[Datasets] Add read benchmark for images (#28724)

This PR add data benchmark for reading images in various settings - different number of images, image sizes, modes, formats. See `read_benchmark.py:run_images_benchmark()` for details.

release/nightly_tests/dataset/read_benchmark.py

@@ -0,0 +1,109 @@
+import os
+import random
+import shutil
+import tempfile
+from typing import List, Optional, Tuple
+
+from PIL import Image
+
+import ray
+from ray.data.dataset import Dataset
+from ray.data.datasource import ImageFolderDatasource
+
+from benchmark import Benchmark
+
+
+def read_images(
+    root: str, size: Optional[Tuple[int, int]] = None, mode: Optional[str] = None
+) -> Dataset:
+
+    return ray.data.read_datasource(
+        ImageFolderDatasource(), root=root, size=size, mode=mode
+    )
+
+
+def generate_images(
+    num_images: int, sizes: List[Tuple[int, int]], modes: List[str], formats: List[str]
+) -> str:
+
+    dimensions = []
+    for mode in modes:
+        if mode in ["1", "L", "P"]:
+            dimension = 1
+        elif mode in ["RGB", "YCbCr", "LAB", "HSV"]:
+            dimension = 3
+        elif mode in ["RGBA", "CMYK", "I", "F"]:
+            dimension = 4
+        else:
+            raise ValueError(f"Found unknown image mode: {mode}.")
+        dimensions.append(dimension)
+
+    images_dir = tempfile.mkdtemp()
+
+    for image_idx in range(num_images):
+        size = random.choice(sizes)
+        file_format = random.choice(formats)
+        mode_idx = random.randrange(len(modes))
+        mode = modes[mode_idx]
+        dimension = dimensions[mode_idx]
+
+        width, height = size
+        file_name = f"{images_dir}/{image_idx}.{file_format}"
+        pixels_per_dimension = []
+        for _ in range(dimension):
+            pixels = os.urandom(width * height)
+            pixels_per_dimension.append(pixels)
+
+        image = Image.new(mode, size)
+        if len(pixels_per_dimension) == 1:
+            image.putdata(pixels_per_dimension[0])
+        else:
+            image.putdata(list(zip(*pixels_per_dimension)))
+        image.save(file_name)
+
+    return images_dir
+
+
+def run_images_benchmark(benchmark: Benchmark):
+    # Set global random seed.
+    random.seed(42)
+
+    test_input = [
+        generate_images(100, [(256, 256)], ["RGB"], ["jpg"]),
+        generate_images(100, [(2048, 2048)], ["RGB"], ["jpg"]),
+        generate_images(
+            1000, [(64, 64), (256, 256)], ["RGB", "L"], ["jpg", "jpeg", "png"]
+        ),
+    ]
+
+    benchmark.run("images-100-256-rbg-jpg", read_images, root=test_input[0])
+    benchmark.run("images-100-2048-rbg-jpg", read_images, root=test_input[1])
+    benchmark.run(
+        "images-100-2048-to-256-rbg-jpg",
+        read_images,
+        root=test_input[1],
+        size=(256, 256),
+    )
+    benchmark.run(
+        "images-1000-mix", read_images, root=test_input[2], size=(256, 256), mode="RGB"
+    )
+
+    for root in test_input:
+        shutil.rmtree(root)
+
+    # TODO(chengsu): run benchmark on 20G and 100G imagenet data.
+    benchmark.run(
+        "images-imagenet-1g",
+        read_images,
+        root="s3://air-example-data-2/1G-image-data-synthetic-raw",
+    )
+
+
+if __name__ == "__main__":
+    ray.init()
+
+    benchmark = Benchmark("read")
+
+    run_images_benchmark(benchmark)
+
+    benchmark.write_result()

release/release_tests.yaml

@@ -4213,7 +4213,7 @@
   team: data
   cluster:
     cluster_env: app_config.yaml
-    cluster_compute: aggregate_benchmark_compute.yaml
+    cluster_compute: single_node_benchmark_compute.yaml
 
   run:
     timeout: 1800
@@ -4222,6 +4222,23 @@
     type: sdk_command
     file_manager: sdk
 
+- name: read_benchmark_single_node
+  group: core-dataset-tests
+  working_dir: nightly_tests/dataset
+
+  frequency: multi
+  team: data
+  cluster:
+    cluster_env: app_config.yaml
+    cluster_compute: single_node_benchmark_compute.yaml
+
+  run:
+    timeout: 1800
+    script: python read_benchmark.py
+
+    type: sdk_command
+    file_manager: sdk
+
 - name: pipelined_training_50_gb
   group: core-dataset-tests
   working_dir: nightly_tests/dataset