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Lambda Face Recognition API
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Docker Compose
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Based on our record, Docker Compose should be more popular than Lambda Face Recognition API. It has been mentiond 60 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Docker Compose Documentation โ official Docker Compose reference. - Source: dev.to / about 1 month ago
Docker Documentation Docker Compose Documentation. - Source: dev.to / 3 months ago
While developing web applications using Docker Compose has many positives, like portability and making it easy to add databases and other services like Redis to your environment, it's important to remember that Docker and containers generally were not originally meant to facilitate the sort of immediate-feedback development workflows which web developers expect. - Source: dev.to / 3 months ago
We started experimenting with AI-powered imports in March, and the initial tests were promising. By analyzing package files, Docker Compose files, Dockerfiles, READMEs, folder structures, and other project files, AI turned out to be remarkably capable of understanding how a project should run on Diploi. - Source: dev.to / 4 months ago
This tutorial walks you through setting up a simple Docker Compose project that serves two Node web servers over HTTPS using Caddy as a reverse proxy. You will learn how to use mkcert to generate wildcard certificates and the minimal configuration needed in the Caddyfile and docker-compose.yml to get it all working. - Source: dev.to / 4 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
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Mattermost - Mattermost is an open source alternative to Slack.
Rancher - Open Source Platform for Running a Private Container Service
Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.
Docker Swarm - Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host.
ipinfo.io - Simple IP address information.