params(name)
            params("${providedBy.size + 1}")
        }
    }

    /**
     * For all modules providing a capability, always use the preferred module, even if there's no conflict.
     * In other words, will forcefully upgrade all modules providing a capability to a selected module.
     *
     * @param to the preferred module
     */
    private

  // +optional
  optional bool privileged = 1;

  // defaultAddCapabilities is the default set of capabilities that will be added to the container
  // unless the pod spec specifically drops the capability.  You may not list a capability in both
  // defaultAddCapabilities and requiredDropCapabilities. Capabilities added here are implicitly
  // allowed, and need not be included in the allowedCapabilities list.
  // +optional

        'you want to build with.\nYou can find the compute capability of your '
        'device at: https://developer.nvidia.com/cuda-gpus. Each capability '
        'can be specified as "x.y" or "compute_xy" to include both virtual and'
        ' binary GPU code, or as "sm_xy" to only include the binary '
        'code.\nPlease note that each additional compute capability '

Each GKE cluster's will have many categories of logs collected by Stackdriver.  Logs can be monitored via
the project's [log viewer](https://cloud.google.com/logging/docs/view/overview) and/or the `gcloud logging read`
capability.

The following example grabs the last 10 `kubelet` logs containing the string "cmdAdd" in the log message.

```console

 * We need to look at Maven, and Nexus as use cases and figure out what each of them needs to be
 * able to do
 *
 * - now what is really the difference between this and loading a component in plexus - custom
 * classloading capability - remote resolution of dependencies - do we want a model for sharing
 * information among plugins, is this more like an extension point - do we need a sort of bus for

# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current
# major release. Example: sm_80 kernels can run on sm_89 GPUs but
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.

### SIG Scalability

[SIG Scalability][] is responsible for scalability testing, measuring and
improving system performance, and answering questions related to scalability.

For the 1.8 release, SIG Scalability focused on automating large cluster
scalability testing in a continuous integration (CI) environment. The SIG
defined a concrete process for scalability testing, created

* Fix `cstring.h` missing file issue with the Libtensorflow archive.

# Release 2.17.0

## TensorFlow

### Breaking Changes

* GPU
    * Support for NVIDIA GPUs with compute capability 5.x (Maxwell generation) has been removed from TF binary distributions (Python wheels).

### Major Features and Improvements

- [ ] Ambient
- [ ] Configuration Infrastructure
- [ ] Docs
- [ ] Dual Stack
- [ ] Installation
- [ ] Networking
- [ ] Performance and Scalability
- [ ] Extensions and Telemetry
- [ ] Security
- [ ] Test and Release
- [ ] User Experience
- [ ] Developer Infrastructure
- [ ] Upgrade
- [ ] Multi Cluster
- [ ] Virtual Machine
- [ ] Control Plane Revisions

**Describe alternatives you've considered**

**Affected product area (please put an X in all that apply)**

[ ] Ambient
[ ] Docs
[ ] Dual Stack
[ ] Installation
[ ] Networking
[ ] Performance and Scalability
[ ] Extensions and Telemetry
[ ] Security
[ ] Test and Release
[ ] User Experience
[ ] Developer Infrastructure

**Affected features (please put an X in all that apply)**

[ ] Multi Cluster