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[data] minor updates for the object detection example #44168

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Mar 20, 2024
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[data] minor updates for the object detection example #44168

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14 changes: 8 additions & 6 deletions doc/source/data/examples/batch_inference_object_detection.ipynb
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Expand Up @@ -429,7 +429,7 @@
"from torchvision.transforms.functional import to_pil_image\n",
"\n",
"labels = [weights.meta[\"categories\"][i] for i in prediction[\"labels\"]]\n",
"box = draw_bounding_boxes(img, \n",
"box = draw_bounding_boxes(img,\n",
" boxes=prediction[\"boxes\"],\n",
" labels=labels,\n",
" colors=\"red\",\n",
Expand All @@ -444,7 +444,7 @@
"source": [
"## Scaling with Ray Data\n",
"\n",
"Then let's see how to scale the previous example to a large set of images. We will use Ray Data to do batch inference in a distributed fashion, leveraging all the CPU and GPU resources in our cluster.\n",
"Then let's see how to scale the previous example to a large set of images. We will use Ray Data to do batch inference in a streaming and distributed fashion, leveraging all the CPU and GPU resources in our cluster.\n",
"\n",
"### Loading the Image Dataset\n",
"\n",
Expand Down Expand Up @@ -536,7 +536,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we use the {meth}`map <ds.data.Dataset.map>` API to apply the function to the whole dataset. By using Ray Data's map, we can scale out the preprocessing to all the resources in our Ray cluster Note, the `map` method is lazy, it won't perform execution until we start to consume the results."
"Then we use the {meth}`map <ds.data.Dataset.map>` API to apply the function to the whole dataset. By using Ray Data's map, we can scale out the preprocessing to all the resources in our Ray cluster. Note, the `map` method is lazy, it won't perform execution until we start to consume the results."
]
},
{
Expand Down Expand Up @@ -944,9 +944,11 @@
"source": [
"ds = ds.map_batches(\n",
" ObjectDetectionModel,\n",
" concurrency=4, # Use 4 GPUs. Change this number based on the number of GPUs in your cluster.\n",
" batch_size=4, # Use the largest batch size that can fit in GPU memory.\n",
" num_gpus=1, # Specify 1 GPU per model replica. Remove this if you are doing CPU inference.\n",
" # Use 4 GPUs. Change this number based on the number of GPUs in your cluster.\n",
" concurrency=4,\n",
" batch_size=4, # Use the largest batch size that can fit in GPU memory.\n",
" # Specify 1 GPU per model replica. Remove this if you are doing CPU inference.\n",
" num_gpus=1,\n",
")"
]
},
Expand Down