andrewssobral

# --------------------------------------------------------
# Camera sample code for Tegra X2/X1
#
# This program could capture and display video from
# IP CAM, USB webcam, or the Tegra onboard camera.
# Refer to the following blog post for how to set up
# and run the code:
#   https://jkjung-avt.github.io/tx2-camera-with-python/
#
# Written by JK Jung <jkjung13@gmail.com>

andrewssobral

Building TensorFlow from source for Jetson TX2 with Jetpack 3.2

Jetpack 3.2 includes Cuda 9 and CuDNN 7 so it is necessary to compile it from source.

Step 1 - Get Java

sudo apt-get install openjdk-8-jdk

Step 2 - Get some dependencies

andrewssobral

// cat cling_opencv_example.cpp | cling                                                                              
.L /usr/lib/libopencv_highgui.so

#include <opencv/highgui.h>
#include <math.h>
#include <iostream>

using namespace std;

template <typename T> T deg2rad(T deg) { return deg*M_PI/180.0; }

andrewssobral

# run with a custom --n
# python run_luigi.py SquaredNumbers --local-scheduler --n 20

import luigi

class PrintNumbers(luigi.Task):
    n = luigi.IntParameter(default=10)

    def requires(self):
        return []

andrewssobral

This configuration worked for me, hope it helps

It is based on: https://becominghuman.ai/deep-learning-gaming-build-with-nvidia-titan-xp-and-macbook-pro-with-thunderbolt2-5ceee7167f8b

and on: https://stackoverflow.com/questions/44744737/tensorflow-mac-os-gpu-support

Hardware

andrewssobral

from jupyter_client.kernelspec import NATIVE_KERNEL_NAME
from jupyter_client.manager import start_new_kernel

km, kc = start_new_kernel(kernel_name=NATIVE_KERNEL_NAME)

def run_cell(client, code, timeout=15):
  print("\nrunning: ", code)
  reply = client.execute_interactive(code, timeout=timeout)
  status = reply['content']['status']
  if status == 'ok':

andrewssobral

#!/bin/bash
#
# pyTorch install script for NVIDIA Jetson TX1/TX2,
# from a fresh flashing of JetPack 2.3.1 / JetPack 3.0 / JetPack 3.1
#
# for the full source, see jetson-reinforcement repo:
#   https://github.com/dusty-nv/jetson-reinforcement/blob/master/CMakePreBuild.sh
#
# note:  pyTorch documentation calls for use of Anaconda,
#        however Anaconda isn't available for aarch64.

andrewssobral

// First make sure the wrapper app is loaded
// https://github.com/widget-/slack-black-theme/issues/48
// alias slackdark='cat ~/slack_dark_theme.js >> /Applications/Slack.app/Contents/Resources/app.asar.unpacked/src/static/ssb-interop.js'
document.addEventListener("DOMContentLoaded", function() {

  // Then get its webviews
  let webviews = document.querySelectorAll(".TeamView webview");

  // Fetch our CSS in parallel ahead of time
  const cssPath = 'https://cdn.rawgit.com/widget-/slack-black-theme/master/custom.css';

andrewssobral

#ifndef THC_GENERIC_FILE
#define THC_GENERIC_FILE "generic/SpatialUpSamplingBilinear.cu"
#else

#include "../linear_upsampling.h"

//#define MAX_THREADS_PER_BLOCK 256
//#define MIN_BLOCKS_PER_MP     2
//__launch_bounds__(MAX_THREADS_PER_BLOCK, MIN_BLOCKS_PER_MP)

andrewssobral

ubuntu@ubuntu1604:~$ intel-realsense-dfu -b 001 -d 006 -f -i Signed_Image_UVC_5_10_6_0.bin
D400 busnum = 1
D400 devnum = 6
D400 FW file path = Signed_Image_UVC_5_10_6_0.bin
FW Version of .bin File   = 5.10.6.0
FW version on device = 5.8.15.0
MM FW Version = 255.255.255.255
FW update required...
Updating FW...
DFU FW version in file = 5.10.6.0