| #!/bin/bash | |
| # This script is used to complete the output of the docker stats command. | |
| # The docker stats command does not compute the total amount of resources (RAM or CPU) | |
| # Get the total amount of RAM, assumes there are at least 1024*1024 KiB, therefore > 1 GiB | |
| #HOST_MEM_TOTAL=$(grep MemTotal /proc/meminfo | awk '{print $2/1024/1024}') | |
| DOCKER_MEM_TOTAL=$(docker info | grep Memory | awk '{print $3}' | grep -o '[0-9]*\.[0-9]*') | |
| # Get the output of the docker stat command. Will be displayed at the end |
| /* recursive mkdir based on | |
| http://nion.modprobe.de/blog/archives/357-Recursive-directory-creation.html | |
| */ | |
| #include <unistd.h> | |
| #include <sys/types.h> | |
| #include <sys/stat.h> | |
| #include <string.h> | |
| #define PATH_MAX_STRING_SIZE 256 |
| import time | |
| import warnings | |
| from contextlib import contextmanager | |
| import torch | |
| class TimeCounter: | |
| names = dict() |
| #!/bin/bash | |
| ## This gist contains instructions about cuda v11.2 and cudnn8.1 installation in Ubuntu 20.04 for Pytorch 1.8 & Tensorflow 2.7.0 | |
| ### steps #### | |
| # verify the system has a cuda-capable gpu | |
| # download and install the nvidia cuda toolkit and cudnn | |
| # setup environmental variables | |
| # verify the installation | |
| ### |
| #include <assert.h> | |
| #include <stdint.h> | |
| #include <stdio.h> | |
| #include <string.h> | |
| #include <windows.h> // 各种位图数据结构 | |
| class Converter | |
| { | |
| public: | |
| Converter() : pixels_(NULL), width_(0), height_(0) {} |
| #!/bin/sh | |
| # Copy the contents of this file to the clipboard, then get a terminal open on your device and enter: | |
| # $ cat > n.sh | |
| # [Ctrl+V] or Right Click, Paste. Then [Ctrl+D]. | |
| # chmod +x n.sh | |
| # To run: ./n.sh eth0 | |
| SLP=1 # display / sleep interval | |
| DEVICE=$1 | |
| IS_GOOD=0 | |
| for GOOD_DEVICE in `grep \: /proc/net/dev | awk -F: '{print $1}'`; do |
| name: "cornernet" | |
| input: "blob1" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 511 | |
| input_dim: 511 | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "blob1" |
| name: "YOLONET" | |
| layer { | |
| name: "data" | |
| type: "Input" | |
| top: "data" | |
| input_param { shape: { dim: 1 dim: 3 dim: 416 dim: 416 } } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" |
| name: "VGG_coco_SSD_300x300_train" | |
| layer { | |
| name: "data" | |
| type: "AnnotatedData" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { |
In the first post I explained how we generate a torch.Tensor object that you can use in your Python interpreter. Next, I will explore the build system for PyTorch. The PyTorch codebase has a variety of components:
