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Developer Runbook
This page contains information on running the application as a developer when you want to make local changes
Welcome to the Deep Learning Playground project team (a DSGT Content Project). This
team's aim is to create an interactive playground for people to build and test their
machine learning and deep learning models in a quicker way. We are constructing a web
application that allows for users to drag and drop their layers (for deep learning),
set the optimizer and relevant parameters, upload their dataset and click the train
button. Once the train buttion is clicked, then the deep learning model is trained and
relevant performance metrics are outputted. Our product is essentially a low-code/no-
code solution to democratizing access to deep learning and machine learning.
Please refer to DLP Onboarding Instructions
Below are the tools/technologies we use
- Python
- React.js
- Flask
- AWS (ECS + Fargate, ECR, EC2, Route53, ACM, Load Balancer, Dynamo DB, S3)
- Docker
- Home
- Terraform
- Bearer-Token-Gen-Script
- Frontend-Backend Communication Documentation
- Backend Documentation (backend)
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driver.py
- AWS Helper Files (backend.aws_helpers)
- Dynamo DB Utility Files (aws_helpers.dynamo_db_utils)
- AWS Secrets Utility Files (aws_secrets_utils)
- AWS Batch Utility Files (aws_batch_utils)
- Firebase Helper Files (backend.firebase_helpers)
- Common Files (backend.common)
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constants.py
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dataset.py
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default_datasets.py
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email_notifier.py
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loss_functions.py
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optimizer.py
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utils.py
- Deep Learning Files (backend.dl)
- Machine Learning Files (backend.ml)
- Frontend Documentation
- Bug Manual
- Developer Runbook
- Examples to locally test DLP
- Knowledge Share