This repository contains application to train models for Image classification and Regression tasks.
- Create a basic app for training and inference
- Support for training regression models
- Support of training multi input/output models
- Incorporate Bayesian Inference for finding uncertainty in the predictions.
- Create Docker Image
- Support for serving the application with gunicorn
- Anything else?
pip install access-niu
python -m access_niu.train --template sample/colors/template.yml
python -m access_niu --projects output
Now use this curl command to parse
curl -X POST \
http://localhost:8000/parse \
-F data=@samples/colors/train/red/1.jpg
###Build image
- clone the git repo
git clone https://github.com/accessai/access-niu.git
- Build the docker image
docker build -t access-niu:latest .
-
Run the docker container.
Note: You can attach a directory as a volume so that you can supply the templates from outside the docker container.
# we will use it as root directory for access-niu application
mkdir accessai
# copy samples folder
cp -r samples accessai/
# train the model
docker run -v $(pwd)/accessai:/accessai access-niu python -m access_niu.train --template samples/colors/template.yml
After running the train command you should get an output folder in the accessai directory
Now start the access_niu server
docker -d run -v $(pwd)/accessai:/accessai -p 8000:8000 access-niu --projects output
Now use this curl command to parse
curl -X POST \
http://localhost:8000/parse \
-F data=@samples/colors/train/red/1.jpg
- This project is inspired from RASA-NLU project.