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Lambda Face Recognition API
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> but it's still a singleton instance, so where do you run it? Most hardware doesn't give you enough uptime for what you need here, because what you actually needed was a re-architecture for distribution / failover / whatever, and while you could ask your LLM to do that you aren't going to run your bank on the result. If only we had a way to solve these issues with tools capable of running Rust programs in that... - Source: Hacker News / about 1 month ago
I run the Jenkins controller in Kubernetes. Helm chart for the deploy, persistent volume for the home dir, a sidecar that injects JCasC config from a ConfigMap. Upgrading Jenkins is just bumping a chart version. Rolling back is rolling back a chart version. Plugin lists are values in a Helm values.yaml file, version-pinned, and reviewed in a pull request like any other change. - Source: dev.to / 3 months ago
Does this scenario sound familiar? It's what happened with containerization before Kubernetes. Kubernetes came along and said: Here's the standard. MCP is doing the same thing for AI tooling. - Source: dev.to / 3 months ago
Building your own runtime layer is the right call in a narrow set of scenarios. The open-source ecosystem has matured enough that deep platform engineering teams can stand up their own orchestration layer on top of the official Model Context Protocol Python or TypeScript SDKs. The SDKs implement the MCP specification over JSON-RPC 2.0 and support both stdio for local process communication and Streamable HTTP for... - Source: dev.to / 3 months ago
Amazon Elastic Kubernetes Service (EKS) is a fully managed service from Amazon Web Services (AWS) that makes it easy to run Kubernetes on AWS without needing to install, operate, or maintain your own Kubernetes control plane. It automates cluster management, security, and scaling, supporting applications on both Amazon EC2 and AWS Fargate. - Source: dev.to / 3 months ago
Setup time matters too. The delta between Runpod and bare-metal providers like Lambda Labs is large. Reaching an equivalent setup on a bare VM requires provisioning the instance, configuring the OS and CUDA drivers, installing Docker, setting up your orchestration layer (Kubernetes or Slurm), deploying your inference container, configuring autoscaling rules, and wiring up your load balancer. Thatโs a realistic... - Source: dev.to / 5 months ago
Let's do the math for a representative setup: GPT-OSS-120B via Together.ai ($0.15/$0.60) vs self-hosting on H100s from Lambda Labs at $2.99/hr ($2,183/mo). A single H100 running a 70B model produces roughly 50 tokens/second on average, which works out to about 130M tokens per month. - Source: dev.to / 6 months ago
How does this compare to https://lambdalabs.com/. - Source: Hacker News / about 3 years ago
Another option is to pay for AWS server with a beefy GPU and enough RAM. It's not too cheap, but isn't expensive either if you aren't planning to run it 24/7. Or get a GPU cluster from a company that offers stuff for ML specifically, it might be easier to set up compared to AWS and in some cases cheaper. Like, for example, lambdalabs that offers H100 gpu for 2 bucks per hour. Source: about 3 years ago
I used some of the cloud GPUs on Vast.ai, but I also tried Lambda Labs, and these days I have my own docker container setup which can be deployed to a VM on Google Cloud and used more programatically. Source: over 3 years ago
Rancher - Open Source Platform for Running a Private Container Service
Mattermost - Mattermost is an open source alternative to Slack.
Helm.sh - The Kubernetes Package Manager
Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
ipinfo.io - Simple IP address information.