# Requirements for NumPy 1.x
numpy ~= 1.26.0
wheel ~= 0.41.2
h5py >= 3.11.0, < 3.15.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

A. Unique TensorFlower <******@****.***> 1773148640 -0700

    srcs = [
        "nvidia-requirements.txt",
        "requirements.in",
        ":nvidia_constraints",
    ],
    extra_args = [
        "--allow-unsafe",
        "--build-isolation",
        "--rebuild",
        "-c $(location :nvidia_constraints)",
    ],
    generate_hashes = True,
    requirements_txt = REQUIREMENTS,

///

## Dependencies errors and return values { #dependencies-errors-and-return-values }

You can use the same dependency *functions* you use normally.

### Dependency requirements { #dependency-requirements }

They can declare request requirements (like headers) or other sub-dependencies:

{* ../../docs_src/dependencies/tutorial006_an_py310.py hl[8,13] *}

### Raise exceptions { #raise-exceptions }

* Using a cloud service that would run a container image for you, etc.

### Package Requirements { #package-requirements }

You would normally have the **package requirements** for your application in some file.

It would depend mainly on the tool you use to **install** those requirements.

The most common way to do it is to have a file `requirements.txt` with the package names and their versions, one per line.

All in a way that provided the best development experience for all the developers.

## Requirements { #requirements }

After testing several alternatives, I decided that I was going to use [**Pydantic**](https://docs.pydantic.dev/) for its advantages.

This is very useful when you need to:

* Have shared logic (the same code logic again and again).
* Share database connections.
* Enforce security, authentication, role requirements, etc.
* And many other things...

All these, while minimizing code repetition.

## First Steps { #first-steps }

Let's see a very simple example. It will be so simple that it is not very useful, for now.

        }
    }

    triggers {
        vcs {
            branchFilter = ""
            enabled = VersionedSettingsBranch.fromDslContext().isMainBranch
        }
    }

    requirements {
        requiresOs(Os.LINUX)
    }

    }
    TesterRequirements requirements;
    try {
      requirements = FeatureUtil.getTesterRequirements(method);
    } catch (ConflictingRequirementsException e) {
      throw new RuntimeException(e);
    }
    if (!features.containsAll(requirements.getPresentFeatures())) {
      if (logger.isLoggable(FINER)) {
        Set<Feature<?>> missingFeatures = copyToSet(requirements.getPresentFeatures());

# Full Stack FastAPI Template { #full-stack-fastapi-template }

Templates, while typically come with a specific setup, are designed to be flexible and customizable. This allows you to modify and adapt them to your project's requirements, making them an excellent starting point. 🏁

You can use this template to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you.