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https://github.com/labmlai/labml

🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
https://github.com/labmlai/labml

analytics deep-learning experiment fastai keras keras-tensorflow machine-learning mobile pytorch pytorch-lightning tensorboard tensorflow tensorflow2 visualization

Last synced: 5 days ago
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🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Host: GitHub
URL: https://github.com/labmlai/labml
Owner: labmlai
License: mit
Created: 2018-11-16T09:34:48.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2024-10-25T17:36:27.000Z (3 months ago)
Last Synced: 2024-10-29T14:50:32.444Z (3 months ago)
Topics: analytics, deep-learning, experiment, fastai, keras, keras-tensorflow, machine-learning, mobile, pytorch, pytorch-lightning, tensorboard, tensorflow, tensorflow2, visualization
Language: Python
Homepage: https://labml.ai
Size: 101 MB
Stars: 2,040
Watchers: 27
Forks: 136
Open Issues: 5
Metadata Files:
- Readme: readme.md
- License: license

Awesome Lists containing this project

awesome-list - labml - Monitor deep learning model training and hardware usage from your mobile phone. (Machine Learning Framework / Experiment Management)
awesome-python-machine-learning-resources - GitHub - 44% open · ⏱️ 15.08.2022): (工作流程和实验跟踪)
StarryDivineSky - labmlai/labml

README

        


Monitor deep learning model training and hardware usage from mobile.


[![PyPI - Python Version](https://badge.fury.io/py/labml.svg)](https://badge.fury.io/py/labml)

[![PyPI Status](https://pepy.tech/badge/labml)](https://pepy.tech/project/labml)

[![Docs](https://img.shields.io/badge/labml-docs-blue)](https://docs.labml.ai/)

[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)





### 🔥 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

  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/labml/blob/master/samples/stocks/analysis.ipynb)

  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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

```

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



    



#### Custom visualizations based on Tensorboard logs



    



[//]: # (## 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, Adithya Narasinghe, Lakshith Nishshanke},

 title = {labml.ai: A library to organize machine learning experiments},

 year = {2020},

 url = {https://labml.ai/},

}

```