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infernet-1.0.0 update

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This commit is contained in:
arshan-ritual
2024-06-06 13:18:48 -04:00
parent 2a11fd3953
commit 40a6c590da
98 changed files with 879 additions and 506 deletions
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5
projects/torch-iris/container/Dockerfile
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@ -7,12 +7,15 @@ ENV PYTHONDONTWRITEBYTECODE 1
ENV PIP_NO_CACHE_DIR 1
ENV RUNTIME docker
ENV PYTHONPATH src
ARG index_url
ENV UV_EXTRA_INDEX_URL ${index_url}
RUN apt-get update
RUN apt-get install -y git curl
# install uv
ADD --chmod=755 https://astral.sh/uv/install.sh /install.sh
ADD https://astral.sh/uv/install.sh /install.sh
RUN chmod 755 /install.sh
RUN /install.sh && rm /install.sh
COPY src/requirements.txt .

2
projects/torch-iris/container/Makefile
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@ -5,7 +5,7 @@ TAG := $(DOCKER_ORG)/example-$(EXAMPLE_NAME)-infernet:latest
.phony: build run
build:
@docker build -t $(TAG) .
@docker build -t $(TAG) --build-arg index_url=$(index_url) .
run:
docker run -p 3000:3000 $(TAG)

4
projects/torch-iris/container/README.md
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@ -8,9 +8,9 @@ repository.
## Overview
We're making use of
the [TorchInferenceWorkflow](https://github.com/ritual-net/infernet-ml-internal/blob/main/src/ml/workflows/inference/torch_inference_workflow.py)
the [TorchInferenceWorkflow](https://github.com/ritual-net/infernet-ml/blob/main/src/ml/workflows/inference/torch_inference_workflow.py)
class to run the model. This is one of many workflows that we currently support in our
[infernet-ml](https://github.com/ritual-net/infernet-ml-internal). Consult the library's
[infernet-ml](https://github.com/ritual-net/infernet-ml). Consult the library's
documentation for more info on workflows that
are supported.

20
projects/torch-iris/container/config.json
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@ -7,7 +7,7 @@
"enabled": true,
"trail_head_blocks": 0,
"rpc_url": "http://host.docker.internal:8545",
"coordinator_address": "0x5FbDB2315678afecb367f032d93F642f64180aa3",
"registry_address": "0x663F3ad617193148711d28f5334eE4Ed07016602",
"wallet": {
"max_gas_limit": 4000000,
"private_key": "0x59c6995e998f97a5a0044966f0945389dc9e86dae88c7a8412f4603b6b78690d"
@ -23,6 +23,10 @@
"port": 6379
},
"forward_stats": true,
"snapshot_sync": {
"sleep": 3,
"batch_size": 100
},
"containers": [
{
"id": "torch-iris",
@ -33,18 +37,8 @@
"allowed_addresses": [],
"allowed_ips": [],
"command": "--bind=0.0.0.0:3000 --workers=2",
"env": {}
},
{
"id": "anvil-node",
"image": "ritualnetwork/infernet-anvil:0.0.0",
"external": true,
"port": "8545",
"allowed_delegate_addresses": [],
"allowed_addresses": [],
"allowed_ips": [],
"command": "",
"env": {}
"env": {},
"accepted_payments": {}
}
]
}

109
projects/torch-iris/container/src/app.py
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@ -1,11 +1,16 @@
import logging
from typing import Any, cast, List
from infernet_ml.utils.common_types import TensorInput
from eth_abi import decode, encode # type: ignore
from infernet_ml.utils.model_loader import (
HFLoadArgs,
)
from infernet_ml.utils.model_loader import ModelSource
from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
from infernet_ml.utils.service_models import InfernetInput, JobLocation
from infernet_ml.workflows.inference.torch_inference_workflow import (
TorchInferenceWorkflow,
TorchInferenceInput,
)
from quart import Quart, request
@ -21,10 +26,10 @@ def create_app() -> Quart:
app = Quart(__name__)
# we are downloading the model from the hub.
# model repo is located at: https://huggingface.co/Ritual-Net/iris-dataset
model_source = ModelSource.HUGGINGFACE_HUB
model_args = {"repo_id": "Ritual-Net/iris-dataset", "filename": "iris.torch"}
workflow = TorchInferenceWorkflow(model_source=model_source, model_args=model_args)
workflow = TorchInferenceWorkflow(
model_source=ModelSource.HUGGINGFACE_HUB,
load_args=HFLoadArgs(repo_id="Ritual-Net/iris-dataset", filename="iris.torch"),
)
workflow.setup()
@app.route("/")
@ -46,16 +51,17 @@ def create_app() -> Quart:
"""
infernet_input: InfernetInput = InfernetInput(**req_data)
if infernet_input.source == InfernetInputSource.OFFCHAIN:
web2_input = cast(dict[str, Any], infernet_input.data)
values = cast(List[List[float]], web2_input["input"])
else:
# On-chain requests are sent as a generalized hex-string which we will
# decode to the appropriate format.
web3_input: List[int] = decode(
["uint256[]"], bytes.fromhex(cast(str, infernet_input.data))
)[0]
values = [[float(v) / 1e6 for v in web3_input]]
match infernet_input:
case InfernetInput(source=JobLocation.OFFCHAIN):
web2_input = cast(dict[str, Any], infernet_input.data)
values = cast(List[List[float]], web2_input["input"])
case InfernetInput(source=JobLocation.ONCHAIN):
web3_input: List[int] = decode(
["uint256[]"], bytes.fromhex(cast(str, infernet_input.data))
)[0]
values = [[float(v) / 1e6 for v in web3_input]]
case _:
raise ValueError("Invalid source")
"""
The input to the torch inference workflow needs to conform to this format:
@ -66,39 +72,52 @@ def create_app() -> Quart:
}
For more information refer to:
https://docs.ritual.net/ml-workflows/inference-workflows/torch_inference_workflow
https://infernet-ml.docs.ritual.net/reference/infernet_ml/workflows/inference/torch_inference_workflow/?h=torch
"""
inference_result = workflow.inference({"dtype": "float", "values": values})
""" # noqa: E501
log.info("Input values: %s", values)
result = [o.detach().numpy().reshape([-1]).tolist() for o in inference_result]
_input = TensorInput(
dtype="float",
shape=(1, 4),
values=values,
)
if infernet_input.source == InfernetInputSource.OFFCHAIN:
"""
In case of an off-chain request, the result is returned as is.
"""
return {"result": result}
else:
"""
In case of an on-chain request, the result is returned in the format:
{
"raw_input": str,
"processed_input": str,
"raw_output": str,
"processed_output": str,
"proof": str,
}
refer to: https://docs.ritual.net/infernet/node/containers for more info.
"""
predictions = cast(List[List[float]], result)
predictions_normalized = [int(p * 1e6) for p in predictions[0]]
return {
"raw_input": "",
"processed_input": "",
"raw_output": encode(["uint256[]"], [predictions_normalized]).hex(),
"processed_output": "",
"proof": "",
}
iris_inference_input = TorchInferenceInput(input=_input)
inference_result = workflow.inference(iris_inference_input)
result = inference_result.outputs
match infernet_input:
case InfernetInput(destination=JobLocation.OFFCHAIN):
"""
In case of an off-chain request, the result is returned as is.
"""
return {"result": result}
case InfernetInput(destination=JobLocation.ONCHAIN):
"""
In case of an on-chain request, the result is returned in the format:
{
"raw_input": str,
"processed_input": str,
"raw_output": str,
"processed_output": str,
"proof": str,
}
refer to: https://docs.ritual.net/infernet/node/containers for more
info.
"""
predictions_normalized = [int(p * 1e6) for p in result]
return {
"raw_input": "",
"processed_input": "",
"raw_output": encode(["uint256[]"], [predictions_normalized]).hex(),
"processed_output": "",
"proof": "",
}
case _:
raise ValueError("Invalid destination")
return app

4
projects/torch-iris/container/src/requirements.txt
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@ -1,6 +1,6 @@
quart==0.19.4
infernet_ml==0.1.0
PyArweave @ git+https://github.com/ritual-net/pyarweave.git
infernet-ml==1.0.0
infernet-ml[torch_inference]==1.0.0
huggingface-hub==0.17.3
sk2torch==1.2.0
torch==2.1.2