# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.
# Users can still include debug info for a specific kernel, e.g. with:
#     --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g

	if err != nil {
		buf, err = os.ReadFile(cgroupV1MemLimitFile)
	}
	if err != nil {
		return 0
	}
	limit, err = strconv.ParseUint(strings.TrimSpace(string(buf)), 10, 64)
	if err != nil {
		// The kernel can return valid but non integer values
		// but still, no need to interpret more
		return 0
	}
	if limit >= 100*humanize.TiByte {
		// No limit set, or unreasonably high. Ignore
		return 0
	}
	return limit
}

        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

| `minio_node_scanner_versions_scanned`      | Total number of object versions scanned since server start. |
| `minio_node_syscall_read_total`            | Total read SysCalls to the kernel. /proc/[pid]/io syscr.    |
| `minio_node_syscall_write_total`           | Total write SysCalls to the kernel. /proc/[pid]/io syscw.   |
| `minio_usage_last_activity_nano_seconds`   | Time elapsed (in nano seconds) since last scan activity.    |

# Bucket Metrics

Go 1.22 changed how the runtime interacts with transparent huge pages on Linux.
In particular, a common default Linux kernel configuration can result in
significant memory overheads, and Go 1.22 no longer works around this default.
To work around this issue without adjusting kernel settings, transparent huge
pages can be disabled for Go memory with the
[`disablethp` setting](/pkg/runtime#hdr-Environment_Variables).

#### 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.

  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph

| `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` |

Each node is responsible of healing its local drives; Each drive will have multiple heal workers which is the quarter of the number of CPU cores of the node or the quarter of the configured nr_requests of the drive (https://www.kernel.org/doc/Documentation/block/queue-sysfs.txt). It is also possible to provide a custom number of workers by using this command: `mc admin config set alias/ heal drive_workers=100` .


```
~ mc admin config set alias/ heal

```

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