Public | Automated Build

Last pushed: a month ago
Short Description
Docker Image of NNabla with CUDA extension.
Full Description

A CUDA Extension of Neural Network Libraries

This repository provides an official CUDA/cuDNN-accelerated extension of the
Neural Network Libraries deep learning framework.

In order to use it, the default context needs to be changed from 'cpu' to

from nnabla.extension_utils import get_extension_context

ctx = get_extension_context('cudnn', device_id='0')

Currently, the binary package install manual and the usage documentation are integrated into the NNabla's documentation.
For build instructions, see below.

Performance notes

Automatic Convolution algorithm selection

If CUDNN is enabled, the extension library automatically finds the fastest Convolution algorithm of CUDNN given a configuration of parameters (filter size, stride, dilation, pad, etc), by exhaustively executing and measuring the time of each computation of algorithms (cudnnFindConvolution*Algorithm). The best algorithm will be cached, then re-used when an identical configuration is passed to our Convolution interface. It is very powerful in speed, even in non-static (dynamic) neural network.

However, it often consumes much memory due to a big workspace memory required by automatically found algorithms, and sometimes doesn't work on a GPU with small memory. To avoid this, you can specify the limit of the workspace size by setting an environment variable NNABLA_CUDNN_WORKSPACE_LIMIT (in bytes) read at runtime (not compilation time). For example, NNABLA_CUDNN_WORKSPACE_LIMIT=134217728 limits the workspace size up to 128 MB. The default value is -1 which means there is no limit of the workspace size.


No FAQ so far.

Docker Pull Command
Source Repository