https://github.com/slai-labs/get-beam

Run GPU inference and training jobs on serverless infrastructure that scales with you.
https://github.com/slai-labs/get-beam

artificial-intelligence cloud-computing cost-optimization data-science deep-learning distributed-computing gpu-acceleration gpu-computing hpc llm-serving llm-training machine-learning ml-infrastructure mlops python serverless serverless-architectures

Last synced: about 2 months ago
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Run GPU inference and training jobs on serverless infrastructure that scales with you.

• Host: GitHub
• URL: https://github.com/slai-labs/get-beam
• Owner: slai-labs
• Created: 2022-10-18T23:35:11.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-20T21:29:38.000Z (4 months ago)
• Last Synced: 2024-05-21T00:10:55.693Z (4 months ago)
• Topics: artificial-intelligence, cloud-computing, cost-optimization, data-science, deep-learning, distributed-computing, gpu-acceleration, gpu-computing, hpc, llm-serving, llm-training, machine-learning, ml-infrastructure, mlops, python, serverless, serverless-architectures
• Language: Shell
• Homepage: https://beam.cloud
• Size: 5.95 MB
• Stars: 89
• Watchers: 3
• Forks: 20
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        








Run GPU inference and training jobs on serverless infrastructure that scales with you









# Get Started

The `examples` folder in this repo contains various examples of programs built with Beam. 

You can run any of these examples yourself. All you need is a free account on [Beam](https://beam.cloud).

# What can you do with Beam?

* 🛰 **Develop locally on remote hardware**. Beam provides a brand new type of cloud development experience. You can write code on your laptop and execute it on cloud hardware immediately, with lightning fast build times.

* 📦 **Instantly containerize any Python function and run it on a GPU.** Configure your runtime in Python - tell us how many GPUs you need and which libraries you want installed, and Beam will spawn a remote environment for you.

* 🚀 **Deploy apps as serverless functions**. Deploy your apps as serverless REST APIs, scheduled cron jobs, or task queues - all with just a single line of Python. It's great for deploying [LangChain](https://docs.beam.cloud/examples/langchain) apps, [Stable Diffusion APIs](https://docs.beam.cloud/examples/stable-diffusion-gpu), or [Dreambooth](https://docs.beam.cloud/examples/dreambooth).

* ⏰ **Run scheduled jobs**. You can run any code on a schedule, and do things like [train machine learning models](https://docs.beam.cloud/examples/recommendation-system) and setup [data pipelines on S3 buckets](https://docs.beam.cloud/examples/s3-schedule).

* 🗂️ **Mount storage volumes**. Read and write data to highly-performant distributed file systems.