akaanirban

Edit 04/11/2021: This gist is quite old now. The current version of PyTorch is 1.8.1, which is miles ahead of version 1.0.1 that I was trying to install when I wrote this gist. Therefore some of the instructions may not apply, or some dependencies may have changed or bugs taken care of. I do not currently have a Raspberry Pi to verify unfortunately. Please proceed with caution. Further, there are may others who have shared their fixes, and direct links to their wheels down in the comments. Cheers !

Install the prerequisites (the last one for numpy):

sudo apt install libopenblas-dev libblas-dev m4 cmake cython python3-yaml libatlas-base-dev

Increase the swap size:

• Stop the swap : sudo dphys-swapfile swapoff
• Modify the size of the swap by editing as root the following file : /etc/dphys-swapfile. Modify the valiable CONF_SWAPSIZE and change its value to CONF_SWAPSIZE=2048

yzh119

from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

def sample_gumbel(shape, eps=1e-20):
    U = torch.rand(shape).cuda()
    return -Variable(torch.log(-torch.log(U + eps) + eps))

mjdietzx

#
# written for Amazon Linux AMI
# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1)
# assumes lambda function defined in ~/main.py
# deployment package created at ~/waya-ai-lambda.zip
#

#
# install python 3.6.1
#

bartolsthoorn

import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torch.autograd import Variable

# (1, 0) => target labels 0+2
# (0, 1) => target labels 1
# (1, 1) => target labels 3
train = []

flyyufelix

ResNet-152 in Keras

This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.

ResNet Paper:

Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

ascendbruce

Use macOS-style shortcuts in Windows / keyboard mappings using a Mac keyboard on Windows

ℹ️ There is a newer alternative project that does similar things and more, check it out at https://github.com/stevenilsen123/mac-keyboard-behavior-in-windows

Make Windows PC's shortcut act like macOS (Mac OS X) (using AutoHotkey (ahk) script)

With this AutoHotKey script, you can use most macOS style shortcuts (eg, cmd+c, cmd+v, ...) on Windows with a standard PC keyboard.

How does it work

awjuliani

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

application2000

These commands are based on a askubuntu answer http://askubuntu.com/a/581497
To install gcc-6 (gcc-6.1.1), I had to do more stuff as shown below.
USE THOSE COMMANDS AT YOUR OWN RISK. I SHALL NOT BE RESPONSIBLE FOR ANYTHING.
ABSOLUTELY NO WARRANTY.

If you are still reading let's carry on with the code.

sudo apt-get update && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \