{"adoption": {"forks": 631, "observed_at": "2026-08-28T04:09:01.845935+00:00", "stars": 4884}, "canonical_url": "https://ross.abutalabs.com/products/visualdl", "card": {"archived": false, "artifact_type": "application", "description": "Deep Learning Visualization Toolkit（『飞桨』深度学习可视化工具 ）", "domain": ["deep-learning", "data-visualization", "machine-learning", "developer-tools"], "enriched": true, "function": ["data-visualization", "monitoring", "machine-learning", "deep-learning"], "health_score": 31, "homepage": "https://www.paddlepaddle.org.cn/paddle/visualdl", "language": "HTML", "license": "Apache-2.0", "license_family": "permissive", "maturity": "stable", "member_repos": ["PaddlePaddle/VisualDL"], "name": "PaddlePaddle/VisualDL", "platform": ["python", "cross-platform"], "pushed_at": "2025-01-22T06:02:52+00:00", "repo": "PaddlePaddle/VisualDL", "stars": 4884, "tags": ["experiment-tracking", "tensorboard-alternative", "paddlepaddle", "onnx", "model-visualization", "training-metrics", "web-server"], "topics": ["visualization", "deep-learning", "paddlepaddle", "onnx", "caffe"], "urls": [], "use_cases": ["visualize training loss and accuracy curves in real time", "inspect a deep learning model's computation graph", "plot PR and ROC curves for classification experiments", "project high-dimensional embeddings to 2D/3D", "compare hyperparameters against model metrics", "share experiment results with a team"], "what_it_is": "VisualDL is a deep learning visualization toolkit for PaddlePaddle that provides charts for tracking training metrics, visualizing model structures, tensor histograms, PR/ROC curves, and high-dimensional data embeddings. It is a Python-based tool with a browser UI for understanding and optimizing model training.", "when_to_avoid": ["you need a full experiment management platform with team collaboration and model registry", "your stack is PyTorch/TensorFlow and you already use TensorBoard or Weights & Biases"], "when_to_choose": ["you train models with PaddlePaddle and want built-in visualization", "you need a TensorBoard-like tool with ONNX model graph support", "you want lightweight Python integration with a few lines of code"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/visualdl", "repo": "PaddlePaddle/VisualDL", "role": "main", "score": 24}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:09:01.845935+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T18:18:14.491692+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ab21144784e302402c9fbef19e834e7845a11acdb8f42f611ff5930c3605ebf0", "fetched_at": "2026-08-28T04:09:01.845935+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PaddlePaddle/VisualDL"}, {"content_hash": "6c227ac7c0f8e7417ada9bfa67c17b81136a622e3fdf3e13dfef50c50201a58d", "fetched_at": "2026-08-29T09:00:36.418884+00:00", "kind": "homepage", "missing": false, "url": "https://www.paddlepaddle.org.cn/paddle/visualdl"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 2, "longevity": 100, "rhythm": 8}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 3178, "days_push": 588, "days_rel": 672, "gap_med": null, "n_releases_24m": 1}, "score": 24, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}