1. GPU-Backed OCI Instances for Deep Learning Inference


    To set up GPU-backed OCI (Oracle Cloud Infrastructure) instances for deep learning inference, we'll use Pulumi to create the necessary cloud infrastructure. This usually involves creating a compute instance with a GPU shape and configuring it for deep learning tasks.

    Here's what needs to be done step-by-step:

    1. Set up an OCI Compute Instance: We will provision an OCI compute instance that offers GPU support. In OCI, certain instance shapes provide GPU capabilities that are suitable for deep learning tasks.

    2. Configure the Instance: After the instance is up and running, we'll need to configure it with the necessary software stack. This typically involves installing CUDA libraries, a deep learning framework like TensorFlow or PyTorch, and other dependencies.

    3. Enable Network Access: If you need to send inference requests to your instance over the internet or a private network, we need to configure the virtual cloud network, firewalls, and potentially load balancers.

    Below is a basic Pulumi program in Python that demonstrates how to create a GPU-backed OCI instance. The instance shape and image would have to be selected based on your specific needs and the region you are deploying to. You would replace 'GPU_SHAPE' with the specific GPU shape you need and 'YOUR_COMPARTMENT_OCID' with your compartment's OCID. Remember to also choose the correct image that comes pre-installed with the necessary drivers and tools for your deep learning tasks.

    import pulumi import pulumi_oci as oci # Use the correct compartment OCID compartment_id = 'YOUR_COMPARTMENT_OCID' # Define the OCI GPU instance gpu_instance = oci.core.Instance('gpuInstance', availability_domain='YOUR_AVAILABILITY_DOMAIN', # Replace with the correct availability domain compartment_id=compartment_id, shape='GPU_SHAPE', # Replace with the GPU instance shape metadata={ 'ssh_authorized_keys': 'YOUR_SSH_PUBLIC_KEY' }, # The image ID should be for an image that is compatible with GPUs (e.g., with preinstalled CUDA) image='YOUR_GPU_COMPATIBLE_IMAGE_ID', # The subnet ID should reference a subnet in your VCN create_vnic_details=oci.core.InstanceCreateVnicDetailsArgs( subnet_id='YOUR_SUBNET_OCID', assign_public_ip=True, ), # Set any tags you may require freeform_tags={ 'Project': 'Deep Learning Inference' } ) # Export the public IP of the GPU instance to access it later public_ip = pulumi.Output.all(gpu_instance.public_ip).apply(lambda args: args[0]) pulumi.export('gpu_instance_public_ip', public_ip)

    In this program, we define a gpu_instance of type oci.core.Instance. We use availability_domain and shape to specify the location and the machine type respectively. Generally, the shape can be something like VM.GPU2.1 or BM.GPU3.8, depending on your needs and the OCI offering at the time. For image, you need to specify an Oracle-provided GPU image or a custom image that you have prepared with the necessary GPU drivers and deep learning libraries.

    Additionally, we set up an SSH key through the metadata field so that you can connect to the instance once it's created. The create_vnic_details argument includes a subnet ID which should be the ID of a subnet in a VCN that you've set up in your OCI account. This enables the instance to receive a public IP address, if desired, to be accessible over the internet.

    Finally, we output the instance's public IP for later use, which could be for SSH access or sending inference requests to the model hosted on the instance.

    Remember, whilst this program will create a GPU-backed instance successfully, you'd still need to set up your environment within the instance. That can mean installing CUDA, cuDNN, TensorFlow, PyTorch, or other frameworks and libraries according to your requirements. These steps would typically be done manually through SSH or by using configuration management tools such as Ansible, or even with Cloud-init scripts provided in the metadata field at the time of instance creation.