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Fix Broken Quickstart Links #2641

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Merged
ptrendx merged 4 commits into from
Feb 11, 2026
Merged

Fix Broken Quickstart Links #2641

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0aee443
fix broken link of quickstart guide
faradawn Feb 2, 2026
d21e875
Update README.rst
faradawn Feb 10, 2026
675756e
moved getting started guide to first and moved jax out of pytorch sec…
faradawn Feb 10, 2026
e329789
Update README.rst
faradawn Feb 11, 2026
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2 changes: 1 addition & 1 deletion README.rst
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Expand Up @@ -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

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6 changes: 3 additions & 3 deletions examples/README.md
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Expand Up @@ -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
Expand All @@ -34,7 +32,9 @@ 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)
- Scripts for training with Accelerate and TE. Supports single GPU, and multi-GPU via DDP, FSDP, and DeepSpeed ZeRO 1-3.
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