Misc doc fixes (#251)

See changes for details. Some extra reasoning behind the changes:
* Prefix all GCS buckets with project_id since GCS buckets are globally
  namespaced.
* gcloud storage buckets add-iam-policy-binding" only accepts bucket
  URLs

benchmarks/README.md

@@ -132,4 +132,4 @@ terraform plan
 terraform apply
 ```
 
-To further interact with the Locust inference benchmark, view the README.md file in `benchmark/tools/locust-load-inference`
+To further interact with the Locust inference benchmark, view the README.md file in `benchmark/tools/locust-load-inference`

benchmarks/benchmark/dataset/ShareGPT_v3_unflitered_cleaned_split/README.md

@@ -1,10 +1,13 @@
 This directory contains the script for uploading a filtered and formatted file of prompts based on the "anon8231489123/ShareGPT_Vicuna_unfiltered" dataset to a given GCS path.
 
 Example usage:
-   python3 upload_sharegpt.py --gcs_path="gs://$BUCKET_NAME/ShareGPT_V3_unfiltered_cleaned_split_filtered_prompts.txt"
+```
+python3 upload_sharegpt.py --gcs_path="gs://$BUCKET_NAME/ShareGPT_V3_unfiltered_cleaned_split_filtered_prompts.txt"
+```
 
 pre-work:
-- upload_sharegpt.py assumes that the bucket already exists. If it does not exist, make sure that you create your bucket $BUCKET_NAME in your project prior to running the script. You can do that with the following command:
+- upload_sharegpt.py may require additional python libraries; see below.
+- upload_sharegpt.py assumes that the bucket already exists. If you've created your cluster via the terraform scripts in `./infra/stage-2`, then the bucket was created for you. (See `terraform.tfvars` in that directory for the name.) If it does not exist, make sure that you create your bucket $BUCKET_NAME in your project prior to running the script. You can do that with the following command:
 ```
 gcloud storage buckets create gs://$BUCKET_NAME --location=BUCKET_LOCATION
 ```
@@ -20,7 +23,7 @@ Assumes in your environment you:
 - have access to use google storage APIs via Application Default Credentials (ADC)
 
 You may need to do the following:
-- run "pip install google-cloud-storage" to install storage client library dependencies
+- run "pip install wget google-cloud-storage" to install storage client library dependencies. (Optionally, you can run this within a venv, i.e. `python3 -m venv ./venv && source ./venv/bin/activate && pip install ...`)
 - run "gcloud auth application-default login" to enable ADC
 
-For more information on running the google cloud storage API, see https://cloud.google.com/python/docs/reference/storage
+For more information on running the google cloud storage API, see https://cloud.google.com/python/docs/reference/storage

benchmarks/benchmark/tools/locust-load-inference/README.md

@@ -58,7 +58,7 @@ The Locust workload requires storage.admin access to view the dataset in the giv
 To give viewer permissions on the gcs bucket to the gcloud service account, run the following:
 
 ```
-gcloud storage buckets add-iam-policy-binding  gs://$BUCKET/$DATASET_FILENAME
+gcloud storage buckets add-iam-policy-binding  gs://$BUCKET
 --member=serviceAccount:$GOOGLE_SERVICE_ACCOUNT@$PROJECT_ID.iam.gserviceaccount.com --role=roles/storage.admin
 ```
 
@@ -237,4 +237,4 @@ To change the benchmark configuration, you will have to rerun terraform destroy
 | <a name="input_sax_model"></a> [sax\_model](#input\_sax\_model)                                                      | Benchmark server configuration for sax model. Only required if framework is sax.                                            | `string`                                                                                                                                                                                                    | `""`                 |    no    |
 | <a name="input_tokenizer"></a> [tokenizer](#input\_tokenizer)                                                        | Benchmark server configuration for tokenizer.                                                                               | `string`                                                                                                                                                                                                    | `"tiiuae/falcon-7b"` |   yes    |
 | <a name="input_use_beam_search"></a> [use\_beam\_search](#input\_use\_beam\_search)                                  | Benchmark server configuration for use beam search.                                                                         | `bool`                                                                                                                                                                                                      | `false`              |    no    |
-<!-- END_TF_DOCS -->
+<!-- END_TF_DOCS -->

benchmarks/benchmark/tools/locust-load-inference/sample-terraform.tfvars

@@ -12,7 +12,7 @@ artifact_registry                        = "us-central1-docker.pkg.dev/$PROJECT_
 inference_server_service                 = "tgi" # inference server service name
 locust_runner_kubernetes_service_account = "sample-runner-ksa"
 output_bucket                            = "benchmark-output"
-gcs_path                                 = "gs://ai-on-gke-benchmark/ShareGPT_V3_unfiltered_cleaned_split_filtered_prompts.txt"
+gcs_path                                 = "gs://${PROJECT_ID}-ai-gke-benchmark-fuse/ShareGPT_V3_unfiltered_cleaned_split_filtered_prompts.txt"
 
 # Benchmark configuration for Locust Docker accessing inference server
 inference_server_framework = "tgi"
@@ -21,4 +21,4 @@ tokenizer                  = "tiiuae/falcon-7b"
 # Benchmark configuration for triggering single test via Locust Runner
 test_duration = 60
 test_users    = 1
-test_rate     = 5
+test_rate     = 5

benchmarks/infra/stage-2/sample-terraform.tfvars

@@ -8,14 +8,14 @@ credentials_config = {
 # terraform output -json  | jq '."project_id".value'
 project_id = "change-me"
 
-bucket_name     = "ai-gke-benchmark-fuse"
+bucket_name     = "${PROJECT_ID}-ai-gke-benchmark-fuse"
 bucket_location = "US"
 
-output_bucket_name     = "benchmark-output"
+output_bucket_name     = "${PROJECT_ID}-benchmark-output"
 output_bucket_location = "US"
 
 google_service_account     = "benchmark-sa"
 kubernetes_service_account = "benchmark-ksa"
 
 benchmark_runner_google_service_account     = "sample-runner-sa"
-benchmark_runner_kubernetes_service_account = "sample-runner-ksa"
+benchmark_runner_kubernetes_service_account = "sample-runner-ksa"