| # Got a bunch of .ckpt files to convert? | |
| # Here's a handy script to take care of all that for you! | |
| # Original .ckpt files are not touched! | |
| # Make sure you have enough disk space! You are going to DOUBLE the size of your models folder! | |
| # | |
| # First, run: | |
| # pip install torch torchsde==0.2.5 safetensors==0.2.5 | |
| # | |
| # Place this file in the **SAME DIRECTORY** as all of your .ckpt files, open a command prompt for that folder, and run: | |
| # python convert_to_safe.py |
| // Original Source Code | |
| var video = document.querySelector("div.video-player video"); | |
| if (video.webkitSupportsPresentationMode && typeof video.webkitSetPresentationMode === "function") { | |
| video.webkitSetPresentationMode(video.webkitPresentationMode === "picture-in-picture" ? "inline" : "picture-in-picture"); | |
| } | |
| // paste below line to as your bookmark in safari | |
| // javascript:(function()%7Bvar%20video%20%3D%20document.querySelector(%22div.video-player%20video%22)%3Bif%20(video.webkitSupportsPresentationMode%20%26%26%20typeof%20video.webkitSetPresentationMode%20%3D%3D%3D%20%22function%22)%20%7Bvideo.webkitSetPresentationMode(video.webkitPresentationMode%20%3D%3D%3D%20%22picture-in-picture%22%20%3F%20%22inline%22%20%3A%20%22picture-in-picture%22)%3B%7D%7D)() |
| var SecTrustEvaluate_handle = | |
| Module.findExportByName('Security', 'SecTrustEvaluate'); | |
| var SecTrustEvaluateWithError_handle = | |
| Module.findExportByName('Security', 'SecTrustEvaluateWithError'); | |
| var SSL_CTX_set_custom_verify_handle = | |
| Module.findExportByName('libboringssl.dylib', 'SSL_CTX_set_custom_verify'); | |
| var SSL_get_psk_identity_handle = | |
| Module.findExportByName('libboringssl.dylib', 'SSL_get_psk_identity'); | |
| var boringssl_context_set_verify_mode_handle = Module.findExportByName( | |
| 'libboringssl.dylib', 'boringssl_context_set_verify_mode'); |
| def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): | |
| """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering | |
| Args: | |
| logits: logits distribution shape (vocabulary size) | |
| top_k >0: keep only top k tokens with highest probability (top-k filtering). | |
| top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering). | |
| Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751) | |
| """ | |
| assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear | |
| top_k = min(top_k, logits.size(-1)) # Safety check |
ssh -i ~/.ssh/udacity-carnd.pem ubuntu@ec2-52-11-14-48.us-west-2.compute.amazonaws.com
이 문서가 여기저기 알려짐에 따라, 이곳에 여러가지 댓글이 달리고 있습니다. 개인적으로는 댓글창을 없애버리고 싶지만 그럴 수 없는 터라, 댓글을 달기 전에 한번씩만 더 생각해주셨으면 합니다.
위 사항들을 포함해 제 마음에 안 드는 댓글들은 임의로 삭제하고 있습니다. 양해 부탁드립니다.
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
Wand==0.3.7, Python 3.4.3, OSX EI Capitan 10.11
when run from wand.image import Image it throw errors:
ImportError: MagickWand shared library not found. You probably had not installed ImageMagick library. Try to install: brew install freetype imagemagick
ok, first I try this:
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| require 'cudnn' | |
| require 'fbcunn' | |
| local timer = torch.Timer() | |
| local tensorsAreProbablySimilar = function(l, r, epsilon) | |
| epsilon = epsilon or 0.00001 | |
| return math.abs((l:norm() - r:norm()) / (l:norm() + r:norm())) < epsilon | |
| end |
