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Visualdl

VisualDL is a Python-supported visualization toolkit that records deep-learning training information in log files and displays metrics, model structures, data samples, tensor distributions, PR and ROC curves, and high-dimensional data projections in a panel.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Visualdl — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use VisualDL when training information is being recorded to log files and you want to inspect progress, model structure, data samples, or evaluation-related visualizations in a panel.

Who it is for

  • Developers working with deep-learning training runs who want visual access to logged metrics and model outputs.
  • Practitioners inspecting model structures from Paddle, ONNX, or Caffe.

Documented uses

  • Track training metric trends and examine relationships between hyperparameters and metrics.
  • Inspect model structures and training data samples.
  • Review tensor distributions and PR or ROC curves.
  • Explore high-dimensional data through lower-dimensional projections.
Before you choose

Requirements & limits

The repository documents Python 3 support and lists browser support for Chrome 79 or later, Firefox 67 or later, Edge 79 or later, and Safari 11.1 or later. The project is licensed under Apache-2.0.

  • The documented logging workflow is built around VisualDL's Python SDK and LogWriter.
  • The README identifies model-structure visualization for Paddle, ONNX, and Caffe.
  • VisualDL stores training information in log files for display in its panel.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare a VisualDL training log writer

Configure a Python `LogWriter` to record training information in a log directory.

Before you start

  • VisualDL installed in a Python 3 environment.
  • A Python training script that can use VisualDL's LogWriter.

Steps

  1. Create a LogWriter, specifying logdir as the path for the log file.

  2. Optionally set max_queue and flush_secs; their documented defaults are 10 and 120 seconds.

  3. Use the logger in the training process to record information; the README describes VisualDL as storing training data and parameters in log files.

Expected outcome

Training information is recorded in VisualDL log files under the selected directory; if `logdir` is omitted, the default is `runs/${CURRENT_TIME}`.

Guide limitations

  • The README provides the LogWriter interface and parameter descriptions but does not include a complete logging call or panel-launch procedure in the available content.
  • This prepares logs; it does not demonstrate viewing or interpreting them.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
PaddlePaddle/VisualDL · Official homepage title · retained in its source language
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