vellamike

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

cprakashagr

## Sublime Text 3 Serial key build is 3103

—– BEGIN LICENSE —–
Michael Barnes
Single User License
EA7E-821385
8A353C41 872A0D5C DF9B2950 AFF6F667
C458EA6D 8EA3C286 98D1D650 131A97AB
AA919AEC EF20E143 B361B1E7 4C8B7F04
B085E65E 2F5F5360 8489D422 FB8FC1AA

peterjc123

# Prerequisites
# 1. MSVC 2017 C++ Build Tools
# 2. CMAKE 3.0 or up
# 3. 64 bits of Windows
# 4. Anaconda / MiniConda 64 bits

# Prerequisites for CUDA
# 1. CUDA 8.0 or up
# 2. NVTX( in CUDA as Visual Studio Integration. if fail to install, you can extract
#     the CUDA installer exe and found the NVTX installer under the CUDAVisualStudioIntegration)

jkjung-avt

# --------------------------------------------------------
# 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 <[email protected]>

jganzabal

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

peter-moran

/*
  Example code for displaying gstreamer video from the CSI port of the Nvidia Jetson in OpenCV.
  Created by Peter Moran on 7/29/17.
  https://gist.github.com/peter-moran/742998d893cd013edf6d0c86cc86ff7f
*/

#include <opencv2/opencv.hpp>

std::string get_tegra_pipeline(int width, int height, int fps) {
    return "nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)" + std::to_string(width) + ", height=(int)" +

nicksam112

#Evolution Strategies with Keras
#Based off of: https://blog.openai.com/evolution-strategies/
#Implementation by: Nicholas Samoray

#README
#Meant to be run on a single machine
#APPLY_BIAS is currently not working, keep to False
#Solves Cartpole as-is in about 50 episodes
#Solves BipedalWalker-v2 in about 1000

karpathy

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

dusty-nv

#!/bin/bash
#
# EDIT:  this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available
#
sudo apt-get install python-pip

# upgrade pip
pip install -U pip
pip --version
# pip 9.0.1 from /home/ubuntu/.local/lib/python2.7/site-packages (python 2.7)

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown: