Prithivida PrithivirajDamodaran

Serving Encoder-Decoder Models using TF Serving

Prithiviraj Damodaran

Step1 : Save Models/Weights

import tensorflow as tf
from tensorflow.keras.models import model_from_json
from tensorflow.keras.models import load_model

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

So you're curious in learning this new thing called Reactive Programming, particularly its variant comprising of Rx, Bacon.js, RAC, and others.

Learning it is hard, even harder by the lack of good material. When I started, I tried looking for tutorials. I found only a handful of practical guides, but they just scratched the surface and never tackled the challenge of building the whole architecture around it. Library documentations often don't help when you're trying to understand some function. I mean, honestly, look at this:

Rx.Observable.prototype.flatMapLatest(selector, [thisArg])

Projects each element of an observable sequence into a new sequence of observable sequences by incorporating the element's index and then transforms an observable sequence of observable sequences into an observable sequence producing values only from the most recent observable sequence.

	/usr/local/lib/python3.10/dist-packages/torch/distributed/launch.py:183: FutureWarning: The module torch.distributed.launch is deprecated
	and will be removed in future. Use torchrun.
	Note that --use-env is set by default in torchrun.
	If your script expects `--local-rank` argument to be set, please
	change it to read from `os.environ['LOCAL_RANK']` instead. See
	https://pytorch.org/docs/stable/distributed.html#launch-utility for
	further instructions

	warnings.warn(
	2024-04-27 16:05:43.073043: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.

	# Run the below if you get no file or command lspci
	# !sudo apt-get install pciutils

	import subprocess

	def get_gpu_driver_info():
	# Find the GPU's chip name
	lspci_output = subprocess.check_output(["lspci", "-nn"]).decode()
	gpu_info_line = next((line for line in lspci_output.split('\n') if '3D controller' in line and 'NVIDIA' in line), None)
	print(f"GPU Information: {gpu_info_line}")

	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt
	%matplotlib inline

	from scipy.stats import binom

	n = [1,2,10,15,25,50]
	p = 0.5
	j = 0

	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt
	%matplotlib inline

	from scipy.stats import binom

	n = [1,2,10,15,25,50]
	p = 0.5
	j = 0

	# Assumes you have the enron email dataset as emails.csv

	import pandas as pd
	data = pd.read_csv("emails.csv")
	pd.set_option('display.max_colwidth',-1)
	new = data["message"].str.split("\n", n = 15, expand = True)

	data["from"] = new[2]
	data["fromn"] = new[8]
	data["to"] = new[3]

	<html>
	<head>
	<style type="text/css">

	@import url('https://fonts.googleapis.com/css?family=Noto+Sans:400,700');


	body{
	background: #f0f0f0;
	font-family: 'Noto Sans', sans-serif;

	The Project Gutenberg EBook of The Adventures of Sherlock Holmes
	by Sir Arthur Conan Doyle
	(#15 in our series by Sir Arthur Conan Doyle)

	Copyright laws are changing all over the world. Be sure to check the
	copyright laws for your country before downloading or redistributing
	this or any other Project Gutenberg eBook.

	This header should be the first thing seen when viewing this Project
	Gutenberg file. Please do not remove it. Do not change or edit the

	# by Prithiviraj Damodaran
	# Based on Peter Norvig’s blog post and toy spell correction logic

	from __future__ import division
	from memo import memo
	import re
	from collections import Counter


	def words(text): return re.findall(r'\w+', text.lower())