docs: Update README to reflect torch 2 support

Update the README file to remove the "experimental" tag from the documentaion.

The existance of the tag was an oversight as Torch 2.x has been supported for 18+ months at this point.

Signed-off-by: J Wyman <[email protected]>

Author: whoisj

README.md

@@ -1,5 +1,5 @@
 <!--
-# Copyright 2020-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2020-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -81,8 +81,8 @@ Currently, Triton requires that a specially patched version of
 PyTorch be used with the PyTorch backend. The full source for
 these PyTorch versions are available as Docker images from
 [NGC](https://ngc.nvidia.com). For example, the PyTorch version
-compatible with the 22.12 release of Triton is available as
-nvcr.io/nvidia/pytorch:22.12-py3.
+compatible with the 25.09 release of Triton is available as
+nvcr.io/nvidia/pytorch:25.09-py3.
 
 Copy over the LibTorch and Torchvision headers and libraries from the
 [PyTorch NGC container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch)
@@ -306,54 +306,13 @@ instance in the
 to ensure that the model instance and the tensors used for inference are
 assigned to the same GPU device as on which the model was traced.
 
-# PyTorch 2.0 Backend \[Experimental\]
+## PyTorch 2.0
 
-> [!WARNING]
-> *This feature is subject to change and removal.*
-
-Starting from 24.01, PyTorch models can be served directly via
-[Python runtime](src/model.py). By default, Triton will use the
-[LibTorch runtime](#pytorch-libtorch-backend) for PyTorch models. To use Python
-runtime, provide the following
-[runtime setting](https://github.com/triton-inference-server/backend/blob/main/README.md#backend-shared-library)
-in the model configuration:
-
-```
-runtime: "model.py"
-```
-
-## Dependencies
-
-### Python backend dependency
-
-This feature depends on
-[Python backend](https://github.com/triton-inference-server/python_backend),
-see
-[Python-based Backends](https://github.com/triton-inference-server/backend/blob/main/docs/python_based_backends.md)
-for more details.
-
-### PyTorch dependency
-
-This feature will take advantage of the
-[`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html#torch-compile)
-optimization, make sure the
-[PyTorch 2.0+ pip package](https://pypi.org/project/torch) is available in the
-same Python environment.
-
-Alternatively, a [Python Execution Environment](#using-custom-python-execution-environments)
-with the PyTorch dependency may be used. It can be created with the
-[provided script](tools/gen_pb_exec_env.sh). The resulting
-`pb_exec_env_model.py.tar.gz` file should be placed at the same
-[backend shared library](https://github.com/triton-inference-server/backend/blob/main/README.md#backend-shared-library)
-directory as the [Python runtime](src/model.py).
-
-## Model Layout
-
-### PyTorch 2.0 models
+### PyTorch 2.0 Models
 
 The model repository should look like:
 
-```
+```bash
 model_repository/
 `-- model_directory
     |-- 1
@@ -362,18 +321,18 @@ model_repository/
     `-- config.pbtxt
 ```
 
-The `model.py` contains the class definition of the PyTorch model. The class
-should extend the
+The `model.py` contains the class definition of the PyTorch model.
+The class should extend the
 [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module).
 The `model.pt` may be optionally provided which contains the saved
 [`state_dict`](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-for-inference)
 of the model.
 
-### TorchScript models
+### TorchScript Models
 
 The model repository should look like:
 
-```
+```bash
 model_repository/
 `-- model_directory
     |-- 1
@@ -383,49 +342,51 @@ model_repository/
 
 The `model.pt` is the TorchScript model file.
 
-## Customization
+### Customization
 
 The following PyTorch settings may be customized by setting parameters on the
 `config.pbtxt`.
 
 [`torch.set_num_threads(int)`](https://pytorch.org/docs/stable/generated/torch.set_num_threads.html#torch.set_num_threads)
-- Key: NUM_THREADS
-- Value: The number of threads used for intraop parallelism on CPU.
+
+* Key: `NUM_THREADS`
+* Value: The number of threads used for intra-op parallelism on CPU.
 
 [`torch.set_num_interop_threads(int)`](https://pytorch.org/docs/stable/generated/torch.set_num_interop_threads.html#torch.set_num_interop_threads)
-- Key: NUM_INTEROP_THREADS
-- Value: The number of threads used for interop parallelism (e.g. in JIT
-interpreter) on CPU.
+
+* Key: `NUM_INTEROP_THREADS`
+* Value: The number of threads used for interop parallelism (e.g. in JIT interpreter) on CPU.
 
 [`torch.compile()` parameters](https://pytorch.org/docs/stable/generated/torch.compile.html#torch-compile)
-- Key: TORCH_COMPILE_OPTIONAL_PARAMETERS
-- Value: Any of following parameter(s) encoded as a JSON object.
-  - fullgraph (*bool*): Whether it is ok to break model into several subgraphs.
-  - dynamic (*bool*): Use dynamic shape tracing.
-  - backend (*str*): The backend to be used.
-  - mode (*str*): Can be either "default", "reduce-overhead" or "max-autotune".
-  - options (*dict*): A dictionary of options to pass to the backend.
-  - disable (*bool*): Turn `torch.compile()` into a no-op for testing.
+
+* Key: `TORCH_COMPILE_OPTIONAL_PARAMETERS`
+* Value: Any of following parameter(s) encoded as a JSON object.
+  * `fullgraph` (`bool`): Whether it is ok to break model into several subgraphs.
+  * `dynamic` (`bool`): Use dynamic shape tracing.
+  * `backend` (`str`): The backend to be used.
+  * `mode` (`str`): Can be either `"default"`, `"reduce-overhead"`, or `"max-autotune"`.
+  * `options` (`dict`): A dictionary of options to pass to the backend.
+  * `disable` (`bool`): Turn `torch.compile()` into a no-op for testing.
 
 For example:
-```
+
+```proto
 parameters: {
-    key: "NUM_THREADS"
-    value: { string_value: "4" }
+  key: "NUM_THREADS"
+  value: { string_value: "4" }
 }
 parameters: {
-    key: "TORCH_COMPILE_OPTIONAL_PARAMETERS"
-    value: { string_value: "{\"disable\": true}" }
+  key: "TORCH_COMPILE_OPTIONAL_PARAMETERS"
+  value: { string_value: "{\"disable\": true}" }
 }
 ```
 
-## Limitations
+### Limitations
 
 Following are few known limitations of this feature:
-- Python functions optimizable by `torch.compile` may not be served directly in
-the `model.py` file, they need to be enclosed by a class extending the
-[`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module).
-- Model weights cannot be shared across multiple instances on the same GPU
-device.
-- When using `KIND_MODEL` as model instance kind, the default device of the
-first parameter on the model is used.
+
+* Python functions optimizable by `torch.compile` may not be served directly in the `model.py` file, they need to be enclosed by a class extending the
+  [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module).
+* Model weights cannot be shared across multiple instances on the same GPU device.
+* When using `KIND_MODEL` as model instance kind, the default device of the first parameter on the model is used.
+