Adding node and cell names for tensorboard graph

[promach (buttercutter)]

I am trying to trace the tensorboard graph for GitHub - buttercutter/gdas: A simple implementation for the GDAS paper : Searching for A Robust Neural Architecture in Four GPU Hours

However, from what I can observe so far, the tensorboard graph generated from torch.utils.tensorboard does not really indicate user-understandable node names and cell names which makes it so difficult for tracking down the connections within the graph.

Any suggestions ?

[Advitya17 (Advitya Gemawat)]

@promach

However, from what I can observe so far, the tensorboard graph generated from torch.utils.tensorboard does not really indicate user-understandable node names and cell names which makes it so difficult for tracking down the connections within the graph.

I have the same question.

[promach (buttercutter)]

I am also checking on GitHub - fossasia/visdom: A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy. now, not sure how to use visdom though

[promach (buttercutter)]

I tried from torch.utils.tensorboard import SummaryWriter and from tensorboardX import SummaryWriter , but both are still giving some non-user-readable nodes and cells names for the output graph.

I suspect the issue might be due to the multiple nested for loops inside the forward function.

Note: I have removed for epoch in range(NUM_EPOCHS): during testing and debugging this issue.

[viniciusarruda]

Any update on this? I’m having the same issue using the EmbeddingBag layer.