karpathy

"""
The most atomic way to train and run inference for 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

dabit3

#!/usr/bin/env python3
# mini-openclaw.py - A minimal OpenClaw clone
# Run: uv run --with anthropic --with schedule python mini-openclaw.py

import anthropic
import subprocess
import json
import os
import re
import threading

smurching

import mlflow

# There are two ways to create parent/child runs in MLflow.

# (1) The most common way is to use the fluent
# mlflow.start_run API, passing nested=True:
with mlflow.start_run():
  num_trials = 10
  mlflow.log_param("num_trials", num_trials)
  best_loss = 1e100

jeremyjordan

import matplotlib.pyplot as plt
import keras.backend as K
from keras.callbacks import Callback


class LRFinder(Callback):
    
    '''
    A simple callback for finding the optimal learning rate range for your model + dataset. 
    

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens