Batch Prediction Jobs with GCP Compute Instances for AI

Question

Pulumi · Accepted Answer

To set up batch prediction jobs with Google Cloud Platform (GCP) Compute Instances for AI purposes, you'll need to create and configure GCP Compute instances that are suitable for running AI and machine learning workloads. You'll likely want to use instances with GPU or TPU support to expedite the processing time for your AI models. The instance will also need the necessary software and frameworks installed, such as TensorFlow or PyTorch.

Below is a Pulumi program in Python that demonstrates how to create a GCP Compute Instance with an attached GPU for AI batch prediction jobs. We will use the `gcp.compute.Instance` resource to launch a new instance, and we'll attach a GPU to it using the `guestAccelerators` property.

We'll start by importing the required Pulumi GCP package and setting up the core properties of the compute instance, including the zone, machine type, and boot disk image.

In this program, we'll use a predefined image that is suitable for AI work, but in a real-world scenario, you might need to customize the image or even use a custom image that includes all your dependencies and specific environment setup.

Make sure you have the GCP provider set up with the required credentials to run this program.

```python
import pulumi
import pulumi_gcp as gcp

# Creating a GCP compute instance for AI batch prediction jobs.
# This instance will have an attached GPU for machine learning workload acceleration.
ai_compute_instance = gcp.compute.Instance("ai-batch-predict-instance",
    machine_type="n1-standard-8", # Example machine type, you may need a different one based on your workload
    # We use 'us-central1-a' for the zone, but you should choose a zone close to your data sources or users.
    zone="us-central1-a",
    boot_disk=gcp.compute.InstanceBootDiskArgs(
        initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs(
            # A common image for AI/ML workloads is the Deep Learning VM images provided by GCP.
            image="projects/deeplearning-platform-release/global/images/family/common-cu113" 
        ),
    ),
    # Adding a GPU accelerator, NVIDIA Tesla T4 in this case.
    guest_accelerators=[gcp.compute.InstanceGuestAcceleratorArgs(
        type="nvidia-tesla-t4",
        count=1,
    )],
    scheduling=gcp.compute.InstanceSchedulingArgs(
        # Allowing the instance to be preemptible can reduce cost but might terminate the instance earlier than expected.
        preemptible=False,
        # Recommending automatic restart for batch jobs so that the instance can resume work after maintenance events.
        automatic_restart=True
    ),
    network_interfaces=[gcp.compute.InstanceNetworkInterfaceArgs(
        # Assuming the default network is used; you may need to specify your VPC network.
        network="default",
        # Example to allow the instance to be accessible from the internet.
        access_configs=[gcp.compute.InstanceNetworkInterfaceAccessConfigArgs(
            nat_ip=gcp.compute.Address("ai-instance-ip").address,
        )]
    )],
    service_account=gcp.compute.InstanceServiceAccountArgs(
        # This service account scope allows full access to all Cloud APIs, which is potentially insecure.
        # You should restrict this to only the scopes necessary for your application.
        scopes=["https://www.googleapis.com/auth/cloud-platform"],
    ),
    # Metadata startup script to install software and start the batch prediction job.
    metadata_startup_script="""#!/bin/bash
    # Commands to install software, download your machine learning model, and start a batch prediction job.
    # e.g., Install conda environments, download datasets, configure environment variables, etc.
    """
)

# Export the instance's public IP to access it if needed.
pulumi.export('ai_instance_public_ip', ai_compute_instance.network_interfaces[0].access_configs[0].nat_ip)
```

Let's go through the core points of the above program:

- `machine_type`: We selected "n1-standard-8" as an example, which signifies an instance with 8 vCPUs. Depending on your batch processing needs, a different machine type may be more appropriate.
- `zone`: It's set to "us-central1-a", you should select a zone that reduces the latency for your data sources and users or offers particular types of Accelerators if needed.
- `boot_disk`: We initialize it with an image specifically designed for deep learning tasks - "common-cu113". This is maintained by GCP and comes with several frameworks and tools pre-installed.
- `guest_accelerators`: We include one NVIDIA Tesla T4 GPU. Based on your workload, you can include more GPUs or opt for other types of accelerators.
- `scheduling`: We set `preemptible` to `False` to reduce the chance of the instance being terminated prematurely, but setting it to `True` could help save on costs for fault-tolerant workloads.
- `network_interfaces`: This section is configured to use the default network for this instance. We also create a public IP for the instance which allows you to access the instance from the internet.
- `service_account`: The service account for the instance is granted full access to Cloud APIs using the `cloud-platform` scope. You should limit this to the minimum necessary scopes for improved security.
- `metadata_startup_script`: This is where you would include a bash script to install additional software, download your ML model, and kick off the prediction job.

After you run this program with Pulumi, it will provision the resources specified, and you should have a running GCP Compute Instance ready to handle batch prediction jobs for AI. You'll be able to access its public IP, as exported in the last line of the program.