Prepare your new block storage, this will house the moved docker folder # format the new block storage device mkfs.ext4 /dev/sdx # create a mount point mkdir /mnt/diskX # mount the file or do equivalent mount in /etc/fstab mount /dev/sdx1 /mnt/diskX # create a new folder to house docker mkdir /mnt/diskX/docker chmod 700 /mnt/diskX/docker Setup the move # stop containers and docker daemon docker ps -q | xargs docker kill service docker stop # if devicemapper exists cd /var/lib/docker/devicemapper/mnt umount .

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Configure ./configure --enable-cuda-sdk --enable-filter=scale_cuda --enable-filter=thumbnail_cuda --enable-cuvid --enable-nvenc --enable-nonfree --enable-libnpp --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 --disable-x86asm Build make -j 10 Example - Extract Frames ffmpeg -hwaccel cuvid -c:v h264_cuvid -i input.mp4 -vf "hwupload_cuda,thumbnail_cuda=100,hwdownload,format=nv12,fps=1/2" -q:v 2 frames/%05d.jpg Links Nvidia Official Page ffmpeg generate thumbnails on GPU PDF: Nvidia Video Technologies

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The 2080 Ti comes out on top as the best GPU in 2018 for training neural nets. Results summary As of October 8, 2018, the NVIDIA RTX 2080 Ti is the best GPU for deep learning research on a single GPU system running TensorFlow. A typical single GPU system with this GPU will be: 37% faster than the 1080 Ti with FP32, 62% faster with FP16 35% faster than the 2080 with FP32, 47% faster with FP16 96% as fast as the Titan V with FP32, 3% faster with FP16 80% as fast as the Tesla V100 with FP32, 82% as fast with FP16 Note that all experiments utilized Tensor Cores when available and are priced out on a complete single GPU system cost.

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Homepage: Worn on TV Search by Show: Supernatural Search by Person: Olivia Pope

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Install git clone https://github.com/magnific0/wondershaper.git Use sudo wondershaper eth0 1000 200 Reset sudo wondershaper clear eth0 GitHub magnific0/wondershaper

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Install sudo apt-get install trickle Run trickle -s -d 10 -u 10 firefox GitHub mariusae/trickle

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Examples youtube-upload --title="A.S. Mutter" anne_sophie_mutter.flv ./youtubeuploader -filename blob.mp4 tokland/youtube-upload Command-line script to upload videos to Youtube using theYoutube APIv3. It should work on any platform (GNU/Linux, BSD, OS X, Windows, …) that runs Python. Install $ sudo pip install --upgrade google-api-python-client oauth2client progressbar2 $ wget https://github.com/tokland/youtube-upload/archive/master.zip $ unzip master.zip $ cd youtube-upload-master $ sudo python setup.py install Setup The package used to include a default client_secrets.json file.

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First thing first, the command used here for both Apache and Nginx were designed to be use in cron because TOR ip often change. But make sure that it’s scheduled for 1 hour at minimum to respect those that gives you free service to lookup tor ip =) TorList If you only have access to .htaccess file For Apache 2.2 This will append new lines to your .

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Use --no-bin-links

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Docs Accelerated Mobile Pages Project Examples Accelerated Mobile Pages for eCommerce SEO SEO: Accelerated Mobile Pages for Ecommerce Learn AMP Conf 2018 Learn AMP by Example AMP Start Integration Lavavel AMP

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