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import argparse
import io
import re
import sys
from pathlib import Path
import random
import subprocess
import shutil
import tempfile

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"""
A simple webserver for serving videos dumped as frames as real videos.
Point it towards a file hierarchy that looks like so:
| root
| |- video_1
| |  |- frame_0001.jpg
| |  |- frame_0002.jpg
| |  |- frame_0003.jpg

Video frames are read in 'natural order' by the `natsort` library.

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$ conda create -n rapids-0.18 -c rapidsai -c nvidia -c conda-forge     -c defaults rapids=0.18 python=3.8 cudatoolkit=11.0
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json)

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USB ID 2357:0120 -> https://github.com/aircrack-ng/rtl8812au
USB ID 2357:0138 -> https://github.com/RinCat/RTL88x2BU-Linux-Driver (follow the manual DKMS setup guide)

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# The following steps are based on
# https://docs.fast.ai/performance.html#installation

$ conda install -y -c conda-forge gxx_linux-64 
$ conda uninstall -y --force jpeg libtiff
$ conda install -y -c conda-forge libjpeg-turbo --no-deps
$ pip uninstall -y pillow
$ export CXX=x86_64-conda-linux-gnu-g++
$ export CC=x86_64-conda-linux-gnu-gcc

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from torch import nn
import torch
from typing import List


def filter_parameters_for_finetuning(module: nn.Module) -> List[torch.Tensor]:
    """

    Args:
        module: A :py:class:`nn.Module` object where some of the children may have

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find . -type '*.pkl' | parallel python do_something_with_pkl.py "{}" --write-to-csv "{.}.csv"
# Expands to
# > python do_something_with_pkl.py my_pickle.pkl --write-to-csv my_pickle.csv

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#1f77b4
#ff7f0e
#2ca02c
#d62728
#9467bd
#8c564b
#e377c2
#7f7f7f
#bcbd22
#17becf

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# To launch the jobs run
# python multigpu_run.py example_args.py

prog = """
import sys
import os
print(sys.argv[1], os.environ['CUDA_VISIBLE_DEVICES'])
print(sys.argv)
if (sys.argv[1] == '2'):
print("Failed!")

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