My ML Cloud

The main idea is to make it easy to run heavy cpu processing on the cloud.

• AWS support only

from my_ml_cloud import process_unit

with process_unit() as instance:
    instance.send_run('ml.py')

Install dependencies

$ pip install -r requirements.txt

First Step

Configure a .env file in the root dir

KEY_NAME=seu_key_name
AWS_ACCESS_KEY=sua_access_key
AWS_SECRET_KEY=sua_secret_key

KEY_PASS=pass_chave

REGION=us-west-2
DEFAULT_INSTANCE_ID=ami-e7b8c0d7
INSTANCE_SIZE=m3.medium
DEFAULT_SECURITY_GROUP=default

SSH_USER=ubuntu
PARAMIKO_DEBUG=True

Second step

Code your ML script. Here's our little example using scikit

import json

from sklearn import datasets
from sklearn import svm

digits = datasets.load_digits()
clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(digits.data[:-1], digits.target[:-1])

result = dict(result=float(clf.predict(digits.data[-1])),
              algo=repr(clf).replace('\n', ''))
print(json.dumps(result, indent=4))

Last step

Call compute.py

$ python compute.py

Terminate ALL cloud instances

$ python terminate.py

Contact

[email protected]