Seldon-一個全方位的機器學習打包部署平台

一、sel動詞英文

Seldon是一個雲原生機器學習平台，為數據科學家和工程師提供了構建、部署和監控生產就緒機器學習模型的工具。Seldon的使命是為開發人員提供可靠的隨時可用的部署，同時為數據科學家提供快速嘗試、迭代和可重複生成的機器學習模型功能。使用Seldon可以輕鬆構建工業級機器學習應用程序，並在任何雲上實現大規模微服務部署。

下面是一個使用Seldon構建機器學習模型的代碼示例：

<?php
    require_once __DIR__ . '/../src/Seldon.php';

    // Initialize the Seldon object with your API key
    $seldon = new Seldon('');
    
    // Define your model's metadata, input type and output type
    $model = array(
        'metadata' => array('name' => 'my-model'),
        'input_type' => 'tensor',
        'output_type' => 'tensor',
    );
    
    // Create the model on Seldon
    $seldon->models->create($model);
?>

二、seldom是什麼意思

Seldom是一個Python庫，它為Selenium WebDriver提供了一個友好的API，以進行Web UI測試。Seldom基於Selenium，並提供了更簡潔、可讀性更高的API，使測試代碼更易於編寫和維護。Seldom還支持測試用例的自動化運行，並提供了一組豐富的斷言方法和測試報告。

下面是一個使用Seldom進行UI自動化測試的代碼示例：

<?php
    require_once('vendor/autoload.php');
    
    use Selenium\Client;
    use Seldom\Seldom;

    // Create the Selenium client and connect to the browser
    $driver = new Client();
    $driver->connect('firefox', 'http://localhost:4444/wd/hub');

    // Create the Seldom object and set the Selenium client
    $seldom = new Seldom($driver);

    // Navigate to the test page
    $seldom->get('http://localhost/tests');

    // Open the dropdown menu
    $seldom->click('div.dropdown-toggle');

    // Click the "Logout" button
    $seldom->click('#logout-button');

    // Assert that the login page is displayed
    $seldom->assertContains('Login', $seldom->getTitle());

    // Close the browser and stop the Selenium client
    $driver->stop();
?>

三、seldon caused any problems

Seldon非常適合用於構建機器學習模型並將其部署到生產環境中。Seldon提供了完整的功能，包括模型版本控制、API版本控制、請求路由、A/B測試、自動縮放和監控。這些功能使Seldon成為一個全面而可靠的機器學習部署平台。

下面是一個使用Seldon部署機器學習模型並進行A/B測試的代碼示例：

<?php
    require_once __DIR__ . '/../src/Seldon.php';

    // Initialize the Seldon object with your API key
    $seldon = new Seldon('');

    // Define your model's metadata, input type and output type
    $model = array(
        'metadata' => array('name' => 'my-model'),
        'input_type' => 'tensor',
        'output_type' => 'tensor',
    );

    // Create the model on Seldon
    $seldon->models->create($model);

    // Define your A/B experiment's metadata, variants and test data
    $experiment = array(
        'metadata' => array('name' => 'my-experiment'),
        'variants' => array(
            'variant-a' => 0.5,
            'variant-b' => 0.5,
        ),
        'test_data' => array(
            array(1, 2, 3, 4, 5),
            array(6, 7, 8, 9, 10),
        ),
    );

    // Create the experiment on Seldon
    $seldon->experiments->create($experiment);

    // Send a request to the model's API endpoint with the test data
    $response = $seldon->predict(array(array(1, 2, 3, 4, 5)));

    // Extract the prediction from the response
    $prediction = $response['data']['tensor']['values'][0];

    // Print the prediction
    echo 'Prediction: ' . implode(', ', $prediction);
    
    // Close the experiment
    $seldon->experiments->close();
?>

四、seldon的中文意思

Seldon是一個基於Kubernetes的機器學習平台，它提供了構建、部署和管理生產級機器學習模型的完整功能。Seldon支持多種機器學習框架，包括TensorFlow、PyTorch和Scikit-learn，並提供了API管理、指標監控、日誌記錄等功能。

下面是一個使用Seldon構建機器學習模型並部署到Kubernetes集群的代碼示例：

<?php
    require_once __DIR__ . '/../src/Seldon.php';

    // Initialize the Seldon object with your Kubernetes config file
    $seldon = new Seldon('');

    // Define your model's metadata, input type and output type
    $model = array(
        'metadata' => array('name' => 'my-model'),
        'input_type' => 'tensor',
        'output_type' => 'tensor',
    );

    // Create the model on Seldon
    $seldon->models->create($model);

    // Define your deployment's metadata and spec
    $deployment = array(
        'metadata' => array('name' => 'my-deployment'),
        'spec' => array(
            'predictors' => array(
                array(
                    'name' => 'my-predictor',
                    'componentSpecs' => array(
                        array(
                            'spec' => array(
                                'containers' => array(
                                    array(
                                        'name' => 'my-container',
                                        'image' => 'my-image:latest',
                                        'env' => array(
                                            array('name' => 'MODEL_NAME', 'value' => 'my-model'),
                                            array('name' => 'API_TYPE', 'value' => 'REST'),
                                            array('name' => 'PREDICTIVE_UNIT_SERVICE_PORT', 'value' => '9000'),
                                        ),
                                        'resources' => array(
                                            'requests' => array(
                                                'memory' => '1Gi',
                                                'cpu' => '1',
                                            ),
                                            'limits' => array(
                                                'memory' => '2Gi',
                                                'cpu' => '2',
                                            ),
                                        ),
                                    ),
                                ),
                            ),
                        ),
                    ),
                    'graph' => array(
                        'name' => 'my-graph',
                        'type' => 'MODEL',
                        'endpoint' => array(
                            'type' => 'REST',
                        ),
                    ),
                    'replicas' => 1,
                ),
            ),
        ),
    );

    // Create the deployment on Seldon
    $seldon->deployments->create($deployment);

    // Send a request to the model's API endpoint with some test data
    $response = $seldon->predict(array(array(1, 2, 3, 4, 5)));

    // Extract the prediction from the response
    $prediction = $response['data']['tensor']['values'][0];

    // Print the prediction
    echo 'Prediction: ' . implode(', ', $prediction);

    // Delete the deployment and model
    $seldon->deployments->delete('my-deployment');
    $seldon->models->delete('my-model');
?>