| /** | |
| *Submitted for verification at Etherscan.io on 2021-09-05 | |
| */ | |
| // SPDX-License-Identifier: MIT | |
| pragma solidity ^0.8.0; | |
| /// [MIT License] | |
| /// @title Base64 |
| #!/bin/bash | |
| # install CUDA Toolkit v8.0 | |
| # instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network)) | |
| CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb" | |
| wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG} | |
| sudo dpkg -i ${CUDA_REPO_PKG} | |
| sudo apt-get update | |
| sudo apt-get -y install cuda |
This tutorial will guide through the steps to create a simple custom layer for Caffe using python. By the end of it, there are some examples of custom layers.
Usually you would create a custom layer to implement a funcionality that isn't available in Caffe, tuning it for your requirements.
Probably just Python and Caffe installed.
| # Make sure you grab the latest version | |
| curl -OL https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip | |
| # Unzip | |
| unzip protoc-3.2.0-linux-x86_64.zip -d protoc3 | |
| # Move protoc to /usr/local/bin/ | |
| sudo mv protoc3/bin/* /usr/local/bin/ | |
| # Move protoc3/include to /usr/local/include/ |
Ubuntu 15.10 have been released for a couple of days. It is a bleeding-edge system coming with Linux kernel 4.2 and GCC 5. However, compiling and running Caffe on this new system is no longer as smooth as on earlier versions. I have done some research related to this issue and finally find a way out. I summarize it here in this short tutorial and I hope more people and enjoy this new system without breaking their works.
The latest NVIDIA driver is officially included in Ubuntu 15.10 repositories. One can install it directly via apt-get.
sudo apt-get install nvidia-352-updates nvidia-modprobe
The nvidia-modprobe utility is used to load NVIDIA kernel modules and create NVIDIA character device files automatically everytime your machine boots up.
Reboot your machine and verify everything works by issuing nvidia-smi or running deviceQuery in CUDA samples.
| #!/usr/bin/env python | |
| # Haversine formula example in Python | |
| # Author: Wayne Dyck | |
| import math | |
| def distance(origin, destination): | |
| lat1, lon1 = origin | |
| lat2, lon2 = destination |
| # SNAKES GAME | |
| # Use ARROW KEYS to play, SPACE BAR for pausing/resuming and Esc Key for exiting | |
| import curses | |
| from curses import KEY_RIGHT, KEY_LEFT, KEY_UP, KEY_DOWN | |
| from random import randint | |
| curses.initscr() | |
| win = curses.newwin(20, 60, 0, 0) |