Link to project: https://shan18.github.io/Flash

Flash is an end-to-end Deep Learning platform that allows users to create, train, and deploy their own neural network models in a matter of minutes without writing a single line of code.

The platform currently supports two types of tasks:

Classify images from your own dataset by using them to train a ResNet-34 or MobileNet v2 model. Training happens via transfer learning where models available will be pre-trained on the ImageNet dataset.

Predict sentiment from sentences by training a LSTM or GRU based sequential model on your own dataset. The models will be trained from scratch.

Using Flash is easy. With just a few clicks you can train and deploy your models automatically. You just have to select your model and upload the dataset, and you're good to go. No code or experience required.

For training a model, you'll have to upload your own dataset and select the model parameters. Depending on the size of the dataset, the model can take anywhere between 3 - 10 minutes to train and deploy your model.

After you upload your configuration, the platform will assign you a unique token. Please save the token as it will used to test the model on the inference page.

You can perform inference on a trained model by using the token provided to you after submitting the training configuration on the training page.

After submitting the token, you'll get a form where you can upload inputs to check the performance of your trained model. The inference page also provides you with the results of the training process by showing you the accuracy of the model on validation set as well as the change in accuracy during training.

Flash uses React Js, AWS Lambda, AWS S3 and AWS EC2 to function properly. To know about how flash functions, read the following links in order

1. Config JSON Files
2. Training Models
3. Performing Inference

To setup Flash from scratch, read the following links in order

1. Install serverless. For reference, check this link
2. Setup config files
3. Training server setup
4. Setting up Infernce
5. Frontend using React

If you need any help or want to report a bug, raise an issue in the repo.