Sync from v0.13

examples/pooling/plugin/prithvi_geospatial_mae_client.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import base64
+import os
+
+import requests
+
+# This example shows how to perform an online inference that generates
+# multimodal data. In this specific case this example will take a geotiff
+# image as input, process it using the multimodal data processor, and
+# perform inference.
+# Requirements :
+# - install TerraTorch v1.1 (or later):
+#   pip install terratorch>=v1.1
+# - start vllm in serving mode with the below args
+#   --model='christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM'
+#   --model-impl terratorch
+#   --trust-remote-code
+#   --skip-tokenizer-init --enforce-eager
+#   --io-processor-plugin terratorch_segmentation
+#   --enable-mm-embeds
+
+
+def main():
+    image_url = "https://huggingface.co/christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM/resolve/main/valencia_example_2024-10-26.tiff"  # noqa: E501
+    server_endpoint = "http://localhost:8000/pooling"
+
+    request_payload_url = {
+        "data": {
+            "data": image_url,
+            "data_format": "url",
+            "image_format": "tiff",
+            "out_data_format": "b64_json",
+        },
+        "priority": 0,
+        "model": "christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM",
+    }
+
+    ret = requests.post(server_endpoint, json=request_payload_url)
+
+    print(f"response.status_code: {ret.status_code}")
+    print(f"response.reason:{ret.reason}")
+
+    response = ret.json()
+
+    decoded_image = base64.b64decode(response["data"]["data"])
+
+    out_path = os.path.join(os.getcwd(), "online_prediction.tiff")
+
+    with open(out_path, "wb") as f:
+        f.write(decoded_image)
+
+
+if __name__ == "__main__":
+    main()