Orchestrate AI workloads in any cloud
dstack is an open-source orchestration engine for running AI workloads in any cloud or data center. Your models, your infrastructure.
Dev environments
Before scheduling a task or deploying a model, you may want to run code interactively.
Dev environments allow you to provision a remote machine set up with your code and favorite IDE with just one command.
Tasks
Tasks allow for convenient scheduling of various batch jobs, such as training, fine-tuning, or data processing, as well as running web applications.
You can run tasks on a single machine or on a cluster of nodes.
Services
Services make it very easy to deploy any kind of model as public, secure, and scalable endpoints.
Pools
Pools enable the efficient reuse of cloud instances and on-premises servers across runs, simplifying their management.
Examples
Llama 3
Deploy Llama 3 as an endpoint using Ollama.
Alignment Handbook
Fine-tune Gemma 7B on a custom dataset using Alignment Handbook.
vLLM
Deploy LLMs as endpoints using vLLM.
Axolotl
Fine-tune Llama 3 on a custom dataset using Axolotl.
TGI
Deploy LLMs as endpoints using TGI.
Ollama
Deploy LLMs as endpoints using Ollama.
TEI
Deploy a text embeddings model as an endpoint using TEI.
Mixtral 7Bx8
Deploy Mixtral 7Bx8 as an endpoint using Ollama.
Get started in a minute
Why community dstack
Andrew Spott
ML Engineer at Stealth Startup
Thanks to @dstack, I get the convenience of having a personal Slurm cluster and using budget-friendly cloud GPUs, without paying the super-high premiums charged by the big three.
Alvaro Bartolome
ML Engineer at Argilla
With @dstack it's incredibly easy to define a configuration within a repository and run it without worrying about GPU availability. It lets you focus on data and your research.
Park Chansung
ML Researcher at ETRI
Thanks to @dstack, I can effortlessly access the top GPU options across different clouds, saving me time and money while pushing my AI work forward.
Eckart Burgwedel
CEO at Uberchord
With @dstack, running an open-source LLM or a dev environment on a cloud GPU is as easy as running a local Docker container. It combines the ease of Docker with the auto-scaling capabilities of K8s.
Peter Hill
Co-Founder at CUDO Compute
@dstack is instrumental in simplifying infrastructure provisioning and AI model development. if your organization is on the lookout for an platform to speed up the adoption of AI, I wholeheartedly recommend @dstack