| `minio_system_process_syscall_read_total`          | Total read SysCalls to the kernel. /proc/[pid]/io syscr. <br><br>Type: counter                                                        | `server` |
| `minio_system_process_syscall_write_total`         | Total write SysCalls to the kernel. /proc/[pid]/io syscw. <br><br>Type: counter                                                       | `server` |

#### Docker runtime Known Issues

- Kernel crash with Aufs storage driver on Debian Jessie ([#27885](https://github.com/kubernetes/kubernetes/issues/27885))
  - Consider running the *new* [kubernetes node problem detector](https://github.com/kubernetes/node-problem-detector) to identify this (and other) kernel issues automatically.

      [ -s "${CLUSTER_KUBECONFIG}" ] && break
      sleep 3
    done

    # Enable core dumps
    retry docker exec "${CLUSTER_NAME}"-control-plane bash -c "sysctl -w kernel.core_pattern=/var/lib/istio/data/core.proxy && ulimit -c unlimited"
  }

  # Now deploy the specified number of KinD clusters and
  # wait till they are provisioned successfully.
  declare -a DEPLOY_KIND_JOBS

```

TensorFlow kernels and TensorFlow's dependencies are still not built with
debugging information with `--config=dbg`, as issues occur on Linux if
there is too much debug info (see [this GitHub
issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If
you want to debug a kernel, you can compile specific files with `-g` using the

  gci-beta-55-8872-47-0:
  Date:           Nov 11, 2016
  Kernel:         ChromiumOS-4.4
  Kubernetes:     v1.4.5
  Docker:         v1.11.2
  Changelog (vs 55-8872-18-0)
    * Cherry-pick runc PR#608: Eliminate redundant parsing of mountinfo
    * Updated kubernetes to v1.4.5

    .SetIsDistributedCommunication()
    .Doc(R"doc(Test communication op throwing Unavailable error.)doc");

// Kernel that throws an Unavailable error.
class TestUnavailableErrorOp : public tensorflow::OpKernel {
 public:
  explicit TestUnavailableErrorOp(tensorflow::OpKernelConstruction* ctx)
      : tensorflow::OpKernel(ctx) {}
  void Compute(tensorflow::OpKernelContext* ctx) override {

# kernel registration - REGISTER_OP, REGISTER_KERNEL_BUILDER, etc.
#
# This setting is currently experimental. The 'v2' implementation does _not_
# correspond to a particular, finalized design; rather, it relates to
# developing one.
#
# The current aim of the 'v2' implementation is to allow 'unused' ops and
# kernels to be discarded by the linker (to the benefit of binary size).

    * Date:           Nov 03, 2016
    * Kernel:         ChromiumOS-4.4
    * Kubernetes:     v1.4.5
    * Docker:         v1.11.2
    * Changelog (vs 55-8872-18-0)
            * Updated kubernetes to v1.4.5
            * Fixed a bug in e2fsprogs that caused mke2fs to take a very long time. Upstream fix: http://git.kernel.org/cgit/fs/ext2/e2fsprogs.git/commit/?h=next&id=d33e690fe7a6cbeb51349d9f2c7fb16a6ebec9c2

Wenn Ihre Anwendung also viel Speicher verbraucht (z. B. bei Modellen für maschinelles Lernen) und Ihr Server über viele CPU-Kerne, **aber wenig Speicher** verfügt, könnte Ihr Container am Ende versuchen, mehr Speicher als vorhanden zu verwenden, was zu erheblichen Leistungseinbußen (oder sogar zum Absturz) führen kann. 🚨

Care should be taken when using vector instructions to ensure that they are available at
runtime.
To use vector instructions a machine must have both the vector facility (bit 129 in the
facility list) and kernel support.
Without kernel support a vector instruction will have no effect (it will be equivalent
to a <code>NOP</code> instruction).
</p>

<p>
Addressing modes:
</p>

<ul>

<li>