| #!/usr/bin/env bash | |
| set -e | |
| cd | |
| case "$OSTYPE" in | |
| darwin*) DOWNLOAD=https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh ;; | |
| linux*) DOWNLOAD=https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh ;; | |
| *) echo "unknown: $OSTYPE" ;; | |
| esac |
| #Credits to https://stackoverflow.com/questions/49697634/tensorboard-export-csv-file-from-command-line | |
| from collections import defaultdict | |
| import argparse | |
| import numpy as np | |
| import tensorflow as tf | |
| from clize import run | |
| import pandas as pd | |
| def save_tag_to_csv(fn): | |
| sess = tf.InteractiveSession() |
| #!/bin/bash | |
| # This file sets the environment variable CUDA_VISIBLE_DEVICES to the MPI local rank to enable multi-GPU usage of this benchmark. Note that this disables any GPU distribution handling by he batch scheduler. | |
| # Background: Most/some batch schedulers set CUDA_VISIBLE_DEVICES to all available GPUs on a node. In that case, the Arbor benchmark would only use the first entry in the list, probably GPU#0. This script changes that. | |
| # -Andreas Herten, Nov 2018 | |
| _verbose=1 | |
| localrank=$CUDA_VISIBLE_DEVICES | |
| if [[ -n "$OMPI_COMM_WORLD_NODE_RANK" ]]; then |
| # use ImageMagick convert | |
| # the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf | |
| convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf |
| """ | |
| A bare bones examples of optimizing a black-box function (f) using | |
| Natural Evolution Strategies (NES), where the parameter distribution is a | |
| gaussian of fixed standard deviation. | |
| """ | |
| import numpy as np | |
| np.random.seed(0) | |
| # the function we want to optimize |
Deploy key is a SSH key set in your repo to grant client read-only (as well as r/w, if you want) access to your repo.
As the name says, its primary function is to be used in the deploy process, where only read access is needed. Therefore keep the repo safe from the attack, in case the server side is fallen.
| """ | |
| Example TensorFlow script for finetuning a VGG model on your own data. | |
| Uses tf.contrib.data module which is in release v1.2 | |
| Based on PyTorch example from Justin Johnson | |
| (https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c) | |
| Required packages: tensorflow (v1.2) | |
| Download the weights trained on ImageNet for VGG: | |
| ``` | |
| wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz |
| { | |
| "embeddings": [ | |
| { | |
| "tensorName": "WIPO", | |
| "tensorShape": [ | |
| 5799, | |
| 300 | |
| ], | |
| "tensorPath": "https://files.fm/down.php?i=yrkmskvv", | |
| "metadataPath": "https://gist.githubusercontent.com/mehdidc/af6727cec814cb37a9bc36f6620bc288/raw/383d6805b294397307b659242328cd446cfded1f/labels.tsv" |
| portable bath bench/seat | |
| bathtub | |
| bath for invalid | |
| a multi-purpose shower device | |
| children's bath | |
| a foot care device | |
| a showering appliance | |
| knockdown bath bench | |
| safety devices | |
| a shower-bath booth structure |
| "FIRST do : curl -fLo ~/.vim/autoload/plug.vim --create-dirs https://raw.githubusercontent.com/junegunn/vim-plug/master/plug.vim | |
| set autoindent | |
| set nocindent | |
| set nosmartindent | |
| set autoindent | |
| set indentexpr= | |
| set tabstop=8 | |
| set expandtab | |
| set shiftwidth=4 | |
| set softtabstop=4 |