Below are my personal notes related to the Nvidia Jetson Nano Dev-board.
Nvidia allows your to fine tune the performance of your Jetson nano. More on this here.
sudo /usr/bin/jetson_clocks.sh --show
#define BIT_LEN 7 | |
float line_value(int line) { | |
int i; | |
int value = 0, count = 0; | |
for(i=0; i < BIT_LEN; ++i) { | |
int mask = (1 << (i)); | |
if((line & (mask)) == (mask)) { | |
value += i: | |
count++; |
#!/bin/bash | |
#Jason T. 2-6-2018 | |
# Check specifically for the run command | |
if [[ $# -ge 2 && $1 == "run" ]]; then | |
# Tell docker to share the following folders with the base system | |
# This allows the docker containers to find CUDA, cuDNN, TensorRT | |
LIB_MAPS="/usr/lib/aarch64-linux-gnu \ | |
/usr/local/cuda \ | |
/usr/local/cuda/lib64" |
Below are my personal notes related to the Nvidia Jetson Nano Dev-board.
Nvidia allows your to fine tune the performance of your Jetson nano. More on this here.
sudo /usr/bin/jetson_clocks.sh --show