This framework tracks the pytorch model:

• On nn.Module level
• Down to the metrics/ features of all tensors, includes
  • inputs/outputs of each module
  • weight/grad tensors
• By minimal extra coding
• Besides WebUI, Visualization compatible with notebook environment

Other lovely features

• Customizable metrics, with easy decorator syntax
• Split the tracking log in the way you like, just mark(k=v,k1=v2...)
• You can easily switch on/off the tracking:
  • Even cost of computation is tiny, torchember don't have to calculate metric for every iteration
  • Hence, you can track eg. only the last steps, only each 200 steps .etc

Installation

pip install torchember

Fast Tutorial

• Instant colab tutorial here:

• Full documentations

WebUI intro Video

• video here

Step1, Track your model

Place you torch ember tracker on your model

from torchember.core import torchEmber
te = torchEmber(model)

The above can track input and output of every module,The following can track status of every module

for i in range(1000):
    ...
    loss.backward()
    optimizer.step()

    te.log_model()

Train your model as usual

Step2 Plan A, visualize in notebook

For colab, kaggle kernels, there isn't an option to run another service, you can visualize the result in notebook.

Of course the following is feasible in usual jupyter notebook.

from torchember.visual import VisualByTensor

vis = VisualByTensor()

• This extra line of code is for colab

  vis.scatter_plot(vis.ember_sub_df)
  

Step2 Plan B, Check the analysis on the WebUI

Run the service from terminal

$ torchember

The default port will be 8080

Or assign a port

$ torchember --port=4200

Visit your analysis at http://[host]:[port]