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Screen Quick Reference

Basic

Description	Command
Start a new session with session name	screen -S <session_name>
List running sessions / screens	screen -ls
Attach to a running session	screen -x
Attach to a running session with name	screen -r

zeroth

[19:16:52] Setting home to 'CONDA_PREFIX'
[WARNING 19:16:52 GPURecognition.cpp:598] Python not yet initialized. Waiting...
2021-03-02 19:16:53.150998: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
[19:16:53] Took 1.027124s to calculate percentiles in 541 frames.
2021-03-02 19:16:55.015413: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-03-02 19:16:55.025723: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x19746b6a0e0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-03-02 19:16:55.025813: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2021-03-02 19:16:55.027816: 

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import numpy as np
from timeit import default_timer as timer
from pathlib import Path
import os
import tifffile
import glob
import argparse
import re
import multiprocessing
from functools import partial

zeroth

from pycudadecon import deskewGPU
import cupy as cp
import numpy as np
import tifffile
import glob
import os
import argparse

"""
need to install https://github.com/tlambert03/pycudadecon

zeroth

import numpy as np
from timeit import default_timer as timer
from pathlib import Path
import os
import tifffile
import glob
import argparse
import re

"""

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import sys
import os
from pycudadecon import RLContext, rl_decon
import tifffile
from pycudadecon.otf import make_otf, TemporaryOTF
import re
import glob
import argparse

"""

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async def main():
    coroutine1 = do_some_work(1)
    coroutine2 = do_some_work(2)
    coroutine3 = do_some_work(4)

    tasks = [
        asyncio.ensure_future(coroutine1),
        asyncio.ensure_future(coroutine2),
        asyncio.ensure_future(coroutine3)
    ]

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from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB


categories = ['alt.atheism', 'soc.religion.christian',
              'comp.graphics', 'sci.med']

twenty_train = fetch_20newsgroups(subset='train',

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section     .text
global      _start                             

_start:                                        
    mov     edx,len                            
    mov     ecx,msg    ;message to write
    mov     ebx,1      ;file descriptor (stdout)
    mov     eax,4      ;system call number (sys_write)
    int     0x80       ;call kernel  
    mov     eax,1      ;system call number (sys_exit)

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/* Index.js */
const Hapi = require('hapi');
const Vision = require('vision');
const Path = require('path');

const server = new Hapi.Server();

// Server connection
server.connection({
    host: 'localhost',