zeryx

import numpy as np
import tensorflow as tf
from multiprocessing import Manager, Pool
import Algorithmia
import os
import re

# This is code for most tensorflow image classification algorithms
# In this example we look at solving batch processing problems with algorithm recursion and pipelining.

zeryx

import torch
from torch import nn
import numpy as np


class GaussianNoise:
    def __init__(self, stddev: float):
        super(GaussianNoise, self).__init__()
        self.stddev = stddev

zeryx

import scala.reflect.runtime.universe._
import play.api.libs.json._
import scala.util.{Failure, Success, Try}

 object AbstractAlgorithm {

   trait AbstractAlgorithm[I, O] {
     def apply(input: I): Try[O]

zeryx

package com.algorithmia.handler
import play.api.libs.json._

import scala.util.{Failure, Success, Try}

trait AbstractAlgorithm[I <: AnyVal, O <: AnyVal] {
  def apply(input:I): Try[O]
  def load(): Try[Unit] = Success(())
  implicit def inputReader: Reads[I] = Try(implicitly[Reads[I]]) match {
    case Failure(_) => Json.reads[I]

zeryx

package com.algorithmia.handler

import java.io.FileOutputStream
import java.io.PrintStream

import scala.util.Try

case class ResponseHandler(){
  val FIFOPATH = "/tmp/algoout"

zeryx

import os
import sys
import json
from shutil import copy
from uuid import uuid4

def load_filenames_file(path):
    with open(path) as f:
        files = set(f.readlines())
    return files

zeryx

FROM SOMEIMAGENAME as runner_intermediate

# We want to cache the following permanently, as these won't change frequently and take a very long time to move around and rebuild.
COPY --from=builder0 --chown=2222:2222 /some/artifact_dir /some/artifact_dir
RUN find /some/artifact_dir > /tmp/artifact_files_BEFORE

FROM SOMEOTHERIMAGE as builder

#This operation occurs on every build operation, therefore to increase perf we want all ops after this to be fast.

zeryx

import torch as th
from torchvision import models, transforms
from PIL import Image
import numpy as np
resnet18 = models.resnet18(pretrained=True)


modules=list(resnet18.children())[:-1]
model = th.nn.Sequential(*modules)

zeryx

import torch as th
from torchvision import models, transforms
from PIL import Image
import numpy as np
model = models.resnet18(pretrained=True)


class Identity(th.nn.Module):
    def __init__(self):
        super(Identity, self).__init__()

zeryx

$ cat algorithmia-r.Rcheck/00check.log 
* using log directory ‘/home/zeryx/code/work/algorithmia-r.Rcheck’
* using R version 3.4.4 (2018-03-15)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--as-cran’
* checking for file ‘algorithmia-r/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘algorithmia’ version ‘0.2.0’
* package encoding: UTF-8