name: Mint Tests

on:
  pull_request:
    branches:
      - master

# This ensures that previous jobs for the PR are canceled when the PR is
# updated.
concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref }}
  cancel-in-progress: true

permissions:
  contents: read

jobs:
  mint-test:
    runs-on: mint
    timeout-minutes: 120
    steps:

export JOB_NAME="$4"
export MINT_MODE="full"

docker system prune -f || true
docker volume prune -f || true
docker volume rm $(docker volume ls -f dangling=true) || true

## change working directory
cd .github/workflows/mint

docker-compose -f minio-${MODE}.yaml up -d
sleep 1m

docker system prune -f || true
docker volume prune -f || true
docker volume rm $(docker volume ls -q -f dangling=true) || true

Carlos Amedee <******@****.***> 1715265901 -0400

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

In many cases you might already know the answer for those questions. 🤓

If you are helping a lot of people with their questions, you will become an official [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}. 🎉

func.func @fakeQuantFoldedWithCast() -> (tensor<8xf32>) {
  %in = arith.constant dense<0.0> : tensor<8xf32>
  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<255.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>

func.func @fakeQuantFoldedWithCast() -> (tensor<8xf32>) {
  %in = arith.constant dense<0.0> : tensor<8xf32>
  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>

		_ = min(1)
		_ = min(1, 2.3, 'a')
		_ = min(1, "foo" /* ERROR "mismatched types" */ )
		_ = min(1, 0i /* ERROR "cannot be ordered" */ )
		_ = min(1, 2 /* ERROR "cannot be ordered" */ + 3i )
	)
}

func min2() {
	_ = assert(min(0) == 0)
	_ = assert(min(0, 1) == 0)
	_ = assert(min(0, -10, 123456789) == -10)
	_ = assert(min(-12345678901234567890, 0) == -12345678901234567890)

	_ = assert(min(1, 2.3) == 1)

---

"_Honestly, what you've built looks super solid and polished. In many ways, it's what I wanted **Hug** to be - it's really inspiring to see someone build that._"

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.