├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── NOTICE ├── README.md ├── THIRD_PARTY_LICENSES ├── docker └── Dockerfile ├── examples └── test.jpg ├── lambda └── app.py └── source ├── 1_Deploy_Model_to_Endpoint.ipynb ├── 2_Test_Endpoint.ipynb ├── 3_Lambda_Setup_and_Deploy.ipynb └── deploy.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | .vscode/ 3 | .ipynb_checkpoints/ 4 | 5 | # Prerequisites 6 | *.d 7 | 8 | # Compiled Object files 9 | *.slo 10 | *.lo 11 | *.o 12 | *.obj 13 | 14 | # Precompiled Headers 15 | *.gch 16 | *.pch 17 | 18 | # Compiled Dynamic libraries 19 | *.so 20 | *.dylib 21 | *.dll 22 | 23 | # Fortran module files 24 | *.mod 25 | *.smod 26 | 27 | # Compiled Static libraries 28 | *.lai 29 | *.la 30 | *.a 31 | *.lib 32 | 33 | # Executables 34 | *.exe 35 | *.out 36 | *.app 37 | 38 | # Prerequisites 39 | *.d 40 | 41 | # Object files 42 | *.o 43 | *.ko 44 | *.obj 45 | *.elf 46 | 47 | # Linker output 48 | *.ilk 49 | *.map 50 | *.exp 51 | 52 | # Precompiled Headers 53 | *.gch 54 | *.pch 55 | 56 | # Libraries 57 | *.lib 58 | *.a 59 | *.la 60 | *.lo 61 | 62 | # Shared objects (inc. Windows DLLs) 63 | *.dll 64 | *.so 65 | *.so.* 66 | *.dylib 67 | 68 | # Executables 69 | *.exe 70 | *.out 71 | *.app 72 | *.i*86 73 | *.x86_64 74 | *.hex 75 | 76 | # Debug files 77 | *.dSYM/ 78 | *.su 79 | *.idb 80 | *.pdb 81 | 82 | # Kernel Module Compile Results 83 | *.mod* 84 | *.cmd 85 | .tmp_versions/ 86 | modules.order 87 | Module.symvers 88 | Mkfile.old 89 | dkms.conf 90 | 91 | # MacOS .DS_Store files 92 | .DS_Store 93 | -------------------------------------------------------------------------------- /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. 5 | -------------------------------------------------------------------------------- /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. 60 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | this software and associated documentation files (the "Software"), to deal in 5 | the Software without restriction, including without limitation the rights to 6 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | 16 | -------------------------------------------------------------------------------- /NOTICE: -------------------------------------------------------------------------------- 1 | Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Host YOLOv5 using SM Endpoint 2 | 3 | --- 4 | 5 | ## [Blog Link](https://aws.amazon.com/blogs/machine-learning/scale-yolov5-inference-with-amazon-sagemaker-endpoints-and-aws-lambda/) 6 | 7 | ## Blog Description: 8 | YOLOv5* model is a popular object detection model known for its runtime efficiency as well as detection accuracy. Many object detection applications use pre-trained YOLOv5 models and are finetuned to detect objects of interest. After data scientists come up with a satisfying model, the model must be deployed to be easily accessible for inference by other members of the organization. Serving a model on Amazon SageMaker Endpoints (https://aws.amazon.com/pm/sagemaker/) can alleviate a lot of the pain points around model scalability and inference cost optimization. Here, we will demonstrate how to host a pre-trained YOLOv5 model on SageMaker Endpoints and use AWS Lambda (https://aws.amazon.com/lambda/) functions to invoke these endpoints. 9 | 10 | - Here we are using Amazon SageMaker Notebook instances to run the Notebooks in the [source/](source/) directory. 11 | - In order to use the Amazon SageMaker Notebook instances, we are using `ml.c5.xlarge` as instance type. 12 | - The kernel used by the Amazon SageMaker Notebooks is `tensorflow2_p38`. 13 | 14 | (*) **_NOTE:_** YOLOv5 is distributed under the GPLv3 license. 15 | 16 | Using the following steps, we can get started from downloading the YOLOv5 model, hosting it on Amazon SageMaker Endpoint, testing it and creating AWS Lambda with OpenCV Layers for running the Endpoint: 17 | 18 | 1. Download YOLOv5 PyTorch model, convert to `tar.gz` format and host on SageMaker Endpoint (More Details: [source/1_Deploy_Model_to_Endpoint.ipynb](source/1_Deploy_Model_to_Endpoint.ipynb)): 19 | * Clone the YOLOv5 repository and convert the model from PyTorch to TensorFlow SavedModel format: 20 | ``` 21 | $ git clone https://github.com/ultralytics/yolov5 22 | $ cd yolov5 23 | $ pip install -r requirements.txt tensorflow-cpu 24 | $ python export.py --weights yolov5l.pt --include saved_model --nms 25 | $ mkdir export && mkdir export/Servo 26 | $ mv yolov5l_saved_model export/Servo/1 27 | ``` 28 | 29 | * Store the model in `tar.gz` format and then upload to Amazon S3: 30 | ``` 31 | $ tar -czvf model.tar.gz export/ 32 | $ aws s3 cp model.tar.gz "" 33 | ``` 34 | 35 | * Edit deploy script from `source/deploy.py`: 36 | - Update the Amazon S3 path of the model: 37 | ``` 38 | model_data = '' 39 | ``` 40 | - Update the role created for SageMaker: 41 | ``` 42 | role = '' 43 | ``` 44 | 45 | * Deploy the model on a SageMaker Endpoint (or follow instructions in the Notebook): 46 | ``` 47 | $ python3 source/deploy.py 48 | ``` 49 | 50 | 2. Testing the Endpoint (More Details: [source/2_Test_Endpoint.ipynb](source/2_Test_Endpoint.ipynb)): 51 | * Setting the Endpoint name and running the Endpoint: 52 | ``` 53 | ENDPOINT_NAME = 'yolov5l-demo' 54 | response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME, ContentType='application/json', Body=payload) 55 | ``` 56 | 57 | 3. Create OpenCV Lambda Layers and deploy AWS Lambda app (More Details: [source/3_Lambda_Setup_and_Deploy.ipynb](source/3_Lambda_Setup_and_Deploy.ipynb)): 58 | * Build & Run Docker and store the output zip in the current directory under layers: 59 | ``` 60 | $ pushd docker 61 | $ docker build --tag aws-lambda-layers:latest 62 | $ docker run -rm -it -v $(pwd):/layers aws-lambda-layers cp /packages/cv2-python37.zip /layers 63 | $ popd 64 | ``` 65 | * Now we can upload the OpenCV layer artifacts to Amazon S3 and create the Lambda layer: 66 | ``` 67 | $ aws s3 cp layers/cv2-python37.zip s3:/// 68 | $ aws lambda publish-layer-version --layer-name cv2 --description "Open CV" --content S3Bucket=,S3Key=/cv2-python37.zip --compatible-runtimes python3.7 69 | ``` 70 | * Edit Lambda function from `lambda/app.py`: 71 | - Update the Amazon S3 Bucket name for image access: 72 | ``` 73 | BUCKET_NAME = "" 74 | ``` 75 | - Update the Amazon S3 prefix where the image will be stored: 76 | ``` 77 | IMAGE_LOCATION = "/image.png" 78 | ``` 79 | * Deploy the Lambda function: 80 | ``` 81 | $ pushd lambda 82 | $ zip app.zip app.py 83 | $ aws s3 cp app.zip s3:/// 84 | $ aws lambda create-function --function-name yolov5-lambda --handler app.lambda_handler --region us-east-1 --runtime python3.7 --environment "Variables={BUCKET_NAME=$BUCKET_NAME,S3_KEY=$S3_KEY}" --code S3Bucket=,S3Key="" 85 | $ popd 86 | ``` 87 | * Once we have the Lambda function and the layer in place, we can connect the layer to the function as follows: 88 | ``` 89 | $ aws lambda update-function-configuration --function-name yolov5-lambda --layers cv2 90 | ``` 91 | 92 | 93 | --- 94 | 95 | ## Contributors: 96 | - [Kevin Song (@kcsong)](kcsong@amazon.com) 97 | - [Romil Shah (@rpshah)](rpshah@amazon.com) 98 | -------------------------------------------------------------------------------- /THIRD_PARTY_LICENSES: -------------------------------------------------------------------------------- 1 | ** YOLOv5; version 6.0 -- https://github.com/ultralytics/yolov5/tree/v6.0 2 | 3 | 4 | GNU GENERAL PUBLIC LICENSE 5 | Version 3, 29 June 2007 6 | 7 | Copyright (C) 2007 Free Software Foundation, Inc. 8 | Everyone is permitted to copy and distribute verbatim copies 9 | of this license document, but changing it is not allowed. 10 | 11 | Preamble 12 | 13 | The GNU General Public License is a free, copyleft license for 14 | software and other kinds of works. 15 | 16 | The licenses for most software and other practical works are designed 17 | to take away your freedom to share and change the works. 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') 31 | orig_image = cv2.imread('/tmp/' + input_imagename) 32 | if orig_image is not None: 33 | start_time_iter = time.time() 34 | # pre-processing input image 35 | image = cv2.resize(orig_image.copy(), (modelWidth, modelHeight), interpolation = cv2.INTER_AREA) 36 | data = np.array(image.astype(np.float32)/255.) 37 | payload = json.dumps([data.tolist()]) 38 | # run inference 39 | response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME, ContentType='application/json', Body=payload) 40 | # get the output results 41 | result = json.loads(response['Body'].read().decode()) 42 | end_time_iter = time.time() 43 | # get the total time taken for inference 44 | inference_time = round((end_time_iter - start_time_iter)*100)/100 45 | logger.info(f'Inference Completed ... ') 46 | 47 | # OUTPUTS - Using the output to utilize in other services downstream 48 | return { 49 | "statusCode": 200, 50 | "body": json.dumps({ 51 | "message": "Inference Time:// " + str(inference_time) + " seconds.", 52 | "results": result 53 | }), 54 | } 55 | -------------------------------------------------------------------------------- /source/1_Deploy_Model_to_Endpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "chubby-policy", 6 | "metadata": {}, 7 | "source": [ 8 | "# Using this Notebook to deploy a YOLOv5 model to Amazon SageMaker Endpoint\n", 9 | "---" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "id": "fourth-yugoslavia", 15 | "metadata": {}, 16 | "source": [ 17 | "## Importing Libraries:" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 1, 23 | "id": "classical-forward", 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "import os\n", 28 | "import tensorflow as tf\n", 29 | "from tensorflow.keras import backend\n", 30 | "from sagemaker.tensorflow import TensorFlowModel\n" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "id": "parallel-blank", 36 | "metadata": {}, 37 | "source": [ 38 | "## Setting up YOLOv5 model for SageMaker:\n", 39 | "\n", 40 | "### Download YOLOv5 pre-trained models:\n", 41 | "- Download any PyTorch models from here: https://github.com/ultralytics/yolov5/releases\n", 42 | "- Here we will use `yolov5s.pt` model from: https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "id": "through-institute", 48 | "metadata": {}, 49 | "source": [ 50 | "## Preparing model.tar.gz for deploying to Sagemaker Endpoint\n", 51 | "Pretrained YoloV5 models can be accessed through https://github.com/ultralytics/yolov5\n", 52 | "\n", 53 | "First, `git clone https://github.com/ultralytics/yolov5`, then within the `yolov5` directory, you can retrieve a pretrained yolov5 model by running the following command: `python export.py --weights yolov5l.pt --include saved_model --nms`. This will create a directory called `yolov5l_saved_model`. \n", 54 | "\n", 55 | "In order to deploy this model onto SageMaker Endpoints, it must be packaged in the following structure:\n", 56 | "\n", 57 | "```\n", 58 | "export\n", 59 | "└──Servo\n", 60 | " └──1\n", 61 | " │ saved_model.pb\n", 62 | " └──assets\n", 63 | " └──variables\n", 64 | " │ variables.data-00000-of-00001\n", 65 | " │ variables.index\n", 66 | "```\n", 67 | "\n", 68 | "To get the correct data structure for `model.tar.gz`, copy the contents from `yolov5l_saved_model` into `export/Servo/1/`. Once you have the correct, directory structure for your model, use `tar -czvf model.tar.gz export` to get your `model.tar.gz`.\n", 69 | "\n", 70 | "Upload `model.tar.gz` to your s3 bucket of your choice, and make sure your ec2 instance or sagemaker instance has the correct IAM roles to access the s3 bucket where `model.tar.gz` will be located. \n", 71 | "\n", 72 | "In my case, my `model.tar.gz` will be uploaded to `s3://sagemaker-us-east-1-171547146718/model.tar.gz`" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "id": "9d4a4fa7", 78 | "metadata": {}, 79 | "source": [ 80 | "## The following commands can be used to generate all the necessary `model.tar.gz` object:" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": null, 86 | "id": "dc99ad60", 87 | "metadata": {}, 88 | "outputs": [], 89 | "source": [ 90 | "!git clone https://github.com/ultralytics/yolov5\n", 91 | "!cd yolov5\n", 92 | "!pip install -r requirements.txt tensorflow-cpu\n", 93 | "!python export.py --weights yolov5l.pt --include saved_model --nms\n", 94 | "!mkdir export && mkdir export/Servo\n", 95 | "!mv yolov5l_saved_model export/Servo/1\n", 96 | "!tar -czvf model.tar.gz export\n", 97 | "!aws s3 cp model.tar.gz \"\"" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "id": "charitable-sustainability", 103 | "metadata": {}, 104 | "source": [ 105 | "## Define the Model Path and the IAM Role:" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": null, 111 | "id": "induced-gasoline", 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "model_data = ''\n", 116 | "role = ''" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "id": "under-incident", 122 | "metadata": {}, 123 | "source": [ 124 | "## Create a SageMaker TensorFlow Model:" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": null, 130 | "id": "tutorial-mobility", 131 | "metadata": {}, 132 | "outputs": [], 133 | "source": [ 134 | "model = TensorFlowModel(model_data=model_data, \n", 135 | " framework_version='2.8', role=role)" 136 | ] 137 | }, 138 | { 139 | "cell_type": "markdown", 140 | "id": "accurate-copper", 141 | "metadata": {}, 142 | "source": [ 143 | "## Deploy the model on SageMaker Endpoint:" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": null, 149 | "id": "sharp-greene", 150 | "metadata": {}, 151 | "outputs": [], 152 | "source": [ 153 | "INSTANCE_TYPE = 'ml.m5.xlarge'\n", 154 | "ENDPOINT_NAME = 'yolov5l-demo'\n", 155 | "\n", 156 | "predictor = model.deploy(initial_instance_count=1, \n", 157 | " instance_type=INSTANCE_TYPE,\n", 158 | " endpoint_name=ENDPOINT_NAME)" 159 | ] 160 | } 161 | ], 162 | "metadata": { 163 | "kernelspec": { 164 | "display_name": "Environment (conda_tensorflow2_p38)", 165 | "language": "python", 166 | "name": "conda_tensorflow2_p38" 167 | }, 168 | "language_info": { 169 | "codemirror_mode": { 170 | "name": "ipython", 171 | "version": 3 172 | }, 173 | "file_extension": ".py", 174 | "mimetype": "text/x-python", 175 | "name": "python", 176 | "nbconvert_exporter": "python", 177 | "pygments_lexer": "ipython3", 178 | "version": "3.8.12" 179 | } 180 | }, 181 | "nbformat": 4, 182 | "nbformat_minor": 5 183 | } 184 | -------------------------------------------------------------------------------- /source/3_Lambda_Setup_and_Deploy.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "attractive-exemption", 6 | "metadata": {}, 7 | "source": [ 8 | "# Using this Notebook to create Lambda Layers and deploy Lambda app to run the Amazon SageMaker Endpoint\n", 9 | "---" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "id": "green-worse", 15 | "metadata": {}, 16 | "source": [ 17 | "## Run Docker to build OpenCV Lambda Layers:" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": null, 23 | "id": "informal-bacon", 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "!pushd ../docker\n", 28 | "!docker build --tag aws-lambda-layers:latest .\n", 29 | "!docker run -rm -it -v $(pwd):/layers aws-lambda-layers cp /packages/cv2-python37.zip /layers\n", 30 | "!popd" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "id": "economic-winter", 36 | "metadata": {}, 37 | "source": [ 38 | "## Push the OpenCV Lambda Layers zip to AWS Lambda and create the Lambda Layer:\n", 39 | "\n", 40 | "- Need to provide the Amazon S3 bucket location to upload the Lambda script:\n", 41 | " `s3:///`" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": null, 47 | "id": "impressive-modern", 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "!aws s3 cp layers/cv2-python37.zip s3:///\n", 52 | "!aws lambda publish-layer-version --layer-name cv2 --description \"Open CV\" --content S3Bucket=,S3Key=/cv2-python37.zip --compatible-runtimes python3.7" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "id": "worldwide-title", 58 | "metadata": {}, 59 | "source": [ 60 | "## Deploy the Lambda function and connect the OpenCV Lambda Layers:\n", 61 | "\n", 62 | "- Need to provide the Amazon S3 bucket location to upload the Lambda script:\n", 63 | " `s3:///`" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": null, 69 | "id": "classical-semiconductor", 70 | "metadata": {}, 71 | "outputs": [], 72 | "source": [ 73 | "!pushd ../lambda\n", 74 | "!zip app.zip app.py\n", 75 | "!aws s3 cp app.zip s3:///\n", 76 | "!aws lambda create-function --function-name yolov5-lambda --handler app.lambda_handler --region us-east-1 --runtime python3.7 --environment \"\"Variables={BUCKET_NAME=$BUCKET_NAME,S3_KEY=$S3_KEY}\"\" --code S3Bucket=,S3Key=\"\"\n", 77 | "!popd\n", 78 | "!aws lambda update-function-configuration --function-name yolov5-lambda --layers cv2" 79 | ] 80 | } 81 | ], 82 | "metadata": { 83 | "kernelspec": { 84 | "display_name": "Python 3", 85 | "language": "python", 86 | "name": "python3" 87 | }, 88 | "language_info": { 89 | "codemirror_mode": { 90 | "name": "ipython", 91 | "version": 3 92 | }, 93 | "file_extension": ".py", 94 | "mimetype": "text/x-python", 95 | "name": "python", 96 | "nbconvert_exporter": "python", 97 | "pygments_lexer": "ipython3", 98 | "version": "3.7.10" 99 | } 100 | }, 101 | "nbformat": 4, 102 | "nbformat_minor": 5 103 | } 104 | -------------------------------------------------------------------------------- /source/deploy.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tensorflow as tf 3 | from tensorflow.keras import backend 4 | from sagemaker.tensorflow import TensorFlowModel 5 | 6 | model_data = '' 7 | role = '' 8 | 9 | model = TensorFlowModel(model_data=model_data, 10 | framework_version='2.3', role=role) 11 | predictor = model.deploy(initial_instance_count=1, 12 | instance_type='ml.m5.xlarge', 13 | endpoint_name='yolov5-demo') 14 | 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