Skip to main content

primitiv: A Neural Network Toolkit. (Python frontend)

Project description

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)

  • scikit-build (0.6.1 or later, only for building)

  • (optional) CUDA (7.5 or later)

  • (optional) OpenCL (1.2 or later) and OpenCL C++ binding v2

Install dependencies:

pip3 install numpy cython scikit-build

Install primitiv without CUDA and OpenCL:

pip3 install primitiv

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

Resources

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

primitiv-0.3.1.dev106.tar.gz (154.6 kB view details)

Uploaded Source

File details

Details for the file primitiv-0.3.1.dev106.tar.gz.

File metadata

File hashes

Hashes for primitiv-0.3.1.dev106.tar.gz
Algorithm Hash digest
SHA256 f4af2b43789f93e462b309b5ee03ab66089bec523092128303e1c47b178d9338
MD5 4fd43d8a889846566fe1f9d584f19296
BLAKE2b-256 f1a6ee1ff0c362ecbda3853d501d37ad57e54025f1fc3ddd5f62685d3e314039

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page