NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
NeuronBlocks is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages.
NeuronBlocks consists of two major components: Block Zoo and Model Zoo.
Users can either pick existing models (config files) in Model Zoo to start model training or create new models by leveraging neural network blocks in Block Zoo just like playing with Lego.
Note: NeuronBlocks requires Python 3.6 and above.
Clone this project.
git clone https://github.com/Microsoft/NeuronBlocks
Install Python packages in requirements.txt by the following command.
pip install -r requirements.txt
Install PyTorch (NeuronBlocks supports PyTorch 0.4.1 and above).
For Linux, run the following command:
pip install "torch>=0.4.1"
For Windows, we suggest you to install PyTorch via Conda by following the instruction of PyTorch.
Get started by trying the given examples. Both Linux/Windows, GPU/CPU are supported. For Windows, we suggest you to use PowerShell instead of CMD.
Tips: in the following instruction, PROJECTROOT denotes the root directory of this project.
# train
cd PROJECT_ROOT
python train.py --conf_path=model_zoo/demo/conf.json
# test
python test.py --conf_path=model_zoo/demo/conf.json
# predict
python predict.py --conf_path=model_zoo/demo/conf.json
For prediction, NeuronBlocks have two modes: Interactive and Batch.
# use the above example
# interactive prediction
python predict.py --conf_path=model_zoo/demo/conf.json --predict_mode='interactive'
# use the above example
# batch prediction
python predict.py --conf_path=model_zoo/demo/conf.json --predict_mode='batch' --predict_data_path=dataset/demo/predict.tsv
For more details, please refer to Tutorial.md and Code documentation.
Engineers or researchers who face the following challenges when using neural network models to address NLP problems:
The advantages of leveraging NeuronBlocks for NLP neural network model training includes:
Model Building: for model building and parameter tuning, users only need to write simple JSON config files, which greatly minimize the effort of implementing new ideas.
Model Sharing It is super easy to share models just through JSON files, instead of nasty codes. For different models or tasks, our users only need to maintain one single centralized source code base.
Code Reusability: Common blocks can be easily shared across various models or tasks, reducing duplicate coding work.
Platform Flexibility: NeuronBlocks can run both on Linux and Windows machines, using both CPU and GPU. It also supports training on GPU platforms like Philly and PAI.
CPU inference | Single-GPU inference | Multi-GPU inference | |
CPU train | ✓ | ✓ | ✓ |
Single-GPU train | ✓ | ✓ | ✓ |
Multi-GPU train | ✓ | ✓ | ✓ |
Model Visualization: A model visualizer is provided for visualization and configure correctness checking, which helps users to visualize the model architecture easily during debugging.
Extensibility: NeuronBlocks is extensible, allowing users to contribute new blocks or contributing novel models (JSON files).
NeuronBlocks operates in an open model. It is designed and developed by STCA NLP Group, Microsoft. Contributions from academia and industry are also highly welcome. For more details, please refer to Contributing.md.
Anyone who are familiar with are highly encouraged to contribute code.
NeuronBlocks -- Building Your NLP DNN Models Like Playing Lego. EMNLP 2019, at https://arxiv.org/abs/1904.09535.
@article{gong2019neuronblocks,
title={NeuronBlocks--Building Your NLP DNN Models Like Playing Lego},
author={Gong, Ming and Shou, Linjun and Lin, Wutao and Sang, Zhijie and Yan, Quanjia and Yang, Ze, Cheng, Feixiang and Jiang, Daxin},
journal={arXiv preprint arXiv:1904.09535},
year={2019}
}
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
If you have any questions, please contact [email protected]
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