To install the current release, which includes support for
[CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu) *(Ubuntu and
Windows)*:

```
$ pip install tensorflow
```

Other devices (DirectX and MacOS-metal) are supported using
[Device Plugins](https://www.tensorflow.org/install/gpu_plugins#available_devices).

A smaller CPU-only package is also available:

```
$ pip install tensorflow-cpu
```

🔹 High Performance – Ideal for demanding storage workloads.

AI storage documentation  (https://min.io/solutions/object-storage-for-ai).

This README provides quickstart instructions on running MinIO on bare metal hardware, including container-based installations. For Kubernetes environments, use the [MinIO Kubernetes Operator](https://github.com/minio/operator/blob/master/README.md).

## Container Installation

v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. This README provides quickstart instructions on running MinIO on bare metal hardware, including container-based installations. For Kubernetes environments, use the [MinIO Kubernetes Operator](https://github.com/minio/operator/blob/master/README.md). ## Container Installation Use the following commands to run a standalone MinIO...

- In dual-stack bare-metal clusters, you can now pass dual-stack IPs to `kubelet --node-ip`.
  eg: `kubelet --node-ip 10.1.0.5,fd01::0005`. This is not yet supported for non-bare-metal
  clusters.
  
  In dual-stack clusters where nodes have dual-stack addresses, hostNetwork pods

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Harshavardhana <******@****.***> 1650484145 -0700

Harshavardhana <******@****.***> 1610360824 -0800