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    Write TensorBoard events with simple function call.

    The current release (v2.5) is tested on anaconda3, with PyTorch 1.11.0 / torchvision 0.12 / tensorboard 2.9.0.

    • Support scalar, image, figure, histogram, audio, text, graph, onnx_graph, embedding, pr_curve, mesh, hyper-parameters and video summaries.

    • FAQ


    pip install tensorboardX

    or build from source:

    pip install 'git+'

    You can optionally install crc32c to speed up.

    pip install crc32c

    Starting from tensorboardX 2.1, You need to install soundfile for the add_audio() function (200x speedup).

    pip install soundfile


    import torch
    import torchvision.utils as vutils
    import numpy as np
    import torchvision.models as models
    from torchvision import datasets
    from tensorboardX import SummaryWriter
    resnet18 = models.resnet18(False)
    writer = SummaryWriter()
    sample_rate = 44100
    freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
    for n_iter in range(100):
        dummy_s1 = torch.rand(1)
        dummy_s2 = torch.rand(1)
        # data grouping by `slash`
        writer.add_scalar('data/scalar1', dummy_s1[0], n_iter)
        writer.add_scalar('data/scalar2', dummy_s2[0], n_iter)
        writer.add_scalars('data/scalar_group', {'xsinx': n_iter * np.sin(n_iter),
                                                 'xcosx': n_iter * np.cos(n_iter),
                                                 'arctanx': np.arctan(n_iter)}, n_iter)
        dummy_img = torch.rand(32, 3, 64, 64)  # output from network
        if n_iter % 10 == 0:
            x = vutils.make_grid(dummy_img, normalize=True, scale_each=True)
            writer.add_image('Image', x, n_iter)
            dummy_audio = torch.zeros(sample_rate * 2)
            for i in range(x.size(0)):
                # amplitude of sound should in [-1, 1]
                dummy_audio[i] = np.cos(freqs[n_iter // 10] * np.pi * float(i) / float(sample_rate))
            writer.add_audio('myAudio', dummy_audio, n_iter, sample_rate=sample_rate)
            writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
            for name, param in resnet18.named_parameters():
                writer.add_histogram(name, param.clone().cpu().data.numpy(), n_iter)
            # needs tensorboard 0.4RC or later
            writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(100), n_iter)
    dataset = datasets.MNIST('mnist', train=False, download=True)
    images = dataset.test_data[:100].float()
    label = dataset.test_labels[:100]
    features = images.view(100, 784)
    writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
    # export scalar data to JSON for external processing


    Using TensorboardX with Comet

    TensorboardX now supports logging directly to Comet. Comet is a free cloud based solution that allows you to automatically track, compare and explain your experiments. It adds a lot of functionality on top of tensorboard such as dataset management, diffing experiments, seeing the code that generated the results and more.

    This works out of the box and just require an additional line of code. See a full code example in this Colab Notebook


    To add more ticks for the slider (show more image history), check or



    tensorboard for pytorch (and chainer, mxnet, numpy, ...)

    🚀 Github 镜像仓库 🚀


    发行版本 15

    Release v2.5


    贡献者 75



    • Python 99.3 %
    • Shell 0.7 %