Skill UI

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Guides TensorFlow Serving setup with step-by-step ML deployment orchestration, enforcing best practices, ready-to-run configurations, and output validation for production inference services.

A comprehensive skill for managing the entire lifecycle of Volcano Jobs within the KubeSphere environment. It supports the scheduling and execution of complex batch workloads, including deep learning tasks using PyTorch and TensorFlow, MPI, and general resource-intensive jobs. Provides detailed API usage and troubleshooting guidance for job scheduling and resource conflicts.