Machine Learning Pipelines with Yandex DataSphere.

Question

Pulumi · Accepted Answer

Creating machine learning pipelines involves setting up a series of data processing and model training steps that automate the process of training and validating machine learning models. Yandex DataSphere is a platform that allows you to develop, run, and manage machine learning models and pipelines.

In the context of Pulumi, you can create the infrastructure required to run a machine learning pipeline, which may include setting up compute instances, storage, and other services. However, the actual development of machine learning pipelines within Yandex's DataSphere would typically be done through the platform itself or using its SDKs, which is beyond the scope of Pulumi.

Below is a program in Python using Pulumi to set up a Dataproc cluster in Yandex Cloud, which is a prerequisite for running machine learning pipelines. The code will provision a cluster with the necessary settings. Please note that this does not create the machine learning pipeline itself, but rather the infrastructure on which you could run such pipelines.

```python
import pulumi
import pulumi_yandex as yandex

# Define the name of the cluster
cluster_name = "dataproc-ml-cluster"

# Instantiate a Dataproc cluster with the necessary configurations
dataproc_cluster = yandex.DataprocCluster(
    cluster_name,
    folder_id="your-yandex-folder-id",  # Replace with your Yandex folder ID
    zone_id="your-zone-id",  # Replace with the zone ID where you want to deploy the cluster
    description="Cluster for ML Pipelines",
    cluster_config={
        "version_id": "1.1",
        "hadoop": {"services": ["HDFS", "YARN"], "ssh_public_keys": ["your-ssh-public-key"]},  # Replace with your SSH public key
        "subcluster_specs": [
            {
                "name": "sdc1",
                "role": "DATANODE",
                "subnet_id": "your-subnet-id",  # Replace with your subnet ID
                "resources": {
                    "resource_preset_id": "s2.small",
                    "disk_size": 15,
                    "disk_type_id": "network-nvme",
                },
                "hosts_count": 2,
            },
            {
                "name": "sdc2",
                "role": "COMPUTENODE",
                "subnet_id": "your-subnet-id",  # Same as the subnet ID above
                "resources": {
                    "resource_preset_id": "s2.medium",
                    "disk_size": 30,
                    "disk_type_id": "network-nvme",
                },
                "hosts_count": 2,
            },
        ],
    },
    service_account_id="your-service-account-id",  # Replace with your service account ID
    ui_proxy=True,
    deletion_protection=False,
)

# Export the cluster ID and UI proxy URL as stack outputs
pulumi.export("cluster_id", dataproc_cluster.id)
pulumi.export("ui_proxy_url", dataproc_cluster.ui_proxy_url)
```

Before running this code:

1. Replace the placeholder values (like `your-yandex-folder-id`, `your-zone-id`, `your-ssh-public-key`, `your-subnet-id`, and `your-service-account-id`) with the actual values from your Yandex Cloud account.
   
2. Make sure you have the Yandex provider configured with the necessary credentials.

When you run this Pulumi program, it will provision a new Dataproc cluster within the Yandex Cloud platform, which can be used for running machine learning jobs and deploying models as part of a pipeline. The program also exports the cluster's ID and UI proxy URL, which enables you to access the Yandex DataProc user interface to manage and monitor your cluster.

Remember that the infrastructure code provided here is solely to get the underlying resources running and does not include the code for the ML pipelines that you would typically develop on the DataSphere platform or via Yandex managed services for machine learning.