gokart23

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gokart23

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          

gokart23

1. 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)

gokart23

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]

gokart23

    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:          

gokart23

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

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

2. 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}'

3. Remove EOS/BOS from a text data file using sed

gokart23

1. Setting up docker to use a different image directory: https://stackoverflow.com/a/34731550
2. 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

gokart23

Steps

1. 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).
2. 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.
3. 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
4. Mount the root partition of the ARM system into a folder (for e.g., sudo mount /dev/sdb2 arm_mountpoint)
5. Copy the QEMU ARM static binary (/usr/bin/qemu-arm-static on my distro) to the mounted root directory's usr/bin

gokart23

#!/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()

gokart23

# 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