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tothandras / Dockerfile
Created May 10, 2020
EfficientDet Tensorflow
View Dockerfile
ARG BASE_IMAGE=nvcr.io/nvidia/l4t-base:r32.4.2
FROM ${BASE_IMAGE}
#
# install prerequisites - https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html#prereqs
#
RUN apt-get update && \
apt-get install -y --no-install-recommends \
python3-pip \
python3-dev \
View Dockerfile
FROM nvcr.io/nvidia/tensorflow:20.01-tf2-py3
RUN apt-get update && \
apt-get install -y libsm6 libxext6 libxrender-dev && \
pip install opencv-python
COPY . .
# RUN echo "Downloading weights..." && \
# wget -O weights/yolov3.weights https://pjreddie.com/media/files/yolov3.weights && \
@tothandras
tothandras / description.md
Last active Jun 22, 2020
Remote coding test
View description.md

GitHub repositories

Implement a single page application using React. Keep your code in version control, create a public GitHub repository.

Tasks

@tothandras
tothandras / random.sh
Created May 11, 2018
Random MAC Address
View random.sh
# source: https://www.howtogeek.com/220462/how-to-find-and-change-your-mac-address-on-os-x/
# Find MAC Address
ifconfig en0 | grep ether
# Change it
sudo ifconfig en0 ether aa:bb:cc:dd:ee:ff
# This will generate and set a new MAC address for en0 every time you run it.
# The changes made from both these commands will be reverted when you restart, so this is not permanent.
@tothandras
tothandras / tensorflow_1_7_high_sierra_gpu.md
Created Apr 24, 2018 — forked from pavelmalik/tensorflow_1_7_high_sierra_gpu.md
Install Tensorflow 1.7 on macOS High Sierra 10.13.3 with CUDA and stock python
View tensorflow_1_7_high_sierra_gpu.md

Tensorflow 1.7 with CUDA on macOS High Sierra 10.13.3 and default python 2.7

Largely based on the Tensorflow 1.6 gist, this should hopefully simplify things a bit. Mixing homebrew python2/python3 with pip ends up being a mess, so here's an approach to uses the built-in python27.

Requirements

  • NVIDIA Web-Drivers 387.10.10.10.25.156 for 10.13.3
  • CUDA-Drivers 387.178
  • CUDA 9.1 Toolkit
  • cuDNN 7.0.5 (latest release for mac os)
View a.js
// a/a.js
class A {
constructor(b) {
this.b = b
}
foo() {
this.b.bar()
}
View logging.md

Logging

Logs are an important source to get insights what's happening when your code runs. It can help to understand how the application works. It can also help to discover errors, but there is no one need to look at this output immediately, it's available for later analysis if needed. Although, it is possible to set up alerts based on logs.

Log format

Here we need to ask ourself, what kind of information can help us to find issues and to understand the call chain, data flow in an application?

  • timestamp: when the given event happened, it helps to establish a timeline of events
  • name or location: what is the source of the log
View keybase.md

Keybase proof

I hereby claim:

  • I am tothandras on github.
  • I am andrastoth (https://keybase.io/andrastoth) on keybase.
  • I have a public key ASDUuMYpEL9jCzi3cuNVTCRu8uc_WqiXeEeVMYt5tBIv2Ao

To claim this, I am signing this object:

View MENTORS.md

Node.js bootcamp

The bootcamp can provide a learning path for a new team member to get familiar with the basic concepts of Node.js and web services. As a mentor you will be responsible to guide the student along the way.

Setup

  1. Create a repository for the student
  2. Push the initial, first commit of this repository to the student's repository
  3. Help the student setting up the environment
View route.spec.js
'use strict'
const expect = require('chai').expect
const objectid = require('objectid')
const request = require('super-request')
const User = require('./user')
const server = require('./server') // your http server
describe('GET /api/v1/user/:userId', () => {
let userId
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