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Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import base64
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import os
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import requests
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# This example shows how to perform an online inference that generates
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# multimodal data. In this specific case this example will take a geotiff
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# image as input, process it using the multimodal data processor, and
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# perform inference.
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# Requirements :
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# - install TerraTorch v1.1 (or later):
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# pip install terratorch>=v1.1
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# - start vllm in serving mode with the below args
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# --model='christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM'
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# --model-impl terratorch
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# --task embed --trust-remote-code
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# --skip-tokenizer-init --enforce-eager
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# --io-processor-plugin terratorch_segmentation
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# --enable-mm-embeds
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def main():
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image_url = "https://huggingface.co/christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM/resolve/main/valencia_example_2024-10-26.tiff" # noqa: E501
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server_endpoint = "http://localhost:8000/pooling"
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request_payload_url = {
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"data": {
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"data": image_url,
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"data_format": "url",
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"image_format": "tiff",
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"out_data_format": "b64_json",
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},
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"priority": 0,
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"model": "christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM",
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}
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ret = requests.post(server_endpoint, json=request_payload_url)
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print(f"response.status_code: {ret.status_code}")
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print(f"response.reason:{ret.reason}")
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response = ret.json()
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decoded_image = base64.b64decode(response["data"]["data"])
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out_path = os.path.join(os.getcwd(), "online_prediction.tiff")
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with open(out_path, "wb") as f:
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f.write(decoded_image)
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if __name__ == "__main__":
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main()
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