VisualDL is a deep learning visualization tool that can help design deep learning jobs. It includes features such as scalar, parameter distribution, model structure and image visualization. Currently it is being developed at a high pace. New features will be continuously added.

At present, most DNN frameworks use Python as their primary language. VisualDL supports Python by nature. Users can get plentiful visualization results by simply add a few lines of Python code into their model before training.

Besides Python SDK, VisualDL was writen in C++ on the low level. It also provides C++ SDK that can be integrated into other platforms.

Why should I use VisualDL?

Visual DL has the following advantages:


At present, most deep learning frameworks are using Python. The status of training process is recorded by logs. This method can show short-term training status. But it can hardly show long-term trends. Visual DL can help you visualize the whole training process and construct plots as below:

Easy to Integrate

Visual DL provides independent Python SDK. If the training task is based on Python, user can simply use Visual DL by installing the Visual DL wheel package and importing it into her/his own project. Please refer to Installation for more details.

Easy to Use

  • Add log collecting logic to your own Python code.
import visualdl

log_writer = LogWriter("./log", sync_cycle=300)
with log_writer.mode('train') as logger:
    loss_writer = logger.scalar("loss")

for step in steps:
    loss = train_process() // Insert your training code here
    loss_writer.add_record(step, loss)
  • Launch Visual DL service and you can see the visualization results.
visualdl --logdir ./log --model_pb <path_to_onnx_model> --port 8080 # onnx model and port are optional

Comprehensive Usability

  1. Scalar: support scalar line/dot data visualization, like the figure above.

    • can show metrics such as loss, accuracy, etc via lines and dots and let user see trends easily
    • can draw several curves in one figure so that it is easy to compare various metrics
  2. Image: support image display.

    • display input and intermediate images, easy to check data sample

    • support display for convolutional layer, easy to see results after each layer

    • support image-generating tasks to preview generated image

  3. Audio: collect audio sampling for playback.

    • display audio input or generated audio for users to play or download audio clip
    • useful for training models for Speech Recognition or Text to Speech
    • help users to investigate issues or check audio quality
  4. Histogram: display of parameter distribution, easy to check distribution curves in each tensor, show the trend of parameter distribution.

    • help users understand the training process and the underneath reason for the change from one parameter distribution to another

    • help users judge if the training is on the track. For example, if parameter change rate becomes close to 0 or grows rapidly, then exploding and vanishing gradients might happen

  5. Graph: visualize the model structure of deep learning networks.

    • Graph supports the preview of ONNX model. Since models of MXNet, Caffe2, PyTorch and CNTK can be converted to ONNX models easily, Visual DL can also support these models indirectly

    • easy to see wrong configuration of a network

    • help understand network structure

  6. High Dimensional: visualize data embeddings by projecting high-dimensional data into 2D / 3D.

    • Help users understand the similarity, correlation of different objects (e.g. word / image)

    • Map objects to vectors in vector space to visualize distance of neighbors and form clusters

    • Support dimension reduction algorithm like PCA, T-SNE

    • In following image(generated by vdl_create_log example), you can tell the similarity of ‘man’ and ‘women’, ‘king’ and ‘queen’ by their position.

    • Here is how it look like in 3D:

Purely Open Source

As a deep learning visualization tool, Visual DL support most deep learning frameworks. On the SDK perspective, it is easy to integrate into Python and C++ projects. Through ONNX, Visual DL’s Graph component can support many popular frameworks such as PaddlePaddle, MXNet, PyTorch and Caffe2.

Welcome everyone to comment and contribute to make Visual DL easier to use, with more features.