Create getting_started.md

Add "Controlling Compiler Behaviour Programatically" section in controlling.md

docs/src/SUMMARY.md

@@ -1,6 +1,7 @@
 # Summary
 
 [Introduction](./overview.md)
+[Getting Started](./getting_started.md)
 
 # User guide
 - [Overview](./overview.md)

docs/src/controlling.md

@@ -11,3 +11,33 @@ You can use the following environment variables to override default behaviour:
 | TT_TORCH_CONSTEVAL_PARAMETERS | Extends consteval to include parameters (e.g., model weights) as well as embedded constants. | False |
 | TT_TORCH_EMBEDDEDD_CONSTANTS | Remove embedded constants from the Torch FX graph and convert them to constant inputs | False |
 | TT_TORCH_IR_LOG_LEVEL | Enables printing MLIR from Torch to TTNN. It supports two modes; `INFO` and `DEBUG`. `INFO` prints MLIR for all conversions steps (Torch, StableHLO, TTIR and TTNN MLIR graphs). `DEBUG` prints intermediate MLIR for all passes (IR dump before and after each pass) additionally. Be warned, `DEBUG` IR printing forces single core compile, so it is much slower. | Disable |
+
+### Controlling Compiler Behaviour Programatically
+
+Instead of using the above environment variables, compiler behaviour can be configured programatically as well.
+
+Here is an example of enabling consteval:
+```py
+from tt_torch.dynamo.backend import backend
+from tt_torch.tools.utils import CompilerConfig
+import torch
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        ...
+
+    def foward(self, ...):
+        ...
+
+model = MyModel()
+
+cc = CompilerConfig()
+cc.enable_consteval = True
+cc.consteval_parameters = True # This will enable constant folding on the parameters in addition to any constants
+
+model = torch.compile(model, backend=backend, options=cc)
+
+inputs = ...
+
+outputs = model(inputs)
+```

docs/src/getting_started.md

@@ -0,0 +1,88 @@
+# Getting Started
+
+## Creating a Virtual Environment (skip if you already have one)
+
+Create a virtual environment if you do not already have one in your project:
+```sh
+python -m venv myvenv
+```
+This will create a virtual environemnt in the folder `myvenv` in the current directory.
+
+Activate the environemnt:
+```sh
+source myvenv/bin/activate
+```
+
+## Installing tt_torch
+
+### Installation Notes
+- `tt_torch` requires a pytorch installation that ships with their ABI.
+    - The `tt_torch` wheel lists the following version of torch as an installation requirement:
+      `torch@https://download.pytorch.org/whl/cpu-cxx11-abi/torch-2.5.0%2Bcpu.cxx11.abi-cp311-cp311-linux_x86_64.whl`
+    - This will be installed by pip upon installing the `tt_torch` wheel
+
+### Installing the tt_torch wheel
+
+Download the latest `tt_torch` wheel from [here](https://github.com/tenstorrent/tt-forge)
+
+Install the wheel:
+```sh
+pip install <PATH_TO_TT_TORCH_WHEEL>.whl
+```
+
+This project requires `torch-mlir`. Install the following wheel as well:
+```sh
+pip install torch-mlir -f https://github.com/llvm/torch-mlir-release/releases/expanded_assets/dev-wheels
+```
+
+### Updating `PYTHONPATH`
+
+In addition to the `tt_torch` python library that gets installed in `<YOUR_ENV_ROOT>/lib/python3.x/site-packages`, some binaries will be installed in `<YOUR_ENV_ROOT>/lib`, and some files from [tt-metal](https://github.com/tenstorrent/tt-metal) will be installed under `<YOUR_ENV_ROOT>/tt-metal`. Python needs to see these installations and so you should update your `PYTHONPATH` environment variable to include them:
+```sh
+export PYTHONPATH=$PYTHONPATH:<YOUR_ENV_ROOT>:<YOUR_ENV_ROOT>/lib
+```
+
+
+## Compiling and Running a Model
+
+Once you have your `torch.nn.Module` compile the model:
+```py
+from tt_torch.dynamo.backend import backend
+import torch
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        ...
+
+    def foward(self, ...):
+        ...
+
+model = MyModel()
+
+model = torch.compile(model, backend=backend)
+
+inputs = ...
+
+outputs = model(inputs)
+```
+
+## Example - Add Two Tensors
+
+Here is an exampe of a small model which adds its inputs running through tt-torch. Try it out!
+
+```py
+from tt_torch.dynamo.backend import backend
+import torch
+
+class AddTensors(torch.nn.Module):
+  def forward(self, x, y):
+    return x + y
+
+
+model = AddTensors()
+tt_model = torch.compile(model, backend=backend)
+
+x = torch.ones(5, 5)
+y = torch.ones(5, 5)
+print(tt_model(x, y))
+```