*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.

    @PostConstruct
    public void init() {
        if (logger.isDebugEnabled()) {
            logger.debug("Initialize {}", this.getClass().getSimpleName());
        }
        load();
    }

    @Override
    public int load() {
        final List<LabelType> labelTypeList = ComponentUtil.getComponent(LabelTypeService.class).getLabelTypeList();
        buildLabelTypeItems(labelTypeList);

func (r *ReplicationStats) getNodeQueueStats(bucket string) (qs ReplQNodeStats) {
	qs.NodeName = globalLocalNodeName
	qs.Uptime = UTCNow().Unix() - globalBootTime.Unix()
	grs := globalReplicationStats.Load()
	if grs != nil {
		qs.ActiveWorkers = grs.ActiveWorkers()
	} else {
		qs.ActiveWorkers = ActiveWorkerStat{}
	}
	qs.XferStats = make(map[RMetricName]XferStats)
	qs.QStats = r.qCache.getBucketStats(bucket)

        File localRepo = getLocalRepositoryPath();
        File pom0 = new File(localRepo, "p0/pom.xml");
        File pom0Basedir = pom0.getParentFile();
        File pom1 = new File(pom0Basedir, "p1/pom.xml");

        // load the child project, which inherits from p0...
        MavenProject project0 = getProjectWithDependencies(pom0);
        MavenProject project1 = getProjectWithDependencies(pom1);

            try {
                mavenWrapperPropertyFileInputStream = new FileInputStream(mavenWrapperPropertyFile);
                Properties mavenWrapperProperties = new Properties();
                mavenWrapperProperties.load(mavenWrapperPropertyFileInputStream);
                url = mavenWrapperProperties.getProperty(PROPERTY_NAME_WRAPPER_URL, url);
            } catch (IOException e) {

            return properties;
        }

        try (Reader reader = Files.newBufferedReader(path)) {
            properties.load(reader);
        } catch (IOException e) {
            LOGGER.warn("Unable to read {}. The --resume / -r feature will not work.", path);
        }

        return properties;
    }

Distributed instances are now accessible on the host using the Minio CLI on port 9000 and the Minio Web Console on port 9001. Proceed to access the Web browser at <http://127.0.0.1:9001/>. Here 4 MinIO server instances are reverse proxied through Nginx load balancing.

### Notes

* By default the Docker Compose file uses the Docker image for latest MinIO server release. You can change the image tag to pull a specific [MinIO Docker image](https://hub.docker.com/r/minio/minio/).

   * Hashing} class is loaded. <b>Do not use this method</b> if hash codes may escape the current
   * process in any way, for example being sent over RPC, or saved to disk. For a general-purpose,
   * non-cryptographic hash function that will never change behavior, we suggest {@link
   * #murmur3_128}.
   *
   * <p>Repeated calls to this method on the same loaded {@code Hashing} class, using the same value

	var err error
	var setupType SetupType

	// Check and load TLS certificates.
	globalPublicCerts, globalTLSCerts, globalIsTLS, err = getTLSConfig()
	logger.FatalIf(err, "Unable to load the TLS configuration")

	// Check and load Root CAs.
	globalRootCAs, err = certs.GetRootCAs(globalCertsCADir.Get())
	logger.FatalIf(err, "Failed to read root CAs (%v)", err)

                return redirect(ErrorAction.class);
            }
            return response;
        } catch (final Exception e) {
            logger.warn("Failed to load: {}", doc, e);
            saveError(messages -> messages.addErrorsNotLoadFromServer(GLOBAL, targetUrl));
            return redirect(ErrorAction.class);
        }
    }