// is specified. It is empty if the KMS does not support
	// a default key.
	DefaultKey string

	// Details provides more details about the KMS endpoint status.
	// including uptime, version and available CPUs.
	// Could be more in future.
	Details kes.State
}

// DEK is a data encryption key. It consists of a
// plaintext-ciphertext pair and the ID of the key
// used to generate the ciphertext.
//

## Recap

You can use **Gunicorn** (or also Uvicorn) as a process manager with Uvicorn workers to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

You could use these tools and ideas if you are setting up **your own deployment system** while taking care of the other deployment concepts yourself.

	sysCPUAvgIdleMD   = NewGaugeMD(sysCPUAvgIdle, "Average CPU idle time")
	sysCPUAvgIOWaitMD = NewGaugeMD(sysCPUAvgIOWait, "Average CPU IOWait time")
	sysCPULoadMD      = NewGaugeMD(sysCPULoad, "CPU load average 1min")
	sysCPULoadPercMD  = NewGaugeMD(sysCPULoadPerc, "CPU load average 1min (percentage)")
	sysCPUNiceMD      = NewGaugeMD(sysCPUNice, "CPU nice time")
	sysCPUStealMD     = NewGaugeMD(sysCPUSteal, "CPU steal time")

Harshavardhana <******@****.***> 1711791711 -0700

### Hardware attacks

Physical GPUs or TPUs can also be the target of attacks. [Published
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other
running models or processes in the same system. GPUs can also have
implementation bugs that might allow attackers to leave malicious code running

-   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:

1. Creating a PR and observing the presubmit test results

   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

global:
  proxy:
    resources:
      requests:
        cpu: 10m
        memory: 40Mi

pilot:
  autoscaleEnabled: false
  traceSampling: 100
  resources:
    requests:
      cpu: 10m
      memory: 100Mi

gateways:
  istio-egressgateway:
    autoscaleEnabled: false
    resources:
      requests:
        cpu: 10m
        memory: 40Mi
  istio-ingressgateway:
    autoscaleEnabled: false

global:
  proxy:
    resources:
      requests:
        cpu: 10m
        memory: 40Mi

pilot:
  autoscaleEnabled: false
  traceSampling: 100
  resources:
    requests:
      cpu: 10m
      memory: 100Mi

gateways:
  istio-egressgateway:
    autoscaleEnabled: false
    resources:
      requests:
        cpu: 10m
        memory: 40Mi
  istio-ingressgateway:
    autoscaleEnabled: false

global:
  proxy:
    resources:
      requests:
        cpu: 10m
        memory: 40Mi

pilot:
  autoscaleEnabled: false
  traceSampling: 100
  resources:
    requests:
      cpu: 10m
      memory: 100Mi

gateways:
  istio-egressgateway:
    autoscaleEnabled: false
    resources:
      requests:
        cpu: 10m
        memory: 40Mi
  istio-ingressgateway:
    autoscaleEnabled: false