spikingjelly.configure module

This py file defines some variables used in SpikingJelly. Here is an example of how you can change them to make effect in your codes:

import spikingjelly spikingjelly.configure.cuda_threads = 512

Do not change them in this way, which will not make effect:

from spikingjelly.configure import cuda_threads cuda_threads = 512

spikingjelly.configure.max_threads_number_for_datasets_preprocess = 16

max_threads_number_for_datasets_preprocess defines the maximum threads for datasets preprocessing, which is 1. reading binary events and saving them to numpy format 2. integrating events to frames.

Note that a too larger max_threads_number_for_datasets_preprocess will overload the disc and slow down the speed.

spikingjelly.configure.cuda_threads = 512

cuda_threads defines the default threads number for CUDA kernel.

It is recommended that cuda_threads is the power of 2.

spikingjelly.configure.cuda_compiler_options = ('-use_fast_math',)

cuda_compiler_options defines the compiler options passed to the backend (NVRTC or NVCC).

For more details, refer to 1. https://docs.nvidia.com/cuda/nvrtc/index.html#group__options 2. https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#command-option-description 3. fangwei123456/spikingjelly#116

spikingjelly.configure.cuda_compiler_backend = 'nvrtc'

cuda_compiler_backend defines the compiler for CUDA(cupy).

It can be set to 'nvcc' or 'nvrtc'.

spikingjelly.configure.save_datasets_compressed = True

If save_datasets_compressed == True, events and frames in spikingjelly.datasets will be saved in compressed npz format.

The compressed npz file consumes less memory in disk but more time in reading.

spikingjelly.configure.save_spike_as_bool_in_neuron_kernel = False

If save_spike_as_bool_in_neuron_kernel == True, the neuron kernel used in the neuron's cupy backend will save the spike as a bool, rather than float/half tensor for backward, which can reduce the memory consumption.

spikingjelly.configure.save_bool_spike_level = 0

save_bool_spike_level take effects on SpikeConv/SpikeLinear, and on neuron's cupy kernel when save_spike_as_bool_in_cuda_utils == True.

If save_bool_spike_level == 0, spikes will be saved in bool. Note that bool uses 8-bit, rather than 1-bit.

If save_bool_spike_level == 1, spikes will be saved in uint8 with each 8-bit storing 8 spikes.

A larger save_bool_spike_level means less memory consumption but slower speed.