Cloud Pipeline solution wraps AWS, GCP and Azure compute and storage resources into a single service. Providing an easy and scalable approach to accomplish a wide range of scientific tasks.

• Data processing: create data processing pipelines and run them in the Cloud in the automated way. Each pipeline represents a workflow script with versioned source code, documentation, and configuration. You can create such scripts in the Cloud Pipeline environment or upload them from the local machine.
• Data storage management: create your data storage, download or upload data or edit files right in the Cloud Pipeline user interface. File version control is supported.
• Tools management: create and deploy your own calculation environment using Docker's container concept. Almost every pipeline requires a specific package of software to run it, which is defined in a docker image. So when you start a pipeline, Cloud Pipeline starts a new cloud instance (nodes) and runs a docker image at it.
• Scientific computing GUI applications: launch and run GUI-based applications using self-service Web interface. It is possible to choose cloud instance configuration, or even use a cluster. Applications are launched as Docker containers exposing Web endpoints or a remote desktop connection (noVNC, NoMachine).

Cloud Pipeline provides a Web-based GUI and also supports CLI, which exposes most of the GUI features.

Cloud Pipeline supports Amazon Web Services , Google Cloud Platform and Microsoft Azure Cloud providers to run computing and store data.

Detailed documentation on the Cloud Pipeline platform is available via:

• Documentation site
• Documentation sources

Cloud Pipeline prebuilt binaries are available from the GitHub Releases page