https://github.com/oldentomato/log2db

DeepLearning Log Send to DB Module
https://github.com/oldentomato/log2db

database logging pymongo pypi-package

Last synced: about 2 months ago
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DeepLearning Log Send to DB Module

• Host: GitHub
• URL: https://github.com/oldentomato/log2db
• Owner: Oldentomato
• License: mit
• Created: 2022-12-29T04:22:56.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-04T10:51:21.000Z (over 3 years ago)
• Last Synced: 2025-11-21T02:38:46.165Z (7 months ago)
• Topics: database, logging, pymongo, pypi-package
• Language: Python
• Homepage:
• Size: 23.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Log2DB 

[![PyPI Latest Release](https://img.shields.io/pypi/v/log2db.svg)](https://pypi.org/project/log2db/)

![Package Format](https://img.shields.io/pypi/format/log2db.svg)

![Python Version](https://img.shields.io/pypi/pyversions/log2db.svg)

[![Package Status](https://img.shields.io/pypi/status/log2db.svg)](https://pypi.org/project/log2db/)

[![GitHub](https://img.shields.io/github/license/oldentomato/Log2DB)](https://github.com/Oldentomato/Log2DB/blob/main/LICENSE) 

[![Build Version](https://img.shields.io/github/v/release/oldentomato/Log2DB)](https://github.com/Oldentomato/Log2DB/releases)

> DeepLearning Log Send to DB Module  

## Introdution

Log2DB is a tool that uploads, manages, and visualizes simple deep learning logs to your own DB.  

## How To Install 

- Download manually:  

https://github.com/Oldentomato/Log2DB/releases  

Download PyPI:

```sh

pip install log2db

```

## How To Use 

- Basic preparation

```python

import log2db as lgdb

#Set the DB Server 

db = lgdb.SetMongoDB(db_url = 'db_url',

                    db_document_name='doc',

                    db_collection_name='coll',

                    port=12345)

coll = db.Connect_DB()

sendlog = lgdb.SendLog(coll)

```

- example

```python

sendlog.on_train_start(

    model_name = 'pytorch_test',

    experiment_count = 3,

    datas_count = len(x_data),

    epoch = 100,

    batch_size = 8,

    learning_rate = 0.01,

    criterion = 'CrossEntropyLoss',

    optimizer = 'SGD(momentum=0.5)',

    model_shape = model,

    LR_scheduler = 'None',

    etc = 'None'

)

...

sendlog.on_epoch_end(epoch=epoch, loss=loss, val_loss=val_loss, acc=acc, val_acc=val_acc)

...

sendmoel.on_train_end(save_graph_url=True)

```

## Dependency

- **Pytorch** https://github.com/pytorch/pytorch

- **Pandas** https://github.com/pandas-dev/pandas

- **Matplotlib** https://github.com/matplotlib/matplotlib

- **pymongo** https://github.com/mongodb/mongo-python-driver

- **scikit-learn** https://github.com/scikit-learn/scikit-learn  

## License

[MIT](LICENSE)