# MinIO Docker Quickstart Guide [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

See our web documentation on [Deploying MinIO in Standalone Mode](Deploy Standalone MinIO in a Container) for a more structured tutorial on deploying MinIO in a container.

## Prerequisites

# Bucket Lifecycle Configuration Quickstart Guide [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

Enable object lifecycle configuration on buckets to setup automatic deletion of objects after a specified number of days or a specified date.

## 1. Prerequisites

De esa manera, no tienes que leerlo todo primero en memoria, y puedes pasar esa función generadora al `StreamingResponse`, y devolverlo.

Esto incluye muchos paquetes para interactuar con almacenamiento en la nube, procesamiento de video y otros.

{* ../../docs_src/custom_response/tutorial008.py hl[2,10:12,14] *}

1. Esta es la función generadora. Es una "función generadora" porque contiene declaraciones `yield` dentro.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

# Distributed MinIO Quickstart Guide [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

#      the cache; this is unavoidable.
export TFCI=py311,linux_x86,public_cache,disk_cache

# Recommended: Configure Docker. (Linux only)
#
#   TF uses hub.docker.com/r/tensorflow/build containers for CI,
#   and scripts on Linux create a persistent container called "tf"
#   which mounts your TensorFlow directory into the container.
#

# Bucket Versioning Guide [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

    @ValueSource(ints = { 0, 1, 10, 100, 1000, 4096 })
    void testDecodeWithVariousAceCounts(int aceCount) throws SMBProtocolDecodingException {
        // This test is theoretical as we can't create huge buffers
        // but tests the boundary conditions
        if (aceCount <= 10) { // Only test small counts practically
            byte[] buffer = new byte[2048];

# MinIO Server Config Guide [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

## Configuration Directory

	s.Split(c.split)
	for s.Scan() {
	}
	if s.Err() != nil {
		t.Fatal("after scan:", s.Err())
	}
	if c != 0 {
		t.Fatalf("stopped with %d left to process", c)
	}
}

// Make sure we can read a huge token if a big enough buffer is provided.
func TestHugeBuffer(t *testing.T) {
	text := strings.Repeat("x", 2*MaxScanTokenSize)
	s := NewScanner(strings.NewReader(text + "\n"))
	s.Buffer(make([]byte, 100), 3*MaxScanTokenSize)