Sriharsh Bhyravajjula darthbhyrava

Building TensorFlow from source (TF 2.1.0, Ubuntu 19.10)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: h

IASNLP-2015 Project list

LTRC, IIIT-Hyderabad

Treebanking

#####1. Shallow Parsers for different languages#####

Description - POS Tagging and Chunking for Gujarati,Odia, Hindi, Bengali, Marathi, Telugu (individual project for each language)
We will implement many supervised algorithms including CRF, HMM, MaxEnt, SVM, some semi-supervised classification methods, finally an unsupervised one. Will try to implement Morph Analyzer if time permits. students need to annotate data, understand the challenges, compare results given by multiple Mentor: Pruthwik M
algorithms- students for each language - 1 / 2

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)