torchember 0.2.6

Tracking and Visualize after the burning PyTorch

Torch Ember

Tracking and visualize after the burning pytorch

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

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

• 30 seconds tutorial

• Full documentations

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, 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]