axis-aws-rekognition
aws-virtual-gpu-device-plugin
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| axis-aws-rekognition | aws-virtual-gpu-device-plugin | |
|---|---|---|
| 2 | 3 | |
| 1 | 132 | |
| - | - | |
| 1.8 | 0.0 | |
| over 2 years ago | over 1 year ago | |
| Go | Jupyter Notebook | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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axis-aws-rekognition
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Boosting the assembly process with AWS serverless & Axis camera
For us, this is a big step towards bringing AWS into our assembly process to enable other use cases with the same setup, at the same time, gain quick wins for us. To support the community and the speed of transformation at manufacturers, we will also release our POC (proof of concept) code at GitHub that brings the powerful OCR function from Rekognition and the result delivered over MQTT together with powerful cameras. With some simple code changes, you could quickly get the object Rekognition with thousands of supported objects/scenes and custom labels to train it for your own object detection with a few images.
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AWS open source news and updates No.41
axis-aws-rekognition Lezgin Bakircioglu got in touch this week to tell me about this project and how they are integrating AI services such as AWS Rekognition into the Axis range of cameras in order to do OCR. He has put together this blog post, Boosting the assembly process with AWS serverless & Axis camera to help you understand how this all fits together. This is still experimental, so get in touch with Lezgin if you think this is something you would like to contribute to.
aws-virtual-gpu-device-plugin
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Share a GPU between pods on AWS EKS
This project (available here) uses the k8s device plugin described by this AWS blog post to make GPU-based nodes publish the amount of GPU resource they have available. Instead of the amount of VRAM available or some abstract metric, this plugin advertises the amount of pods/processes that can be connected to the GPU. This is controlled by what is called by NVIDIA as Multi-Process Service (MPS).
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[D] Serverless solutions for GPU inference (if there's such a thing)
AWS has apparently already started using this type of tech as of this year (see lost below). They mention virtual gpus but this particular solution probably won't help OP unfortunately. https://aws.amazon.com/blogs/opensource/virtual-gpu-device-plugin-for-inference-workload-in-kubernetes/
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AWS open source news and updates No.41
The post explores GPU device plugin to address how to set fractional number of GPU resource for each pod by implementing the Kubernetes device plugin and Nvidia MPS. This project has been open sourced on GitHub.
What are some alternatives?
amazon-s3-presigned-urls-aws-sam - For more serverless learning resources, visit https://serverlessland.com/.
kserve - Standardized Serverless ML Inference Platform on Kubernetes
cfn-diagram - CLI tool to visualise CloudFormation/SAM/CDK stacks as visjs networks, draw.io or ascii-art diagrams.
aws-eks-share-gpu - How to share the same GPU between pods on AWS EKS
acaps - Axis System Integration ACAP
k8s-device-plugin - NVIDIA device plugin for Kubernetes
ha-mqtt-iot - A simple configurable MQTT client that allows actions to be taken or values be reported from a PC
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
aws-sdk-js-v3 - Modularized AWS SDK for JavaScript.
terraform-provider-kubernetes - Terraform Kubernetes provider
awslambdahandler - Linter for AWS Lambda handler
booster - Software development framework specialized in building highly scalable microservices with CQRS and Event-Sourcing. It uses the semantics of the code to build a fully working GraphQL API that supports real-time subscriptions.