Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (10.8%) to scientific vocabulary
Keywords
Repository
BuilT - Build a Trainer of deep neural networks
Basic Info
Statistics
- Stars: 5
- Watchers: 6
- Forks: 3
- Open Issues: 4
- Releases: 1
Topics
Metadata Files
README.md
BuilT(Build a Trainer)
Easily build a trainer for your Depp Neural Network model and experiment as many as you want to find optimal combination of components(model, optimizer, scheduler) and hyper-parameters in a well-organized manner. - No more boilerplate code to train and evaluate your DNN model. just focus on your model. - Simply swap your dataset, model, optimizer and scheduler in the configuration file to find optimal combination. Your code doesn't need to be changed!!!. - Support Cross Validation, OOF(Out of Fold) Prediction - Support WandB(https://wandb.ai/) or tensorboard logging. - Support checkpoint management(Save and load a model. Resume the previous training) - BuilT easily integrates with Kaggle(https://www.kaggle.com/) notebook. (todo: add notebook link)
Installation
Please follow the instruction below to install BuilT.
Installation of BuilT package from the source code
git clone https://github.com/UoA-CARES/BuilT.git
cd BuilT
python setup.py install
Installation of BuilT package using pip
BuilT can be installed using pip(https://pypi.org/project/BuilT/).
pip install built
Usage
Configuration
Builder
Trainer
Dataset
Model
Loss
Optimizer
Scheduler
Logger
Metric
Inference
Ensemble
Examples
MNIST hand-written image classification
(todo)
Sentiment Classification
(todo)
Developer Guide
(todo)
conda create -n conda_BuilT python=3.7
conda activate conda_BuilT
pip install -r requirements.txt
Reference
https://packaging.python.org/tutorials/packaging-projects/
Owner
- Name: CARES
- Login: UoA-CARES
- Kind: organization
- Location: University of Auckland
- Website: https://cares.blogs.auckland.ac.nz/
- Repositories: 7
- Profile: https://github.com/UoA-CARES
UoA Centre of Automation and Robotic Engineer Science
GitHub Events
Total
Last Year
Committers
Last synced: over 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| jongyoon | s****e@g****m | 121 |
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 25
- Total pull requests: 24
- Average time to close issues: 3 months
- Average time to close pull requests: about 4 hours
- Total issue authors: 2
- Total pull request authors: 3
- Average comments per issue: 0.6
- Average comments per pull request: 0.17
- Merged pull requests: 20
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- jlim262 (20)
- inkyusa (5)
Pull Request Authors
- jlim262 (15)
- inkyusa (8)
- rrayhka (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 82 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 4
- Total maintainers: 1
pypi.org: built
Easily build your trainer for DNNs.
- Homepage: https://github.com/UoA-CARES/BuilT
- Documentation: https://built.readthedocs.io/
- License: MIT License
-
Latest release: 0.0.4
published over 4 years ago
Rankings
Maintainers (1)
Dependencies
- PyYAML *
- easydict *
- pandas *
- sklearn *
- tensorboard *
- torch *
- torchvision *
- tqdm *
- wandb *
- PyYAML *
- easydict *
- pandas *
- sklearn *
- torch *
- torchvision *
- tqdm *
- wandb *