AWS Doppel

Distribute and run code on AWS.

Deploying code to production is becoming easier, but leveraging cloud resources to train a large model or run dozen of instances in parallel to tune hyperparameters can still be a struggle, from choosing your instance type, to debugging and retrieving your logs. AWS Doppel makes it as easy and transparent as possible!

One click deployment

AWS Doppel automatically creates all the AWS resources, provisions EC2 instances and run your code

• Cheapest spot or on-demand instances meeting your requirements (CPU, GPU, memory) are automatically selected
• Your code is automatically sent to your EC2 instances and installed via setup.py or added to the PYTHONPATH
• Context data are uploaded from your machine to a new S3 bucket, and downloaded to your EC2 instances
• Add requirements, installed via pip, or packages available on your local machine

context = DoppelContext() \
    .add_data(key='train.pkl', bucket='doppel-project', source=r'C:\data\train.pkl') \
    .add_data(key='test.pkl', bucket='doppel-project', source=r'C:\data\test.pkl')
context.upload_data()

project = DoppelProject(
    name='doppel-project',
    path=r'..\my-project',
    entry_point='-m project.train',
    packages=[r'C:\app\aws-doppel'],
    python='3.7.6',
    n_instances=1,
    min_memory=128,
    context=context,
    env_vars={'PYTHONHASHSEED': '1'})
project.start()

Transparent execution

Execute the same code locally or remotely using the doppel context. Depending on the environment:

• context.get_logger() redirects your logs to the console or to a new AWS Cloudwatch journal
• context.path() points to your local data source or to the instance data directory
• context.save(), context.save_pickle() and context.save_json() saves your data to your local folder or your S3 bucket
• terminate(context) stops the EC2 instance and saves money!

import logging
from doppel import terminate
from project.data import Loader


context.get_logger()
try:
    loader = Loader(context.path())
    X, y = loader.load()
    model = MyModel().fit(X, y)
    context.save(model.save(), 'my_model.pkl')

except Exception as e:
    logging.info(str(e))

finally:
    logging.info('Finished')
    terminate(context)