ritual/projects/onnx-iris/onnx-iris.md
2024-06-06 13:18:48 -04:00

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# Running an ONNX Model on Infernet
Welcome to this comprehensive guide where we'll explore how to run an ONNX model on Infernet, using our [infernet-container-starter](https://github.com/ritual-net/infernet-container-starter/)
examples repository. This tutorial is designed to give you and end-to-end understanding of how you can run your own
custom pre-trained models, and interact with them on-chain and off-chain.
**Model:** This example uses a pre-trained model to classify iris flowers. The code for the model
is located at our [`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/onnx-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
```
## Making Inference Requests via Node API (a la Web2 request)
### Build the `onnx-iris` container
From the top-level directory of this repository, simply run the following command to build the `onnx-iris` container:
```bash copy
make build-container project=onnx-iris
```
After the container is built, you can deploy an infernet-node that utilizes that
container by running the following command:
```bash copy
make deploy-container project=onnx-iris
```
Now, you can make inference requests to the infernet-node. In a new tab, run:
```bash copy
curl -X POST "http://127.0.0.1:4000/api/jobs" \
-H "Content-Type: application/json" \
-d '{"containers":["onnx-iris"], "data": {"input": [[1.0380048, 0.5586108, 1.1037828, 1.712096]]}}'
```
You should get an output similar to the following:
```json
{
"id": "074b9e98-f1f6-463c-b185-651878f3b4f6"
}
```
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 -X GET "http://127.0.0.1:4000/api/jobs?id=074b9e98-f1f6-463c-b185-651878f3b4f6"
```
Should return:
```json
[
{
"id": "074b9e98-f1f6-463c-b185-651878f3b4f6",
"result": {
"container": "onnx-iris",
"output": [
[
[
0.0010151526657864451,
0.014391022734344006,
0.9845937490463257
]
]
]
},
"status": "success"
}
]
```
The `output` corresponds to the model's prediction for each of the classes:
```python
['setosa', 'versicolor', 'virginica']
```
In this case, the model predicts that the input corresponds to the class `virginica`with
a probability of `0.9845937490463257`(~98.5%).
#### 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 accuracy. This can be seen
[here](contracts/src/IrisClassifier.sol#19) in the contract's
code.
### Monitoring the EVM Logs
The infernet node configuration for this project includes
an [infernet anvil node](projects/hello-world/README.mdllo-world/README.md#77) with pre-deployed contracts. You can view 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=onnx-iris make deploy-contracts
```
In your anvil logs you should see the following:
```bash
eth_getTransactionReceipt
Transaction: 0xeed605eacdace39a48635f6d14215b386523766f80a113b4484f542d862889a4
Contract created: 0x13D69Cf7d6CE4218F646B759Dcf334D82c023d8e
Gas used: 714269
Block Number: 1
Block Hash: 0x4e6333f91e86a0a0be357b63fba9eb5f5ba287805ac35aaa7698fd05445730f5
Block Time: "Mon, 19 Feb 2024 20:31:17 +0000"
eth_blockNumber
```
beautiful, we can see that a new contract has been created
at `0x663F3ad617193148711d28f5334eE4Ed07016602`. 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=onnx-iris make call-contract
```
In the anvil logs, you should see the following:
```bash
eth_sendRawTransaction
_____ _____ _______ _ _ _
| __ \|_ _|__ __| | | | /\ | |
| |__) | | | | | | | | | / \ | |
| _ / | | | | | | | |/ /\ \ | |
| | \ \ _| |_ | | | |__| / ____ \| |____
|_| \_\_____| |_| \____/_/ \_\______|
predictions: (adjusted by 6 decimals, 1_000_000 = 100%, 1_000 = 0.1%)
Setosa: 1015
Versicolor: 14391
Virginica: 984593
Transaction: 0x77c7ff26ed20ffb1a32baf467a3cead6ed81fe5ae7d2e419491ca92b4ac826f0
Gas used: 111091
Block Number: 3
Block Hash: 0x78f98f4d54ebdca2a8aa46c3b9b7e7ae36348373dbeb83c91a4600dd6aba2c55
Block Time: "Mon, 19 Feb 2024 20:33:00 +0000"
eth_blockNumber
eth_newFilter
eth_getFilterLogs
```
Beautiful! We can see that the same result has been posted to the contract.
### Next Steps
From here, you can bring your own pre-trained ONNX 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 [other examples](../../readme.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)