{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Monkey-patches PyTorch tensors to display human-readable summaries with statistics, histograms, and anomaly detection instead of raw numerical dumps.","skillfed_tags":["pytorch-debugging","jupyter-friendly","tensor-inspection"],"use_cases":["Debugging neural network activations in Jupyter notebooks without scrolling through thousands of raw values","Quickly spotting NaN or Inf propagation during training by inspecting intermediate tensors","Verifying tensor shapes and memory usage at a glance during model prototyping","Inspecting gradient statistics after backpropagation to detect gradient explosion or vanishing","Exploring multi-dimensional data (e.g., image batches) by recursively drilling into sub-tensors with `.deeper`"],"what_it_does":"Lovely Tensors replaces PyTorch's default tensor repr with a compact, human-readable summary that shows shape, element count, memory footprint, value range, mean, standard deviation, and a histogram\u2014all in one line. It detects and flags NaN, Inf, and all-zero tensors, and works with named dimensions and gradient tracking. The package monkey-patches torch.Tensor's display methods so the improved output appears automatically in notebooks and REPLs without changing your code.\n\nIt is designed for interactive debugging and exploration, not for production logging. The summary format is stable across tensor operations, making it easy to spot data anomalies during model development. It also provides optional verbose and plain-text modes, and a `.deeper` method to recursively inspect nested tensor structures.","worth_installing":"Yes. Low install friction, no security issues, permissive license, and active maintenance make it a safe addition. If you work with PyTorch in notebooks or interactive environments, the quality-of-life improvement from readable tensor summaries justifies the minimal dependency footprint. Not essential for production code, but valuable for development and debugging."},"id":"lovely-tensors","links":{"html":"https://skillfed.io/packages/lovely-tensors","md":"https://skillfed.io/packages/lovely-tensors.md","pypi":"https://pypi.org/project/lovely-tensors/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-11","license_spdx":null,"license_treatment":"permissive","name":"lovely-tensors","python_support":"supports_current","summary":"\u2764\ufe0f Lovely Tensors"},"popularity":{"monthly_downloads":75939,"position":14666,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.22"}