Automated configurations for high-performance machine types

This page describes automatically set default Cloud Storage FUSE values used for high-performance Google Cloud machine types, which are designed to optimize performance for demanding, high-throughput workloads. Values that are manually set at the time of mount will override these defaults.

Machine types

Configurations are automated for the following high-performance machine types:

Series type	Machine type
A2 machine series
	a2-megagpu-16g
	a2-ultragpu-8g
A3 machine series
	a3-edgegpu-8g
	a3-highgpu-8g
	a3-megagpu-8g
	a3-ultragpu-8g
A4 machine series
	4-highgpu-8g-lowmem
TPU v5e
	ct5l-hightpu-8t
	ct5lp-hightpu-8t
TPU v5p
	ct5p-hightpu-4t
	ct5p-hightpu-4t-tpu
TPU v6e (Trillium)
	ct6e-standard-4t
	ct6e-standard-4t-tpu
	ct6e-standard-8t
	ct6e-standard-8t-tpu

Automated configurations

When a supported machine type is detected, Cloud Storage FUSE automatically applies the following configuration values:

Configuration file field	CLI option	Automated configuration value
metadata-cache.negative-ttl-secs	--metadata-cache-negative-ttl-secs	0
metadata-cache.ttl-secs 1	--metadata-cache-ttl-secs 1	-1
metadata-cache.stat-cache-max-size-mb	--stat-cache-max-size-mb	1024
metadata-cache.type-cache-max-size-mb	--type-cache-max-size-mb	128
implicit-dirs	--implicit-dirs	true
file-system.rename-dir-limit	--rename-dir-limit	200000

¹ Setting this configuration to -1 significantly boosts performance by always serving files from the cache. Be aware that this configuration bypasses consistency checks, which can lead to serving outdated data. For details on managing data consistency, refer to File, stat, and type cache invalidation .

Further performance tuning

When you use a high-performance Google Cloud machine type, the configurations detailed on this page are automatically applied. However, you can further fine-tune your machine for optimal performance using the Performance tuning best practices guide.

If you're running training, serving, or checkpointing and JIT cache workloads on Google Kubernetes Engine clusters that use Cloud GPUs or Cloud TPU to access large datasets in Cloud Storage, you can streamline your setup by utilizing pre-configured YAML files to mount your Cloud Storage buckets directly into your pods more efficiently. For more information and instructions on how to use pre-configured Google Kubernetes Engine YAML files, see Use pre-configured Google Kubernetes Engine YAML files to optimize Cloud Storage FUSE performance .

What's next

• Learn how to tune Cloud Storage FUSE for optimal performance .

• Use a pre-configured Google Kubernetes Engine YAML file to configure tuning best practices.