qstat -F gpus,gputype,mem_total -q gpu
qlogin -pe gpu-titanx 1
#!/bin/bash | |
if [ $# -lt 2 ] | |
then | |
echo "usage: $0 [input_dir] [output_dir]" | |
echo "chooses 20 video files randomly from the input directory and run Faster R-CNN" | |
exit 1 | |
fi | |
input_dir="$1" |
c:v=libx264 preset=slow profile:v=high crf=%quality coder=1 pix_fmt=yuv420p movflags=+faststart g=12 bf=2 c:a=aac ab=%audiobitrate+'k' profile:a=aac_low f=mp4 |
ffmpeg -hwaccel cuvid -c:v h264_cuvid -i <input.MP4> -c:v h264_nvenc -rc:v vbr_hq -cq:v 19 -b:v 10000k -maxrate:v 20000k -profile:v high -color_range pc -colorspace bt709 -color_trc bt709 -color_primaries bt709 -c:a copy <output.mp4> |
#!/bin/bash | |
if [ $# -lt 2 ] | |
then | |
echo "usage: $0 [input_dir] [output_dir] [output_resolution=224x224]" | |
echo "Crops the video into 4 corners and 1 centre, and flips the video horizontally to do the same, resulting in 10 augmentation per video." | |
exit 1 | |
fi | |
input_dir="$1" |
#docker-compose.yml : Make container as described here. | |
#Author : Hyeonsu Lyu, hslyu@unist.ac.kr, +82 10-5117-9780 | |
# Kiyoon Kim (kiyoon.kim@ed.ac.uk) | |
#First version, Jan. 30, 2019 | |
version: '2' | |
services: | |
aislab-docker: | |
container_name: base_env | |
image: kiyoon/docker-for-ML:cuda10.1-cudnn7 |
from nvidia.dali.pipeline import Pipeline | |
from nvidia.dali.plugin import pytorch | |
import nvidia.dali.ops as ops | |
import nvidia.dali.types as types | |
import argparse | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--file_list', type=str, default='file_list.txt', | |
help='DALI file_list for VideoReader') |
#!/usr/bin/env python3 | |
# For command line usage, include the token and chat ids in advance in the script. | |
# If you want to use only the functions, there's no need to include this. | |
telegram_token = "" | |
# Get chat id by opening the URL: https://api.telegram.org/bot{token}/getUpdates | |
telegram_chat_ids = [""] | |
TIME_DURATION_UNITS = ( | |
('week', 60*60*24*7), | |
('day', 60*60*24), | |
('hour', 60*60), | |
('min', 60), | |
('sec', 1) | |
) | |
def human_time_duration(seconds): |
### get the first node name as master address | |
### e.g. master(aislab-[2-5],gnoded1) == aislab-2 | |
import argparse | |
def get_parser(): | |
parser = argparse.ArgumentParser(description="Extract master node name from Slurm node list", | |
formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
parser.add_argument("nodelist", help="Slurm nodelist") | |
return parser |