Node Version Manager (https://github.com/nvm-sh/nvm) works perfectly across native node installations as well as emulated Rosetta installations. The trick I am using here is to install one LTS version of node under Rosetta and another stable version as native binary.
| # git clone https://github.com/NVlabs/stylegan2 | |
| import os | |
| import numpy as np | |
| from scipy.interpolate import interp1d | |
| from scipy.io import wavfile | |
| import matplotlib.pyplot as plt | |
| import PIL.Image | |
| import moviepy.editor | |
| import dnnlib |
| class Ralamb(Optimizer): | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): | |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) | |
| self.buffer = [[None, None, None] for ind in range(10)] | |
| super(Ralamb, self).__init__(params, defaults) | |
| def __setstate__(self, state): | |
| super(Ralamb, self).__setstate__(state) |
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| ## Created by: Hang Zhang, Rutgers University, Email: [email protected] | |
| ## Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected] | |
| ## Copyright (c) 2017-2018 | |
| ## | |
| ## This source code is licensed under the MIT-style license found in the | |
| ## LICENSE file in the root directory of this source tree | |
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| """Encoding Data Parallel""" |
| # WARNING: These steps seem to not work anymore! | |
| #!/bin/bash | |
| # Purge existign CUDA first | |
| sudo apt --purge remove "cublas*" "cuda*" | |
| sudo apt --purge remove "nvidia*" | |
| # Install CUDA Toolkit 10 | |
| wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb |
| Africa/Abidjan | |
| Africa/Accra | |
| Africa/Addis_Ababa | |
| Africa/Algiers | |
| Africa/Asmara | |
| Africa/Asmera | |
| Africa/Bamako | |
| Africa/Bangui | |
| Africa/Banjul | |
| Africa/Bissau |
| import os | |
| import requests | |
| import time | |
| import sys | |
| video_quality = "540p" # available qualities 360p, 540p, 720p | |
| """ | |
| In order to get courseId go to a course page open network tab in browser dev tools and search for "onDemandSpecializations" | |
| in search input of network tab and then go to videos section of say week 1 , check the query param "courseId" its value is what we use can use as value for "one_of_specialization_course_id" |
| conda uninstall --force pillow -y | |
| # install libjpeg-turbo to $HOME/turbojpeg | |
| git clone https://github.com/libjpeg-turbo/libjpeg-turbo | |
| pushd libjpeg-turbo | |
| mkdir build | |
| cd build | |
| cmake .. -DCMAKE_INSTALL_PREFIX:PATH=$HOME/turbojpeg | |
| make | |
| make install |
| """Script to illustrate usage of tf.estimator.Estimator in TF v1.3""" | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data as mnist_data | |
| from tensorflow.contrib import slim | |
| from tensorflow.contrib.learn import ModeKeys | |
| from tensorflow.contrib.learn import learn_runner | |
| # Show debugging output |
