https://wandb.ai/rom1504/dalle2_train_decoder/runs/mic5buox/files/decoder_config.json
get dalle2
get the config file
get these 2 .sh
run sbatch start_big.sh
https://wandb.ai/rom1504/dalle2_train_decoder/runs/mic5buox/files/decoder_config.json
get dalle2
get the config file
get these 2 .sh
run sbatch start_big.sh
A. Schneider, "Real-Time Volumetric Cloudscapes," in GPU Pro 7: Advanced Rendering Techniques, 2016, pp. 97-127. (Follow up presentations here, and here.)
S. Hillaire, "Physically Based Sky, Atmosphere and Cloud Rendering in Frostbite" in Physically Based Shading in Theory and Practice course, SIGGRAPH 2016. [video] [course notes] [scatter integral shadertoy]
[R. Högfeldt, "Convincing Cloud Rendering – An Implementation of Real-Time Dynamic Volumetric Clouds in Frostbite"](https://odr.chalmers.se/hand
We frequently use pydantic at Pluralsight to validate data at the edge of a well typed domain. I've been trying to sell my coworkers on using hypothesis for testing, and thought it might go easier if they could generate test data from existing pydantic schemas. I found that it was almost trivial for data classes, but BaseModel subclasses (which are unfortunately much more common in our code) don't play as nicely, and deeply nested schemas can get you into trouble. If anyone has any advice on how to get around the errors that come from the last test, I'd be super greatful.
| import {chunk} from 'lodash'; | |
| import * as admin from "firebase-admin"; | |
| import * as storage from '@google-cloud/storage'; | |
| const gcs = storage(); | |
| //https://github.com/GoogleCloudPlatform/google-cloud-node/issues/2254 | |
| gcs.interceptors.push({ | |
| request: function(reqOpts) { | |
| reqOpts.forever = false; | |
| return reqOpts |
| # QEmu | |
| brew install qemu | |
| # Home for out tests | |
| mkdir ~/arm-emu | |
| cd ~/arm-emu | |
| # Download initrd and kernel | |
| wget http://ftp.de.debian.org/debian/dists/jessie/main/installer-armel/current/images/versatile/netboot/initrd.gz |
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| {-# LANGUAGE OverloadedStrings #-} | |
| module Main where | |
| import Text.Parsec | |
| import Text.Parsec.String | |
| import Text.Parsec.Char | |
| import Control.Monad | |
| import Data.List | |
| import Data.Either |
This gist is part of a blog post. Check it out at:
http://jasonrudolph.com/blog/2011/08/09/programming-achievements-how-to-level-up-as-a-developer
| #!/usr/bin/env python | |
| """ | |
| Usage: | |
| ./rtail.py user@host:path/foo.log bar.log host2:/path/baz.log | |
| """ | |
| import optparse | |
| import os |