Fix Broken Quickstart Links (#2641)

* fix broken link of quickstart guide
Signed-off-by: Faradawn Yang <73060648+faradawn@users.noreply.github.com>

* Update README.rst
Co-authored-by: Paweł Gadziński <62263673+pggPL@users.noreply.github.com>
Signed-off-by: Faradawn Yang <73060648+faradawn@users.noreply.github.com>

* moved getting started guide to first and moved jax out of pytorch section
Signed-off-by: Faradawn Yang <73060648+faradawn@users.noreply.github.com>

* Update README.rst
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Signed-off-by: Faradawn Yang <73060648+faradawn@users.noreply.github.com>

---------
Signed-off-by: Faradawn Yang <73060648+faradawn@users.noreply.github.com>
Co-authored-by: Paweł Gadziński <62263673+pggPL@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

README.rst

@@ -137,7 +137,7 @@ Flax
       for _ in range(10):
         loss, (param_grads, other_grads) = fwd_bwd_fn(params, other_variables, inp)
 
-For a more comprehensive tutorial, check out our `Quickstart Notebook <https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb>`_.
+For a more comprehensive tutorial, check out our `Getting Started Guide <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/getting_started.html>`_.
 
 .. overview-end-marker-do-not-remove
 

examples/README.md

@@ -23,8 +23,6 @@ Additionally, we offer [Jupyter notebook tutorials](https://github.com/NVIDIA/Tr
   - **FP8 Weight Caching**: Avoiding redundant FP8 casting during multiple gradient accumulation steps to improve efficiency.
 - [Introduction to FP8](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/fp8_primer.ipynb)
   - Overview of FP8 datatypes (E4M3, E5M2), mixed precision training, delayed scaling strategies, and code examples for FP8 configuration and usage.
-- [TE Quickstart](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb)
-  - Introduction to TE, building a Transformer Layer using PyTorch, and instructions on integrating TE modules like Linear and LayerNorm.
 - [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/mnist)
 
 # JAX
@@ -34,6 +32,8 @@ Additionally, we offer [Jupyter notebook tutorials](https://github.com/NVIDIA/Tr
   - Model Parallelism: Divide a model across multiple GPUs for parallel training.
   - Multiprocessing with Model Parallelism: Multiprocessing for model parallelism, including multi-node support and hardware affinity setup.
 - [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/jax/mnist)
+- [TE JAX Integration Tutorial](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/te_jax_integration.ipynb)
+  - Introduction to integrating TE into an existing JAX model framework, building a Transformer Layer, and instructions on integrating TE modules like Linear and LayerNorm.
 
 # Third party
 - [Hugging Face Accelerate + TE](https://github.com/huggingface/accelerate/tree/main/benchmarks/fp8/transformer_engine)