Andrews Cordolino Sobral andrewssobral

## Crack Sublime Text Windows and Linux.md

      
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                andrewssobral
                / Crack Sublime Text Windows and Linux.md
            
            
              Created
              August 3, 2021 15:14
                — forked from JerryLokjianming/Crack Sublime Text Windows and Linux.md
            
              
                Crack Sublime Text 3.2.2 Build 3211 and Sublime Text 4 Alpha 4098 with Hex
              
          
YouTube Channel https://www.youtube.com/c/jerrylokjianming


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


Download & Install Sublime Text 3.2.2 Build 3211
Visit https://hexed.it/


## synergy-jetson.sh
sudo apt install xorg-dev libssl-dev qtbase5-dev libgdk-pixbuf2.0-dev libnotify-dev qttools5-dev-tools qttools5-dev
git clone https://github.com/symless/synergy-core.git
cd synergy-core
mkdir build
cd build
cmake ..
make
# https://github.com/symless/synergy-core/wiki/Command-Line
# ./bin/synergyc $SYNERGY_SERVER_IP

## gstreamer-opencv.md

      
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                andrewssobral
                / gstreamer-opencv.md
            
            
              Last active
              August 11, 2021 21:24
            
              
                Jetson-Nano GStreamer OpenCV Plugins
              
          
    Basic tutorial 10: GStreamer tools

https://gstreamer.freedesktop.org/documentation/tutorials/basic/gstreamer-tools.html

# (optional)
$ export GST_PLUGIN_PATH=/usr/lib/aarch64-linux-gnu/gstreamer-1.0:/usr/lib/aarch64-linux-gnu/gstreamer-1.0/deepstream

$ apt list --installed | grep streamer


## pytorch-distributed-slurm-example.py
# https://github.com/ShigekiKarita/pytorch-distributed-slurm-example/blob/master/main_distributed.py
# lauch 2 gpus x 2 nodes (= 4 gpus)
# srun -N2 -p gpu --gres gpu:2 python main_distributed.py --dist-backend nccl --multiprocessing-distributed --dist-file dist_file
import argparse
import os
import random
import shutil
import time
import warnings
import sys

## pytorch_lightning_distributed_training.py
import os
import multiprocessing

import torch
import torch.nn.functional as F
import torchmetrics
import pytorch_lightning as pl

from argparse import ArgumentParser
from torch import nn

## cuda_combination.py
"""
Created on Sat Sep 28 00:47:38 2019
@author: Kenan Kilictepe (Entegral / www.entegral.com.tr)
"""

import numba
from numba import cuda
import numpy as np
from pdb import set_trace

## keras_mnist_cnn.py
'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

import keras
from keras.datasets import mnist
from keras.models import Sequential

## ssecheck.cpp
/*
	Check SSE/AVX support.
	This application can detect the instruction support of
	SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, SSE4a, SSE5, and AVX.
	grep avx /proc/cpuinfo
	g++ ssecheck.cpp -o ssecheck
*/

#include <iostream>
#ifdef _MSC_VER

## pytorch_gpu_test.py
import torch

print(torch.cuda.current_device())
# 0

print(torch.cuda.device(0))
# <torch.cuda.device at 0x...>

print(torch.cuda.device_count())
# 1

## tensorflow_test.py
import tensorflow as tf
import numpy as np
import time

from tensorflow.contrib.rnn import BasicLSTMCell

# https://github.com/tensorflow/tensorflow/issues/24828
try:
    from tensorflow.compat.v1 import ConfigProto
    from tensorflow.compat.v1 import InteractiveSession
	sudo apt install xorg-dev libssl-dev qtbase5-dev libgdk-pixbuf2.0-dev libnotify-dev qttools5-dev-tools qttools5-dev
	git clone https://github.com/symless/synergy-core.git
	cd synergy-core
	mkdir build
	cd build
	cmake ..
	make
	# https://github.com/symless/synergy-core/wiki/Command-Line
	# ./bin/synergyc $SYNERGY_SERVER_IP
	# https://github.com/ShigekiKarita/pytorch-distributed-slurm-example/blob/master/main_distributed.py
	# lauch 2 gpus x 2 nodes (= 4 gpus)
	# srun -N2 -p gpu --gres gpu:2 python main_distributed.py --dist-backend nccl --multiprocessing-distributed --dist-file dist_file
	import argparse
	import os
	import random
	import shutil
	import time
	import warnings
	import sys
	import os
	import multiprocessing

	import torch
	import torch.nn.functional as F
	import torchmetrics
	import pytorch_lightning as pl

	from argparse import ArgumentParser
	from torch import nn
	"""
	Created on Sat Sep 28 00:47:38 2019
	@author: Kenan Kilictepe (Entegral / www.entegral.com.tr)
	"""

	import numba
	from numba import cuda
	import numpy as np
	from pdb import set_trace
	'''Trains a simple convnet on the MNIST dataset.

	Gets to 99.25% test accuracy after 12 epochs
	(there is still a lot of margin for parameter tuning).
	16 seconds per epoch on a GRID K520 GPU.
	'''

	import keras
	from keras.datasets import mnist
	from keras.models import Sequential
	/*
	Check SSE/AVX support.
	This application can detect the instruction support of
	SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, SSE4a, SSE5, and AVX.
	grep avx /proc/cpuinfo
	g++ ssecheck.cpp -o ssecheck
	*/

	#include <iostream>
	#ifdef _MSC_VER
	import torch

	print(torch.cuda.current_device())
	# 0

	print(torch.cuda.device(0))
	# <torch.cuda.device at 0x...>

	print(torch.cuda.device_count())
	# 1
	import tensorflow as tf
	import numpy as np
	import time

	from tensorflow.contrib.rnn import BasicLSTMCell

	# https://github.com/tensorflow/tensorflow/issues/24828
	try:
	from tensorflow.compat.v1 import ConfigProto
	from tensorflow.compat.v1 import InteractiveSession