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

          RUN make
          EOF

      - name: Build
        uses: docker/build-push-action@v3
        with:
          context: .
          file: Dockerfile.fips.test
          push: false
          load: true
          tags: minio/fips-test:latest

      # This should fail if grep returns non-zero exit
      - name: Test binary
        run: |
          docker run --rm minio/fips-test:latest ./minio --version

	MOVD $2147483649, R5            // 6405800060a50001 or 0600800038a00001
	// Hex constant 0xFFFFFFFF80000001
	MOVD $-2147483647, R5           // 3ca0800060a50001 or 0603800038a00001
	// Hex constant 0xFFFFFFFE00000002 (load of constant on < power10, pli on >= power10
	MOVD $-8589934590, R5           // 3ca00000e8a50000 or 0602000038a00002

	// For backwards compatibility, MOVW $const,Rx and MOVWZ $const,Rx assemble identically

	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)

  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

	STP	(R3, R4), 0x1234567(R27)                         // ERROR "REGTMP used in large offset store"
	LDP	0x1234567(R27), (R3, R4)                         // ERROR "REGTMP used in large offset load"
	STP	(R26, R27), 700(R2)                              // ERROR "cannot use REGTMP as source"
	MOVK	$0, R10                                          // ERROR "zero shifts cannot be handled correctly"

{} # 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:...

// 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

	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"

   
   
   *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.