# Getting started with ONNX optimization

In this section, we will learn about the 4 main steps needed to optimize your ONNX models:

- Input your model and data
- Run the optimization
- Save your optimized model
- Load and run your optimized model in production

## 1) Input model and data

Info

In order to optimize a model with `Speedster`

, first you should input the model you want to optimize and load some sample data that will be needed to test the optimization performances (latency, throughput, accuracy loss, etc).

```
import numpy as np
# Load a resnet as example
# Model was downloaded from here:
# https://github.com/onnx/models/tree/main/vision/classification/resnet
model = "resnet50-v1-12.onnx"
# Provide input data for the model
input_data = [((np.random.randn(1, 3, 224, 224).astype(np.float32), ), np.array([0])) for _ in range(100)]
```

Now your input model and data are ready, you can move on to Run the optimization section ๐.

## 2) Run the optimization

Once the `model`

and `input_data`

have been defined, everything is ready to use Speedster's `optimize_model`

function to optimize your model.

The function takes the following arguments as inputs:

`model`

: model to be optimized in your preferred framework (ONNX in this case)`input_data`

: sample data needed to test the optimization performances (latency, throughput, accuracy loss, etc)`optimization_time`

: if "constrained" mode,`Speedster`

takes advantage only of compilers and precision reduction techniques, such as quantization. "unconstrained" optimization_time allows it to exploit more time-consuming techniques, such as pruning and distillation`metric_drop_ths`

: maximum drop in your preferred accuracy metric that you are willing to trade to gain in acceleration

and returns the accelerated version of your model ๐.

```
from speedster import optimize_model
# Run Speedster optimization
optimized_model = optimize_model(
model,
input_data=input_data,
optimization_time="constrained",
metric_drop_ths=0.05
)
```

Internally, `Speedster`

tries to use all the compilers and optimization techniques at its disposal along the software to hardware stack to optimize the model. From these, it will choose the ones with the lowest latency on the specific hardware.

At the end of the optimization, you are going to see the results in a summary table like the following:

If the speedup you obtained is good enough for your application, you can move to the Save your optimized model section to save your model and use it in production.

If you want to squeeze out even more acceleration out of the model, please see the `optimize_model`

API section. Consider if in your application you can trade off a little accuracy for much higher performance and use the `metric`

, `metric_drop_ths`

and `optimization_time`

arguments accordingly.

## 3) Save your optimized model

After accelerating the model, it can be saved using the `save_model`

function:

Now you are all set to use your optimized model in production. To explore how to do it, see the Load and run your optimized model in production section.

## 4) Load and run your optimized model in production

Once the optimized model has been saved, it can be loaded with the `load_model`

function:

The optimized model can be used for accelerated inference in the same way as the original model:

```
# Use the accelerated version of your ONNX model in production
output = optimized_model(input_sample)
```

Info

The first 1-2 inferences could be a bit slower than expected because some compilers still perform some optimizations during the first iterations. After this warm-up time, the next ones will be faster than ever.

If you want to know more about how to squeeze out more performances from your models, please visit the Advanced options section.