Ryan Reece rreece

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

TensorFlow Best Practices as of Q1 2018

By Adam Anderson

adam.b.anderson.96@gmail.com

Preface

This write-up assumes you have an general understanding of the TensorFlow programming model, but maybe you haven't kept up to date with the latest library features/standard practices.

	"""
	The most atomic way to train and inference a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	# Sebastian Raschka 2025
	#
	#
	# Usage:
	# python llama-vs-gemma.py \
	# --auth_token hf_... \
	# --model_name meta-llama/Llama-3.2-1B \
	# --prompt medium

	import argparse

	"""
	A minimal, fast example generating text with Llama 3.1 in MLX.

	To run, install the requirements:

	pip install -U mlx transformers fire

	Then generate text with:

	python l3min.py "How tall is K2?"

	import pyhf
	pyhf.set_backend('pytorch', 'minuit')

	nSig = 4.166929245
	errSig = 4.166929245
	nBkg = 0.11
	errBkgUp = 0.20
	errBkgDown = 0.11

	model_json = {

	import numpy as np
	from pynvml.smi import nvidia_smi
	import pycuda.gpuarray as ga
	import pycuda.driver as cuda

	nvsmi = nvidia_smi.getInstance()

	def getGPUMemoryUsage(gpu_index=0):
	return nvsmi.DeviceQuery("memory.used")["gpu"][gpu_index]['fb_memory_usage']['used']

	### JHW 2018
	import numpy as np
	import umap



	# This code from the excellent module at:
	# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module

	import random

	import torch
	from torch import LongTensor
	from torch.nn import Embedding, LSTM
	from torch.autograd import Variable
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
	#
	# Step 1: Construct Vocabulary
	# Step 2: Load indexed data (list of instances, where each instance is list of character indices)