# Running a Torch Model on Infernet Welcome to this comprehensive guide where we'll explore how to run a `pytorch` model on Infernet. If you've followed our ONNX example, you'll find this guide to be quite similar. **Model:** This example uses a pre-trained model to classify iris flowers. The code for the model is located at the [simple-ml-models](https://github.com/ritual-net/simple-ml-models/tree/main/iris_classification) repository. ## Pre-requisites For this tutorial you'll need to have the following installed. 1. [Docker](https://docs.docker.com/engine/install/) 2. [Foundry](https://book.getfoundry.sh/getting-started/installation) ### Ensure `docker` & `foundry` exist To check for `docker`, run the following command in your terminal: ```bash copy docker --version # Docker version 25.0.2, build 29cf629 (example output) ``` You'll also need to ensure that docker-compose exists in your terminal: ```bash copy which docker-compose # /usr/local/bin/docker-compose (example output) ``` To check for `foundry`, run the following command in your terminal: ```bash copy forge --version # forge 0.2.0 (551bcb5 2024-02-28T07:40:42.782478000Z) (example output) ``` ### Clone the starter repository If you haven't already, clone the infernet-container-starter repository. All of the code for this tutorial is located under the `projects/torch-iris` directory. ```bash copy # Clone locally git clone --recurse-submodules https://github.com/ritual-net/infernet-container-starter # Navigate to the repository cd infernet-container-starter ``` ### Build the `torch-iris` container From the top-level directory of this repository, simply run the following command to build the `torch-iris` container: ```bash copy make build-container project=torch-iris ``` After the container is built, you can deploy an infernet-node that utilizes that container by running the following command: ```bash make deploy-container project=torch-iris ``` ## Making Inference Requests via Node API (a la Web2 request) Now, you can make inference requests to the infernet-node. In a new tab, run: ```bash curl -X POST "http://127.0.0.1:4000/api/jobs" \ -H "Content-Type: application/json" \ -d '{"containers":["torch-iris"], "data": {"input": [[1.0380048, 0.5586108, 1.1037828, 1.712096]]}}' ``` You should get an output similar to the following: ```json { "id": "6d5e47f0-5907-4ab2-9523-862dccb80d67" } ``` Now, you can check the status of the job by running (make sure job id matches the one you got from the previous request): ```bash curl "http://127.0.0.1:4000/api/jobs?id=6d5e47f0-5907-4ab2-9523-862dccb80d67" ``` Should return: ```json [ { "id": "6d5e47f0-5907-4ab2-9523-862dccb80d67", "result": { "container": "torch-iris", "output": { "input_data": [ [ 1.038004755973816, 0.5586107969284058, 1.1037827730178833, 1.7120959758758545 ] ], "input_shapes": [ [ 4 ] ], "output_data": [ [ 0.0016699483385309577, 0.021144982427358627, 0.977185070514679 ] ] } }, "status": "success" } ] ``` #### Note Regarding the Input The inputs provided above correspond to an iris flower with the following characteristics. Refer to the 1. Sepal Length: `5.5cm` 2. Sepal Width: `2.4cm` 3. Petal Length: `3.8cm` 4. Petal Width: `1.1cm` Putting this input into a vector and scaling it, we get the following scaled input: ```python [1.0380048, 0.5586108, 1.1037828, 1.712096] ``` 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 information on how the input is scaled. For more context on the Iris dataset, refer to the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris). ## Making Inference Requests via Contracts (a la Web3 request) The [contracts](contracts) directory contains a simple forge project that can be used to interact with the Infernet Node. Here, we have a very simple contract, [IrisClassifier](contracts/src/IrisClassifier.sol), that requests a compute job from the Infernet Node and then retrieves the result. We are going to make the same request as above, but this time using a smart contract. Since floats are not supported in Solidity, we convert all floats to `uint256` by multiplying the input vector entries by `1e6`: ```solidity 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; ``` We have multiplied the input by 1e6 to have enough decimals accuracy. This can be seen [here](contracts/src/IrisClassifier.sol#19) in the contract's code. ### Infernet's Anvil Testnet To request an on-chain job, you'll need to deploy contracts using the infernet sdk. We already have a public [anvil node](https://hub.docker.com/r/ritualnetwork/infernet-anvil) docker image which has the corresponding infernet sdk contracts deployed, along with a node that has registered itself to listen to on-chain subscription events. * Registry Address: `0x663F3ad617193148711d28f5334eE4Ed07016602` * Node Address: `0x70997970C51812dc3A010C7d01b50e0d17dc79C8` (This is the second account in the anvil's accounts.) ### Monitoring the EVM Logs The infernet node configuration for this project includes our anvil node. You can monitor the logs of the anvil node to see what's going on. In a new terminal, run: ```bash docker logs -f anvil-node ``` As you deploy the contract and make requests, you should see logs indicating the requests and responses. ### Deploying the Contract Simply run the following command to deploy the contract: ```bash project=torch-iris make deploy-contracts ``` In your anvil logs you should see the following: ```bash eth_feeHistory eth_sendRawTransaction eth_getTransactionReceipt Transaction: 0x8e7e96d0a062285ee6fea864c43c29af65b962d260955e6284ab79dae145b32c Contract created: 0x13D69Cf7d6CE4218F646B759Dcf334D82c023d8e Gas used: 725947 Block Number: 1 Block Hash: 0x88c1a1af024cca6f921284bd61663b1d500aa6d22d06571f0a085c2d8e1ffe92 Block Time: "Mon, 19 Feb 2024 16:44:00 +0000" eth_blockNumber eth_newFilter eth_getFilterLogs eth_blockNumber ``` beautiful, we can see that a new contract has been created at `0x13D69Cf7d6CE4218F646B759Dcf334D82c023d8e`. That's the address of the `IrisClassifier` contract. We are now going to call this contract. To do so, we are using the [CallContract.s.sol](contracts/script/CallContract.s.sol) script. Note that the address of the contract [is hardcoded in the script](contracts/script/CallContract.s.sol#L13), and should match the address we see above. Since this is a test environment and we're using a test deployer address, this address is quite deterministic and shouldn't change. Otherwise, change the address in the script to match the address of the contract you just deployed. ### Calling the Contract To call the contract, run the following command: ```bash project=torch-iris make call-contract ``` In the anvil logs, you should see the following: ```bash eth_sendRawTransaction _____ _____ _______ _ _ _ | __ \|_ _|__ __| | | | /\ | | | |__) | | | | | | | | | / \ | | | _ / | | | | | | | |/ /\ \ | | | | \ \ _| |_ | | | |__| / ____ \| |____ |_| \_\_____| |_| \____/_/ \_\______| about to decode babyyy predictions: (adjusted by 6 decimals, 1_000_000 = 100%, 1_000 = 0.1%) Setosa: 1669 Versicolor: 21144 Virginica: 977185 Transaction: 0x252158ab9dd2178b6a11e417090988782861d208d8e9bb01c4e0635316fd95c9 Gas used: 111762 Block Number: 3 Block Hash: 0xfba07bd65da8dde644ba07ff67f0d79ed36f388760f27dcf02d96f7912d34c4c Block Time: "Mon, 19 Feb 2024 16:54:07 +0000" eth_blockNumbereth_blockNumber eth_blockNumber ``` Beautiful! We can see that the same result has been posted to the contract. For more information about the container, consult the [container's readme.](container/README.md) ### Next Steps From here, you can bring your own trained pytorch model, and with minimal changes, you can make it both work with an infernet-node as well as a smart contract. ### More Information 1. Check out our [ONNX example](../onnx-iris/onnx-iris.md) if you haven't already. 2. [Infernet Callback Consumer Tutorial](https://docs.ritual.net/infernet/sdk/consumers/Callback) 3. [Infernet Nodes Docoumentation](https://docs.ritual.net/infernet/node/introduction) 4. [Infernet-Compatible Containers](https://docs.ritual.net/infernet/node/containers)