Create and manage ML pipelines with ZenML Dashboard
Track, rank and evaluate open LLMs and chatbots
Measure execution times of BERT models using WebGPU and WASM
Generate and view leaderboard for LLM evaluations
Upload a machine learning model to Hugging Face Hub
Analyze model errors with interactive pages
Visualize model performance on function calling tasks
Export Hugging Face models to ONNX
View LLM Performance Leaderboard
Convert Hugging Face model repo to Safetensors
View RL Benchmark Reports
Evaluate RAG systems with visual analytics
Browse and submit model evaluations in LLM benchmarks
Zenml Server is a powerful tool designed to create and manage ML pipelines with ease. It serves as the web-based interface for ZenML, allowing users to streamline and organize their machine learning workflows. The server provides a centralized platform to monitor, configure, and optimize ML pipelines, making it an essential component for efficient ML workflow management.
• Pipeline Management: Easily create, edit, and monitor ML pipelines through an intuitive interface.
• Multi-User Support: Collaborate with teams seamlessly with role-based access control.
• Real-Time Monitoring: Track pipeline executions and gain insights into performance metrics.
• Extensibility: Integrate with various tools and frameworks in the ML ecosystem.
• Collaboration Tools: Share pipelines, experiments, and results with team members.
zenml server start.http://localhost:8000 to access the Zenml Dashboard.What is Zenml Server used for?
Zenml Server is used to manage and monitor ML pipelines. It provides a centralized interface for creating, running, and analyzing machine learning workflows.
How do I install Zenml Server?
Zenml Server is part of the ZenML package. You can install it using pip install zenml and then start the server with zenml server start.
Can I use Zenml Server with my existing ML tools?
Yes, Zenml Server supports integration with popular ML tools and frameworks. It is designed to be extensible and compatible with a wide range of machine learning ecosystems.