![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Faite en python moderne

Tout est basé sur la déclaration de type standard de **Python 3.8** (grâce à Pydantic). Pas de nouvelles syntaxes à apprendre. Juste du Python standard et moderne.

Prozentsatz von deren Ressourcen zu nutzen (CPU, Arbeitsspeicher, Festplatte, Netzwerkbandbreite, usw.).

Wenn Sie andererseits über zwei Server verfügen und **100 % ihrer CPU und ihres RAM** nutzen, wird irgendwann ein Prozess nach mehr Speicher fragen und der Server muss die Festplatte als „Speicher“ verwenden (was tausendmal langsamer sein kann) oder er könnte sogar **abstürzen**. Oder ein Prozess muss möglicherweise einige Berechnungen durchführen und müsste warten, bis die CPU wieder frei ist....

pkg archive/zip, method (*Writer) Flush() error

# CL 97140043 compress/flate: add Reset() to allow reusing large buffers to compress multiple buffers, James Robinson <******@****.***>
pkg compress/flate, type Resetter interface { Reset }
pkg compress/flate, type Resetter interface, Reset(io.Reader, []uint8) error
pkg compress/zlib, type Resetter interface { Reset }

			t.Fatalf("failed to found constraint %v", name)
		}
	}
}

type DynamicUser struct {
	gorm.Model
	Name      string
	CompanyID string `gorm:"index"`
}

// To test auto migrate crate indexes for dynamic table name
// https://github.com/go-gorm/gorm/issues/4752
func TestMigrateIndexesWithDynamicTableName(t *testing.T) {
	// Create primary table
	if err := DB.AutoMigrate(&DynamicUser{}); err != nil {

   *
   * <p>The expected (but not guaranteed) performance of the special implementation differs from
   * {@code getStackTrace} in one main way: The {@code lazyStackTrace} call itself returns quickly
   * by delaying the per-stack-frame work until each element is accessed. Roughly speaking:
   *
   * <ul>
   *   <li>{@code getStackTrace} takes {@code stackSize} time to return but then negligible time to
   *       retrieve each element of the returned list.

	for i := range chunk {
		chunk[i] = '.'
	}
	for i := 0; i < chunks; i++ {
		_, err := f.Write(chunk)
		if err != nil {
			t.Fatal("write chunk:", err)
		}
	}
	if frag := int(size % chunkSize); frag > 0 {
		_, err := f.Write(chunk[:frag])
		if err != nil {
			t.Fatal("write chunk:", err)
		}
	}
	end := []byte("END\n")
	_, err = f.Write(end)
	if err != nil {
		t.Fatal("write end:", err)
	}

    peer.acceptFrame() // ACK
    peer.sendFrame().ping(false, 2, 0)
    peer.acceptFrame()
    peer.play()

    // Play it back.
    val connection = connect(peer)

    // fake a settings frame with clear flag set.
    val settings2 = Settings()
    settings2[Settings.MAX_CONCURRENT_STREAMS] = 60000
    connection.readerRunnable.applyAndAckSettings(true, settings2)
    connection.withLock {

		{"test-bucket-list-object", "Asia", "A", "", 1, resultCases[4], nil, true},
		// Setting a non-existing directory to be prefix (12-13).
		{"empty-bucket", "europe/france/", "", "", 1, ListObjectsInfo{}, nil, true},
		{"empty-bucket", "africa/tunisia/", "", "", 1, ListObjectsInfo{}, nil, true},
		// Testing on empty bucket, that is, bucket without any objects in it (14).

Si vous avez de bonnes connaissances techniques (coroutines, threads, code bloquant, etc.) et êtes curieux de comment **FastAPI** gère `async def` versus le `def` classique, cette partie est faite pour vous.

///

### Fonctions de chemin

# Note that linux cpu and cuda builds share the same toolchain now.
build:release_gpu_linux --config=cuda_clang_official
# Local test jobs has to be 4 because parallel_gpu_execute is fragile, I think
test:release_gpu_linux --test_timeout=300,450,1200,3600 --local_test_jobs=4 --run_under=//tensorflow/tools/ci_build/gpu_build:parallel_gpu_execute

# Deprecated release CPU config with non-hermetic toolchains.