Skip to content

Latest commit

 

History

History
executable file
·
48 lines (37 loc) · 2.96 KB

File metadata and controls

executable file
·
48 lines (37 loc) · 2.96 KB

Ludwig

The AI Engineer presents Ludwig

Overview

Ludwig is an open source low-code framework for building custom AI models like LLMs easily. Just declare model architecture in YAML. Supports multi-task learning, distributed training, model exporting & more.

Description

Ludwig is an open-source low-code framework for building custom AI models like large language models (LLMs) and other deep neural networks.

Key Highlights

  • 🛠️ Build models quickly - Just declare model architecture in YAML. No coding is needed.
  • ⚡️ Optimized for scale - Distributed training, model compression, faster optimizers to handle large datasets.
  • 🧩 Modular and extensible - Experiment with different architectures, tasks, and features as modules.
  • 📈 Metrics and visualization - Compare models easily with built-in benchmarking.
  • 🎚️ Complete control - Customize every aspect, like layers, activation functions, etc.
  • 🏭 Production-ready - Docker containers, export TorchScript models, Kubernetes, etc.

Whether you want to build an LLM model tailored to your use case or optimize an existing architecture, Ludwig makes the process incredibly intuitive with its low-code approach. Its rich features, like hyperparameter optimization, multi-task learning capabilities and seamless scaling, enable rapid experimentation and development.

With Ludwig, you get the best of simplicity through configuration AND extreme customizability when needed - no coding required.

🤔 Why should The AI Engineer care about Ludwig?

  1. ⚡️ Productivity - Faster development cycles building models with no boilerplate code.
  2. 📊 Governance - Standardized benchmarks and metrics aid model quality enforcement.
  3. 🧩 Modularity - Flexible components enable custom solutions tailored to specific needs.
  4. 🔌 Integrations - Works out-of-the-box with HuggingFace, Ray, and other libs.
  5. 🛡 Reliability - Battle-tested foundations like PyTorch bring enterprise-grade robustness.

In summary, Ludwig brings productivity to engineers through radically simplified access to best practices in deep learning. By handling complexity, it maximizes innovation capability.

📊 Ludwig Stats

  • 👷🏽‍♀️ Builders: Piero Molino, Travis Addair, Devvret Rishi, Justin Zhao,
  • 💾 Used in 214 repositories
  • 👩🏽‍💻 Contributors: 145
  • 💫 GitHub Stars: 10.2k
  • 🍴 Forks: 1.1k
  • 👁️ Watch: 189
  • 🪪 License: Apache-2.0
  • 🔗 Links: Below 👇🏽

🖇️ Ludwig Links


🧙🏽 Follow The AI Engineer for daily insights tailored to AI engineers and subscribe to our newsletter. We are the AI community for hackers!

⚠️ If you want me to highlight your favorite AI library, open-source or not, please share it in the comments section!