Password-store keeps your passwords (or any other sensitive information) saved in GnuPG encrypted files organized in ~/.password-store. For more information about GPG, consult the GNU Privacy Handbook.
To get started, install pass and generate a keypair.
$ brew install pass
$ gpg --gen-key
$ gpg --list-keys| /** | |
| * Copyright 2016 NVIDIA Corporation. All rights reserved. | |
| * | |
| * Please refer to the NVIDIA end user license agreement (EULA) associated | |
| * with this source code for terms and conditions that govern your use of | |
| * this software. Any use, reproduction, disclosure, or distribution of | |
| * this software and related documentation outside the terms of the EULA | |
| * is strictly prohibited. | |
| * | |
| */ |
| #!/bin/bash | |
| rm -rf /home/yen3/ubuntu.qcow2 | |
| qemu-img create -f qcow2 /home/yen3/ubuntu.qcow2 10G | |
| virsh undefine ubuntu1604arm64 --nvram | |
| install_from_localtion() { | |
| virt-install -n ubuntu1604arm64 --memory 1024 --arch aarch64 --vcpus 1 \ | |
| --disk /home/yen3/ubuntu.qcow2,device=disk,bus=virtio \ |
In most of deep learning projects, the training scripts always start with lines to load in data, which can easily take a handful minutes. Only after data ready can start testing my buggy code. It is so frustratingly often that I wait for ten minutes just to find I made a stupid typo, then I have to restart and wait for another ten minutes hoping no other typos are made.
In order to make my life easy, I devote lots of effort to reduce the overhead of I/O loading. Here I list some useful tricks I found and hope they also save you some time.
use Numpy Memmap to load array and say goodbye to HDF5.
I used to relay on HDF5 to read/write data, especially when loading only sub-part of all data. Yet that was before I realized how fast and charming Numpy Memmapfile is. In short, Memmapfile does not load in the whole array at open, and only later "lazily" load in the parts that are required for real operations.
Sometimes I may want to copy the full array to memory at once, as it makes later operations
| all: | |
| nvcc -arch=sm_35 -std=c++11 -O2 -Icudnn/include -L cudnn/lib64 -L /usr/local/lib test.cu -o test -lcudnn -I. -D$(FLAGS) |
