Accelerate provides access to numerical libraries optimized for performance on Intel CPUs and NVidia GPUs.
- Bindings to CUDA libraries: cuBLAS, cuFFT, cuSPARSE, cuRAND, and sorting algorithms from the CUB and Modern GPU libraries
- Speed-boosted linear algebra operations in NumPy, SciPy, scikit-learn and NumExpr libraries using Intel’s Math Kernel Library (MKL).
- Increased-speed Fast Fourier Transformations (FFT) in NumPy.
64-bit operating system: Linux, OS X or Windows
- Supported Python and Numpy combinations:
- Python 2.7 with Numpy 1.9 or 1.10
- Python 3.4 with Numpy 1.9 or 1.10
- Python 3.5 with Numpy 1.9 or 1.10
For the CUDA features:
- NVidia driver version 349.00 or later
- CUDA toolkit 7.0
- At least one CUDA GPU with compute capability 2.0 or above
Accelerate is included with Anaconda Workgroup and Anaconda Enterprise subscriptions.
To start a 30-day free trial just download and install the Anaconda Accelerate package.
If you already have Anaconda (free Python distribution) installed:
conda update conda conda install accelerate
If you do not have Anaconda installed, you can download it here.
Anaconda Accelerate can also be installed into your own (non-Anaconda) Python environment. For more information about Accelerate please contact firstname.lastname@example.org.
If you have Anaconda (free Python distribution) installed:
conda update conda conda update accelerate
If you already have NumbaPro installed, you must manually upgrade NumbaPro to install the NumbaPro compatibility layer:
conda update conda conda update numbapro
- Release Notes
- CUDA Libraries
- CUDA Sorting