-   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

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## HTML "dfn" elements { #html-dfn-elements }

* <dfn title="A group of machines that are configured to be connected and work together in some way.">cluster</dfn>
* <dfn title="A method of machine learning that uses artificial neural networks with numerous hidden layers between input and output layers, thereby developing a comprehensive internal structure">Deep Learning</dfn>

// CONTROL EQUIPMENT IN HAZARDOUS ENVIRONMENTS REQUIRING FAIL-SAFE
// PERFORMANCE, SUCH AS IN THE OPERATION OF NUCLEAR FACILITIES, AIRCRAFT
// NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL, DIRECT LIFE
// SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH THE FAILURE OF THE
// SOFTWARE COULD LEAD DIRECTLY TO DEATH, PERSONAL INJURY, OR SEVERE
// PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH RISK ACTIVITIES").  WIDGET WORKSHOP

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

Les conteneurs Linux s'exécutent en utilisant le même noyau Linux que l'hôte (machine, machine virtuelle, serveur cloud, etc.). Cela signifie simplement qu'ils sont très légers (comparés à des machines virtuelles complètes émulant un système d'exploitation entier).

Ainsi, les conteneurs consomment **peu de ressources**, une quantité comparable à l'exécution directe des processus (alors qu'une machine virtuelle consommerait beaucoup plus).

use the "integTest" task.

If in doubt about which command to use, simply run <gradle path>:check

== Testing packaging

The packaging tests use Vagrant virtual machines or cloud instances to verify
that installing and running Elasticsearch distributions works correctly on
supported operating systems. These tests should really only be run on ephemeral

    - [Deprecated metrics](#deprecated-metrics)
  - [Notable Features](#notable-features)
  - [API Changes](#api-changes)
  - [Detailed Bug Fixes And Changes](#detailed-bug-fixes-and-changes)
    - [API Machinery](#api-machinery)
    - [Apps](#apps)
    - [Auth](#auth)
    - [AWS](#aws)
    - [Azure](#azure)
    - [CLI](#cli)
    - [Cloud Provider](#cloud-provider)
    - [Cluster Lifecycle](#cluster-lifecycle)
    - [GCP](#gcp)

describes the peculiarities that apply when writing assembly code to interact with Go.
</p>

<p>
The most important thing to know about Go's assembler is that it is not a direct representation of the underlying machine.
Some of the details map precisely to the machine, but some do not.
This is because the compiler suite (see
<a href="https://9p.io/sys/doc/compiler.html">this description</a>)
needs no assembler pass in the usual pipeline.

Voir la section `### HTML abbr elements` dans l’invite générale dans `scripts/translate.py`.

////

## Éléments HTML « dfn » { #html-dfn-elements }

* <dfn title="Un groupe de machines configurées pour être connectées et travailler ensemble d’une certaine manière.">grappe</dfn>

* Use local disk for ConfigMap volume instead of tmpfs ([#25306](https://github.com/kubernetes/kubernetes/pull/25306), [@pmorie](https://github.com/pmorie))
* Alpha support for scheduling pods on machines with NVIDIA GPUs whose kubelets use the `--experimental-nvidia-gpus` flag, using the alpha.kubernetes.io/nvidia-gpu resource  ([#24836](https://github.com/kubernetes/kubernetes/pull/24836), [@therc](https://github.com/therc))