wassname (Michael J Clark) wassname

STOP DOING MATH

NUMBERS WERE NOT SUPPOSED TO BE GIVEN NAMES
YEARS OF COUNTING yet NO REAL-WORLD USE FOUND for going higher than your FINGERS
Wanted to go higher anyway for a laugh? We had a tool for that: It was called "GUESSING"
"Yes please give me ZERO of something. Please give me INFINITE of it" - Statements dreamed up by the utterly Deranged

LOOK at what Mathematicians have been demanding your Respect for all this time, with all the calculators & abacus we built for them

	"""
	sometimes you want to run or adapt a cli script from jupyter, here a decent way to do it
	# https://gist.github.com/wassname/f1c23636cc04b39176bd82f45c6e398a
	"""

	def jupyter_argparse(s: str) -> list:
	"""
	Usage
	s = '''
	--rank 16

	"""
	When splitting text for Language Models, aim for two properties:

	- Limit tokens to a maximum size (e.g., 400)
	- Use natural boundaries for splits (e.g. ".")

	Many splitters don't enforce a token size limit, causing errors like "device assert" or "out of memory." Others focus on character length rather than token length. To address these issues:

	- Use RecursiveCharacterTextSplitter from the langchain library
	- Set the last separator to an empty string '' to ensure there is always a splitting point, thus maintaining token limits

	#!/bin/bash

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###

	### to verify your gpu is cuda enable check

	"""
	This is a template for starting with pytorch lightning, it includes many extra things because it's easier to delete than reinvent.

	It is written for these versions:
	- lightning==2.0.2
	- pytorch-optimizer==2.8.0
	"""

	import torch
	import torch.nn as nn

	import pytorch_lightning as pl
	from pytorch_lightning.loggers import CSVLogger, WandbLogger
	from pathlib import Path
	import pandas as pd

	def read_metrics_csv(metrics_file_path):
	df_hist = pd.read_csv(metrics_file_path)
	df_hist["epoch"] = df_hist["epoch"].ffill()
	df_histe = df_hist.set_index("epoch").groupby("epoch").mean()
	return df_histe

	"""
	Modified from https://github.com/timeseriesAI/tsai/blob/main/tsai/models/InceptionTimePlus.py

	I've added my favourite modifications
	- causal padding in the conv
	- causal padding the max pool
	- coord=True by default
	- dilation so it can work over larger context lengths
	- specify kernels to be [3, 13, 39] instead of the default [13, 19, 39] or whatver. This makes sure we have a small kernel as well which can help performance.

	from typing import Callable

	from loguru import logger
	import torch
	from torch import nn


	def convert_layers(model: nn.Module, original: nn.Module, value: bool):
	"""
	Turn dropout on