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fix links in docs (#5446)
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Peter Park
@@ -338,7 +338,7 @@ with ROCm.
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* - Library
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- Description
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* - `torchaudio <https://docs.pytorch.org/audio/stable/index.html>`_
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* - `torchaudio <https://docs.pytorch.org/audio/stable/index.html>`_
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- Audio and signal processing library for PyTorch. Provides utilities for
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audio I/O, signal and data processing functions, datasets, model
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implementations, and application components for audio and speech
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@@ -365,11 +365,11 @@ with ROCm.
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and popular datasets for natural language processing, including
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tokenization, vocabulary management, and text embeddings.
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**Note:** ``torchtext`` does not implement ROCm-specific kernels.
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**Note:** ``torchtext`` does not implement ROCm-specific kernels.
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ROCm acceleration is provided through the underlying PyTorch framework
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and ROCm library integration. Only official release exists.
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* - `torchdata <https://docs.pytorch.org/data/beta/index.html>`_
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* - `torchdata <https://meta-pytorch.org/data/beta/index.html#torchdata>`_
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- Beta library of common modular data loading primitives for easily
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constructing flexible and performant data pipelines, with features still
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in prototype stage.
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@@ -471,7 +471,7 @@ Known issues and notes for PyTorch 2.7 with ROCm 7.0
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================================================================================
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- The ``matmul.allow_fp16_reduced_precision_reduction`` and
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``matmul.allow_bf16_reduced_precision_reduction`` options under
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``torch.backends.cuda`` are not supported. As a result,
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``matmul.allow_bf16_reduced_precision_reduction`` options under
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``torch.backends.cuda`` are not supported. As a result,
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reduced-precision reductions using FP16 or BF16 accumulation types are not
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available.
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@@ -75,8 +75,8 @@ This helps ensure optimal scaling for multi-accelerator tasks.
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make
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2. Run the suggested RCCL tests -- see `RCCL benchmarking
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<https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#rccl-benchmarking-results>`_
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in the Instinct performance benchmarking documentation for instructions.
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<https://instinct.docs.amd.com/projects/system-acceptance/en/latest/network/rdma-benchmarking.html#rccl-benchmarking-results>`_
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in the AMD Instinct customer acceptance guide.
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TransferBench test
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==================
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@@ -369,7 +369,7 @@ benchmark results:
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benchmark. Run them outside of any Docker container.
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1. Make sure ``$HF_HOME`` is set before running the test. See
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`ROCm benchmarking <https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/readme.md>`__
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`ROCm benchmarking <https://github.com/ROCm/MAD/blob/develop/scripts/jax-maxtext/gpu-rocm/readme.md>`__
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for more details on downloading the Llama models before running the
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benchmark.
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@@ -202,16 +202,14 @@ Getting started
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The following examples demonstrate how to get started with single node
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and multi-node training using the benchmarking scripts provided at
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`<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__.
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`<https://github.com/ROCm/maxtext/>`__.
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.. important::
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The provided scripts launch a Docker container and execute a benchmark. Ensure you run these commands outside of any existing Docker container.
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Before running any benchmarks, ensure the ``$HF_HOME`` environment variable is
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set correctly and points to your Hugging Face cache directory. Refer to the
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README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__
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for more detailed instructions.
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set correctly and points to your Hugging Face cache directory.
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Single node training benchmarking examples
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------------------------------------------
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@@ -299,28 +299,28 @@ Run training
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- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
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* - ``torchdata``
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- `TorchData <https://pytorch.org/data/beta/index.html>`_
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- `TorchData <https://meta-pytorch.org/data/beta/index.html#torchdata>`__
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* - ``tomli``
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- `Tomli <https://pypi.org/project/tomli/>`_
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- `Tomli <https://pypi.org/project/tomli/>`__
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* - ``tiktoken``
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- `tiktoken <https://github.com/openai/tiktoken>`_
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- `tiktoken <https://github.com/openai/tiktoken>`__
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* - ``blobfile``
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- `blobfile <https://pypi.org/project/blobfile/>`_
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- `blobfile <https://pypi.org/project/blobfile/>`__
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* - ``tabulate``
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- `tabulate <https://pypi.org/project/tabulate/>`_
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- `tabulate <https://pypi.org/project/tabulate/>`__
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* - ``wandb``
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- `Weights & Biases <https://github.com/wandb/wandb>`_
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- `Weights & Biases <https://github.com/wandb/wandb>`__
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* - ``sentencepiece``
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- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
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- `SentencePiece <https://github.com/google/sentencepiece>`__ 0.2.0
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* - ``tensorboard``
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- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
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- `TensorBoard <https://www.tensorflow.org/tensorboard>`__ 2.18.0
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.. container:: model-doc pyt_train_flux
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@@ -336,50 +336,50 @@ Run training
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- `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
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* - ``datasets``
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- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
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- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`__ 3.2.0
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* - ``sentencepiece``
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- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
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- `SentencePiece <https://github.com/google/sentencepiece>`__ 0.2.0
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* - ``tensorboard``
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- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
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- `TensorBoard <https://www.tensorflow.org/tensorboard>`__ 2.18.0
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* - ``csvkit``
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- `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
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- `csvkit <https://csvkit.readthedocs.io/en/latest/>`__ 2.0.1
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* - ``deepspeed``
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- `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
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- `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`__ 0.16.2
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* - ``diffusers``
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- `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
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- `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`__ 0.31.0
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* - ``GitPython``
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- `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
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- `GitPython <https://github.com/gitpython-developers/GitPython>`__ 3.1.44
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* - ``opencv-python-headless``
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- `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
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- `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`__ 4.10.0.84
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* - ``peft``
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- `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
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- `PEFT <https://huggingface.co/docs/peft/en/index>`__ 0.14.0
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* - ``protobuf``
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- `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
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- `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`__ 5.29.2
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* - ``pytest``
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- `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
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- `PyTest <https://docs.pytest.org/en/stable/>`__ 8.3.4
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* - ``python-dotenv``
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- `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
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- `python-dotenv <https://pypi.org/project/python-dotenv/>`__ 1.0.1
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* - ``seaborn``
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- `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
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- `Seaborn <https://seaborn.pydata.org/>`__ 0.13.2
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* - ``transformers``
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- `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
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- `Transformers <https://huggingface.co/docs/transformers/en/index>`__ 4.47.0
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``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
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* `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
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* `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`__
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{% for model_group in model_groups %}
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{% for model in model_group.models %}
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