https://github.com/codlocker/aws-face-recognition
Set up a docker environment to run Face Recognition software
https://github.com/codlocker/aws-face-recognition
Last synced: 4 days ago
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Set up a docker environment to run Face Recognition software
- Host: GitHub
- URL: https://github.com/codlocker/aws-face-recognition
- Owner: codlocker
- Created: 2023-03-18T02:41:49.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-04-19T22:44:33.000Z (about 2 years ago)
- Last Synced: 2024-10-08T02:51:05.106Z (9 months ago)
- Language: Python
- Size: 7.82 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
### Group Name :
- Dynamo
### Group Members :
- Ipsit Sahoo, Sandipan De, Varad Vijay Deshmukh
Installation steps:
- Ensure the test_case folder is downloaded from https://github.com/nehavadnere/cse546-project-lambda/tree/master/test_cases and set it up in the same directory as the project folder
- Ensure workload.py is in the same folder as well
- Run requirements.txt to ensure boto3 is installed for running the workload.py | pip install -r requirements.txt
- Install Docker setup (https://docs.docker.com/desktop/install/windows-install/)
- In your docker environment, set-up the docker configuration file (Dockerfile) to run on this folder and push the changes to ECR.
- Once the docker has been setup, you can run workload.py to execute the test-cases.
RESOURCE NAMES:
1. input S3 nucket name : inputbucket-cse546
2. output S3 bucket name : outputbucket-cse546
3. Dynamo DB : After configuring AWS CLI, push data to dynamo_db using upload_data_to_dynamo.py
4. Here's how the end to end pipe looks like
- Once the docker has been setup, you can run workload.py to execute the test-cases.
Skeleton Forked from : https://github.com/nehavadnere/cse546-project-lambda
#### LOCAL ENVIRONMENT SETUP
- Create a new python environment
- Run: `pip install -r requirements.txt`
- Uncomment line 177 in handler.py to test in your non-docker IDE environment.
- Execute `python handler.py`
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