LayerVision is a type of Machine Learning application that I have been working on to allow data scientists, engineers, and researchers to deploy and keep track of their deep learning models with more ease. In its current state, it is only a proof-of-concept for an application of such sort.
This version of the application is built on a Flask backend and a HTML & CSS frontend. It will incorporate some elements of what is shown in the previous versions of the application.
With this application, you are able to:
- Give hyperparameters to model.
- Edit model hyperparameters.
- Delete redundant models.
- View loss and accuracy of the model with graphs, including:
- Training Loss
- Training Accuracy
- Testing Loss
- Testing Accuracy
- Feed the set hyperparameters of the model to the python file with ease.
To install, you can clone the repository in your specified file on the command prompt:
git clone https://github.com/axie123/LayerVision.gitor download the zip file and unpack it in the folder.
To run the program, you need to go into the location of the repo in you computer either on your Linux or Windows Bash/Shell. Once there, type
python app.pyto run. You can also use Anaconda Prompt. After that, go to http://http://127.0.0.1:5000/.
You need the following libraries installed to use this application:
- Flask
- SQLAlchemy (Flask)
- PyTorch
You can just use the requirements file to download the necessary dependencies.