feat: publishing infernet-container-starter v0.2.0

projects/onnx-iris/contracts/.gitignore

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+# Compiler files
+cache/
+out/
+
+# Ignores development broadcast logs
+!/broadcast
+/broadcast/*/31337/
+/broadcast/**/dry-run/
+
+# Docs
+docs/
+
+# Dotenv file
+.env

projects/onnx-iris/contracts/Makefile

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+# phony targets are targets that don't actually create a file
+.phony: deploy
+
+# anvil's third default address
+sender := 0x5de4111afa1a4b94908f83103eb1f1706367c2e68ca870fc3fb9a804cdab365a
+RPC_URL := http://localhost:8545
+
+# deploying the contract
+deploy:
+	@PRIVATE_KEY=$(sender) forge script script/Deploy.s.sol:Deploy --broadcast --rpc-url $(RPC_URL)
+
+# calling sayGM()
+call-contract:
+	@PRIVATE_KEY=$(sender) forge script script/CallContract.s.sol:CallContract --broadcast --rpc-url $(RPC_URL)

projects/onnx-iris/contracts/README.md

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+# `ONNX` Consumer Contracts
+
+This is a [foundry](https://book.getfoundry.sh/) project that implements a simple Consumer
+contract, [`IrisClassifier`](./src/IrisClassifier.sol).
+
+This readme explains how to compile and deploy the contract to the Infernet Anvil Testnet network.
+
+> [!IMPORTANT]
+> Ensure that you are running the following scripts with the Infernet Anvil Testnet network.
+> The [tutorial](../../hello-world/README.mdADME.md) at the root of this repository explains how to
+> bring up an infernet node.
+
+### Installing the libraries
+
+```bash
+forge install
+```
+
+### Compiling the contracts
+
+```bash
+forge compile
+```
+
+### Deploying the contracts
+The deploy script at `script/Deploy.s.sol` deploys the `IrisClassifier` contract to the Infernet Anvil Testnet network.
+
+We have the [following make target](./Makefile#L9) to deploy the contract. Refer to the Makefile
+for more understanding around the deploy scripts.
+```bash
+make deploy
+```
+
+### Requesting a job
+We also have a script called `CallContract.s.sol` that requests a job to the `IrisClassifier` contract.
+Refer to the [script](./script/CallContract.s.sol) for more details. Similar to deployment,
+you can run that script using the following convenience make target.
+```bash
+make call-contract
+```
+Refer to the [Makefile](./Makefile#L14) for more details.

projects/onnx-iris/contracts/foundry.toml

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+[profile.default]
+src = "src"
+out = "out"
+libs = ["lib"]
+via_ir = true
+
+# See more config options https://github.com/foundry-rs/foundry/blob/master/crates/config/README.md#all-options

projects/onnx-iris/contracts/remappings.txt

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+forge-std/=lib/forge-std/src
+infernet-sdk/=lib/infernet-sdk/src

projects/onnx-iris/contracts/script/CallContract.s.sol

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+// SPDX-License-Identifier: BSD-3-Clause-Clear
+pragma solidity ^0.8.0;
+
+import {Script, console2} from "forge-std/Script.sol";
+import {IrisClassifier} from "../src/IrisClassifier.sol";
+
+contract CallContract is Script {
+    function run() public {
+        // Setup wallet
+        uint256 deployerPrivateKey = vm.envUint("PRIVATE_KEY");
+        vm.startBroadcast(deployerPrivateKey);
+
+        IrisClassifier irisClassifier = IrisClassifier(0x663F3ad617193148711d28f5334eE4Ed07016602);
+
+        irisClassifier.classifyIris();
+
+        vm.stopBroadcast();
+    }
+}

projects/onnx-iris/contracts/script/Deploy.s.sol

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+// SPDX-License-Identifier: BSD-3-Clause-Clear
+pragma solidity ^0.8.13;
+
+import {Script, console2} from "forge-std/Script.sol";
+import {IrisClassifier} from "../src/IrisClassifier.sol";
+
+contract Deploy is Script {
+    function run() public {
+        // Setup wallet
+        uint256 deployerPrivateKey = vm.envUint("PRIVATE_KEY");
+        vm.startBroadcast(deployerPrivateKey);
+
+        // Log address
+        address deployerAddress = vm.addr(deployerPrivateKey);
+        console2.log("Loaded deployer: ", deployerAddress);
+
+        address coordinator = 0x5FbDB2315678afecb367f032d93F642f64180aa3;
+        // Create consumer
+        IrisClassifier classifier = new IrisClassifier(coordinator);
+        console2.log("Deployed IrisClassifier: ", address(classifier));
+
+        // Execute
+        vm.stopBroadcast();
+        vm.broadcast();
+    }
+}

projects/onnx-iris/contracts/src/IrisClassifier.sol

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+// SPDX-License-Identifier: BSD-3-Clause-Clear
+pragma solidity ^0.8.13;
+
+import {console2} from "forge-std/console2.sol";
+import {CallbackConsumer} from "infernet-sdk/consumer/Callback.sol";
+
+
+contract IrisClassifier is CallbackConsumer {
+    string private EXTREMELY_COOL_BANNER = "\n\n"
+    "_____  _____ _______ _    _         _\n"
+    "|  __ \\|_   _|__   __| |  | |  /\\   | |\n"
+    "| |__) | | |    | |  | |  | | /  \\  | |\n"
+    "|  _  /  | |    | |  | |  | |/ /\\ \\ | |\n"
+    "| | \\ \\ _| |_   | |  | |__| / ____ \\| |____\n"
+    "|_|  \\_\\_____|  |_|   \\____/_/    \\_\\______|\n\n";
+
+    constructor(address coordinator) CallbackConsumer(coordinator) {}
+
+    function classifyIris() public {
+        /// @dev Iris data is in the following format:
+        /// @dev [sepal_length, sepal_width, petal_length, petal_width]
+        /// @dev the following vector corresponds to the following properties:
+        ///     "sepal_length": 5.5cm
+        ///     "sepal_width": 2.4cm
+        ///     "petal_length": 3.8cm
+        ///     "petal_width": 1.1cm
+        /// @dev The data is normalized & scaled.
+        /// refer to [this function in the model's repository](https://github.com/ritual-net/simple-ml-models/blob/03ebc6fb15d33efe20b7782505b1a65ce3975222/iris_classification/iris_inference_pytorch.py#L13)
+        /// for more info on normalization.
+        /// @dev The data is adjusted by 6 decimals
+
+        uint256[] memory iris_data = new uint256[](4);
+        iris_data[0] = 1_038_004;
+        iris_data[1] = 558_610;
+        iris_data[2] = 1_103_782;
+        iris_data[3] = 1_712_096;
+
+        _requestCompute(
+            "onnx-iris",
+            abi.encode(iris_data),
+            20 gwei,
+            1_000_000,
+            1
+        );
+    }
+
+    function _receiveCompute(
+        uint32 subscriptionId,
+        uint32 interval,
+        uint16 redundancy,
+        address node,
+        bytes calldata input,
+        bytes calldata output,
+        bytes calldata proof
+    ) internal override {
+        console2.log(EXTREMELY_COOL_BANNER);
+        (bytes memory raw_output, bytes memory processed_output) = abi.decode(output, (bytes, bytes));
+        (uint256[] memory classes) = abi.decode(raw_output, (uint256[]));
+        uint256 setosa = classes[0];
+        uint256 versicolor = classes[1];
+        uint256 virginica = classes[2];
+        console2.log("predictions: (adjusted by 6 decimals, 1_000_000 = 100%, 1_000 = 0.1%)");
+        console2.log("Setosa: ", setosa);
+        console2.log("Versicolor: ", versicolor);
+        console2.log("Virginica: ", virginica);
+    }
+}