ANY WARRANTY; without even the implied warranty of 646 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 647 | GNU General Public License for more details. 648 | 649 | You should have received a copy of the GNU General Public License 650 | along with this program. If not, see . 651 | 652 | Also add information on how to contact you by electronic and paper mail. 653 | 654 | If the program does terminal interaction, make it output a short 655 | notice like this when it starts in an interactive mode: 656 | 657 | Copyright (C) 658 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 659 | This is free software, and you are welcome to redistribute it 660 | under certain conditions; type `show c' for details. 661 | 662 | The hypothetical commands `show w' and `show c' should show the appropriate 663 | parts of the General Public License. Of course, your program's commands 664 | might be different; for a GUI interface, you would use an "about box". 665 | 666 | You should also get your employer (if you work as a programmer) or school, 667 | if any, to sign a "copyright disclaimer" for the program, if necessary. 668 | For more information on this, and how to apply and follow the GNU GPL, see 669 | . 670 | 671 | The GNU General Public License does not permit incorporating your program 672 | into proprietary programs. If your program is a subroutine library, you 673 | may consider it more useful to permit linking proprietary applications with 674 | the library. If this is what you want to do, use the GNU Lesser General 675 | Public License instead of this License. But first, please read 676 | . -------------------------------------------------------------------------------- /assets/AWSArchitecture.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/host-yolov8-on-sagemaker-endpoint/aac7214397f49d0a6fa7adde2abb5f8eb0367b54/assets/AWSArchitecture.png -------------------------------------------------------------------------------- /assets/LaunchStack.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/host-yolov8-on-sagemaker-endpoint/aac7214397f49d0a6fa7adde2abb5f8eb0367b54/assets/LaunchStack.png -------------------------------------------------------------------------------- /assets/Step1_StackName.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/host-yolov8-on-sagemaker-endpoint/aac7214397f49d0a6fa7adde2abb5f8eb0367b54/assets/Step1_StackName.png -------------------------------------------------------------------------------- /assets/Step2_StackIAM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/host-yolov8-on-sagemaker-endpoint/aac7214397f49d0a6fa7adde2abb5f8eb0367b54/assets/Step2_StackIAM.png -------------------------------------------------------------------------------- /assets/Step3_StackSuccess.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/host-yolov8-on-sagemaker-endpoint/aac7214397f49d0a6fa7adde2abb5f8eb0367b54/assets/Step3_StackSuccess.png -------------------------------------------------------------------------------- /sm-notebook/1_DeployEndpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "33dcbabf-2c40-4c28-bf1c-daf9ffef3ca3", 6 | "metadata": {}, 7 | "source": [ 8 | "## 1.1 Import Python Libraries" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": null, 14 | "id": "663dc165-7f93-42b0-ad01-f1651650d1b7", 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "import os, sagemaker, subprocess, boto3\n", 19 | "from datetime import datetime\n", 20 | "from sagemaker import s3\n", 21 | "from sagemaker import get_execution_role\n", 22 | "from sagemaker.pytorch import PyTorchModel\n", 23 | "from sagemaker.deserializers import JSONDeserializer" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "id": "4183a04e-2ab1-4b76-9fdb-4a55b6db923d", 29 | "metadata": {}, 30 | "source": [ 31 | "## 1.2 Install Ultralytics for YOLOv8 model\n", 32 | "\n", 33 | "There are following models to choose from:\n", 34 | "1. Object Detection:\n", 35 | " - yolov8l.pt\n", 36 | " - yolov8m.pt\n", 37 | " - yolov8n.pt\n", 38 | " - yolov8s.pt\n", 39 | " - yolov8x.pt\n", 40 | " - yolov8x6.pt\n", 41 | "2. Segmentation:\n", 42 | " - yolov8l-seg.pt\n", 43 | " - yolov8m-seg.pt\n", 44 | " - yolov8n-seg.pt\n", 45 | " - yolov8s-seg.pt\n", 46 | " - yolov8x-seg.pt\n", 47 | "3. Classification:\n", 48 | " - yolov8l-cls.pt\n", 49 | " - yolov8m-cls.pt\n", 50 | " - yolov8n-cls.pt\n", 51 | " - yolov8s-cls.pt\n", 52 | " - yolov8x-cls.pt\n", 53 | "4. Pose:\n", 54 | " - yolov8n-pose.pt\n", 55 | " - yolov8s-pose.pt\n", 56 | " - yolov8m-pose.pt\n", 57 | " - yolov8l-pose.pt\n", 58 | " - yolov8x-pose.pt\n", 59 | " - yolov8x-pose-p6.pt\n", 60 | "\n", 61 | "For further information, check the link: https://docs.ultralytics.com" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": null, 67 | "id": "aa890ad6", 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "!pip3 install ultralytics\n", 72 | "from ultralytics import YOLO\n", 73 | "\n", 74 | "## Choose a model:\n", 75 | "model_name = 'yolov8l.pt'\n", 76 | "\n", 77 | "YOLO(model_name)\n", 78 | "\n", 79 | "bashCommand = f\"tar -cpzf model.tar.gz {model_name} code/\"\n", 80 | "process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)\n", 81 | "output, error = process.communicate()" 82 | ] 83 | }, 84 | { 85 | "attachments": {}, 86 | "cell_type": "markdown", 87 | "id": "9c4f30e2-4916-457a-9cf5-f24f4944835a", 88 | "metadata": {}, 89 | "source": [ 90 | "## 1.3 Zip the code and model into `model.tar.gz` and upload it to specific S3 bucket\n", 91 | "Here permission is granted to the S3 bucket created with CDK and not any other bucket" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": null, 97 | "id": "ultimate-triangle", 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [ 101 | "s3_client = boto3.client('s3')\n", 102 | "response = s3_client.list_buckets()\n", 103 | "for bucket in response['Buckets']:\n", 104 | " if 'yolov8' in bucket[\"Name\"]:\n", 105 | " bucket = 's3://' + bucket[\"Name\"]\n", 106 | " break\n", 107 | "\n", 108 | "print(f'Bucket: {bucket}')\n", 109 | "sess = sagemaker.Session(default_bucket=bucket.split('s3://')[-1])\n", 110 | "\n", 111 | "prefix = \"yolov8/demo-custom-endpoint\"" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": null, 117 | "id": "2d4c5032-8a51-4539-8ef6-cfa8f07e2d8b", 118 | "metadata": {}, 119 | "outputs": [], 120 | "source": [ 121 | "sm_client = boto3.client(service_name=\"sagemaker\")\n", 122 | "runtime_sm_client = boto3.client(service_name=\"sagemaker-runtime\")\n", 123 | "\n", 124 | "account_id = boto3.client(\"sts\").get_caller_identity()[\"Account\"]\n", 125 | "region = boto3.Session().region_name\n", 126 | "\n", 127 | "role = get_execution_role()\n", 128 | "print(f'Role: {role}')\n", 129 | "\n", 130 | "model_data = s3.S3Uploader.upload(\"model.tar.gz\", bucket + \"/\" + prefix)\n", 131 | "print(f'Model Data: {model_data}')" 132 | ] 133 | }, 134 | { 135 | "attachments": {}, 136 | "cell_type": "markdown", 137 | "id": "91c395a5", 138 | "metadata": {}, 139 | "source": [ 140 | "## 1.4 Create the SageMaker PyTorchModel" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": null, 146 | "id": "dfdced23-e766-4ead-9a16-7c45dec8c0a6", 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [ 150 | "model = PyTorchModel(entry_point='inference.py',\n", 151 | " model_data=model_data, \n", 152 | " framework_version='1.12', \n", 153 | " py_version='py38',\n", 154 | " role=role,\n", 155 | " env={'TS_MAX_RESPONSE_SIZE':'20000000', 'YOLOV8_MODEL': model_name},\n", 156 | " sagemaker_session=sess)" 157 | ] 158 | }, 159 | { 160 | "attachments": {}, 161 | "cell_type": "markdown", 162 | "id": "polyphonic-outline", 163 | "metadata": {}, 164 | "source": [ 165 | "## 1.5 Deploy the model on SageMaker Endpoint:" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "execution_count": null, 171 | "id": "convinced-miller", 172 | "metadata": { 173 | "tags": [] 174 | }, 175 | "outputs": [], 176 | "source": [ 177 | "INSTANCE_TYPE = 'ml.m5.4xlarge'\n", 178 | "ENDPOINT_NAME = 'yolov8-pytorch-' + str(datetime.utcnow().strftime('%Y-%m-%d-%H-%M-%S-%f'))\n", 179 | "\n", 180 | "# Store the endpoint name in the history to be accessed by 2_TestEndpoint.ipynb notebook\n", 181 | "%store ENDPOINT_NAME\n", 182 | "print(f'Endpoint Name: {ENDPOINT_NAME}')\n", 183 | "\n", 184 | "predictor = model.deploy(initial_instance_count=1, \n", 185 | " instance_type=INSTANCE_TYPE,\n", 186 | " deserializer=JSONDeserializer(),\n", 187 | " endpoint_name=ENDPOINT_NAME)" 188 | ] 189 | } 190 | ], 191 | "metadata": { 192 | "kernelspec": { 193 | "display_name": "conda_amazonei_pytorch_latest_p37", 194 | "language": "python", 195 | "name": "conda_amazonei_pytorch_latest_p37" 196 | }, 197 | "language_info": { 198 | "codemirror_mode": { 199 | "name": "ipython", 200 | "version": 3 201 | }, 202 | "file_extension": ".py", 203 | "mimetype": "text/x-python", 204 | "name": "python", 205 | "nbconvert_exporter": "python", 206 | "pygments_lexer": "ipython3", 207 | "version": "3.7.13" 208 | } 209 | }, 210 | "nbformat": 4, 211 | "nbformat_minor": 5 212 | } 213 | -------------------------------------------------------------------------------- /sm-notebook/2_TestEndpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "id": "ee63f4f8", 7 | "metadata": {}, 8 | "source": [ 9 | "## 2.1 Import Python Libraries" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "id": "1b6b4621", 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "import boto3, cv2, time, numpy as np, matplotlib.pyplot as plt, random\n", 20 | "import base64, json" 21 | ] 22 | }, 23 | { 24 | "attachments": {}, 25 | "cell_type": "markdown", 26 | "id": "158dcccc-905b-4448-8570-94015bc67d19", 27 | "metadata": {}, 28 | "source": [ 29 | "## 2.2 Check if Endpoint creation is successful and create the predictor" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "id": "74487be9-e5dd-4a4c-aed6-75a2e895aace", 36 | "metadata": {}, 37 | "outputs": [], 38 | "source": [ 39 | "sm_client = boto3.client(service_name=\"sagemaker\")\n", 40 | "\n", 41 | "# Restore the endpoint name stored in the 2_DeployEndpoint.ipynb notebook\n", 42 | "%store -r ENDPOINT_NAME\n", 43 | "print(f'Endpoint Name: {ENDPOINT_NAME}')\n", 44 | "\n", 45 | "endpoint_created = False\n", 46 | "while True:\n", 47 | " response = sm_client.list_endpoints()\n", 48 | " for ep in response['Endpoints']:\n", 49 | " print(f\"Endpoint Status = {ep['EndpointStatus']}\")\n", 50 | " if ep['EndpointName']==ENDPOINT_NAME and ep['EndpointStatus']=='InService':\n", 51 | " endpoint_created = True\n", 52 | " break\n", 53 | " if endpoint_created:\n", 54 | " break\n", 55 | " time.sleep(5)" 56 | ] 57 | }, 58 | { 59 | "attachments": {}, 60 | "cell_type": "markdown", 61 | "id": "83ec72a4", 62 | "metadata": {}, 63 | "source": [ 64 | "## 2.3 Run Inference and Generate output results" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": null, 70 | "id": "e0180fcb-ea19-4fc5-80a9-fdaad7f8e8b8", 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "infer_start_time = time.time()\n", 75 | "# Read the image into a numpy array\n", 76 | "orig_image = cv2.imread('bus.jpg')\n", 77 | "\n", 78 | "# Calculate the parameters for image resizing\n", 79 | "image_height, image_width, _ = orig_image.shape\n", 80 | "model_height, model_width = 640, 640\n", 81 | "x_ratio = image_width/model_width\n", 82 | "y_ratio = image_height/model_height\n", 83 | "\n", 84 | "# Resize the image as numpy array\n", 85 | "resized_image = cv2.resize(orig_image, (model_height, model_width))\n", 86 | "# Conver the array into jpeg\n", 87 | "resized_jpeg = cv2.imencode('.jpg', resized_image)[1]\n", 88 | "# Serialize the jpg using base 64\n", 89 | "payload = base64.b64encode(resized_jpeg).decode('utf-8')\n", 90 | "\n", 91 | "runtime= boto3.client('runtime.sagemaker')\n", 92 | "response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME,\n", 93 | " ContentType='text/csv',\n", 94 | " Body=payload)\n", 95 | "response_body = response['Body'].read()\n", 96 | "result = json.loads(response_body.decode('ascii'))\n", 97 | "\n", 98 | "infer_end_time = time.time()\n", 99 | "\n", 100 | "\n", 101 | "print(f\"Inference Time = {infer_end_time - infer_start_time:0.4f} seconds\")\n", 102 | "\n", 103 | "if 'boxes' in result:\n", 104 | " for idx,(x1,y1,x2,y2,conf,lbl) in enumerate(result['boxes']):\n", 105 | " # Draw Bounding Boxes\n", 106 | " x1, x2 = int(x_ratio*x1), int(x_ratio*x2)\n", 107 | " y1, y2 = int(y_ratio*y1), int(y_ratio*y2)\n", 108 | " color = (random.randint(10,255), random.randint(10,255), random.randint(10,255))\n", 109 | " cv2.rectangle(orig_image, (x1,y1), (x2,y2), color, 4)\n", 110 | " cv2.putText(orig_image, f\"Class: {int(lbl)}\", (x1,y1-40), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2, cv2.LINE_AA)\n", 111 | " cv2.putText(orig_image, f\"Conf: {int(conf*100)}\", (x1,y1-10), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2, cv2.LINE_AA)\n", 112 | " if 'masks' in result:\n", 113 | " # Draw Masks\n", 114 | " mask = cv2.resize(np.asarray(result['masks'][idx]), dsize=(image_width, image_height), interpolation=cv2.INTER_CUBIC)\n", 115 | " for c in range(3):\n", 116 | " orig_image[:,:,c] = np.where(mask>0.5, orig_image[:,:,c]*(0.5)+0.5*color[c], orig_image[:,:,c])\n", 117 | "\n", 118 | "if 'probs' in result:\n", 119 | " # Find Class\n", 120 | " lbl = result['probs'].index(max(result['probs']))\n", 121 | " color = (random.randint(10,255), random.randint(10,255), random.randint(10,255))\n", 122 | " cv2.putText(orig_image, f\"Class: {int(lbl)}\", (20,20), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2, cv2.LINE_AA)\n", 123 | " \n", 124 | "if 'keypoints' in result:\n", 125 | " # Define the colors for the keypoints and lines\n", 126 | " keypoint_color = (random.randint(10,255), random.randint(10,255), random.randint(10,255))\n", 127 | " line_color = (random.randint(10,255), random.randint(10,255), random.randint(10,255))\n", 128 | "\n", 129 | " # Define the keypoints and the lines to draw\n", 130 | " # keypoints = keypoints_array[:, :, :2] # Ignore the visibility values\n", 131 | " lines = [\n", 132 | " (0, 1), (0, 2), (1, 3), (2, 4), # Head\n", 133 | " (5, 6), (5, 7), (7, 9), (6, 8), (8, 10), # Torso\n", 134 | " (11, 12), (11, 13), (13, 15), (12, 14), (14, 16) # Legs\n", 135 | " ]\n", 136 | "\n", 137 | " # Draw the keypoints and the lines on the image\n", 138 | " for keypoints_instance in result['keypoints']:\n", 139 | " # Draw the keypoints\n", 140 | " for keypoint in keypoints_instance:\n", 141 | " if keypoint[2] == 0: # If the keypoint is not visible, skip it\n", 142 | " continue\n", 143 | " cv2.circle(orig_image, (int(x_ratio*keypoint[:2][0]),int(y_ratio*keypoint[:2][1])), radius=5, color=keypoint_color, thickness=-1)\n", 144 | "\n", 145 | " # Draw the lines\n", 146 | " for line in lines:\n", 147 | " start_keypoint = keypoints_instance[line[0]]\n", 148 | " end_keypoint = keypoints_instance[line[1]]\n", 149 | " if start_keypoint[2] == 0 or end_keypoint[2] == 0: # If any of the keypoints is not visible, skip the line\n", 150 | " continue\n", 151 | " cv2.line(orig_image, (int(x_ratio*start_keypoint[:2][0]),int(y_ratio*start_keypoint[:2][1])),(int(x_ratio*end_keypoint[:2][0]),int(y_ratio*end_keypoint[:2][1])), color=line_color, thickness=2)\n", 152 | "\n", 153 | "plt.imshow(cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB))\n", 154 | "plt.show()" 155 | ] 156 | }, 157 | { 158 | "attachments": {}, 159 | "cell_type": "markdown", 160 | "id": "9f167efa-02b2-434c-ae35-8109154b6df8", 161 | "metadata": {}, 162 | "source": [ 163 | "## 2.4 Cleanup by removing Endpoint, Endpoint Config and Model" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "id": "c94b6f40-8a02-47ff-b576-806705aeb20f", 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [ 173 | "response = sm_client.describe_endpoint_config(EndpointConfigName=ENDPOINT_NAME)\n", 174 | "print(response)\n", 175 | "endpoint_config_name = response['EndpointConfigName']\n", 176 | "\n", 177 | "# Delete Endpoint\n", 178 | "sm_client.delete_endpoint(EndpointName=ENDPOINT_NAME)\n", 179 | "\n", 180 | "# Delete Endpoint Configuration\n", 181 | "sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n", 182 | "\n", 183 | "# Delete Model\n", 184 | "for prod_var in response['ProductionVariants']:\n", 185 | " model_name = prod_var['ModelName']\n", 186 | " sm_client.delete_model(ModelName=model_name) " 187 | ] 188 | } 189 | ], 190 | "metadata": { 191 | "kernelspec": { 192 | "display_name": "data-pipeline", 193 | "language": "python", 194 | "name": "data-pipeline" 195 | }, 196 | "language_info": { 197 | "codemirror_mode": { 198 | "name": "ipython", 199 | "version": 3 200 | }, 201 | "file_extension": ".py", 202 | "mimetype": "text/x-python", 203 | "name": "python", 204 | "nbconvert_exporter": "python", 205 | "pygments_lexer": "ipython3", 206 | "version": "3.9.6" 207 | } 208 | }, 209 | "nbformat": 4, 210 | "nbformat_minor": 5 211 | } 212 | -------------------------------------------------------------------------------- /sm-notebook/bus.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/host-yolov8-on-sagemaker-endpoint/aac7214397f49d0a6fa7adde2abb5f8eb0367b54/sm-notebook/bus.jpg -------------------------------------------------------------------------------- /sm-notebook/code/inference.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch, os, json, base64, cv2, time 3 | from ultralytics import YOLO 4 | 5 | def model_fn(model_dir): 6 | print("Executing model_fn from inference.py ...") 7 | env = os.environ 8 | model = YOLO(os.path.join(model_dir, env['YOLOV8_MODEL'])) 9 | return model 10 | 11 | def input_fn(request_body, request_content_type): 12 | print("Executing input_fn from inference.py ...") 13 | if request_content_type: 14 | jpg_original = base64.b64decode(request_body) 15 | jpg_as_np = np.frombuffer(jpg_original, dtype=np.uint8) 16 | img = cv2.imdecode(jpg_as_np, flags=-1) 17 | else: 18 | raise Exception("Unsupported content type: " + request_content_type) 19 | return img 20 | 21 | def predict_fn(input_data, model): 22 | print("Executing predict_fn from inference.py ...") 23 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 24 | model.to(device) 25 | with torch.no_grad(): 26 | result = model(input_data) 27 | return result 28 | 29 | def output_fn(prediction_output, content_type): 30 | print("Executing output_fn from inference.py ...") 31 | infer = {} 32 | for result in prediction_output: 33 | if 'boxes' in result._keys and result.boxes is not None: 34 | infer['boxes'] = result.boxes.cpu().numpy().data.tolist() 35 | if 'masks' in result._keys and result.masks is not None: 36 | infer['masks'] = result.masks.cpu().numpy().data.tolist() 37 | if 'keypoints' in result._keys and result.keypoints is not None: 38 | infer['keypoints'] = result.keypoints.cpu().numpy().data.tolist() 39 | if 'probs' in result._keys and result.probs is not None: 40 | infer['probs'] = result.probs.cpu().numpy().data.tolist() 41 | return json.dumps(infer) -------------------------------------------------------------------------------- /sm-notebook/code/requirements.txt: -------------------------------------------------------------------------------- 1 | nvgpu 2 | opencv-python 3 | torchvision 4 | seaborn 5 | ultralytics 6 | pynvml 7 | omegaconf==2.3.0 8 | -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/README.md: -------------------------------------------------------------------------------- 1 | # GitHub to SageMaker Notebook 2 | This Python implementation creates 1 S3 Bucket and 1 SageMaker Notebook 3 | 4 | ![AWSArchitecture](../assets/AWSArchitecture.png) 5 | 6 | ## Creation and Deployment 7 | The project uses virtual environment. Use the following steps to setup the CDK Stack: 8 | ``` 9 | [To manually create a virtualenv on MacOS and Linux] 10 | $ python3 -m venv .venv 11 | 12 | [Activate the virtual environment] 13 | $ source .venv/bin/activate 14 | 15 | [Install the pre-requisites] 16 | $ pip3 install -r requirements.txt 17 | 18 | [CDK Creation and Deployment] 19 | $ cdk synth 20 | $ cdk bootstrap 21 | $ cdk deploy 22 | ``` 23 | 24 | ## Cleanup 25 | To clear the stack and resources: 26 | ``` 27 | $ cdk destroy 28 | ``` 29 | 30 | ## Useful commands 31 | * `cdk ls` list all stacks in the app 32 | * `cdk synth` emits the synthesized CloudFormation template 33 | * `cdk deploy` deploy this stack to your default AWS account/region 34 | * `cdk diff` compare deployed stack with current state 35 | * `cdk docs` open CDK documentation -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/app.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import os 3 | import aws_cdk as cdk 4 | from cdk.yolov8_sagemaker import YOLOv8SageMakerStack 5 | 6 | app = cdk.App() 7 | YOLOv8SageMakerStack(app, "YOLOv8SageMakerStack",) 8 | 9 | app.synth() -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/cdk.json: -------------------------------------------------------------------------------- 1 | { 2 | "app": "python app.py", 3 | "watch": { 4 | "include": [ 5 | "**" 6 | ], 7 | "exclude": [ 8 | "README.md", 9 | "cdk*.json", 10 | "requirements*.txt", 11 | "source.bat", 12 | "**/__init__.py", 13 | "python/__pycache__", 14 | "tests" 15 | ] 16 | }, 17 | "context": { 18 | "@aws-cdk/aws-apigateway:usagePlanKeyOrderInsensitiveId": true, 19 | "@aws-cdk/core:stackRelativeExports": true, 20 | "@aws-cdk/aws-rds:lowercaseDbIdentifier": true, 21 | "@aws-cdk/aws-lambda:recognizeVersionProps": true, 22 | "@aws-cdk/aws-cloudfront:defaultSecurityPolicyTLSv1.2_2021": true, 23 | "@aws-cdk-containers/ecs-service-extensions:enableDefaultLogDriver": true, 24 | "@aws-cdk/aws-ec2:uniqueImdsv2TemplateName": true, 25 | "@aws-cdk/core:checkSecretUsage": true, 26 | "@aws-cdk/aws-iam:minimizePolicies": true, 27 | "@aws-cdk/core:target-partitions": [ 28 | "aws", 29 | "aws-cn" 30 | ] 31 | } 32 | } -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/cdk/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/host-yolov8-on-sagemaker-endpoint/aac7214397f49d0a6fa7adde2abb5f8eb0367b54/yolov8-pytorch-cdk/cdk/__init__.py -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/cdk/yolov8_sagemaker.py: -------------------------------------------------------------------------------- 1 | import os 2 | from constructs import Construct 3 | from aws_cdk import ( 4 | Aws, 5 | Stack, 6 | aws_s3 as s3, 7 | aws_iam as iam, 8 | aws_sagemaker as sagemaker, 9 | aws_ec2 as ec2, 10 | ) 11 | import aws_cdk as cdk 12 | 13 | region = Aws.REGION 14 | account = Aws.ACCOUNT_ID 15 | 16 | # CDK Stack for 17 | # 1. Create S3 18 | # 2. Create SageMaker Notebook and use GitHub as Source 19 | 20 | class YOLOv8SageMakerStack(Stack): 21 | """ 22 | The SageMaker Notebook is used to deploy the custom model on a SageMaker endpoint and test it. 23 | """ 24 | 25 | def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None: 26 | super().__init__(scope, construct_id, **kwargs) 27 | 28 | ## Create S3 bucket 29 | self.bucket = s3.Bucket( 30 | self, "yolov8-s3", 31 | auto_delete_objects=True, 32 | removal_policy=cdk.RemovalPolicy.DESTROY) 33 | 34 | ## IAM Roles 35 | # Create role for Notebook instance 36 | nRole = iam.Role( 37 | self, 38 | "yolov8-notebookAccessRole", 39 | assumed_by=iam.ServicePrincipal('sagemaker')) 40 | 41 | # Attach the right policies for SageMaker Notebook instance 42 | nPolicy = iam.Policy( 43 | self, 44 | "yolov8-notebookAccessPolicy", 45 | policy_name="yolov8-notebookAccessPolicy", 46 | statements=[ 47 | iam.PolicyStatement(actions=['sagemaker:*'], resources=['*']), 48 | iam.PolicyStatement(actions=['s3:ListAllMyBuckets'], resources=['arn:aws:s3:::*']), 49 | iam.PolicyStatement(actions=['iam:PassRole', 'ecr:*', "logs:*"], resources=['*', '*', '*']), 50 | iam.PolicyStatement(actions=['s3:*'], resources=[self.bucket.bucket_arn, self.bucket.bucket_arn+'/*']), 51 | ]).attach_to_role(nRole) 52 | 53 | ## Create SageMaker Notebook instances cluster 54 | nid = 'yolov8-sm-notebook' 55 | notebook = sagemaker.CfnNotebookInstance( 56 | self, 57 | nid, 58 | instance_type='ml.m5.4xlarge', 59 | volume_size_in_gb=5, 60 | notebook_instance_name=nid, 61 | role_arn=nRole.role_arn, 62 | additional_code_repositories=["https://github.com/aws-samples/host-yolov8-on-sagemaker-endpoint"], 63 | ) -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/requirements-dev.txt: -------------------------------------------------------------------------------- 1 | pytest==6.2.5 -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/requirements.txt: -------------------------------------------------------------------------------- 1 | aws-cdk-lib==2.139.1 2 | constructs<=11.0.0 3 | jsonpickle~=2.2.0 4 | boto3~=1.24.1 -------------------------------------------------------------------------------- /yolov8-pytorch-cdk/source.bat: -------------------------------------------------------------------------------- 1 | @echo off 2 | 3 | rem The sole purpose of this script is to make the command 4 | rem 5 | rem source .venv/bin/activate 6 | rem 7 | rem (which activates a Python virtualenv on Linux or Mac OS X) work on Windows. 8 | rem On Windows, this command just runs this batch file (the argument is ignored). 9 | rem 10 | rem Now we don't need to document a Windows command for activating a virtualenv. 11 | 12 | echo Executing .venv\Scripts\activate.bat for you 13 | .venv\Scripts\activate.bat 14 | --------------------------------------------------------------------------------