bastman

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

mkolod

object DiffieHellmanMerkle {

  import java.math.BigInteger
  import scala.language.implicitConversions
  import scala.util.Random

  def main(args: Array[String]): Unit =
    println(
      diffieHellmanMerkle(generator = 3, modulus = 17, alicePrivateKey = 54, bobPrivateKey = 24)
    )

subfuzion

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

myme5261314

import tensorflow as tf
import numpy as np
import input_data
import Image
from util import tile_raster_images


def sample_prob(probs):
    return tf.nn.relu(
        tf.sign(

krishnanraman

Dataset: COREL subset ( 7 classes, 100 images per class  => 7*100 = 700 jpgs )
COREL: https://sites.google.com/site/dctresearch/Home/content-based-image-retrieval

Training Test Ratio: 80-20

Equal number of true & false samples ie. train on 80 dinos & 80 random non-dinos out of 700-100 = 600 non-dinos.
So training sample size = 80 + 80 = 160
Test sample = 20 dinos + 20 non-dinos

Train 1 SVM classifier per class => 7 SVM classifiers

kapilt

from botocore.credentials import RefreshableCredentials
from botocore.session import get_session

from boto3 import Session


def assumed_session(role_arn, session_name, session=None):
    """STS Role assume a boto3.Session                                                                                                                                                                                                                                                                                                                 
                                                                                                                                                                                                                                                                                                                                                       
    With automatic credential renewal.                                                                                                                 

tedsta

extern crate deeplearn;
extern crate matrix;

use deeplearn::Graph;
use deeplearn::op::{MatMul, Relu};

fn main() {
    let ctx = matrix::Context::new();

    // Setup the graph

Chandler

print("UPDATE AUG 2023: this script is beyond old and broken")
print("You may find interesting and more up to date resources in the comments of the gist")
exit()
      
from slacker import Slacker
import json
import argparse
import os

# This script finds all channels, private channels and direct messages

inanna-malick

//send messages via Pusher API in play 2.4
import play.api.libs.json.{ Writes, Json }
import play.api.libs.ws.{ WSResponse, WSClient }
import play.api.libs.ws.ning.NingWSClient
import java.security.MessageDigest
import java.math.BigInteger
import javax.crypto.Mac
import javax.crypto.spec.SecretKeySpec
import scala.concurrent.{ ExecutionContext, Future }

saliksyed

""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k