# Command Line

Usage:

	go tool asm [flags] file

The specified file must be a Go assembly file.
The same assembler is used for all target operating systems and architectures.
The GOOS and GOARCH environment variables set the desired target.

Flags:

	-D name[=value]
		Predefine symbol name with an optional simple value.
		Can be repeated to define multiple symbols.
	-I dir1 -I dir2

# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.

# Usage: buildall.bash [-e] [pattern]
#
# buildall.bash builds the standard library for all Go-supported
# architectures.
#
# Originally the Go build system used it as a smoke test to quickly
# flag portability issues in builders named "misc-compile" or "all-compile".
# As of CL 464955, the build system uses make.bash -compile-only instead,

#!/bin/bash

set -e
# Enable tracing if set.
[ -n "$BASH_XTRACEFD" ] && set -x

function _init() {
	## All binaries are static make sure to disable CGO.
	export CGO_ENABLED=0

	## List of architectures and OS to test coss compilation.
	SUPPORTED_OSARCH="linux/ppc64le linux/mips64 linux/amd64 linux/arm64 linux/s390x darwin/arm64 darwin/amd64 freebsd/amd64 windows/amd64 linux/arm linux/386 netbsd/amd64 linux/mips openbsd/amd64"
}

```Dockerfile
FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

COPY ./app /app/app
```

### Raspberry Pi et autres architectures

Si vous utilisez Docker sur un Raspberry Pi (qui a un processeur ARM) ou toute autre architecture, vous pouvez créer un `Dockerfile` à partir de zéro, basé sur une image de base Python (qui est multi-architecture) et utiliser Uvicorn seul.

Dans ce cas, votre `Dockerfile` pourrait ressembler à ceci :

```Dockerfile

///

/// tip

🚥 👆 💭, "📊", "🎓", &amp; "🍏" 📛 🎰 🏫 <abbr title="Technically, Deep Learning model architectures">🏷</abbr>.

///

### 📣 *➡ 🔢*

⤴️ ✍ *➡ 🔢* ⏮️ 🆎 ✍ ⚙️ 🔢 🎓 👆 ✍ (`ModelName`):

```Python hl_lines="16"
{!../../docs_src/path_params/tutorial005.py!}
```

### ✅ 🩺

///

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

```Python hl_lines="16"

## Consequences

* X
* Y
* Z
```

## Status

ACCEPTED

## Consequences

* We start to use Architectural Decision Records
* We use the proposed template from this ADR
* We locate `.md` files in the folder `/architecture-standards`
* We highly encourage usage of ADR to communicate decisions

# ADR-0004 - Introduce a UI architecture module to the core platform

## Date

2024-02-07

## Context

The Gradle core platform provides many services to the Gradle platforms and builds logic. One such group of services allows logic to interact with the build user, to provide diagnostics, progress information, prompt for questions, and so on. Currently, these services are part of the core platform runtime architecture module.

An architecture module is responsible for providing a coherent set of features and:

- Provides a set of APIs and services for use from outside the module.
- Has a private implementation.
- Is owned by a single team. A team may own multiple architecture modules.

The modules are arranged into several different "Gradle platforms".

# Gradle architecture documentation

This directory contains documentation that describes Gradle's architecture and how the various pieces fit together and work.

## Architecture decision records (ADRs)

The Gradle team uses ADRs to record architectural decisions that the team has made.

See [Architecture decisions records](standards) for the list of ADRs.