Create and manage ML pipelines with ZenML Dashboard
Evaluate code generation with diverse feedback types
Find recent high-liked Hugging Face models
Measure over-refusal in LLMs using OR-Bench
View and submit LLM benchmark evaluations
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Display and submit language model evaluations
Browse and evaluate language models
Explore GenAI model efficiency on ML.ENERGY leaderboard
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
Calculate survival probability based on passenger details
Convert Stable Diffusion checkpoint to Diffusers and open a PR
Calculate memory usage for LLM models
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.