infernet-1.0.0 update
This commit is contained in:
arshan-ritual
@ -1,12 +1,18 @@
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import logging
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from typing import Any, cast, List
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from infernet_ml.utils.common_types import TensorInput
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import numpy as np
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from eth_abi import decode, encode # type: ignore
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from infernet_ml.utils.model_loader import ModelSource
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from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
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from infernet_ml.utils.model_loader import (
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HFLoadArgs,
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ModelSource,
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)
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from infernet_ml.utils.service_models import InfernetInput, JobLocation
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from infernet_ml.workflows.inference.onnx_inference_workflow import (
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ONNXInferenceWorkflow,
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ONNXInferenceInput,
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ONNXInferenceResult,
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)
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from quart import Quart, request
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from quart.json.provider import DefaultJSONProvider
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@ -29,10 +35,11 @@ def create_app() -> Quart:
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app = Quart(__name__)
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# we are downloading the model from the hub.
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# model repo is located at: https://huggingface.co/Ritual-Net/iris-dataset
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model_source = ModelSource.HUGGINGFACE_HUB
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model_args = {"repo_id": "Ritual-Net/iris-dataset", "filename": "iris.onnx"}
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workflow = ONNXInferenceWorkflow(model_source=model_source, model_args=model_args)
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workflow = ONNXInferenceWorkflow(
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model_source=ModelSource.HUGGINGFACE_HUB,
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load_args=HFLoadArgs(repo_id="Ritual-Net/iris-dataset", filename="iris.onnx"),
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)
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workflow.setup()
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@app.route("/")
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@ -43,7 +50,7 @@ def create_app() -> Quart:
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return "ONNX Iris Classifier Example Program"
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@app.route("/service_output", methods=["POST"])
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async def inference() -> dict[str, Any]:
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async def inference() -> Any:
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req_data = await request.get_json()
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"""
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InfernetInput has the format:
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@ -52,50 +59,56 @@ def create_app() -> Quart:
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"""
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infernet_input: InfernetInput = InfernetInput(**req_data)
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if infernet_input.source == InfernetInputSource.OFFCHAIN:
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web2_input = cast(dict[str, Any], infernet_input.data)
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values = cast(List[List[float]], web2_input["input"])
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else:
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# On-chain requests are sent as a generalized hex-string which we will
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# decode to the appropriate format.
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web3_input: List[int] = decode(
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["uint256[]"], bytes.fromhex(cast(str, infernet_input.data))
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)[0]
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values = [[float(v) / 1e6 for v in web3_input]]
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match infernet_input:
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case InfernetInput(source=JobLocation.OFFCHAIN):
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web2_input = cast(dict[str, Any], infernet_input.data)
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values = cast(List[List[float]], web2_input["input"])
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case InfernetInput(source=JobLocation.ONCHAIN):
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web3_input: List[int] = decode(
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["uint256[]"], bytes.fromhex(cast(str, infernet_input.data))
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)[0]
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values = [[float(v) / 1e6 for v in web3_input]]
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"""
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The input to the onnx inference workflow needs to conform to ONNX runtime's
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input_feed format. For more information refer to:
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https://docs.ritual.net/ml-workflows/inference-workflows/onnx_inference_workflow
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"""
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result: dict[str, Any] = workflow.inference({"input": values})
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_input = ONNXInferenceInput(
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inputs={"input": TensorInput(shape=(1, 4), dtype="float", values=values)},
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)
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result: ONNXInferenceResult = workflow.inference(_input)
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if infernet_input.source == InfernetInputSource.OFFCHAIN:
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"""
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In case of an off-chain request, the result is returned as is.
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"""
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return result
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else:
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"""
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In case of an on-chain request, the result is returned in the format:
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{
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"raw_input": str,
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"processed_input": str,
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"raw_output": str,
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"processed_output": str,
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"proof": str,
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}
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refer to: https://docs.ritual.net/infernet/node/containers for more info.
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"""
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predictions = cast(List[List[List[float]]], result)
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predictions_normalized = [int(p * 1e6) for p in predictions[0][0]]
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return {
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"raw_input": "",
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"processed_input": "",
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"raw_output": encode(["uint256[]"], [predictions_normalized]).hex(),
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"processed_output": "",
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"proof": "",
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}
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match infernet_input:
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case InfernetInput(destination=JobLocation.OFFCHAIN):
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"""
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In case of an off-chain request, the result is returned as is.
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"""
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return result
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case InfernetInput(destination=JobLocation.ONCHAIN):
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"""
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In case of an on-chain request, the result is returned in the format:
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{
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"raw_input": str,
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"processed_input": str,
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"raw_output": str,
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"processed_output": str,
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"proof": str,
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}
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refer to: https://docs.ritual.net/infernet/node/containers for more
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info.
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"""
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predictions = result[0]
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predictions_normalized = [int(p * 1e6) for p in predictions.values]
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return {
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"raw_input": "",
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"processed_input": "",
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"raw_output": encode(["uint256[]"], [predictions_normalized]).hex(),
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"processed_output": "",
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"proof": "",
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}
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case _:
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raise ValueError("Invalid destination")
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return app
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@ -1,7 +1,4 @@
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quart==0.19.4
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infernet_ml==0.1.0
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PyArweave @ git+https://github.com/ritual-net/pyarweave.git
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infernet-ml==1.0.0
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infernet-ml[onnx_inference]==1.0.0
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web3==6.15.0
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onnx==1.15.0
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onnxruntime==1.16.3
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torch==2.1.2
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