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🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
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labmlai/labml
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- Monitor running experiments from mobile phone or laptop
- Monitor hardware usage on any computerwith a single command
- Integrate with just 2 lines of code (see examples below)
- Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
- API for custom visualizations
- Pretty logs of training progress
- Open source!
To installMongoDB, refer to the officialdocumentationhere.
Install the package using pip:
pip install labml-app
# Start the server on the default port (5005)labml app-server# To start the server on a different port, use the following commandlabml app-server --port PORT
Optional: to setup and configure Nginx in your server, please refertothis.
You can access the user interface either by visitinghttp://localhost:{port} or, if configured on a separate machine,by navigating tohttp://{server-ip}:{port}.
- Install the package using pip.
pip install labml
- Create a file named
.labml.yamlat the top level of your project folder, and add the following line to the file:
app_url:http://localhost:{port}/api/v1/default# If you are setting up the project on a different machine, include the following line instead,app_url:http://{server-ip}:{port}/api/v1/default
fromlabmlimporttracker,experimentwithexperiment.record(name='sample',exp_conf=conf):foriinrange(50):loss,accuracy=train()tracker.save(i, {'loss':loss,'accuracy':accuracy})
fromlabmlimporttracker,experimentuuid=experiment.generate_uuid()# make sure to sync this in every machineexperiment.create(uuid=uuid,name='distributed training sample',distributed_rank=0,distributed_world_size=8, )withexperiment.start():foriinrange(50):loss,accuracy=train()tracker.save(i, {'loss':loss,'accuracy':accuracy})
- API to create experiments
- Track training metrics
- Monitored training loop and other iterators
- API for custom visualizations
- Configurations management API
- Logger for stylized logging
# Install packages and dependenciespip install labml psutil py3nvml# Start monitoringlabml monitor
If you use LabML for academic research, please cite the library using the following BibTeX entry.
@misc{labml, author = {Varuna Jayasiri, Nipun Wijerathne, Adithya Narasinghe, Lakshith Nishshanke}, title = {labml.ai: A library to organize machine learning experiments}, year = {2020}, url = {https://labml.ai/},}About
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱