Bill Wang billhhh

I've used this in the past to grab offline copies of some of the Google API docs.

$ wget --no-parent --recursive --page-requisites --html-extension --convert-links "http://code.google.com/apis/gears/"

# --no-parent - Don't fetch anything in the parent directory of the URL specified
# --page-requisites - Download everything required for the page to display correctly
# --recursive - Recursively download (use default depth of 5)
# --html-extension - Add .html to downloaded html files (this isn't necessarily required for the google code example but might be useful on other sites)

--convert-links - Update links in the downloaded document to point to, either the downloaded copy of a file or a full URL

IMPORTANT

See the following links for further updates to Github Desktop for Ubuntu. These are official instructions. (also mentioned by fetwar on Nov 3, 2023)

For the sake of "maintaining the tradition" here is the updated version.

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	"""
	citation:

	@misc{brown2025grpodemo,
	title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
	author={Brown, William},

	deb http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse
	deb-src http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse

	deb http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse
	deb-src http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse

	deb http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse
	deb-src http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse

	deb http://archive.ubuntu.com/ubuntu/ focal-backports main restricted universe multiverse

	import nibabel as nib
	import numpy as np

	data = np.ones((32, 32, 15, 100), dtype=np.int16) # dummy data in numpy matrix
	img = nib.Nifti1Image(data, np.eye(4)) # Save axis for data (just identity)

	img.header.get_xyzt_units()
	img.to_filename(os.path.join('build','test4d.nii.gz')) # Save as NiBabel file

	def contextual_loss(x, y, h=0.5):
	"""Computes contextual loss between x and y.

	Args:
	x: features of shape (N, C, H, W).
	y: features of shape (N, C, H, W).

	Returns:
	cx_loss = contextual loss between x and y (Eq (1) in the paper)
	"""

	# tested on AWS p2.xlarge August 29, 2018

	# install CUDA
	sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
	CUDA_URL="https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64"
	wget -c ${CUDA_URL} -O cuda.deb
	sudo dpkg --install cuda.deb
	sudo apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
	sudo apt-get update
	sudo apt-get install -y cuda

	# Imports
	from sklearn.datasets import fetch_olivetti_faces
	import numpy as np

	# Download Olivetti faces dataset
	olivetti = fetch_olivetti_faces()
	x = olivetti.images
	y = olivetti.target

	# Print info on shapes and reshape where necessary

	import torch
	import torch.nn as nn
	import torch.nn.functional as F

	# Focal loss implementation inspired by
	# https://github.com/c0nn3r/RetinaNet/blob/master/focal_loss.py
	# https://github.com/doiken23/pytorch_toolbox/blob/master/focalloss2d.py
	class MultiClassBCELoss(nn.Module):
	def __init__(self,
	use_weight_mask=False,