Executors

Local

The local executor declares that an executable will be run on the same machine from which the launch script is invoked.

xm_local.Local(
    docker_options=xm_local.DockerOptions(...),
)

Making GPUs available to local containers is possible by requesting the local_gpu resource through a requirements object.

xm_local.Local(
    xm.JobRequirements(local_gpu=1)
)

Note: Only local NVIDIA GPUs can be requested for the Local executor through the resource requirements object. Any other requirements will be ignored.

Vertex AI (Cloud AI Platform)

The Vertex executor declares that an executable will be run on the Vertex AI platform.

The Vertex executor takes in a resource requirements object.

xm_local.Vertex(
    xm.JobRequirements(
        cpu=1,  # Measured in vCPUs.
        ram=4 * xm.GiB,
        T4=1,  # NVIDIA Tesla T4.
    ),
)

xm_local.Vertex(
    xm.JobRequirements(
        cpu=1,  # Measured in vCPUs.
        ram=4 * xm.GiB,
        TPU_V2=8,  # TPU v2.
    ),
)

As of June 2021, the currently supported accelerator types are:

P100
V100
P4
T4
A100
TPU_V2
TPU_V3

Vertex AI Specification

The Vertex AI executor allows you specify a remote image repository to push to.

xm_local.Vertex.Spec(
    push_image_tag='gcr.io/<project>/<image>:<tag>',
)

Kubernetes (experimental)

The Kubernetes executor declares that an executable will be run on a Kubernetes cluster. As of October 2021, Kubernetes is not fully supported.

The Kubernetes executor pulls from your local kubeconfig. The XManager command-line has helpers to set up a Google Kubernetes Engine (GKE) cluster.

pip install caliban==0.4.1
xmanager cluster create

# cleanup
xmanager cluster delete

You can store the GKE credentials in your kubeconfig:

gcloud container clusters get-credentials <cluster-name>

Kubernetes Specification

The Kubernetes executor allows you specify a remote image repository to push to.

xm_local.Kubernetes.Spec(
    push_image_tag='gcr.io/<project>/<image>:<tag>',
)

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