MOV	$0, 0(SP)			// ERROR "constant load must target register"
	MOV	$0, 8(SP)			// ERROR "constant load must target register"
	MOV	$1234, 0(SP)			// ERROR "constant load must target register"
	MOV	$1234, 8(SP)			// ERROR "constant load must target register"
	MOVB	$1, X5				// ERROR "unsupported constant load"
	MOVH	$1, X5				// ERROR "unsupported constant load"
	MOVW	$1, X5				// ERROR "unsupported constant load"

	MOVF	F0, 4(X5)				// 27a20200
	MOVF	F0, F1					// d3000020

	MOVD	4(X5), F0				// 07b04200
	MOVD	F0, 4(X5)				// 27b20200
	MOVD	F0, F1					// d3000022

	// TLS load with local-exec (LUI + ADDIW + ADD of TP + load)
	MOV	tls(SB), X5				// b70f00009b8f0f00b38f4f0083b20f00
	MOVB	tls(SB), X5				// b70f00009b8f0f00b38f4f0083820f00

	// TLS store with local-exec (LUI + ADDIW + ADD of TP + store)

                        description: Maximum % of hosts in the load balancing pool
                          for the upstream service that can be ejected.
                        format: int32
                        type: integer
                      minHealthPercent:
                        description: Outlier detection will be enabled as long as
                          the associated load balancing pool has at least min_health_percent

  healthcheck:
    test: ["CMD", "mc", "ready", "local"]
    interval: 5s
    timeout: 5s
    retries: 5

# starts 4 docker containers running minio server instances.
# using nginx reverse proxy, load balancing, you can access
# it through port 9000.
services:
  minio1:
    <<: *minio-common
    hostname: minio1
    volumes:
      - edata1-1:/edata1
      - edata1-2:/edata2
      - edata1-3:/edata3

{} # Setup how istiod Service is configured. See https://kubernetes.io/docs/concepts/services-networking/dual-stack/#services ipFamilyPolicy: "" ipFamilies: [] # To generate an internal load balancer: # --set serviceAnnotations.cloud.google.com/load-balancer-type=internal #serviceAnnotations: # cloud.google.com/load-balancer-type: "internal" podAnnotations: {} type: LoadBalancer #change to NodePort, ClusterIP or LoadBalancer if need be ########## secretVolumes: - name: ingressgateway-certs secretName:...

  command: server http://minio{1...4}/data{1...3}
  env_file:
    - ./minio.env
  expose:
    - "9000"
    - "9001"

# starts 4 docker containers running minio server instances.
# using nginx reverse proxy, load balancing, you can access
# it through port 9000.
services:
  minio1:
    <<: *minio-common
    hostname: minio1
    volumes:
      - data1-1:/data1
      - data1-2:/data2
      - data1-3:/data3

  healthcheck:
    test: ["CMD", "mc", "ready", "local"]
    interval: 5s
    timeout: 5s
    retries: 5

# starts 4 docker containers running minio server instances.
# using nginx reverse proxy, load balancing, you can access
# it through port 9000.
services:
  minio1:
    <<: *minio-common
    hostname: minio1
    volumes:
      - cdata1-1:/cdata1
      - cdata1-2:/cdata2

  minio2:

// This input was created by taking the mips64 testcase and modified
// by hand.

#include "../../../../../runtime/textflag.h"

TEXT foo(SB),DUPOK|NOSPLIT,$0

	//inst:
	//
	// load ints and bytes
	//
	//	LMOVW rreg ',' rreg
	//	{
	//		outcode(int($1), &$2, 0, &$4);
	//	}
	MOVW	R1, R2
	MOVW	LO, R1
	MOVW	HI, R1
	MOVW	R1, LO
	MOVW	R1, HI
	MOVW	R1, R2
	MOVW	LO, R1
	MOVW	HI, R1

                        description: Maximum % of hosts in the load balancing pool
                          for the upstream service that can be ejected.
                        format: int32
                        type: integer
                      minHealthPercent:
                        description: Outlier detection will be enabled as long as
                          the associated load balancing pool has at least min_health_percent

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   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.