@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);

	req.Header.Set(xhttp.MinIOPeerCall, "true")

	resp, err := httpClient.Do(req)
	if err != nil {
		return err
	}
	xhttp.DrainBody(resp.Body)

	return nil
}

// connect to list of endpoints and load all Erasure disk formats, validate the formats are correct
// and are in quorum, if no formats are found attempt to initialize all of them for the first
// time. additionally make sure to close all the disks used in this attempt.

            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/).

        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);

   
   
   *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.

*   Azure Load Balancer Auto Mode: Services can be annotated to allow auto selection of available load balancers and to provide specific availability sets that host the load balancers (for example, `service.beta.kubernetes.io/azure-load-balancer-mode=auto|as1,as2...`)

### **CLI**

#### **Kubectl**

	if isSSEC && objInfo.UserDefined[ReplicationSsecChecksumHeader] != "" {
		userMeta[ReplicationSsecChecksumHeader] = objInfo.UserDefined[ReplicationSsecChecksumHeader]
	}

	// really big value but its okay on heavily loaded systems. This is just tail end timeout.
	cctx, ccancel := context.WithTimeout(ctx, 10*time.Minute)
	defer ccancel()
	_, err = c.CompleteMultipartUpload(cctx, bucket, object, uploadID, uploadedParts, minio.PutObjectOptions{

	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)

	}

	if len(x509Certs) == 0 {
		return nil, ErrTLSUnexpectedData(nil).Msgf("Empty public certificate file %s", certFile)
	}

	return x509Certs, nil
}

// LoadX509KeyPair - load an X509 key pair (private key , certificate)
// from the provided paths. The private key may be encrypted and is
// decrypted using the ENV_VAR: MINIO_CERT_PASSWD.