This release is a pre-release and may not be stable for production use.
Features
Dynamic and incremental graph construction
On-demand memory allocation
Automatic minibatch broadcasting
Mostly device-independent
Simple usage
Install
Prerequisites:
Python 3 (3.5 or later)
NumPy (1.11.0 or later)
Cython (0.27 or later)
CMake (3.1.0 or later)
scikit-build (0.6.1 or later)
(optional) CUDA (7.5 or later)
(optional) OpenCL (1.2 or later) and OpenCL C++ binding v2
Install dependencies:
pip3 install numpy cython cmake scikit-build
Build and install primitiv without CUDA and OpenCL:
pip3 install primitiv
Build and install primitiv with CUDA and/or OpenCL support:
# Enable only CUDA pip3 install primitiv --global-option --enable-cuda # Enable both CUDA and OpenCL pip3 install primitiv --global-option --enable-cuda --global-option --enable-opencl
Notes
For now, we provide only a source pacakge, and pip command downloads a source package and builds it before installing. This is useful for users to install this library with CUDA/OpenCL backends while keeping compatibility with the manylinux1 standard described in PEP 513.
Resources
1 maintainer
Metadata
1 maintainer
Release files for primitiv 0.4.0.dev158
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| primitiv-0.4.0.dev158.tar.gz | 185.5 kB | Details |
Release files / primitiv-0.4.0.dev158.tar.gz
| Download URL | primitiv-0.4.0.dev158.tar.gz |
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| Size | 185.5 kB |
| Tags | Source |
|
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