feat: publishing infernet-container-starter v0.2.0
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projects/torch-iris/torch-iris.md
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# Running a Torch Model on Infernet
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Welcome to this comprehensive guide where we'll explore how to run a `pytorch` model on Infernet. If you've followed
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our ONNX example, you'll find this guide to be quite similar.
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**Model:** This example uses a pre-trained model to classify iris flowers. The code for the model
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is located at the [simple-ml-models](https://github.com/ritual-net/simple-ml-models/tree/main/iris_classification)
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repository.
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## Pre-requisites
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For this tutorial you'll need to have the following installed.
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1. [Docker](https://docs.docker.com/engine/install/)
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2. [Foundry](https://book.getfoundry.sh/getting-started/installation)
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### Ensure `docker` & `foundry` exist
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To check for `docker`, run the following command in your terminal:
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```bash copy
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docker --version
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# Docker version 25.0.2, build 29cf629 (example output)
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```
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You'll also need to ensure that docker-compose exists in your terminal:
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```bash copy
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which docker-compose
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# /usr/local/bin/docker-compose (example output)
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```
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To check for `foundry`, run the following command in your terminal:
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```bash copy
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forge --version
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# forge 0.2.0 (551bcb5 2024-02-28T07:40:42.782478000Z) (example output)
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```
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### Clone the starter repository
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If you haven't already, clone the infernet-container-starter repository. All of the code for this tutorial is located
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under the `projects/torch-iris` directory.
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```bash copy
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# Clone locally
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git clone --recurse-submodules https://github.com/ritual-net/infernet-container-starter
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# Navigate to the repository
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cd infernet-container-starter
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```
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### Build the `torch-iris` container
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From the top-level directory of this repository, simply run the following command to build the `torch-iris` container:
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```bash copy
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make build-container project=torch-iris
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```
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After the container is built, you can deploy an infernet-node that utilizes that
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container by running the following command:
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```bash
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make deploy-container project=torch-iris
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```
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## Making Inference Requests via Node API (a la Web2 request)
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Now, you can make inference requests to the infernet-node. In a new tab, run:
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```bash
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curl -X POST "http://127.0.0.1:4000/api/jobs" \
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-H "Content-Type: application/json" \
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-d '{"containers":["torch-iris"], "data": {"input": [[1.0380048, 0.5586108, 1.1037828, 1.712096]]}}'
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```
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You should get an output similar to the following:
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```json
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{
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"id": "6d5e47f0-5907-4ab2-9523-862dccb80d67"
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}
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```
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Now, you can check the status of the job by running (make sure job id matches the one
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you got from the previous request):
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```bash
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curl "http://127.0.0.1:4000/api/jobs?id=6d5e47f0-5907-4ab2-9523-862dccb80d67"
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```
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Should return:
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```json
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[
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{
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"id": "6d5e47f0-5907-4ab2-9523-862dccb80d67",
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"result": {
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"container": "torch-iris",
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"output": {
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"input_data": [
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[
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1.038004755973816,
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0.5586107969284058,
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1.1037827730178833,
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1.7120959758758545
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]
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],
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"input_shapes": [
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[
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4
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]
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],
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"output_data": [
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[
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0.0016699483385309577,
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0.021144982427358627,
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0.977185070514679
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]
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]
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}
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},
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"status": "success"
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}
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]
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```
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#### Note Regarding the Input
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The inputs provided above correspond to an iris flower with the following
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characteristics. Refer to the
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1. Sepal Length: `5.5cm`
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2. Sepal Width: `2.4cm`
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3. Petal Length: `3.8cm`
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4. Petal Width: `1.1cm`
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Putting this input into a vector and scaling it, we get the following scaled input:
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```python
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[1.0380048, 0.5586108, 1.1037828, 1.712096]
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```
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Refer
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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)
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for more information on how the input is scaled.
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For more context on the Iris dataset, refer to
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the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris).
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## Making Inference Requests via Contracts (a la Web3 request)
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The [contracts](contracts) directory contains a simple forge
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project that can be used to interact with the Infernet Node.
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Here, we have a very simple
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contract, [IrisClassifier](contracts/src/IrisClassifier.sol),
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that requests a compute job from the Infernet Node and then retrieves the result.
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We are going to make the same request as above, but this time using a smart contract.
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Since floats are not supported in Solidity, we convert all floats to `uint256` by
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multiplying the input vector entries by `1e6`:
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```solidity
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uint256[] memory iris_data = new uint256[](4);
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iris_data[0] = 1_038_004;
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iris_data[1] = 558_610;
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iris_data[2] = 1_103_782;
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iris_data[3] = 1_712_096;
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```
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We have multiplied the input by 1e6 to have enough decimals accuracy. This can be seen
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[here](contracts/src/IrisClassifier.sol#19) in the contract's
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code.
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### Infernet's Anvil Testnet
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To request an on-chain job, you'll need to deploy contracts using the infernet sdk.
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We already have a public [anvil node](https://hub.docker.com/r/ritualnetwork/infernet-anvil) docker image which has the
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corresponding infernet sdk contracts deployed, along with a node that has
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registered itself to listen to on-chain subscription events.
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* Coordinator Address: `0x5FbDB2315678afecb367f032d93F642f64180aa3`
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* Node Address: `0x70997970C51812dc3A010C7d01b50e0d17dc79C8` (This is the second account in the anvil's accounts.)
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### Monitoring the EVM Logs
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The infernet node configuration for this project includes our anvil node. You can monitor the logs of the anvil node to
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see what's going on. In a new terminal, run:
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```bash
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docker logs -f anvil-node
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```
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As you deploy the contract and make requests, you should see logs indicating the
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requests and responses.
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### Deploying the Contract
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Simply run the following command to deploy the contract:
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```bash
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project=torch-iris make deploy-contracts
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```
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In your anvil logs you should see the following:
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```bash
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eth_feeHistory
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eth_sendRawTransaction
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eth_getTransactionReceipt
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Transaction: 0x8e7e96d0a062285ee6fea864c43c29af65b962d260955e6284ab79dae145b32c
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Contract created: 0x663f3ad617193148711d28f5334ee4ed07016602
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Gas used: 725947
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Block Number: 1
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Block Hash: 0x88c1a1af024cca6f921284bd61663b1d500aa6d22d06571f0a085c2d8e1ffe92
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Block Time: "Mon, 19 Feb 2024 16:44:00 +0000"
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eth_blockNumber
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eth_newFilter
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eth_getFilterLogs
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eth_blockNumber
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```
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beautiful, we can see that a new contract has been created
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at `0x663f3ad617193148711d28f5334ee4ed07016602`. That's the address of
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the `IrisClassifier` contract. We are now going to call this contract. To do so,
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we are using
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the [CallContract.s.sol](contracts/script/CallContract.s.sol)
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script. Note that the address of the
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contract [is hardcoded in the script](contracts/script/CallContract.s.sol#L13),
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and should match the address we see above. Since this is a test environment and we're
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using a test deployer address, this address is quite deterministic and shouldn't change.
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Otherwise, change the address in the script to match the address of the contract you
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just deployed.
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### Calling the Contract
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To call the contract, run the following command:
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```bash
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project=torch-iris make call-contract
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```
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In the anvil logs, you should see the following:
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```bash
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eth_sendRawTransaction
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_____ _____ _______ _ _ _
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| __ \|_ _|__ __| | | | /\ | |
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| |__) | | | | | | | | | / \ | |
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| _ / | | | | | | | |/ /\ \ | |
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| | \ \ _| |_ | | | |__| / ____ \| |____
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|_| \_\_____| |_| \____/_/ \_\______|
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about to decode babyyy
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predictions: (adjusted by 6 decimals, 1_000_000 = 100%, 1_000 = 0.1%)
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Setosa: 1669
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Versicolor: 21144
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Virginica: 977185
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Transaction: 0x252158ab9dd2178b6a11e417090988782861d208d8e9bb01c4e0635316fd95c9
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Gas used: 111762
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Block Number: 3
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Block Hash: 0xfba07bd65da8dde644ba07ff67f0d79ed36f388760f27dcf02d96f7912d34c4c
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Block Time: "Mon, 19 Feb 2024 16:54:07 +0000"
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eth_blockNumbereth_blockNumber
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eth_blockNumber
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```
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Beautiful! We can see that the same result has been posted to the contract.
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For more information about the container, consult
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the [container's readme.](container/README.md)
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### Next Steps
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From here, you can bring your own trained pytorch model, and with minimal changes, you can make it both work with an
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infernet-node as well as a smart contract.
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### More Information
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1. Check out our [ONNX example](../onnx-iris/onnx-iris.md) if you haven't already.
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2. [Infernet Callback Consumer Tutorial](https://docs.ritual.net/infernet/sdk/consumers/Callback)
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3. [Infernet Nodes Docoumentation](https://docs.ritual.net/infernet/node/introduction)
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4. [Infernet-Compatible Containers](https://docs.ritual.net/infernet/node/containers)
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