Update README and versions for 1.40.0 / 24.05 (#883)

* Update README and versions for 1.40.0 / 24.05

Author: mc-nv

README.md

@@ -1,5 +1,5 @@
 <!--
-Copyright (c) 2020-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+Copyright (c) 2020-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -18,5 +18,98 @@ limitations under the License.
 
 # Triton Model Analyzer
 
-> [!Warning]
-> You are currently on the `24.05` branch which tracks under-development and unreleased features.
+Triton Model Analyzer is a CLI tool which can help you find a more optimal configuration, on a given piece of hardware, for single, multiple, ensemble, or BLS models running on a [Triton Inference Server](https://github.com/triton-inference-server/server/). Model Analyzer will also generate reports to help you better understand the trade-offs of the different configurations along with their compute and memory requirements.
+<br><br>
+
+# Features
+
+### Search Modes
+
+- [Quick Search](docs/config_search.md#quick-search-mode) will **sparsely** search the [Max Batch Size](https://github.com/triton-inference-server/server/blob/r24.05/docs/user_guide/model_configuration.md#maximum-batch-size),
+  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/r24.05/docs/user_guide/model_configuration.md#dynamic-batcher), and
+  [Instance Group](https://github.com/triton-inference-server/server/blob/r24.05/docs/user_guide/model_configuration.md#instance-groups) spaces by utilizing a heuristic hill-climbing algorithm to help you quickly find a more optimal configuration
+
+- [Automatic Brute Search](docs/config_search.md#automatic-brute-search) will **exhaustively** search the
+  [Max Batch Size](https://github.com/triton-inference-server/server/blob/r24.05/docs/user_guide/model_configuration.md#maximum-batch-size),
+  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/r24.05/docs/user_guide/model_configuration.md#dynamic-batcher), and
+  [Instance Group](https://github.com/triton-inference-server/server/blob/r24.05/docs/user_guide/model_configuration.md#instance-groups)
+  parameters of your model configuration
+
+- [Manual Brute Search](docs/config_search.md#manual-brute-search) allows you to create manual sweeps for every parameter that can be specified in the model configuration
+
+### Model Types
+
+- [Ensemble Model Search](docs/config_search.md#ensemble-model-search): Model Analyzer can help you find the optimal
+  settings when profiling an ensemble model, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
+
+- [BLS Model Search](docs/config_search.md#bls-model-search): Model Analyzer can help you find the optimal
+  settings when profiling a BLS model, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
+
+- [Multi-Model Search](docs/config_search.md#multi-model-search-mode): Model Analyzer can help you
+  find the optimal settings when profiling multiple concurrent models, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
+
+- [LLM Search](docs/config_search.md#llm-search-mode): Model Analyzer can help you
+  find the optimal settings when profiling large language models, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
+
+### Other Features
+
+- [Detailed and summary reports](docs/report.md): Model Analyzer is able to generate
+  summarized and detailed reports that can help you better understand the trade-offs
+  between different model configurations that can be used for your model.
+
+- [QoS Constraints](docs/config.md#constraint): Constraints can help you
+  filter out the Model Analyzer results based on your QoS requirements. For
+  example, you can specify a latency budget to filter out model configurations
+  that do not satisfy the specified latency threshold.
+  <br><br>
+
+# Examples and Tutorials
+
+### **Single Model**
+
+See the [Single Model Quick Start](docs/quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on a simple PyTorch model.
+
+### **Multi Model**
+
+See the [Multi-model Quick Start](docs/mm_quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on two models running concurrently on the same GPU.
+
+### **Ensemble Model**
+
+See the [Ensemble Model Quick Start](docs/ensemble_quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on a simple Ensemble model.
+
+### **BLS Model**
+
+See the [BLS Model Quick Start](docs/bls_quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on a simple BLS model.
+<br><br>
+
+# Documentation
+
+- [Installation](docs/install.md)
+- [Model Analyzer CLI](docs/cli.md)
+- [Launch Modes](docs/launch_modes.md)
+- [Configuring Model Analyzer](docs/config.md)
+- [Model Analyzer Metrics](docs/metrics.md)
+- [Model Config Search](docs/config_search.md)
+- [Checkpointing](docs/checkpoints.md)
+- [Model Analyzer Reports](docs/report.md)
+- [Deployment with Kubernetes](docs/kubernetes_deploy.md)
+  <br><br>
+
+# Reporting problems, asking questions
+
+We appreciate any feedback, questions or bug reporting regarding this
+project. When help with code is needed, follow the process outlined in
+the Stack Overflow (https://stackoverflow.com/help/mcve)
+document. Ensure posted examples are:
+
+- minimal – use as little code as possible that still produces the
+  same problem
+
+- complete – provide all parts needed to reproduce the problem. Check
+  if you can strip external dependency and still show the problem. The
+  less time we spend on reproducing problems the more time we have to
+  fix it
+
+- verifiable – test the code you're about to provide to make sure it
+  reproduces the problem. Remove all other problems that are not
+  related to your request/question.