You can also connect using any S3-compatible tool, such as the MinIO Client `mc` commandline tool. See
[Test using MinIO Client `mc`](#test-using-minio-client-mc) for more information on using the `mc` commandline tool. For application developers,
see <https://min.io/docs/minio/linux/developers/minio-drivers.html> to view MinIO SDKs for supported languages.

## Architecture Standards

**Experimental!**

We'd like to capture our architectural decisions about the build tool as [Architectural Decision Records (ADRs)](https://adr.github.io/).
For now we just have this global repository of ADRs.
If we see fit, we can break these out to per-platform ones, or keep a hybrid approach to having global and platform-specific ADSs.

Our aim is to keep the process lightweight and approachable.

version or implementation.

For example, you may use your own Prometheus and Grafana installs, or you may use a specialized/custom
certificate provisioning tool, or use components that are centrally managed and running in a different cluster.

This is a work in progress - building on top of the multi-cluster installer.

> **NOTE:** **Each pool you add must have the same erasure coding parity configuration as the original pool, so the same data redundancy SLA is maintained.**

NOTE:

- `timeToFirstByte` and `timeToResponse` will be expressed in Nanoseconds.
- Additionally in the case of the erasure coded setup `tags.objectErasureMap` provides per object details about
  - Pool number the object operation was performed on.
  - Set number the object operation was performed on.
  - The list of drives participating in this operation belong to the set.

```json
{
  "version": "1",

    -   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

You may check how your changes will affect TensorFlow by:

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):