| import optuna | |
| import xgboost as xgb | |
| from sklearn.metrics import mean_squared_error # or any other metric | |
| from sklearn.model_selection import train_test_split | |
| # Load the dataset | |
| X, y = ... # load your own | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # Define the objective function for Optuna |
| """ | |
| This is a workaround for `examples/run_mlm.py` for pretraining models | |
| with big text files line-by-line. | |
| For the time being, `datasets` is facing some issues dealing with really | |
| big text files, so we use a custom dataset until this is fixed. | |
| August 3th 2021 | |
| Author: Juan Manuel Pérez |
| import threading | |
| from playwright.sync_api import sync_playwright | |
| from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor | |
| class Tls(threading.local): | |
| def __init__(self) -> None: | |
| self.playwright = sync_playwright().start() | |
| print("Create playwright instance in Thread", threading.current_thread().name) |
Folks, Leave me a comment / URL if something you like is missing!
| Resource | Description |
|---|---|
| Kube Academy | https://kube.academy/ |
| kuernetes-101 | https://kube.academy/courses/kubernetes-101/ |
| Docs Home | https://kubernetes.io/docs/home/ |
| CKS CKA CKAD Simulator | https://killer.sh/ |
| ########################################################### | |
| # How to NEVER use lambdas. An inneficient and yet educa- # | |
| # tonal [sic] guide to the proper misuse of the lambda # | |
| # construct in Python 3.x. [DO NOT USE ANY OF THIS EVER] # | |
| # original by (and apologies to): e000 (13/6/11) # | |
| # now in Python 3 courtesy of: khuxkm (17/9/20) # | |
| ########################################################### | |
| ## Part 1. Basic LAMBDA Introduction ## | |
| # If you're reading this, you've probably already read e000's |
| # ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py | |
| import torch | |
| import math | |
| def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3): | |
| delta = (res[0] / shape[0], res[1] / shape[1]) | |
| d = (shape[0] // res[0], shape[1] // res[1]) | |
| grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1 |
| ''' | |
| A multiprocessing Pool drop-in replacement for the pytorch | |
| DataLoader class. Built to work around an apparent bug in | |
| the default pytorch DataLoader, in which it hangs indefinitely. | |
| It is possible to reach a sustained 95-100% GPU usage (as | |
| reported by `nvidia-smi`) using this implementation. | |
| Requirements: | |
| pip install filelock |
Instructions to hack up WSL2 on Windows 10 Build 18917 to run k3s (Kubernetes) and rio
https://docs.microsoft.com/en-us/windows/wsl/wsl2-install
I already had Ubuntu-18.04 installed in wsl 1. So I just did wsl --set-version Ubuntu-18.04 2
Using Ubuntu 18.04 (I'm sure any distro will work), inside WSL2 download https://thirdpartysource.microsoft.com/download/Windows%20Subsystem%20for%20Linux%20v2/May%202019/WSLv2-Linux-Kernel-master.zip and extract to a folder. The latest version of the kernel source is available at (https://github.com/microsoft/WSL2-Linux-Kernel)
| # On mac, run these first: | |
| # - brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release | |
| # - brew update | |
| # - brew install msodbcsql17 mssql-tools | |
| # | |
| # Install requirements: | |
| # - pip install pyodbc sqlalchemny | |
| import urllib | |
| from sqlalchemy import create_engine |
| import numpy as np | |
| import gym | |
| import matplotlib.pyplot as plt | |
| # Import and initialize Mountain Car Environment | |
| env = gym.make('MountainCar-v0') | |
| env.reset() | |
| # Define Q-learning function | |
| def QLearning(env, learning, discount, epsilon, min_eps, episodes): |
