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atoll

A microservice for data analysis pipelines

atoll requires Python 3.4+.

Note that the coral package defines a Coral-specific instance of atoll (with community-related metrics and so on), where as atoll is the base library which implements "pipelines-as-a-service".

The available metrics for coral can be found in docs/library.md.

Installation

You can use atoll in your own projects as well! Install the latest version like so (at this point in development, the PyPi version may not be up-to-date):

pip install git+https://github.com/coralproject/atoll

Note: this does not install Coral-specific modules. This will only give you the pipeline framework!

Configuration

When running atoll as a microservice, you can specify a YAML config file by setting the ATOLL_CONF env variable, e.g.

export ATOLL_CONF=/path/to/my/conf.yaml

The default configuration is:

worker_broker: amqp://guest:guest@localhost/
worker_backend: amqp
executor_host: 127.0.0.1:8786

The worker_broker and worker_backend options define how celery, which is used to handle requests, is configured.

The executor_host is for distributed computation of pipelines - see the "Distributed" section below.

Setup

If you are running the microservice and using asynchronous requests (i.e. callbacks), you need a Celery stack.

The provided run script makes it easy to get this up and running. Install Docker if you do not have it, and then the following commands are available:

# Pull the necessary Docker images,
# setup the Docker containers
./run setup

# Start the RabbitMQ container (the Celery broker)
./run rabbitmq

# Start a Celery worker
./run worker

# View the cluster status
./run status

# Spin down the stack
./run stop

Distributed computing support

NOTE: Distributed computing support is still somewhat experimental, at this moment it supports the full atoll pipeline API but its stability is not guaranteed.

atoll can run its pipelines either on a single computer with multiprocessing or across a cluster (using the distributed library).

To run pipelines across a cluster, you will need to provide the following configuration option:

  • executor_host: where the cluster executor is, by default, 127.0.0.1:8786

See the config above for an example.

Then, to run a pipeline on the cluster, just pass distributed=True when calling the pipeline, e.g:

pipeline = Pipeline().map(foo).map(bar)
results = pipeline(input, distributed=True)

Setting up a cluster

Setting up a cluster is fairly easy - just setup the necessary (virtual) environment on and passwordless SSH access to each worker node.

Then, from the leader (i.e. the executor) machine, run:

dcluster <worker ip> <worker ip> ...

The IP of this executor machine is what goes in the executor_host configuration option.

Refer to the distributed documentation for more details.

Deployment

To deploy, clone this repo then cd into deploy.

First, run setup.sh to setup your local machine for deployment.

Then setup the git ssh keys and server key in the deploy/keys folder (ask me for them if necessary).

You may need to edit hosts.ini to point to the proper servers.

Then you should be able to run deploy.sh <ENV NAME> to deploy the Coral Atoll instance, where ENV NAME is one of [development production].

Once you deploy, you can run test.py to sanity-check that the service is working properly.

See deploy/readme.md for more info.

Documentation

See the docs: (hosted docs coming soon)

For now, you can build them yourself:

git submodule update --init
cd docs
make clean; make html

Then open _build/html/index.html

Development

If you are interested in contributing to atoll, great! Here's how you can get it setup for development.

Ensure that you have Python 3.4 or later installed on your system.

For OSX, this can be accomplished with brew:

brew install python3

A virtual environment is recommended as well:

pip3 install virtualenv
virtualenv -p python3 ~/env/atoll --no-site-packages

Then activate the virtualenv:

source ~/env/atoll/bin/activate

Then clone this repo somewhere on your system:

git clone https://github.com/coralproject/atoll.git

Install the requirements:

pip install -r requirements.txt

To run the coral-atoll instance, change into the repo directory and run:

python coral.py

The server will then run at localhost:5001.

Notes

  • Until joblib switches from pickle to dill for serialization (see joblib/joblib#240) we can't use lambdas: functions used in pipelines must be defined at the top-level of a module.