1. Object Storage for AI Dataset Management on OVH Cloud


    To manage AI datasets on OVH Cloud, you would typically need a way to store large amounts of data that can be easily accessed, frequently even from various geographic locations and by multiple users or systems. Object storage is ideal for this use case because of its scalability, durability, and ease of access.

    In the context of Pulumi, you could accomplish this by using the Object Storage service provided by OVH Cloud. To interact with OVH Cloud within Pulumi, you would use the oci Pulumi package, as it contains the necessary resources for managing object storage, specifically the oci.ObjectStorage.Bucket and oci.ObjectStorage.StorageObject resources.

    Here is a Pulumi program written in Python which creates a new Bucket where you can store AI datasets and a StorageObject, representing an individual file within that Bucket.

    First, we would be defining a bucket in the OVH Cloud Object Storage service. This is the primary storage container that will hold our AI datasets. Then, we would create a storage object within that bucket to represent our AI dataset file.

    Please replace 'unique-bucket-name' with your desired unique bucket name and 'ai-dataset-file-path' with the file path of your AI dataset.

    import pulumi import pulumi_oci as oci # Create an Object Storage Bucket to store AI datasets ai_dataset_bucket = oci.ObjectStorage.Bucket("aiDatasetBucket", namespace="your-namespace", # Replace with your OCI Object Storage namespace compartment_id="your-compartment-id", # Replace with your OCI Compartment ID name="unique-bucket-name", # Replace with a unique bucket name storage_tier="Standard", public_access_type="NoPublicAccess" # This bucket will not be publicly accessible ) # Create a Storage Object to represent an AI dataset ai_dataset_object = oci.ObjectStorage.StorageObject("aiDatasetObject", bucket=ai_dataset_bucket.name, namespace=ai_dataset_bucket.namespace, object="ai-dataset-file-path", # Replace with the AI dataset file path source=pulumi.FileAsset("local-file-path-to-ai-dataset") # Replace with the local path to your AI dataset file ) # Export the bucket's name and the object's URL pulumi.export('ai_dataset_bucket_name', ai_dataset_bucket.name) pulumi.export('ai_dataset_object_url', ai_dataset_object.object.apply( lambda name: f'https://{ai_dataset_bucket.namespace}.compat.objectstorage.{ai_dataset_bucket.region}.oraclecloud.com/{ai_dataset_bucket.name}/{name}' ))

    In the program above:

    • oci.ObjectStorage.Bucket is used to create a new bucket. We give it a unique name and specify the storage tier (which is Standard in this case). The public_access_type is set to NoPublicAccess to prevent public access to the datasets.

    • oci.ObjectStorage.StorageObject is used to create a storage object within the bucket we just created. We specify the bucket name and the path of the object inside the bucket. The source parameter points to the actual dataset file on your local system. The FileAsset class is used to represent files on the local filesystem that Pulumi will manage as assets.

    • We use pulumi.export() to export the bucket name and object's URL after the bucket and object are created. This will allow you to access the names and URLs as outputs once Pulumi has finished deploying your infrastructure.

    Please ensure to replace placeholder values with actual information from your OVH Cloud environment, such as your namespace, compartment ID, and file paths. Adjust the access type and storage tier according to your requirements.