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]>

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)

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

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

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

JerryLokjianming

---

How to Crack Sublime Text 3.2.2 Build 3211 with Hex Editor (Windows | Without License) ↓

1. Download & Install Sublime Text 3.2.2 Build 3211
2. Visit https://hexed.it/
3. Open file select sublime_text.exe
4. Offset 0x8545: Original 84 -> 85
5. Offset 0x08FF19: Original 75 -> EB
6. Offset 0x1932C7: Original 75 -> 74 (remove UNREGISTERED in title bar, so no need to use a license)

mblondel

# Authors: Mathieu Blondel, Vlad Niculae
# License: BSD 3 clause

import numpy as np


def _gen_pairs(gen, max_iter, max_inner, random_state, verbose):
    rng = np.random.RandomState(random_state)

    # if tuple, interpret as randn

sorny

X11 forwarding on macOS and docker

A quick guide on how to setup X11 forwarding on macOS when using docker containers requiring a DISPLAY. Works on both Intel and M1 macs!

This guide was tested on:

• macOS Catalina 10.15.4
• docker desktop 2.2.0.5 (43884) - stable release
• XQuartz 2.7.11 (xorg-server 1.18.4)
• Macbook Pro (Intel)

seddonm1

On an Orin NX 16G the memory was too low to compile and the SWAP file had to be increased.

/etc/systemd/nvzramconfig.sh
change:

```
# Calculate memory to use for zram (1/2 of ram)
totalmem=`LC_ALL=C free | grep -e "^Mem:" | sed -e 's/^Mem: *//' -e 's/  *.*//'`
mem=$((("${totalmem}" / 2 / "${NRDEVICES}") * 1024))
```

lewtun

# This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), 
# adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs.
#
# Usage:
#   - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate`
#   - Install deepspeed: `pip install deepspeed==0.9.5`
#   - Install TRL from main: pip install git+https://github.com/huggingface/trl.git
#   - Clone the repo: git clone github.com/huggingface/trl.git
#   - Copy this Gist into trl/examples/scripts
#   - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py