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 Pipelineenvironment 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 Pipelineuser 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 Pipelinestarts 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:
Cloud Pipeline prebuilt binaries are available from the GitHub Releases page