<div align="center" style="margin-bottom: 100px;">
<h1>Monitor deep learning model training and hardware usage from mobile.</h1>
[](https://badge.fury.io/py/labml)
[](https://pepy.tech/project/labml)
[](https://docs.labml.ai/)
[](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)
<img src="https://github.com/labmlai/labml/blob/master/images/cover-dark.png" alt=""/>
</div>
### 🔥 Features
* Monitor running experiments from mobile phone or laptop
* Monitor hardware usage on any computer
with 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
[](https://colab.research.google.com/github/labmlai/labml/blob/master/samples/stocks/analysis.ipynb)
[](https://colab.research.google.com/github/vpj/poker/blob/master/kuhn_cfr/kuhn_cfr.ipynb)
* Pretty logs of training progress
* Open source!
### Hosting the experiments server
#### Prerequisites
To install `MongoDB`, refer to the official
documentation [here](https://www.mongodb.com/docs/manual/tutorial/install-mongodb-on-ubuntu/).
#### Installation
Install the package using pip:
```bash
pip install labml-app
```
#### Starting the server
```sh
# Start the server on the default port (5005)
labml app-server
# To start the server on a different port, use the following command
labml app-server --port PORT
```
***Optional: to setup and configure Nginx in your server, please refer
to [this](https://github.com/labmlai/labml/blob/master/guides/server-setup.md).***
You can access the user interface either by visiting `http://localhost:{port}` or, if configured on a separate machine,
by navigating to `http://{server-ip}:{port}`.
### Monitor Experiments
#### Installation
1. Install the package using pip.
```bash
pip install labml labml-app
```
2. Create a file named `.labml.yaml` at the top level of your project folder, and add the following line to the file:
```yaml
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
```
#### PyTorch example
```python
from labml import tracker, experiment
with experiment.record(name='sample', exp_conf=conf):
for i in range(50):
loss, accuracy = train()
tracker.save(i, {'loss': loss, 'accuracy': accuracy})
```
#### Distributed training example
```python
from labml import tracker, experiment
uuid = experiment.generate_uuid() # make sure to sync this in every machine
experiment.create(uuid=uuid,
name='distributed training sample',
distributed_rank=0,
distributed_world_size=8,
)
with experiment.start():
for i in range(50):
loss, accuracy = train()
tracker.save(i, {'loss': loss, 'accuracy': accuracy})
```
### 📚 Documentation
* [Python API Reference](https://docs.labml.ai)
* [Samples](https://github.com/labmlai/labml/tree/master/samples)
##### Guides
* [API to create experiments](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/experiment.ipynb)
* [Track training metrics](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/tracker.ipynb)
* [Monitored training loop and other iterators](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/monitor.ipynb)
* [API for custom visualizations](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/analytics.ipynb)
* [Configurations management API](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/configs.ipynb)
* [Logger for stylized logging](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/logger.ipynb)
### 🖥 Screenshots
#### Formatted training loop output
<div align="center">
<img src="https://raw.githubusercontent.com/vpj/lab/master/images/logger_sample.png" alt="Sample Logs"/>
</div>
#### Custom visualizations based on Tensorboard logs
<div align="center">
<img src="https://raw.githubusercontent.com/vpj/lab/master/images/analytics.png" alt="Analytics"/>
</div>
[//]: # (## Tools)
[//]: # ()
[//]: # (### [Training models on cloud](https://github.com/labmlai/labml/tree/master/remote))
[//]: # ()
[//]: # (```bash)
[//]: # (# Install the package)
[//]: # (pip install labml_remote)
[//]: # ()
[//]: # (# Initialize the project)
[//]: # (labml_remote init)
[//]: # ()
[//]: # (# Add cloud server(s) to .remote/configs.yaml)
[//]: # ()
[//]: # (# Prepare the remote server(s))
[//]: # (labml_remote prepare)
[//]: # ()
[//]: # (# Start a PyTorch distributed training job)
[//]: # (labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0)
[//]: # (```)
### [Monitoring hardware usage](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md)
```sh
# Install packages and dependencies
pip install labml psutil py3nvml
# Start monitoring
labml monitor
```
[//]: # (## Other Guides)
[//]: # ()
[//]: # (#### [Setting up a local Ubuntu workstation for deep learning](https://github.com/labmlai/labml/blob/master/guides/local-ubuntu.md))
[//]: # ()
[//]: # (#### [Setting up a cloud computer for deep learning](https://github.com/labmlai/labml/blob/master/guides/remote-python.md))
## Citing
If you use LabML for academic research, please cite the library using the following BibTeX entry.
```bibtext
@misc{labml,
author = {Varuna Jayasiri, Nipun Wijerathne},
title = {labml.ai: A library to organize machine learning experiments},
year = {2020},
url = {https://labml.ai/},
}
```
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"description": "<div align=\"center\" style=\"margin-bottom: 100px;\">\n\n<h1>Monitor deep learning model training and hardware usage from mobile.</h1>\n\n[](https://badge.fury.io/py/labml)\n[](https://pepy.tech/project/labml)\n[](https://docs.labml.ai/)\n[](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)\n\n<img src=\"https://github.com/labmlai/labml/blob/master/images/cover-dark.png\" alt=\"\"/>\n</div>\n\n### \ud83d\udd25 Features\n\n* Monitor running experiments from mobile phone or laptop\n* Monitor hardware usage on any computer\n with a single command\n* Integrate with just 2 lines of code (see examples below)\n* Keeps track of experiments including infomation like git commit, configurations and hyper-parameters\n* API for custom visualizations\n [](https://colab.research.google.com/github/labmlai/labml/blob/master/samples/stocks/analysis.ipynb)\n [](https://colab.research.google.com/github/vpj/poker/blob/master/kuhn_cfr/kuhn_cfr.ipynb)\n* Pretty logs of training progress\n* Open source!\n\n### Hosting the experiments server\n\n#### Prerequisites\n\nTo install `MongoDB`, refer to the official\n documentation [here](https://www.mongodb.com/docs/manual/tutorial/install-mongodb-on-ubuntu/).\n\n#### Installation\n\nInstall the package using pip:\n\n```bash\npip install labml-app\n```\n\n#### Starting the server\n\n```sh\n# Start the server on the default port (5005)\nlabml app-server\n\n# To start the server on a different port, use the following command\nlabml app-server --port PORT\n```\n\n***Optional: to setup and configure Nginx in your server, please refer\nto [this](https://github.com/labmlai/labml/blob/master/guides/server-setup.md).***\n\nYou can access the user interface either by visiting `http://localhost:{port}` or, if configured on a separate machine,\nby navigating to `http://{server-ip}:{port}`.\n\n### Monitor Experiments\n\n#### Installation\n\n1. Install the package using pip.\n\n```bash\npip install labml labml-app\n```\n\n2. Create a file named `.labml.yaml` at the top level of your project folder, and add the following line to the file:\n\n```yaml\napp_url: http://localhost:{port}/api/v1/default\n\n# If you are setting up the project on a different machine, include the following line instead,\napp_url: http://{server-ip}:{port}/api/v1/default\n```\n\n#### PyTorch example\n\n```python\nfrom labml import tracker, experiment\n\nwith experiment.record(name='sample', exp_conf=conf):\n for i in range(50):\n loss, accuracy = train()\n tracker.save(i, {'loss': loss, 'accuracy': accuracy})\n```\n\n#### Distributed training example\n\n```python\nfrom labml import tracker, experiment\n\nuuid = experiment.generate_uuid() # make sure to sync this in every machine\nexperiment.create(uuid=uuid,\n name='distributed training sample',\n distributed_rank=0,\n distributed_world_size=8,\n )\nwith experiment.start():\n for i in range(50):\n loss, accuracy = train()\n tracker.save(i, {'loss': loss, 'accuracy': accuracy})\n```\n\n### \ud83d\udcda Documentation\n\n* [Python API Reference](https://docs.labml.ai)\n* [Samples](https://github.com/labmlai/labml/tree/master/samples)\n\n##### Guides\n\n* [API to create experiments](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/experiment.ipynb)\n* [Track training metrics](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/tracker.ipynb)\n* [Monitored training loop and other iterators](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/monitor.ipynb)\n* [API for custom visualizations](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/analytics.ipynb)\n* [Configurations management API](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/configs.ipynb)\n* [Logger for stylized logging](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/logger.ipynb)\n\n### \ud83d\udda5 Screenshots\n\n#### Formatted training loop output\n\n<div align=\"center\">\n <img src=\"https://raw.githubusercontent.com/vpj/lab/master/images/logger_sample.png\" alt=\"Sample Logs\"/>\n</div>\n\n#### Custom visualizations based on Tensorboard logs\n\n<div align=\"center\">\n <img src=\"https://raw.githubusercontent.com/vpj/lab/master/images/analytics.png\" alt=\"Analytics\"/>\n</div>\n\n[//]: # (## Tools)\n\n[//]: # ()\n[//]: # (### [Training models on cloud](https://github.com/labmlai/labml/tree/master/remote))\n\n[//]: # ()\n[//]: # (```bash)\n\n[//]: # (# Install the package)\n\n[//]: # (pip install labml_remote)\n\n[//]: # ()\n[//]: # (# Initialize the project)\n\n[//]: # (labml_remote init)\n\n[//]: # ()\n[//]: # (# Add cloud server(s) to .remote/configs.yaml)\n\n[//]: # ()\n[//]: # (# Prepare the remote server(s))\n\n[//]: # (labml_remote prepare)\n\n[//]: # ()\n[//]: # (# Start a PyTorch distributed training job)\n\n[//]: # (labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0)\n\n[//]: # (```)\n\n### [Monitoring hardware usage](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md)\n\n```sh\n# Install packages and dependencies\npip install labml psutil py3nvml\n\n# Start monitoring\nlabml monitor\n```\n\n[//]: # (## Other Guides)\n\n[//]: # ()\n[//]: # (#### [Setting up a local Ubuntu workstation for deep learning](https://github.com/labmlai/labml/blob/master/guides/local-ubuntu.md))\n\n[//]: # ()\n[//]: # (#### [Setting up a cloud computer for deep learning](https://github.com/labmlai/labml/blob/master/guides/remote-python.md))\n\n## Citing\n\nIf you use LabML for academic research, please cite the library using the following BibTeX entry.\n\n```bibtext\n@misc{labml,\n author = {Varuna Jayasiri, Nipun Wijerathne},\n title = {labml.ai: A library to organize machine learning experiments},\n year = {2020},\n url = {https://labml.ai/},\n}\n```\n",
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