Description:
ML workflow orchestration system designed for reproducibility and collaboration
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Type: Formula
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Latest Version: 0.20.0@0
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Tracked Since: Dec 17, 2025
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Links:
Homepage |
@dstackai |
formulae.brew.sh
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Category: Ai ml
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Tags:
ai
ml
orchestration
devops
reproducibility
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Install:
brew install dstack
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About:
dstack is an open-source orchestration system that enables developers to define and run ML workflows using Python. It abstracts infrastructure management, allowing seamless execution across local machines, cloud providers, or on-premise clusters. The platform focuses on reproducibility and collaboration, making it easier for teams to develop and share AI applications.
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Key Features:
- Declarative YAML or Python API for defining workflows
- Automatic provisioning of cloud and local infrastructure
- Built-in support for dev environments, tasks, and services
- Git integration for version control and reproducibility
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Use Cases:
- Developing and training ML models in reproducible cloud environments
- Running large-scale batch inference jobs on spot instances
- Collaborating on AI projects with shared, versioned configurations
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Alternatives:
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Kubeflow
– Kubeflow is Kubernetes-native and more complex, while dstack offers a simpler, cloud-agnostic experience.
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Weights & Biases
– W&B focuses on experiment tracking, whereas dstack provides full environment and workflow orchestration.
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