dstack provides an open framework for confidential AI computing and deployments.
Open source
24 alternativesFiber
Fiber presents a simple approach to distributed computing for AI workloads.
About Fiber
Fiber is a Python-based distributed computing library for computer clusters. It was developed to support large-scale parallel scientific computation projects such as POET. It provides the same API as Python's standard multiprocessing library, so code written with multiprocessing can run across a cluster.
Fiber installs with pip, packages code and dependencies with Docker, and launches on Kubernetes with a run command. It can allocate CPU, memory, and GPU resources and includes error handling for worker pools.
Key features
- Multiprocessing-compatible API for cluster-level programs
- Installs with pip install fiber
- Packages code and dependencies using Docker
- Runs on Kubernetes via fiber run command
- Dynamically allocates CPU, memory, and GPU resources
- Built-in error handling for worker pools
Best for: Python developers familiar with multiprocessing who need to run programs on computer clusters.
Good for
- Compatible with Python's standard multiprocessing API
- Runs without a separate cluster deployment step
- Allocates CPU, memory, and GPU as needed
Fiber at a glance
- Website
- uber.github.io/fiber
- Type
- Online tool
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Fiber FAQ
How do I install Fiber?
Run pip install fiber.
How do I run a program on a cluster?
Use fiber run python code_with_fiber.py to launch code packaged with Docker on Kubernetes.
Do I need to learn a new API to use Fiber?
No, it provides the same API as Python's standard multiprocessing library.
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