| # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. | |
| # *Only* converts the UNet, VAE, and Text Encoder. | |
| # Does not convert optimizer state or any other thing. | |
| # Written by jachiam | |
| import argparse | |
| import os.path as osp | |
| import torch |
| FROM python:3.6-slim-stretch as csvbuilder | |
| # This one uses csvs-to-sqlite to compile the DB, and then uses datasette | |
| # inspect to generate inspect-data.json Compiling pandas takes way too long | |
| # under alpine so we use slim-stretch for this one instead. | |
| RUN apt-get update && apt-get install -y python3-dev gcc | |
| COPY *.csv csvs/ | |
| RUN pip install csvs-to-sqlite datasette | |
| RUN csvs-to-sqlite csvs/names.csv data.db -f "name" -c "legislature" -c "country" |
| from keras.optimizers import Adam | |
| from keras import backend as K | |
| from keras.datasets import mnist | |
| from keras.utils.np_utils import to_categorical | |
| from keras.metrics import categorical_accuracy | |
| from keras.initializations import glorot_uniform, zero | |
| import numpy as np | |
| # inputs and targets are placeholders | |
| input_dim = 28*28 |
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
| """Short and sweet LSTM implementation in Tensorflow. | |
| Motivation: | |
| When Tensorflow was released, adding RNNs was a bit of a hack - it required | |
| building separate graphs for every number of timesteps and was a bit obscure | |
| to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`. | |
| Currently the APIs are decent, but all the tutorials that I am aware of are not | |
| making the best use of the new APIs. | |
| Advantages of this implementation: |
| # Install | |
| # via http://askubuntu.com/questions/510056/how-to-install-google-chrome | |
| wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | sudo apt-key add - | |
| sudo sh -c 'echo "deb http://dl.google.com/linux/chrome/deb/ stable main" >> /etc/apt/sources.list.d/google-chrome.list' | |
| sudo apt-get update | |
| sudo apt-get install google-chrome-stable | |
| # Update |
Source: http://www.jejik.com/articles/2007/02/a_simple_unix_linux_daemon_in_python/
by Sander Marechal
I've written a simple Python class for creating daemons on unix/linux systems. It was pieced together for various other examples, mostly corrections to various Python Cookbook articles and a couple of examples posted to the Python mailing lists. It has support for a pidfile to keep track of the process. I hope it's useful to someone.
| import argparse, os, sys | |
| from Crypto.Cipher import AES | |
| from Crypto.Hash import HMAC | |
| from Crypto.Protocol.KDF import PBKDF2 | |
| # check arguments | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("file", help="the file we want to encrypt/decrypt") | |
| parser.add_argument("key", help="your key") |
