Reference documentation and code samples for the Google Cloud Gke Recommender V1 Client class StorageConfig.
Storage configuration for a model deployment.
Generated from protobuf message google.cloud.gkerecommender.v1.StorageConfig
Namespace
Google \ Cloud \ GkeRecommender \ V1Methods
__construct
Constructor.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ model_bucket_uri |
string
Optional. The Google Cloud Storage bucket URI to load the model from. This URI must point to the directory containing the model's config file ( |
↳ xla_cache_bucket_uri |
string
Optional. The URI for the GCS bucket containing the XLA compilation cache. If using TPUs, the XLA cache will be written to the same path as |
getModelBucketUri
Optional. The Google Cloud Storage bucket URI to load the model from. This
URI must point to the directory containing the model's config file
(config.json) and model weights. A tuned GCSFuse setup can improve
LLM Pod startup time by more than 7x. Expected format:
gs://<bucket-name>/<path-to-model>.
| Returns | |
|---|---|
| Type | Description |
string |
|
setModelBucketUri
Optional. The Google Cloud Storage bucket URI to load the model from. This
URI must point to the directory containing the model's config file
(config.json) and model weights. A tuned GCSFuse setup can improve
LLM Pod startup time by more than 7x. Expected format:
gs://<bucket-name>/<path-to-model>.
| Parameter | |
|---|---|
| Name | Description |
var |
string
|
| Returns | |
|---|---|
| Type | Description |
$this |
|
getXlaCacheBucketUri
Optional. The URI for the GCS bucket containing the XLA compilation cache.
If using TPUs, the XLA cache will be written to the same path as
model_bucket_uri. This can speed up vLLM model preparation for repeated
deployments.
| Returns | |
|---|---|
| Type | Description |
string |
|
setXlaCacheBucketUri
Optional. The URI for the GCS bucket containing the XLA compilation cache.
If using TPUs, the XLA cache will be written to the same path as
model_bucket_uri. This can speed up vLLM model preparation for repeated
deployments.
| Parameter | |
|---|---|
| Name | Description |
var |
string
|
| Returns | |
|---|---|
| Type | Description |
$this |
|