The HPE Cognitive Computing Toolkit (CCT) is a GPU-accelerated platform for deep learning and other advanced analytics. It provides an embedded domain-specific language (DSL) designed to maximize ease of programmability, while preserving the semantics necessary to generate efficient GPU code. CCT is particularly powerful for applications that require combining deep learning techniques with more conventional signal processing or computer vision algorithms. The CCT DSL lives inside the Scala language, on top of the Java Virtual Machine (JVM).
To get started, you’ll need a machine with a relatively current NVIDIA GPU and a 1.8 JDK installed. While CCT emits OpenCL GPU kernels and thus may run on AMD or Intel GPUs as well, these are not regularly tested hardware configurations. For more detailed installation instructions go to the Download and Setup page.
The Getting Started page provides an introduction to the CCT platform using examples from this repository, cct-tutorial.
The CCT Programming Guide provides more in-depth documentation of the CCT platform. Note that this is an early document, and still refers to CCT by its original internal working name (Cog ex Machina or Cog).
These and other CCT documentation pages are located here.
CCT includes four user-visible components. The core (cct-core)provides the compiler, runtime, visual debugger, and standard library. The I/O library (cct-io) includes several useful sensors for standard data types. This is a separate module becuase it has significant dependencies. The NN library (cct-nn) includes support for deep learning and similar gradient descent methods. The sandbox (cct-sandbox) includes a number of library routines that don't cluster into coarse enough chunks to justify independent libraries. All four modules are included as dependencies for the tutorial.
Scaladocs for these modules is available here: