infernet-1.0.0 update
This commit is contained in:
arshan-ritual
@ -1,11 +1,16 @@
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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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from eth_abi import decode, encode # type: ignore
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from infernet_ml.utils.model_loader import (
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HFLoadArgs,
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)
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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.service_models import InfernetInput, JobLocation
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from infernet_ml.workflows.inference.torch_inference_workflow import (
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TorchInferenceWorkflow,
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TorchInferenceInput,
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)
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from quart import Quart, request
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@ -21,10 +26,10 @@ 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.torch"}
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workflow = TorchInferenceWorkflow(model_source=model_source, model_args=model_args)
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workflow = TorchInferenceWorkflow(
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model_source=ModelSource.HUGGINGFACE_HUB,
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load_args=HFLoadArgs(repo_id="Ritual-Net/iris-dataset", filename="iris.torch"),
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)
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workflow.setup()
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@app.route("/")
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@ -46,16 +51,17 @@ 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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case _:
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raise ValueError("Invalid source")
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"""
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The input to the torch inference workflow needs to conform to this format:
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@ -66,39 +72,52 @@ def create_app() -> Quart:
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}
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For more information refer to:
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https://docs.ritual.net/ml-workflows/inference-workflows/torch_inference_workflow
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https://infernet-ml.docs.ritual.net/reference/infernet_ml/workflows/inference/torch_inference_workflow/?h=torch
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"""
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inference_result = workflow.inference({"dtype": "float", "values": values})
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""" # noqa: E501
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log.info("Input values: %s", values)
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result = [o.detach().numpy().reshape([-1]).tolist() for o in inference_result]
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_input = TensorInput(
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dtype="float",
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shape=(1, 4),
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values=values,
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)
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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": 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[float]], result)
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predictions_normalized = [int(p * 1e6) for p in predictions[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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iris_inference_input = TorchInferenceInput(input=_input)
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inference_result = workflow.inference(iris_inference_input)
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result = inference_result.outputs
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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": 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_normalized = [int(p * 1e6) for p in result]
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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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@ -1,6 +1,6 @@
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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[torch_inference]==1.0.0
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huggingface-hub==0.17.3
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sk2torch==1.2.0
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torch==2.1.2
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