keinos/php8-ml repository overview

⁠Dockerfile of PHP8-ML

Dockerfile of PHP-ML⁠, the Machine Learning library for PHP. (PHP8-ish + JIT on Alpine Linux)

docker pull keinos/php8-ml:latest

For ARMv6l(arm32v6) architecture such as RaspberryPi Zero W use the below image.

docker pull keinos/php8-ml:arm32v6

⁠Basic Image Info

• PHP: Closest version to PHP8 and JIT enabled.
• Source: https://github.com/KEINOS/Dockerfile_of_php8-ML⁠ @ GitHub
• Image: https://hub.docker.com/r/keinos/php8-ml⁠ @ Docker Hub
  • Target architecture: x86_64 (Intel. Maybe AMD too but ARM)
• Base Image: keinos/php8-jit⁠
• User: www-data
• Entry Point: not set
• Composer: Installed under /usr/local/bin/composer
• Working Directory: /app (alias of /home/www-data)
• PHP-ML: Install via composer (composer require php-ai/php-ml)

  • Ref: Repo⁠ @ GitHub

  • Ref: Docs⁠ @ ReadTheDocs

  • Sample /app/composer.json :

    {
        "require": {
            "php-ai/php-ml": "^0.4.1"
        }
    }
    

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⁠Sample Usage

Mount the script on /app and run.

$ docker run --rm -it -v $(pwd)/sample.php:/app/sample.php keinos/php8-ml:latest /bin/sh
/ $ cd /app
/ $ ls
sample.php
/ $ composer require php-ai/php-ml
...
/ $ php ./sample.php
b

• Sample script:

  <?php
  /* sample.php */
  
  require_once __DIR__ . '/vendor/autoload.php';
  
  use Phpml\Classification\KNearestNeighbors;
  
  $samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
  $labels  = ['a', 'a', 'a', 'b', 'b', 'b'];
  
  $classifier = new KNearestNeighbors();
  $classifier->train($samples, $labels);
  
  echo $classifier->predict([3, 2]), PHP_EOL;
  // expect return: 'b'
  

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⁠Build

1. Clone this repo.

2. Decide tag name to use as a base image from:

   • arm32v6 arm(ARMv7) amd latest(default, x86_64)
   • [See available tags⁠]

3. Build image specifing the base image tag.

   The below sample will build with the base image keinos/php8-jit:arm32v6 as myname/php8-ml:local.

   docker build --build-arg NAME_TAG_IMAGE=arm32v6 -t myname/php8-ml:local .
   

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