Torch Ember
Tracking and visualize after the burning pytorch
This framework tracks the pytorch model:
- On
nn.Modulelevel - 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]
1 maintainer
Metadata
1 maintainer
Release files for torchember 0.2.7
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