fix: update project name, version, system data support matrix (#3)

Author: tianhaox

README.md

@@ -3,14 +3,14 @@ SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All
 SPDX-License-Identifier: Apache-2.0
 -->
 
-# aiconfigurator
+# AIConfigurator
 Today, in disaggregated serving, it's quite difficult to find a proper config
 to get benefits from disaggregation such as how many prefill workers and decode workers 
 do I need and what about the parallelism for each worker. Combined with SLA: 
 TTFT(Time-To-First-Token) and TPOT(Time-Per-Output-Token), it becomes even more complicated 
 to solve the throughput @ latency problem.
 
-We're introducing aiconfigurator to help you find a good reference to start with in your 
+We're introducing AIConfigurator to help you find a good reference to start with in your 
 disaggregated serving journey. The tool will try to search the space to get a good deployment config 
 based on your requirement including which model you want to serve, how many GPUs you have and what's 
 the GPU. Automatically generate the config files for you to deploy with Dynamo.
@@ -52,7 +52,7 @@ With **-h**, you can have more information about optional args to customize your
 
 ```
 ********************************************************************************
-*                      Dynamo aiconfigurator Final Results                     *
+*                      Dynamo AIConfigurator Final Results                     *
 ********************************************************************************
   ----------------------------------------------------------------------------
   Input Configuration & SLA Target:
@@ -205,6 +205,15 @@ TRTLLM Versions: 0.20.0, 1.0.0rc3
 Parallel modes: Tensor-parallel; Pipeline-parallel; Expert Tensor-parallel/Expert-parallell; Attention DP for DEEPSEEK and MoE  
 Scheduling: Static; IFB(continuous batching); Disaggregated serving; MTP for DEEPSEEK
 
+### System Data Support Matrix
+
+| System | Framework(Version) | Status |
+|--------|-------------------|--------|
+| h100_sxm | TRTLLM(0.20.0, 1.0.0rc3) | ✅ |
+| h200_sxm | TRTLLM(0.20.0, 1.0.0rc3) | ✅ |
+| b200_sxm | TRTLLM(NA) | 🚧 |
+
+
 ## Data Collection
 Data collection is a standalone process for collecting the database for aiconfigurator. By default, you don't have to collect the data by yourself.
 Small versions of database will not introduce huge perf difference. Say, you can use 1.0.0rc3 data of trtllm on h200_sxm and deploy the generated 

pyproject.toml

@@ -3,7 +3,7 @@
 
 [project]
 name = "aiconfigurator"
-version = "0.1.0"
+version = "0.1.1"
 authors = [
     { name = "NVIDIA Inc.", email = "sw-dl-dynamo@nvidia.com" },
 ]

src/aiconfigurator/cli/main.py

@@ -89,7 +89,7 @@ class AIConfiguratorConfig:
 
 @dataclass
 class AIConfiguratorResult:
-    """Result of Dynamo aiconfigurator"""
+    """Result of Dynamo AIConfigurator"""
     chosen_system_type: str # "agg" or "disagg" or "none"    
     has_disagg_benefit: bool
     benefit_ratio: float # disagg_throughput / agg_throughput
@@ -997,7 +997,7 @@ def main(args):
     logging.basicConfig(level=logging.DEBUG if args.debug else logging.INFO, 
                         format='%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s')
 
-    logger.info(f"Loading Dynamo aiconfigurator version: {__version__}")
+    logger.info(f"Loading Dynamo AIConfigurator version: {__version__}")
 
     # Create aiconfigurator and its config
     aiconfigurator = AIConfigurator()
@@ -1037,7 +1037,7 @@ def main(args):
         logger.info(f"Configuration completed in {end_time - start_time:.2f} seconds")
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Dynamo aiconfigurator for Disaggregated Serving Deployment")
+    parser = argparse.ArgumentParser(description="Dynamo AIConfigurator for Disaggregated Serving Deployment")
     configure_parser(parser)
     args = parser.parse_args()
     main(args)

src/aiconfigurator/main.py

@@ -8,7 +8,7 @@
 
 def main():
     parser = argparse.ArgumentParser(
-        description='Dynamo aiconfigurator for disaggregated serving deployment.'
+        description='Dynamo AIConfigurator for disaggregated serving deployment.'
     )
     subparsers = parser.add_subparsers(dest='command', help='Command to run', required=True)
 

tools/simple_sdk_demo/sla_service/sla_service.py

@@ -27,7 +27,7 @@ class PrettyJSONResponse(Response):
     def render(self, content: Any) -> bytes:
         return orjson.dumps(content, option=orjson.OPT_INDENT_2)
 
-app = FastAPI(title="Dynamo aiconfigurator SLA API", description="Dynamo aiconfigurator SLA API", default_response_class=PrettyJSONResponse)
+app = FastAPI(title="Dynamo AIConfigurator SLA API", description="Dynamo AIConfigurator SLA API", default_response_class=PrettyJSONResponse)
 
 @app.get("/sla/supported_models")
 def get_supported_models():