Just 2 new lines of code get you on the cloud.

Use thousands of cores without managing a single server.

The PiCloud Platform gives you the freedom to develop your algorithms and software without sinking time into all of the plumbing that comes with provisioning, managing, and maintaining servers.

								> def f(x):              # write or import your function
								      ...
								
								> import cloud           # import our library
								> id = cloud.call(f, x)  # run f on the cloud

    	                        > cloud.status(id)       # track its progress
	                            'processing'
                                > cloud.result(id)       # grab the result

								> cloud.map(f, datapoints) # parallelize your analysis

Install any library or binary written in any language.

Step 1. SSH into your Environment and Install Anything

							$ ssh picloud@ec2-w-x-y-z.amazonaws.com

							$ apt-get install anything
							$ cd prog; ./configure; make; make install

Step 2. Use it

							> cloud.call(f, _env='name_of_your_environment')

What's an environment?

Think of an environment as a filesystem. We give you the ability to customize the filesystem your jobs use. You can install anything your jobs might need whether it's a program, library or data dependency, in any format or programming language.

What makes it powerful?

You setup the environment once. When you run a large workload, we automatically distribute your environment to all the cores that need them, regardless of the number.

Learn more →

Store all your data without limits.

cloud.files

Our object store makes it easy to keep your data on the cloud for efficient processing.

							> cloud.files.put('corpus.txt')   # store
							> cloud.files.get('corpus.txt')   # retrieve

Crunch files with the map and reduce paradigm.

							> cloud.files.map('corpus.txt', mapper, reducer))

cloud.volumes

Our volume store enables you to synchronize your datasets with our cloud storage, and access them from the cloud as files mounted on the filesystem. This is ideal for large HDF5 datasets.

							> cloud.volumes.sync('data_directory', 'your-volume'))

From the command-line interface:

							$ picloud volumes sync data_directory your-volume:

Drill down into your Computational History

The days of sifting through server logs are over.

We consolidate all of your history into a single, easy-to-use interface. With our web dashboard, cloud library, or command-line interface, you can see a task's: