Welcome to DLSA Pages
DLSA is Intel optimized representative End-to-end Fine-Tuning & Inference pipeline for Document level sentiment analysis using BERT model implemented with Hugging face transformer API.
Prerequisites
Download the repo
#download the repo
git clone https://github.com/intel/document-level-sentiment-analysis.git
cd frameworks.ai.end2end-ai-pipelines.dlsa/profiling-transformers
git checkout v1.0.0
Download the datasets:
mkdir datasets
cd datasets
#download and extract SST-2 dataset
wget https://dl.fbaipublicfiles.com/glue/data/SST-2.zip && unzip SST-2.zip && mv SST-2 sst
#download and extract IMDB dataset
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz && tar -zxf aclImdb_v1.tar.gz
Note: Make sure the network connections work well for downloading the datasets.
Deploy the test environment
Download Miniconda and install it
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
sh Miniconda3-latest-Linux-x86_64.sh
Note: If you have already installed conda on your system, just skip this step.
Prepare the conda environment for DLSA
conda create -n dlsa python=3.8 --yes
conda activate dlsa
sh install.sh
Running DLSA Inference Pipeline
| Implementations | Model | API | Framework | Precision |
|---|---|---|---|---|
| Run with HF Transformers | HF Model | Trainer | PyTorch + IPEX | FP32,BF16 |
| Run with Stock Pytorch | HF Mode | Non-trainer | PyTorch | FP32 |
| Run with IPEX | HF Mode | Non-trainer | PyTorch + IPEX | FP32,BF16,INT8 |
Running DLSA Fine-Tuning Pipeline
Single Node Fine-Tuning
| Implementations | Model | Instance | API | Framework | Precision |
|---|---|---|---|---|---|
| Run with HF Transformers + IPEX | HF Model | Single | Trainer | PyTorch + IPEX | FP32, BF16 |
| Run with Stock Pytorch | HF Model | Single | Non-trainer | PyTorch | FP32 |
| Run with IPEX (Single Instance) | HF Model | Single | Non-trainer | PyTorch + IPEX | FP32,BF16 |
| Run with IPEX (Multi Instance) | HF Model | Multiple | Non-trainer | PyTorch + IPEX | FP32,BF16 |
Issue Tracking
E2E DLSA tracks both bugs and enhancement requests using Github. We welcome input, however, before filing a request, please make sure you do the following: Search the Github issue database.