pathways-cli 0.1.1

Pathways CLI to easily bring up pathways clusters.

pwy: Standalone Pathways GKE Cluster CLI Tool

pwy is a lightweight, standalone Python CLI utility designed to generate, apply, and manage interactive Pathways workloads on Google Kubernetes Engine (GKE) using Kubernetes JobSets.

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Features

• Automated TPU Topology Calculations: Translates simple TPU resource types (v6e-4, v6e-16, etc.) into GKE topologies, VM counts, and instance settings.
• Spot VM Support: Dynamically injects GKE node selectors and tolerations for running workloads on cost-effective Spot VMs.
• Colocated Python Support: Simplifies distributed checkpointing (e.g. via Orbax) by configuring and enabling colocated host CPU sidecars and proxy endpoints automatically.
• Interactive & Batch Execution: Supports spinning up pathways servers with infinite sleep drivers for interactive debugging, or executing training scripts directly.
• Dry-run Manifest Generation: Preview and inspect the GKE JobSet manifest without applying it to the cluster.

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Installation

Install pathways-cli from PyPI using your preferred package manager:

# Using pip
pip install pathways-cli

# Or using uv (recommended for fast tool management)
uv tool install pathways-cli

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Usage

Once installed, you can invoke the pwy CLI directly:

1. Provision / Preview a Cluster (pwy up)

Starts a Pathways JobSet or dry-runs the configuration.

pwy up \
  --tpu-type v6e-16 \
  --gcs-scratch-location gs://my-bucket/pathways-staging \
  --num-slices 1 \
  --dry-run

Key Options:

• --tpu-type: (Required) TPU type (e.g., v6e-4, v6e-8, v6e-16, v6e-32, v6e-64).
• --gcs-scratch-location: (Required) GCS scratch path for pathways synchronization.
• --num-slices: Number of TPU slices to run (default: 1).
• --jax-client-image: Custom client container image (default: python:3.12-slim).
• --command: Run a custom training/eval script in the client container. If omitted, defaults to sleep infinity (interactive mode).
• --enable-spot: Add node affinity and toleration settings for Spot VMs.
• --colocated-python: Enables colocated CPU Python sidecar/init containers on GKE workers and enables external proxy routing.
• --dry-run: Prints the generated YAML to stdout instead of calling kubectl apply.
• --name: Name of the Kubernetes JobSet resource (default: pathways-interactive).
• --namespace: Target Kubernetes namespace (default: default).

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2. Teardown a Cluster (pwy down)

Deletes the running Pathways JobSet.

pwy down --name pathways-interactive --namespace default

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3. Verification Example

Once the interactive cluster is running, you can verify execution by execing into the client container:

1. Find the client pod name:

   POD_NAME=$(kubectl get pods -l jobset.sigs.k8s.io/jobset-name=pathways-interactive -o jsonpath='{.items[?(@.metadata.labels.jobset\\.sigs\\.k8s\\.io/replicatedjob-name=="pwhd")].metadata.name}')
   

2. Install JAX and Pathways utils:

   kubectl exec $POD_NAME -c client -- pip install jax pathwaysutils
   

3. Run a Python snippet to initialize and list devices:

   kubectl exec $POD_NAME -c client -- python3 -c "import pathwaysutils; pathwaysutils.initialize(); import jax; print(jax.devices())"
   

   The command output should print the available virtual TPU devices (e.g., coordinates and memory spaces of the allocated chips).

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TPU Type Mappings

pwy handles all resource-limit math and topologies automatically. It supports a wide range of TPU generations, including:

• TPU v6e: v6e-4 up to v6e-256 (including v6e-8-1 with 8 chips per VM)
• TPU v5p: v5p-8 up to v5p-17920
• TPU v5e (v5LitePod): v5litepod-8 up to v5litepod-256
• TPU v4: v4-8 up to v4-4096
• TPU 7x: 7x-8 up to 7x-8192

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Running Tests

To execute the unit test suite:

uv run pytest