Karthik Duddu gokart23

Creating binary "input values" file for TFLite benchmarking tool:

import numpy as np

inp_feat_array = ....
...
inp_feat_array.astype(TYPE_OF_TFLITE_ARRAY_DTYPE).write(FNAME)

Filter CSV based on number of words in a field using awk:

awk -F ',' 'split($2,a," ") == 2 {print $2}' fname

Convert Kubernetes events to local timezone (for comparison with other logs):

kubectl get events -o json | jq '.items[] | .lastTimestamp + "," + .message' | tr -d '"' | awk -F',' '{cmd="date +\"%d/%b/%Y:%T\" --date=" $1; cmd | getline conv_date; print "[" conv_date "] (kubectl events) " $2}'

Remove EOS/BOS from a text data file using sed

Setting up docker to use a different image directory: https://stackoverflow.com/a/34731550
Jellyfin docker image: docker run --name "jellyfin-server" --restart=unless-stopped -d -v $(pwd)/config/:/config -v $(pwd)/cache/:/cache -v /home/nemty/media:/media --net=host jellyfin/jellyfin

Steps

Install qemu-user-static (yay -S qemu-user-static).
- This might need you to install pcre-static and update PGP keys (follow the tips in the comments here).
If not already present, install systemd-binfmt, the revamped version of binfmt-support tools
- Your system has to support transparent Qemu user emulation, but fortunately, that is mostly enabled once the steps here have been followed.
Check the status of the systemd-binfmt unit (systemctl status systemd-binfmt) and (re)start if needed (sudo systemctl restart systemd-binfmt)
- This unit has ARM support by default, but check the current documentation to make install it if needed
Mount the root partition of the ARM system into a folder (for e.g., sudo mount /dev/sdb2 arm_mountpoint)
Copy the QEMU ARM static binary (/usr/bin/qemu-arm-static on my distro) to the mounted root directory's usr/bin

	import tensorflow as tf
	import re

	from model import modeling
	from model import optimization
	from pretrain import pretrain_data
	from pretrain import pretrain_helpers
	from util import training_utils
	from util import utils

	import tensorflow as tf
	import tensorflow_text as text

	tf.enable_eager_execution()

	CLS_TOKEN, SEP_TOKEN, MASK_TOKEN = 101, 102, 103

	def merge_dims(rt, axis=0):
	to_expand = rt.nested_row_lengths()[axis]
	to_elim = rt.nested_row_lengths()[axis + 1]

	def mask_all_out(self, x, lengths):
	"""
	Returns tensor with all tokens masked out one at a time, with respect for seq lengths

	Inputs:
	x : (slen, bsz) LongTensor
	lengths : (bsz) LongTensor

	Returns:

	#!/bin/env python3
	import argparse, sys
	import os, subprocess
	import xml.etree.ElementTree as ET

	parser = argparse.ArgumentParser()
	parser.add_argument("--rssfile", help="Location of RSS feed file downloaded from Panopto",
	required=True, type=argparse.FileType('r'))

	args = parser.parse_args()

	# Kubernetes Server: 1.3

	# If this is the first time that apply is being called
	curl -X POST -H "Content-Type: application/json" "${KUBERNETES_API_SERVER}"/apis/extensions/v1beta1/namespaces/my-namespace/deployments/ --data @my-deployment-spec.json

	# If this is the first time that apply is being called
	curl -X PATCH -H "Content-Type: application/json-patch+json" "${KUBERNETES_API_SERVER}"/apis/extensions/v1beta1/namespaces/my-namespace/deployments/my-deployment --data @my-deployment-spec.json