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modify: VGG tutorial
train loader fail
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.ci/docker/build.sh

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shift
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export UBUNTU_VERSION="20.04"
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export CUDA_VERSION="12.4.1"
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export BASE_IMAGE="ubuntu:${UBUNTU_VERSION}"
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export BASE_IMAGE="nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
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echo "Building ${IMAGE_NAME} Docker image"
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docker build \

.ci/docker/common/common_utils.sh

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}
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pip_install() {
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as_ci_user conda run -n py_$ANACONDA_PYTHON_VERSION pip install --progress-bar off $*
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as_ci_user conda run -n py_$ANACONDA_PYTHON_VERSION pip3 install --progress-bar off $*
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}

.ci/docker/requirements.txt

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sphinx_design
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docutils==0.16
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sphinx-copybutton
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sphinx_sitemap==2.6.0
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pypandoc==1.12
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pandocfilters
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markdown
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torchx
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torchrl==0.5.0
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tensordict==0.5.0
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ax-platform>==0.4.0
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nbformat>==5.9.2
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ax-platform>=0.4.0
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nbformat>=5.9.2
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datasets
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transformers
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torchmultimodal-nightly # needs to be updated to stable as soon as it's avaialable
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gymnasium[mujoco]==0.27.0
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timm
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iopath
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pygame==2.1.2
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pygame==2.6.0
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pycocotools
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semilearn==0.3.2
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torchao==0.0.3
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segment_anything==1.0
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segment_anything==1.0

.gitmodules

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[submodule "src/pytorch-sphinx-theme"]
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path = src/pytorch-sphinx-theme
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url = https://github.com/pytorch/pytorch_sphinx_theme

.jenkins/metadata.json

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"intermediate_source/model_parallel_tutorial.py": {
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"needs": "linux.16xlarge.nvidia.gpu"
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},
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"recipes_source/torch_export_aoti_python.py": {
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"needs": "linux.g5.4xlarge.nvidia.gpu"
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},
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"advanced_source/pendulum.py": {
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"needs": "linux.g5.4xlarge.nvidia.gpu",
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"_comment": "need to be here for the compiling_optimizer_lr_scheduler.py to run."

README.md

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Here is how you can create a new tutorial (for a detailed description, see [CONTRIBUTING.md](./CONTRIBUTING.md)):
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NOTE: Before submitting a new tutorial, read [PyTorch Tutorial Submission Policy](./tutorial_submission_policy.md).
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1. Create a Python file. If you want it executed while inserted into documentation, save the file with the suffix `tutorial` so that the file name is `your_tutorial.py`.
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2. Put it in one of the `beginner_source`, `intermediate_source`, `advanced_source` directory based on the level of difficulty. If it is a recipe, add it to `recipes_source`. For tutorials demonstrating unstable prototype features, add to the `prototype_source`.
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3. For Tutorials (except if it is a prototype feature), include it in the `toctree` directive and create a `customcarditem` in [index.rst](./index.rst).
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## Building locally
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The tutorial build is very large and requires a GPU. If your machine does not have a GPU device, you can preview your HTML build without actually downloading the data and running the tutorial code:
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The tutorial build is very large and requires a GPU. If your machine does not have a GPU device, you can preview your HTML build without actually downloading the data and running the tutorial code:
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1. Install required dependencies by running: `pip install -r requirements.txt`.
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- If you have a GPU-powered laptop, you can build using `make docs`. This will download the data, execute the tutorials and build the documentation to `docs/` directory. This might take about 60-120 min for systems with GPUs. If you do not have a GPU installed on your system, then see next step.
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- You can skip the computationally intensive graph generation by running `make html-noplot` to build basic html documentation to `_build/html`. This way, you can quickly preview your tutorial.
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> If you get **ModuleNotFoundError: No module named 'pytorch_sphinx_theme' make: *** [html-noplot] Error 2** from /tutorials/src/pytorch-sphinx-theme or /venv/src/pytorch-sphinx-theme (while using virtualenv), run `python setup.py install`.
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## Building a single tutorial
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You can build a single tutorial by using the `GALLERY_PATTERN` environment variable. For example to run only `neural_style_transfer_tutorial.py`, run:
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## About contributing to PyTorch Documentation and Tutorials
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* You can find information about contributing to PyTorch documentation in the
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PyTorch Repo [README.md](https://github.com/pytorch/pytorch/blob/master/README.md) file.
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* You can find information about contributing to PyTorch documentation in the
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PyTorch Repo [README.md](https://github.com/pytorch/pytorch/blob/master/README.md) file.
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* Additional information can be found in [PyTorch CONTRIBUTING.md](https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md).
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_static/css/custom.css

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transition: none;
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transform-origin: none;
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}
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.pytorch-left-menu-search input[type=text] {
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background-image: none;
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}
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.gsc-control-cse {
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padding-left: 0px !important;
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padding-bottom: 0px !important;
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}
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.gsc-search-button .gsc-search-button-v2:focus {
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border: transparent !important;
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outline: none;
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box-shadow: none;
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}
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.gsc-search-button-v2:active {
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border: none !important;
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}
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.gsc-search-button-v2 {
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border: none !important;
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}

_templates/layout.html

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</script>
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{%- endblock %}
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{% block sidebartitle %}
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{% if theme_display_version %}
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{%- set nav_version = version %}
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{% if READTHEDOCS and current_version %}
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{%- set nav_version = current_version %}
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{% endif %}
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{% if nav_version %}
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<div class="version">
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{{ nav_version }}
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</div>
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{% endif %}
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{% endif %}
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<div class="searchbox">
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<script async src="https://cse.google.com/cse.js?cx=e65585f8c3ea1440e"></script>
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<div class="gcse-search"></div>
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</div>
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{% endblock %}
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{% block footer %}
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{{ super() }}

advanced_source/cpp_custom_ops.rst

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known as a "meta kernel" or "abstract impl"). FakeTensors are Tensors that have
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metadata (such as shape, dtype, device) but no data: the FakeTensor kernel for an
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operator specifies how to compute the metadata of output tensors given the metadata of input tensors.
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The FakeTensor kernel should return dummy Tensors of your choice with
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the correct Tensor metadata (shape/strides/``dtype``/device).
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We recommend that this be done from Python via the `torch.library.register_fake` API,
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though it is possible to do this from C++ as well (see

advanced_source/dynamic_quantization_tutorial.py

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model.load_state_dict(
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torch.load(
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model_data_filepath + 'word_language_model_quantize.pth',
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map_location=torch.device('cpu')
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map_location=torch.device('cpu'),
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weights_only=True
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)
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)
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