// bucket.Methods(http.MethodPut).Path("/{object:.+}").HeadersRegexp("X-Amz-Copy-Source", ".*?(\\/|%2F).*?").HandlerFunc(api.CopyObjectPartHandler).Queries("partNumber", "{partNumber:[0-9]+}", "uploadId", "{uploadId:.*}")
	// Its necessary to set the "X-Amz-Copy-Source" header for the request to be accepted by the handler.
	nilReq.Header.Set("X-Amz-Copy-Source", url.QueryEscape(SlashSeparator+nilBucket+SlashSeparator+nilObject))

`tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with...

* Implementers of the cloud provider interface will note the addition of a context to this interface. Trivial code modification will be necessary for a cloud provider to continue to compile. ([#59287](https://github.com/kubernetes/kubernetes/pull/59287), [@cheftako](https://github.com/cheftako))

	maps.Copy(oi2Map, oi2.UserTags)
	if (oi2.UserTagCount > 0 && !reflect.DeepEqual(oi2Map, t.ToMap())) || (oi2.UserTagCount != len(t.ToMap())) {
		return replicateMetadata
	}

	// Compare only necessary headers
	compareKeys := []string{
		"Expires",
		"Cache-Control",
		"Content-Language",
		"Content-Disposition",
		"X-Amz-Object-Lock-Mode",
		"X-Amz-Object-Lock-Retain-Until-Date",

    val framesThatFillWindow =
      roundUp(Settings.DEFAULT_INITIAL_WINDOW_SIZE, peer.maxOutboundDataLength())

    // Write the mocking script. This accepts more data frames than necessary!
    peer.sendFrame().settings(Settings())
    peer.acceptFrame() // SETTINGS ACK
    peer.acceptFrame() // SYN_STREAM on stream 1
    for (i in 0 until framesThatFillWindow) {

  * After your master is upgraded to 1.6, you will need to update your PodSpecs to set the `tolerations` field of the PodSpec and remove the `scheduler.alpha.kubernetes.io/tolerations` annotation on the Pod. (It's not strictly necessary to remove the annotation, as it will have no effect once you upgrade to 1.6.) Between the time the master is upgraded to 1.6 and when you do this, tolerations attached to Pods that are created will have no effect. Pods that are already running will...

- resource metrics endpoint `/metrics/resource/v1alpha1` as well as all metrics under this endpoint have...

- Simple script based hyperkube image that bundles all the necessary binaries. This is an equivalent replacement for the image based on the go based hyperkube command + image. ([#84662](https://github.com/kubernetes/kubernetes/pull/84662), [@dims](https://github.com/dims))

- Avoid unnecessary GCE API calls when adding IP alises or reflecting them in Node object in GCE cloud provider. ([#90242](https://github.com/kubernetes/kubernetes/pull/90242), [@wojtek-t](https://github.com/wojtek-t)) [SIG Apps, Cloud Provider and Network]

- **Cluster Lifecycle**
  - [alpha] Ensure critical cluster infrastructure pods (Heapster, DNS, etc.) can schedule by evicting regular pods when necessary to make the critical pods schedule. ([docs](http://kubernetes.io/docs/admin/rescheduler/#guaranteed-scheduling-of-critical-add-on-pods)) ([kubernetes/features#62](https://github.com/kubernetes/enhancements/issues/62))