1. Object Detection Model Training with Scaleway GPU Instances.


    To train an object detection model using Scaleway GPU instances, we first need to set up and configure a Scaleway cloud environment. This will typically involve creating a GPU-enabled virtual machine, configuring the environment with the necessary libraries and frameworks for machine learning (like TensorFlow, PyTorch, etc.), and then running the training process.

    While Pulumi doesn't have direct support for Scaleway's services, we can use Pulumi with providers like AWS, GCP, or Azure to create similar infrastructures on these clouds. Below is an explanation followed by an example program that demonstrates how you might set up a machine learning environment on GCP with a GPU instance using Pulumi.

    First, you need to ensure that you have Pulumi installed and the GCP plugin set up with appropriate credentials. The program will involve the following steps:

    1. Define a Google Cloud virtual machine instance with an attached GPU.
    2. Specify the required machine type and disk image suitable for machine learning tasks.
    3. Attach the GPU to the VM instance and specify the necessary settings, like the number of GPUs and their type.

    Here's a Pulumi program that demonstrates how to set up such an environment on GCP.

    import pulumi import pulumi_gcp as gcp # This code assumes that you have already set up Google Cloud SDK and configured # your credentials for Pulumi using `gcp.config`. # Define the machine type and the boot disk image to use. # This example uses a pre-defined Deep Learning VM image, # but you should check for the latest or preferred image for your use case. machine_type = "n1-standard-4" # A machine type with 4 vCPUs boot_disk_image = "deeplearning-platform-release/tf2-latest-cu110" # Create a new virtual machine instance with an attached GPU gpu_instance = gcp.compute.Instance("gpu-instance", machine_type=machine_type, boot_disk=gcp.compute.InstanceBootDiskArgs( initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs( image=boot_disk_image ), ), # Specify the zone where you want to launch the VM. # Ensure that it has the necessary resources available, # including the required GPU types. zone="us-central1-a", # Attach the GPU to the instance. This example attaches one NVIDIA Tesla K80. # You must ensure that GPU quotas allow for using the specified GPUs. guest_accelerators=[gcp.compute.InstanceGuestAcceleratorArgs( type="nvidia-tesla-k80", count=1 )], # Attach a service account to the instance with necessary permissions # if you need to access other GCP resources like GCS buckets. service_account=gcp.compute.InstanceServiceAccountArgs( email="default", scopes=["https://www.googleapis.com/auth/cloud-platform"] ), # Network-related configuration. The instance will have an external IP by default. network_interfaces=[gcp.compute.InstanceNetworkInterfaceArgs( network="default" )] ) # Export the external IP address of the GPU instance so you can connect to it. pulumi.export('gpu_instance_external_ip', gpu_instance.network_interfaces[0].access_configs[0].nat_ip)

    This program sets up a basic GCP GPU instance that could be used for your machine learning training workload. After creating the instance, you would typically SSH into it to configure your object detection training environment, including downloading datasets, installing dependencies, and starting the training process.

    Keep in mind that although the resources specified here meet general object detection model training requirements, you may need to adjust the resources (e.g., machine type, number of GPUs, disk size) depending on the specific requirements of your training workloads.