* Added the new resource_name field to scaled_up/down_gpu_nodes_total,
representing the resource name for the gpu.
* Changed metrics registrations to use GpuConfig
* Added GetNodeGpuConfig to cloud provider which returns a GpuConfig
struct containing the gpu label, type and resource name if the node
has a GPU.
* Added initial implementaion of the GetNodeGpuConfig to all cloud
providers.
* Add GPU-related scaled_up & scaled_down metrics
* Fix name to match SD naming convention
* Fix import after master rebase
* Change the logic to include GPU-being-installed nodes
Nodes with GPU are expensive and it's likely a bunch of pods
using them will be created in a batch. In this case we can
wait a bit for all pods to be created to make more efficient
scale-up decision.
When a machine with GPU becomes ready it can take
up to 15 minutes before it reports that GPU is allocatable.
This can cause Cluster Autoscaler to trigger a second
unnecessary scale up.
The workaround sets allocatable to capacity for GPU so that
a node that waits for GPUs to become ready to use will be
considered as a place where pods requesting GPUs can be
scheduled.
Piotr Betkier
Jayant Jain
Hakan Bostan
Flavian
Bartłomiej Wróblewski
Maciek Pytel
Julien Balestra
Jiaxin Shan
Łukasz Osipiuk
Aleksandra Malinowska
Karol Gołąb
Łukasz Osipiuk
Marcin Wielgus
Beata Skiba