krish-adi

"""
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

krish-adi

Parallel Python within the same process or hacking around the cursed GIL with a hand-rolled library loader

From its obscure beginnings in Amsterdam, the Python programming language has become a fundamental building block of our digital society. It is used literally everywhere and by everyone for a mind-boggingly wide variety of tasks.

Python is also the lingua franca of Data Science, tying together tools for data loading, wrangling, analysis and AI. There is a massive ecosystem of contributed Python packages, which - for example - allows reading every obscure data format under the sun. This makes Python and its ecosystem extremely valuable for analytical data management systems: Users are likely somewhat familiar with Python due to its immense popularity and the ecosystem provides solutions for most data problems. As a result, Python is being integrated into SQL systems, typically through so-called User-Defined Functions (UDFs). For example, [Apach

krish-adi

// Updated: Aug. 15, 2024
// Run: node testRegex.js testText.txt
// Used in https://jina.ai/tokenizer
const fs = require('fs');
const util = require('util');

// Define variables for magic numbers
const MAX_HEADING_LENGTH = 7;
const MAX_HEADING_CONTENT_LENGTH = 200;
const MAX_HEADING_UNDERLINE_LENGTH = 200;

krish-adi

Parameter	Type	Description	Example
<table_name>	string	Data table name	'TS_DATA_TABLE'
<time_column_name>	string	Timestamp column name	'DS'
<value_column_name>	string	Value of interest	'Y'
<forecast_period>	integer	Forecast period	40
``	integer	Unit alias of the given period	'M' (month)

krish-adi

import ee 

# Authenticate and initialize
email = "my-service-account@...gserviceaccount.com"
key_file = "/path-to-key/key-file.json"

credentials = ee.ServiceAccountCredentials(email=email, key_file=key_file)
ee.Initialize(credentials)

krish-adi

app.display(name='text1', value='# Enter point value for finding its elevation')
lon = app.number(name='Longitude of the Point', value=86.9250)
lat = app.number(name='Latitude of the Point', value=27.9881)
point_name = app.text(name='Enter the name of point', value='Mt. Everest')

# Point object in EE
ee_point = ee.Geometry.Point([lon, lat])
app.ee_layer(ee_object=ee_point, vis_params={"color": "red"}, 
             name=point_name, description=f"The Point representing {point_name}.")

krish-adi

from greppo import app
import ee

# Authenticate and initialize
email = "my-service-account@...gserviceaccount.com"
key_file = "/path-to-key/key-file.json"
credentials = ee.ServiceAccountCredentials(email=email, key_file=key_file)
ee.Initialize(credentials)

# Adds a band containing image date as years since 1991.

krish-adi

from greppo import app
import ee

# Authenticate and initialize
email = "my-service-account@...gserviceaccount.com"
key_file = "/path-to-key/key-file.json"
credentials = ee.ServiceAccountCredentials(email=email, key_file=key_file)
ee.Initialize(credentials)

# Select the satellite dataset from the catalog

krish-adi

from greppo import app
import geopandas as gpd

app.display(name='title', value='Vector demo')
app.display(name='description',
            value='A Greppo demo app for vector data using GeoJSON data.')

app.base_layer(
    name="Open Street Map",
    visible=True,

krish-adi

from greppo import app
import geopandas as gpd

regions = gpd.read_file("./regions.geojson")
roads = gpd.read_file("./roads.geojson")
cities = gpd.read_file("./cities.geojson")

app.display(name='text-2',
            value='The following displays the count of polygons, lines and points as a barchart.')