Algomancer

object HelloWorld {
  def main(args: Array[String]) {
    println("Hello, world!")
  }
}

Algomancer

def foldLeft[B](z: B)(g: (B, A) => B): B

Algomancer

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "codedeploy:RegisterApplicationRevision",
                "codedeploy:GetApplicationRevision"
            ],
            "Resource": [

Algomancer

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::YOUR_S3_BUCKET_NAME/*"

Algomancer

from IPython.display import Image, display, HTML

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)
        if n.op == 'Const':

Algomancer

import logging
from logging.handlers import RotatingFileHandler

from flask import Flask

app = Flask(__name__)

@app.route('/')
def foo():
    app.logger.warning('A warning occurred (%d apples)', 42)

Algomancer

from torch.autograd import Variable

from torch.nn import functional as F



class SELU(nn.Module):

    def __init__(self):

Algomancer

#include <stdio.h>
#include <stdlib.h>

#define N 16 /* buffer size */

int main(void) {
  char name[N]; /* buffer */
  
  /* prompt user for name */
  printf("What's your name? ");

Algomancer

# Install useful stuff
! apt install --yes ssh screen nano htop ranger git > /dev/null
# SSH setting
! echo "root:carbonara" | chpasswd
! echo "PasswordAuthentication yes" > /etc/ssh/sshd_config
! echo "PermitUserEnvironment yes" >> /etc/ssh/sshd_config
! echo "PermitRootLogin yes" >> /etc/ssh/sshd_config
! service ssh restart > /dev/null
# Download ngrok
! wget -q -c -nc https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip

Algomancer

# Author Adam Hibble @algomancer
import torch 
import torch.nn.functional as F
import torch.nn as nn
import tqdm

def get_padding(padding_type, kernel_size):
    assert padding_type in ['SAME', 'VALID']
    if padding_type == 'SAME':
        return tuple((k - 1) // 2 for k in kernel_size)