Onuralp cx0

# install nougat
pip install "nougat-ocr[api, dataset]"
# crop the table from paper (preserve pdf)
# using default 0.1.0-small model
nougat /tmp/2304.08485.table3.only.pdf -o /tmp/" --markdown

\begin{table}

Change the number of columns and rows on the launchpad grid.

defaults write com.apple.dock springboard-columns -int 8
defaults write com.apple.dock springboard-rows -int 8
defaults write com.apple.dock ResetLaunchPad -bool TRUE
killall Dock

Use Default to reset.

Use GitHub Copilot in the command line.

pip uninstall gh
brew install gh
gh auth login
gh extension install github/gh-copilot
gh extension upgrade gh-copilot
gh copilot suggest 'read text file line by line and return the total number of alphanum chars in each line'

User prompt:

You have access to source code for torch.distributed.pipelining package and relevant documentation for implementing pipeline parallelism. Write a function that creates an accurate pipeline_order for Schedule1F1B given world_size and n_microbatches such that _format_pipeline_order will return the correct output. Make sure to provide a minimal example to verify function performs as expected.

CoT:

Crafting a pipeline: I’m thinking through a function to set up the pipeline order for Schedule1F1B in PyTorch, aiming to efficiently manage forward and backward passes across stages. Progress is steady, ensuring an interleaved pattern that optimizes pipeline stage utilization.
Crafting the schedule: I’m working on a 1F1B pipeline schedule, alternating forward and backward pass microbatches at each stage. Ensuring optimal utilization, I aim to refine a formula for this arrangement.
Piecing together: I’m using a well-known formula from “GPipe” and “torch.distributed.pipeline”

	# check the ports
	# openssl s_client -connect ega.ebi.ac.uk:8443
	# openssl s_client -connect ega.ebi.ac.uk:8052

	# pyEGA3 - EGA python client version 3.4.0
	# Python version - 3.7.3

	pyega3 -cf default_credential_file.json fetch EGAF00001383154
	pyega3 -cf default_credential_file.json fetch EGAD00001000440 --saveto Desktop/ega_output/

	#!/usr/bin/bash

	# ClinVar weekly updates: https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/
	# Download assembly-specific variant annotation (Release date: 2022-09-19)
	wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz

	awk '{print "\t"$0"\t"}' acmg.filtered.genes.list \\|
	rg -zf - variant_summary.txt.gz \\|
	rg 'GRCh38' \\|
	rg 'single nucleotide variant' \\|

	# tinygrad implementation: https://github.com/tinygrad/tinygrad/blob/master/examples/beautiful_mnist.py
	%time
	import torch
	import torch.nn as nn
	import torch.optim as optim
	from torchvision import datasets
	import torchvision.transforms as transforms
	from torch.utils.data import DataLoader
	from tqdm import trange

	from openai import OpenAI

	client = OpenAI()

	response = client.chat.completions.create(
	model="gpt-4-turbo",
	messages=[
	{
	"role": "user",
	"content": [