[RayJob] [Doc] Add real-world Ray Job use case tutorial for KubeRay

ray-operator/config/samples/ray-job.batch-inference.yaml

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+apiVersion: ray.io/v1alpha1
+kind: RayJob
+metadata:
+  name: rayjob-sample
+spec:
+  entrypoint: python /home/ray/samples/sample_code.py
+  rayClusterSpec:
+    rayVersion: '2.6.3'
+    headGroupSpec:
+      rayStartParams:
+        dashboard-host: '0.0.0.0'
+      template:
+        spec:
+          containers:
+            - name: ray-head
+              image: rayproject/ray-ml:2.6.3-gpu
+              ports:
+                - containerPort: 6379
+                  name: gcs-server
+                - containerPort: 8265
+                  name: dashboard
+                - containerPort: 10001
+                  name: client
+              resources:
+                limits:
+                  nvidia.com/gpu: "4"
+                  cpu: "54"
+                  memory: "54Gi"
+                requests:
+                  nvidia.com/gpu: "4"
+                  cpu: "54"
+                  memory: "54Gi"
+              volumeMounts:
+                - mountPath: /home/ray/samples
+                  name: code-sample
+          nodeSelector:
+            cloud.google.com/gke-accelerator: nvidia-tesla-t4
+          volumes:
+            - name: code-sample
+              configMap:
+                name: ray-job-code-sample
+                items:
+                  - key: sample_code.py
+                    path: sample_code.py
+
+######################Ray code #################################
+# This sample is from https://docs.ray.io/en/latest/data/examples/huggingface_vit_batch_prediction.html
+# It is mounted into the container.
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: ray-job-code-sample
+data:
+  sample_code.py: |
+    import ray
+
+    s3_uri = "s3://anonymous@air-example-data-2/imagenette2/val/"
+
+    ds = ray.data.read_images(
+        s3_uri, mode="RGB"
+    )
+    ds
+    from typing import Dict
+    import numpy as np
+
+    from transformers import pipeline
+    from PIL import Image
+
+    BATCH_SIZE = 16
+
+    class ImageClassifier:
+        def __init__(self):
+            # If doing CPU inference, set `device="cpu"` instead.
+            self.classifier = pipeline("image-classification", model="google/vit-base-patch16-224", device=0) # TODO:archit
+
+        def __call__(self, batch: Dict[str, np.ndarray]):
+            # Convert the numpy array of images into a list of PIL images which is the format the HF pipeline expects.
+            outputs = self.classifier(
+                [Image.fromarray(image_array) for image_array in batch["image"]], 
+                top_k=1, 
+                batch_size=BATCH_SIZE)
+
+            # `outputs` is a list of length-one lists. For example:
+            # [[{'score': '...', 'label': '...'}], ..., [{'score': '...', 'label': '...'}]]
+            batch["score"] = [output[0]["score"] for output in outputs]
+            batch["label"] = [output[0]["label"] for output in outputs]
+            return batch
+
+    predictions = ds.map_batches(
+        ImageClassifier,
+        compute=ray.data.ActorPoolStrategy(size=4), # Change this number based on the number of GPUs in your cluster.
+        num_gpus=1, # Specify 1 GPU per model replica.
+        batch_size=BATCH_SIZE # Use the largest batch size that can fit on our GPUs
+    )
+
+    prediction_batch = predictions.take_batch(5)
+
+    from PIL import Image
+    print("A few sample predictions: ")
+    for image, prediction in zip(prediction_batch["image"], prediction_batch["label"]):
+        img = Image.fromarray(image)
+        # Display the image
+        img.show()
+        print("Label: ", prediction)
+
+    # Write to local disk, or external storage, e.g. S3
+    # ds.write_parquet("s3://my_bucket/my_folder")