Simple uWSGI with Flask web server implementing a Machine Learning REST API.
Python obviously has some exceptional ML libraries. To facilitate using them I have written a simple REST API that should work for most ML models, as well as a Docker container around the whole thing.
Edit app/config.yml to add your AWS credentials.
aws:
BUCKET : '<bucker name>'
AWS_ACCESS_KEY_ID : '<ACCESS_KEY_ID here>'
AWS_SECRET_ACCESS_KEY : '<ACCESS_SECRET_KEY here>'
And to handle each file passed in the POST request, use app/ML.py:
def handleTrainFile(contents):
# Dear ML engineer, feel free to handle the training
# of your model here.
print 'Received /train file, contents...'
print contents
Body:
{
files: [ <S3 file path>, ...]
}
Example:
{
files: [ USERS/data/1234.csv ]
}
Response:
{
"jobId": "1"
}
Error: 500
Body:
{
files: [ <S3 file path>, ... ]
}
Response:
{
"jobId": "1"
}
Error: 500
Body:
{
files: [ <s3 file path>, ... ]
}
Response:
{
output: { output vector }
}
Error: 500
Response:
{ status: [completed|running|cancelled] }
Error: *500*