Update documentation to reflect command change

Author: jaredoconnell

README.md

@@ -68,12 +68,12 @@ For information on starting other supported inference servers or platforms, see
 
 #### 2. Run a GuideLLM Benchmark
 
-To run a GuideLLM benchmark, use the `guidellm benchmark` command with the target set to an OpenAI-compatible server. For this example, the target is set to 'http://localhost:8000', assuming that vLLM is active and running on the same server. Otherwise, update it to the appropriate location. By default, GuideLLM automatically determines the model available on the server and uses it. To target a different model, pass the desired name with the `--model` argument. Additionally, the `--rate-type` is set to `sweep`, which automatically runs a range of benchmarks to determine the minimum and maximum rates that the server and model can support. Each benchmark run under the sweep will run for 30 seconds, as set by the `--max-seconds` argument. Finally, `--data` is set to a synthetic dataset with 256 prompt tokens and 128 output tokens per request. For more arguments, supported scenarios, and configurations, jump to the [Configurations Section](#configurations) or run `guidellm benchmark --help`.
+To run a GuideLLM benchmark, use the `guidellm benchmark run` command with the target set to an OpenAI-compatible server. For this example, the target is set to 'http://localhost:8000', assuming that vLLM is active and running on the same server. Otherwise, update it to the appropriate location. By default, GuideLLM automatically determines the model available on the server and uses it. To target a different model, pass the desired name with the `--model` argument. Additionally, the `--rate-type` is set to `sweep`, which automatically runs a range of benchmarks to determine the minimum and maximum rates that the server and model can support. Each benchmark run under the sweep will run for 30 seconds, as set by the `--max-seconds` argument. Finally, `--data` is set to a synthetic dataset with 256 prompt tokens and 128 output tokens per request. For more arguments, supported scenarios, and configurations, jump to the [Configurations Section](#configurations) or run `guidellm benchmark --help`.
 
 Now, to start benchmarking, run the following command:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
   --target "http://localhost:8000" \
   --rate-type sweep \
   --max-seconds 30 \
@@ -110,11 +110,11 @@ For further details on determining the optimal request rate and SLOs, refer to t
 
 ### Configurations
 
-GuideLLM offers a range of configurations through both the benchmark CLI command and environment variables, which provide default values and more granular controls. The most common configurations are listed below. A complete list is easily accessible, though, by running `guidellm benchmark --help` or `guidellm config` respectively.
+GuideLLM offers a range of configurations through both the benchmark CLI command and environment variables, which provide default values and more granular controls. The most common configurations are listed below. A complete list is easily accessible, though, by running `guidellm benchmark run --help` or `guidellm config` respectively.
 
 #### Benchmark CLI
 
-The `guidellm benchmark` command is used to run benchmarks against a generative AI backend/server. The command accepts a variety of arguments to customize the benchmark run. The most common arguments include:
+The `guidellm benchmark run` command is used to run benchmarks against a generative AI backend/server. The command accepts a variety of arguments to customize the benchmark run. The most common arguments include:
 
 - `--target`: Specifies the target path for the backend to run benchmarks against. For example, `http://localhost:8000`. This is required to define the server endpoint.
 

docs/datasets.md

@@ -20,7 +20,7 @@ The following arguments can be used to configure datasets and their processing:
 ### Example Usage
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
     --target "http://localhost:8000" \
     --rate-type "throughput" \
     --max-requests 1000 \
@@ -49,7 +49,7 @@ For different use cases, here are the recommended dataset profiles to pass as ar
 #### Example Commands
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
     --target "http://localhost:8000" \
     --rate-type "throughput" \
     --max-requests 1000 \
@@ -59,7 +59,7 @@ guidellm benchmark \
 Or using a JSON string:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
     --target "http://localhost:8000" \
     --rate-type "throughput" \
     --max-requests 1000 \
@@ -90,7 +90,7 @@ GuideLLM supports datasets from the Hugging Face Hub or local directories that f
 #### Example Commands
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
     --target "http://localhost:8000" \
     --rate-type "throughput" \
     --max-requests 1000 \
@@ -100,7 +100,7 @@ guidellm benchmark \
 Or using a local dataset:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
     --target "http://localhost:8000" \
     --rate-type "throughput" \
     --max-requests 1000 \
@@ -152,7 +152,7 @@ GuideLLM supports various file formats for datasets, including text, CSV, JSON,
 #### Example Commands
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
     --target "http://localhost:8000" \
     --rate-type "throughput" \
     --max-requests 1000 \

docs/outputs.md

@@ -5,7 +5,7 @@ GuideLLM provides flexible options for outputting benchmark results, catering to
 For all of the output formats, `--output-extras` can be used to include additional information. This could include tags, metadata, hardware details, and other relevant information that can be useful for analysis. This must be supplied as a JSON encoded string. For example:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
   --target "http://localhost:8000" \
   --rate-type sweep \
   --max-seconds 30 \
@@ -26,21 +26,21 @@ By default, GuideLLM displays benchmark results and progress directly in the con
 
 ### Disabling Console Output
 
-To disable the progress outputs to the console, use the `disable-progress` flag when running the `guidellm benchmark` command. For example:
+To disable the progress outputs to the console, use the `disable-progress` flag when running the `guidellm benchmark run` command. For example:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
   --target "http://localhost:8000" \
   --rate-type sweep \
   --max-seconds 30 \
   --data "prompt_tokens=256,output_tokens=128" \
   --disable-progress
 ```
 
-To disable console output, use the `--disable-console-outputs` flag when running the `guidellm benchmark` command. For example:
+To disable console output, use the `--disable-console-outputs` flag when running the `guidellm benchmark run` command. For example:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
   --target "http://localhost:8000" \
   --rate-type sweep \
   --max-seconds 30 \
@@ -50,10 +50,10 @@ guidellm benchmark \
 
 ### Enabling Extra Information
 
-GuideLLM includes the option to display extra information during the benchmark runs to monitor the overheads and performance of the system. This can be enabled by using the `--display-scheduler-stats` flag when running the `guidellm benchmark` command. For example:
+GuideLLM includes the option to display extra information during the benchmark runs to monitor the overheads and performance of the system. This can be enabled by using the `--display-scheduler-stats` flag when running the `guidellm benchmark run` command. For example:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
   --target "http://localhost:8000" \
   --rate-type sweep \
   --max-seconds 30 \
@@ -81,7 +81,7 @@ GuideLLM supports saving benchmark results to files in various formats, includin
 Example command to save results in YAML format:
 
 ```bash
-guidellm benchmark \
+guidellm benchmark run \
   --target "http://localhost:8000" \
   --rate-type sweep \
   --max-seconds 30 \