| """Stream a response from the OpenAI completion API.""" | |
| import os | |
| import re | |
| import sys | |
| import time | |
| import random | |
| import openai | |
| openai.api_key = open(os.path.expanduser("~/.openai")).read().strip() |
| #!/bin/bash | |
| ### steps #### | |
| # Verify the system has a cuda-capable gpu | |
| # Download and install the nvidia cuda toolkit and cudnn | |
| # Setup environmental variables | |
| # Verify the installation | |
| ### | |
| ### to verify your gpu is cuda enable check |
| 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 |
These are instructions for taking screenshots of an entire webpage, not just the part of the webpage visible in the browser.
This requires Safari 11.3, which comes on macOS 10.3.4.
Here's a simple implementation of bilinear interpolation on tensors using PyTorch.
I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).
For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).
In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle
Settings: System Preferences » Keyboard » Key Repeat/Delay Until Repeat
Use the commands below to increase the key repeat rate on macOS beyond the possible settings via the user interface. The changes aren't applied until you restart your computer.
| """ | |
| Create train, valid, test iterators for CIFAR-10 [1]. | |
| Easily extended to MNIST, CIFAR-100 and Imagenet. | |
| [1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4 | |
| """ | |
| import torch | |
| import numpy as np |
| # Steps to build and install tmux from source. | |
| # Takes < 25 seconds on EC2 env [even on a low-end config instance]. | |
| VERSION=2.5 | |
| sudo yum -y remove tmux | |
| sudo yum -y install wget tar libevent-devel ncurses-devel | |
| wget https://github.com/tmux/tmux/releases/download/${VERSION}/tmux-${VERSION}.tar.gz | |
| tar xzf tmux-${VERSION}.tar.gz | |
| rm -f tmux-${VERSION}.tar.gz | |
| cd tmux-${VERSION} |
| import torch | |
| from torch import nn | |
| __all__ = ['FCDenseNet', 'fcdensenet_tiny', 'fcdensenet56_nodrop', | |
| 'fcdensenet56', 'fcdensenet67', 'fcdensenet103', | |
| 'fcdensenet103_nodrop'] | |
| class DenseBlock(nn.Module): |
Today I wanted to make a recording of me running some commands inside a docker-container.
❯ docker-compose run app bash
root@e9bb2af4dc11:/usr/local/go/src/example.com/dev/project#
Needless to say it looked a bit bland with no colors and a long prompt that prevents me from recording a small terminal and show the full commands I'm