Add Pathways Integration

examples/pathways_example.py

@@ -0,0 +1,49 @@
+import os
+
+os.environ["KERAS_BACKEND"] = "jax"
+
+import keras
+import numpy as np
+from keras import layers
+
+import keras_remote
+
+
+# A simple model that will be executed remotely on pathways
+@keras_remote.run(accelerator="v5litepod-1", backend="pathways")
+def train_simple_model():
+  print("Running Pathways job on JAX Backend!")
+
+  # Create a simple dataset
+  x = np.random.rand(1000, 10)
+  y = np.random.randint(0, 2, size=(1000, 1))
+
+  # A simple sequential model
+  model = keras.Sequential(
+    [
+      keras.Input(shape=(10,)),
+      layers.Dense(32, activation="relu"),
+      layers.Dense(16, activation="relu"),
+      layers.Dense(1, activation="sigmoid"),
+    ]
+  )
+
+  model.compile(
+    optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
+  )
+
+  print("Model Architecture:")
+  model.summary()
+
+  # Train the model
+  print("\nStarting Training...")
+  history = model.fit(x, y, epochs=5, batch_size=32, validation_split=0.2)
+
+  print("\nTraining completed successfully on Pathways!")
+  return history.history
+
+
+if __name__ == "__main__":
+  print("Submitting Pathways training job...")
+  result_history = train_simple_model()
+  print("Final validation accuracy:", result_history["val_accuracy"][-1])

keras_remote/backend/execution.py

@@ -14,7 +14,7 @@
 import cloudpickle
 from absl import logging
 
-from keras_remote.backend import gke_client
+from keras_remote.backend import gke_client, pathways_client
 from keras_remote.constants import get_default_zone, zone_to_region
 from keras_remote.infra import container_builder
 from keras_remote.utils import packager, storage
@@ -105,13 +105,17 @@ def cleanup_job(self, job: Any, ctx: JobContext) -> None:
     ...
 
 
-class GKEBackend:
-  """Backend adapter for GKE."""
+class BaseK8sBackend:
+  """Base class for Kubernetes-based backends."""
 
   def __init__(self, cluster: Optional[str] = None, namespace: str = "default"):
     self.cluster = cluster
     self.namespace = namespace
 
+
+class GKEBackend(BaseK8sBackend):
+  """Backend adapter for standard GKE Jobs."""
+
   def submit_job(self, ctx: JobContext) -> Any:
     """Submit job to GKE cluster."""
     return gke_client.submit_k8s_job(
@@ -134,6 +138,31 @@ def cleanup_job(self, job: Any, ctx: JobContext) -> None:
     gke_client.cleanup_job(job_name, namespace=self.namespace)
 
 
+class PathwaysBackend(BaseK8sBackend):
+  """Backend adapter for ML Pathways using LeaderWorkerSet."""
+
+  def submit_job(self, ctx: JobContext) -> Any:
+    """Submit LWS job to GKE cluster."""
+    return pathways_client.submit_pathways_job(
+      display_name=ctx.display_name,
+      container_uri=ctx.image_uri,
+      accelerator=ctx.accelerator,
+      project=ctx.project,
+      job_id=ctx.job_id,
+      bucket_name=ctx.bucket_name,
+      namespace=self.namespace,
+    )
+
+  def wait_for_job(self, job: Any, ctx: JobContext) -> None:
+    """Wait for Pathways LWS completion."""
+    pathways_client.wait_for_job(ctx.job_id, namespace=self.namespace)
+
+  def cleanup_job(self, job: Any, ctx: JobContext) -> None:
+    """Clean up LWS resources."""
+    job_name = pathways_client._get_job_name(ctx.job_id)
+    pathways_client.cleanup_job(job_name, namespace=self.namespace)
+
+
 def _find_requirements(start_dir: str) -> Optional[str]:
   """Search up directory tree for requirements.txt."""
   search_dir = start_dir

keras_remote/backend/gke_client.py

@@ -300,7 +300,7 @@ def _create_job_spec(
 
   pod_template = client.V1PodTemplateSpec(
     metadata=client.V1ObjectMeta(
-      labels={"app": "keras-remote", "job-id": job_id}
+      labels={"app": "keras-remote", "job-id": job_id, "job-name": job_name}
     ),
     spec=client.V1PodSpec(**pod_spec_kwargs),
   )