* It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
         * All you need is a good internet connection and you are all set.
      * Try to build TF from sources by changing CPU optimization flags.
   
   *Please let us know if this helps.*

        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",
        "//learning/serving/experimental/remote_predict/...",
        "//perftools/accelerators/xprof/convert/...",
        "//perftools/accelerators/xprof/integration_tests/...",
        "//smartass/brain/...",
        "//tensorflow/...",
        "//tensorflow_decision_forests/...",
        "//tensorflow_federated/...",

    - [**Node**](#node-3)
      - [**Pod API**](#pod-api)
      - [**Hardware Accelerators**](#hardware-accelerators)
      - [**Container Runtime**](#container-runtime)
      - [**Kubelet**](#kubelet)
        - [**Other**](#other-5)
    - [**OpenStack**](#openstack-2)
    - [**Scheduling**](#scheduling-2)
      - [**Hardware Accelerators**](#hardware-accelerators-1)
      - [**Other**](#other-6)
    - [**Storage**](#storage-3)

    resource availability for critical system pods.
* **[alpha]** `--experimental-nvidia-gpus` flag is replaced by `Accelerators` alpha
  feature gate along with addition of support for multiple Nvidia GPUs.
  - To use GPUs, pass `Accelerators=true` as part of `--feature-gates` flag.
  - More information [here](https://vishh.github.io/docs/user-guide/gpus/).

implementation bugs that might allow attackers to leave malicious code running
and leak or tamper with applications from other users. Please report
vulnerabilities to the vendor of the affected hardware accelerator.

## Reporting vulnerabilities

### Vulnerabilities in TensorFlow

This document covers different use cases for TensorFlow together with comments

  - [Known Issues](#known-issues)
    - [Summary API in kubelet doesn't have accelerator metrics](#summary-api-in-kubelet-doesnt-have-accelerator-metrics)
  - [Urgent Upgrade Notes](#urgent-upgrade-notes)
    - [(No, really, you MUST read this before you upgrade)](#no-really-you-must-read-this-before-you-upgrade)

- When `DisableAcceleratorUsageMetrics` is set, do not collect accelerator metrics using cAdvisor. ([#101712](https://github.com/kubernetes/kubernetes/pull/101712), [@SergeyKanzhelev](https://github.com/SergeyKanzhelev)) [SIG Instrumentation and Node]

- The kube-scheduler ComponentConfig v1beta2 is deprecated in v1.25. ([#111547](https://github.com/kubernetes/kubernetes/pull/111547), [@kerthcet](https://github.com/kerthcet))
- The kubelet no longer supports collecting accelerator metrics through cAdvisor. The feature gate `DisableAcceleratorUsageMetrics` is now GA and cannot be disabled. ([#110940](https://github.com/kubernetes/kubernetes/pull/110940), [@pacoxu](https://github.com/pacoxu))

- Adds the ability to disable Accelerator/GPU metrics collected by Kubelet ([#91930](https://github.com/kubernetes/kubernetes/pull/91930), [@RenaudWasTaken](https://github.com/RenaudWasTaken)) [SIG Node]

        `StatefulNnApiDelegate::Options::max_number_delegated_partitions` to 3.
    *   Add capability to disable `NNAPI` CPU and check `NNAPI` Errno.
    *   Fix crashes when using `NNAPI` with target accelerator specified with
        model containing Conv2d or FullyConnected or LSTM nodes with quantized
        weights.
    *   Fix `ANEURALNETWORKS_BAD_DATA` execution failures with