| from urllib.request import urlopen | |
| from io import BytesIO | |
| from zipfile import ZipFile | |
| def download_and_unzip(url, extract_to='.'): | |
| http_response = urlopen(url) | |
| zipfile = ZipFile(BytesIO(http_response.read())) | |
| zipfile.extractall(path=extract_to) |
| date | title | tags | |
|---|---|---|---|
2020-02-29 |
Proper CUDA and cuDNN installation |
|
You're here, so you're probably already hurting because of CUDA and cuDNN compatibility, and I won't have to motivate you, or explain why you'd want to have standalone CUDA and cuDNN installations, if you're going to develop using Tensorflow in the long term.
| model.save('model_weights.pth') | |
| # ... Later ... | |
| model = core.Model.load('model_weights.pth', ['alien', 'bat', 'witch']) |
| More recent resolution: | |
| 1. cd ~/../../etc (go to etc folder in WSL). | |
| 2. echo "[network]" | sudo tee wsl.conf (Create wsl.conf file and add the first line). | |
| 3. echo "generateResolvConf = false" | sudo tee -a wsl.conf (Append wsl.conf the next line). | |
| 4. wsl --terminate Debian (Terminate WSL in Windows cmd, in case is Ubuntu not Debian). | |
| 5. cd ~/../../etc (go to etc folder in WSL). | |
| 6. sudo rm -Rf resolv.conf (Delete the resolv.conf file). | |
| 7. In windows cmd, ps or terminal with the vpn connected do: Get-NetIPInterface or ipconfig /all for get the dns primary and | |
| secondary. |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>A simple clock</title> | |
| </head> | |
| <body translate="no" > |
List of desktop Nvidia GPUS ordered by CUDA core count
I created it for those who use Neural Style
Guys, please add your hardware setups, neural-style configs and results in comments!
| GPU | CUDA cores | Memory | Processor frequency | Memory Bandwidth (GB/sec) |
|---|---|---|---|---|
| Geforce GTX TITAN Z | 5760 | 12 GB | 705 / 876 | 673 |
| import cv2 as cv | |
| def integral_test(X): # returns True if X is convex | |
| img_t = np.reshape(X, (28, 28)) # preprocess: reshaping | |
| int_result = cv.integral(img_t.astype(np.uint8)) # calculating the integral-image | |
| patterns_to_test = [block_patterns, big_block_patterns, horizontal_patterns, vertical_patterns, | |
| horizontal_patterns2, vertical_patterns2, bigger_block_patterns, horizontal_patterns3, | |
| vertical_patterns3, bbigger_block_patterns, bbbigger_block_patterns, bbbbigger_block_patterns] | |
| for p in range(len(patterns_to_test)): # run test on all patterns until a match is found |
| import obspython as obs | |
| import subprocess | |
| import os | |
| import re | |
| import datetime | |
| # Info for potential OBS Python hackers! | |
| # Tip 1 - Read the "OBS Studio Backend Design" documentation page. Read the documentation table of contents. | |
| # Tip 2 - be sure to add obspython.py to your script path to enable completion. | |
| # Tip 3 - Some of the Python API is generated at runtime, so it won't show up in obspython.py. |
| import requests # dependency | |
| url = "<your url>" # webhook url, from here: https://i.imgur.com/f9XnAew.png | |
| # for all params, see https://discordapp.com/developers/docs/resources/webhook#execute-webhook | |
| data = { | |
| "content" : "message content", | |
| "username" : "custom username" | |
| } |
