Skill UI

Deploys and runs ML experiments on local, remote, Vast.ai, or Modal GPUs by detecting the target environment, syncing code, ensuring W&B logging when needed, and launching training jobs safely.

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

A powerful tool for orchestrating large-scale Machine Learning experiments across remote GPU servers. It handles complex job manifests, multi-seed sweeps, sequential wave transitions, and advanced failure recovery (OOM-aware retry, stale screen cleanup). Ideal for complex grid search and pipeline chaining where simple batch running fails.