rednafi

"""
This is a self contained custom data structure with dict like
key-value storage capabilities.

* Can store the key-value pairs in any sqlalchemy supported db
* Employs thread safe transactional scope
* Modular, just change the session_scope to use a different db
* This example uses sqlite db for demonstration purpose

The code is inspired by Raymond Hettinger's talk `Build powerful,

rednafi

import json
import os
import sys
from datetime import timedelta

import httpx
import redis
from dotenv import load_dotenv
from fastapi import FastAPI
from asgiref.sync import sync_to_async

harveyconnor

This will guide you through setting up a replica set in a docker environment using.

• Docker Compose
• MongoDB Replica Sets
• Mongoose
• Mongoose Transactions

Thanks to https://gist.github.com/asoorm for helping with their docker-compose file!

rluts

from channels.auth import AuthMiddlewareStack
from rest_framework.authtoken.models import Token
from django.contrib.auth.models import AnonymousUser
from django.db import close_old_connections


class TokenAuthMiddleware:
    """
    Token authorization middleware for Django Channels 2
    """

chetanppatil

#!/bin/bash

# Download Postman
cd /tmp || exit
echo "Downloading Postman..."
wget -q https://dl.pstmn.io/download/latest/linux?arch=64 -O postman.tar.gz

# Extract and install Postman to /opt
echo "Extracting and installing to /opt..."
sudo tar -xzf postman.tar.gz -C /opt/

paduvi

import torch
import torch.nn as nn
import torch.nn.init as init

dropout_prob = 0.5


class FlatCnnLayer(nn.Module):
    def __init__(self, embedding_size, sequence_length, filter_sizes=[3, 4, 5], out_channels=128):
        super(FlatCnnLayer, self).__init__()

hrshovon

This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native
compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen
to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for 
native compiling without any issues.
Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing 
that out.
Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5.
Let us use the term "build machine" for your PC where you are building opencv and "target machine" for the ARM single board computer.

1.Run the following commands in both machines(I think installing these in target machine only would do) to install the necessary libraries etc.(mine worked with them,so they should be enough 

eamartin

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

StuartFarmer

import csv
import random

import numpy as np
import matplotlib.pyplot as plt

import math

import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32,allow_gc=False"