No project description provided
Project description
Mobile first web app to monitor PyTorch & TensorFlow model training
Relax while your models are training instead of sitting in front of a computer
This is an open-source library to push updates of your ML/DL model training to mobile. Here's a sample experiment
You can host this on your own. We also have a small AWS instance running. and you are welcome to use it. Please consider using your own installation if you are running lots of experiments.
Notable Features
- Mobile first design: web version, that gives you a great mobile experience on a mobile browser.
- Model Gradients, Activations and Parameters: Track and compare these indicators independently. We provide a separate analysis for each of the indicator types.
- Summary and Detail Views: Summary views would help you to quickly scan and understand your model progress. You can use detail views for more in-depth analysis.
- Track only what you need: You can pick and save the indicators that you want to track in the detail view. This would give you a customised summary view where you can focus on specific model indicators.
- Standard ouptut: Check the terminal output from your mobile. No need to SSH.
📚 How to track experiments?
How to run app locally?
Install the PIP package
pip install labml-app
Start the server
labml app-server
Set the web api url to http://localhost:5005/api/v1/track?
when you run experiments.
You can also set this on .labml.yaml
.
from labml import tracker, experiment
with experiment.record(name='sample', token='http://localhost:5005/api/v1/track?'):
for i in range(50):
loss, accuracy = train()
tracker.save(i, {'loss': loss, 'accuracy': accuracy})
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
labml_app-0.0.101.tar.gz
(79.5 kB
view hashes)
Built Distribution
labml_app-0.0.101-py3-none-any.whl
(106.9 kB
view hashes)
Close
Hashes for labml_app-0.0.101-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d494f8e0addb674893c8de0a42a10ca4820f6424a5f3f39a1a33b6f5fb5c8554 |
|
MD5 | ecf79a6662d5585746503e3e43f06710 |
|
BLAKE2b-256 | 179e336f85938839b1e32642df01f58c108071c7433361560b658df4bd79948b |