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[Docs] Improve Kubernetes documentation (#3138)
* [Docs] Kubernetes guide * [Docs] Kubernetes guide Rework `Backends` and `Fleets` pages to reflect the changes related to Kubernetes * [Docs] Improve Kubernetes documentation Updated `README`, `Overview`, `Installation` * [Docs] Improve Kubernetes documentation Minor updates, incl. the description of `Default image`, and `privileged` for NCCL tests * [Docs] Improve Kubernetes documentation Updated `FAQ`
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README.md

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`dstack` provides a unified control plane for running development, training, and inference on GPUs — across cloud VMs, Kubernetes, or on-prem clusters. It helps your team avoid vendor lock-in and reduce GPU costs.
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`dstack` is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters.
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#### Accelerators
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It streamlines development, training, and inference, and is compatible with any hardware, open-source tools, and frameworks.
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#### Hardware
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`dstack` supports `NVIDIA`, `AMD`, `Google TPU`, `Intel Gaudi`, and `Tenstorrent` accelerators out of the box.
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#### Set up the server
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##### (Optional) Configure backends
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##### Configure backends
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To orchestrate compute across cloud providers or existing Kubernetes clusters, you need to configure backends.
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To use `dstack` with cloud providers, configure backends
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via the `~/.dstack/server/config.yml` file.
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Backends can be set up in `~/.dstack/server/config.yml` or through the [project settings page](../concepts/projects.md#backends) in the UI.
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For more details on how to configure backends, check [Backends](https://dstack.ai/docs/concepts/backends).
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For more details, see [Backends](../concepts/backends.md).
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> For using `dstack` with on-prem servers, create [SSH fleets](https://dstack.ai/docs/concepts/fleets#ssh)
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> once the server is up.
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> When using `dstack` with on-prem servers, backend configuration isn’t required. Simply create [SSH fleets](../concepts/fleets.md#ssh) once the server is up.
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##### Start the server
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