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Backend Documentation (backend)
Samarth Chandna edited this page Jul 18, 2022
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This page contains documentation for the files in the /backend
directory of the Github repo. This page is regularly updated when new changes are added to these files.
This file is a very important file in the backend. It essentially acts as the point of entry into the backend
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ml_drive()
: Endpoint that trains classical ML model based on user specifications -
dl_drive()
: Endpoint that trains classical DL model based on user specifications -
root()
: Flask Route to send files from one directory to another -
train_and_output()
: Flask Route that right now trains DL model (frontend will hit this endpoint to rundl_drive()
and JSONified output is sent back) -
send_email_route()
: Endpoint to send email notification to user about their model + training result attachments. This function acts as a wrapper to an AWS API Gateway endpoint that invokes a lambda to send email notification (via AWS SES) through thesend_email()
function. TLDR, the flow issend_email_route()
->send_email()
-> API Gateway -> AWS Lambda -> AWS SES
Note that we run the app on host 0.0.0.0 to allow for backend and frontend to be served on the same port. It's crucial for our app to work properly in production
- Home
- Terraform
- Bearer-Token-Gen-Script
- Frontend-Backend Communication Documentation
- Backend Documentation (backend)
-
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)
-
constants.py
-
dataset.py
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default_datasets.py
-
email_notifier.py
-
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