hyperparam-sweep
Systematically search hyperparameter space. Use when tuning learning rate, batch size, or other hyperparameters.
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Systematically search hyperparameter space. Use when tuning learning rate, batch size, or other hyperparameters.
Interactive diagnostic workflow for training problems. Use when training is failing, loss is stuck, gradients explode, NaN occurs, or convergence is poor.
Execute a neural network training run with mandatory monitoring and best-practice defaults. Use when user wants to train a model, start training, or run a training job.
Design rigorous experiments before running them. Use when planning experiments, testing hypotheses, or designing ablation studies.
View training progress in real-time. Use when checking on running training, viewing live metrics, or opening dashboards.
Run model validation on a dataset. Use when testing model performance, comparing checkpoints, or running final evaluation.
Analyze training logs and explain what's happening. Use when reviewing training runs, understanding loss curves, or diagnosing completed training.
Run scaling experiments to understand model/data/compute relationships. Use when investigating scaling laws, compute-optimal training, or model size decisions.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.