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Deprecate Tensorboard tutorials
1 parent 2f3f3fa commit 86239af

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.jenkins/build.sh

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@@ -42,11 +42,6 @@ if [[ "${JOB_TYPE}" == "worker" ]]; then
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# Temp remove for 1.10 release.
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# python $DIR/remove_runnable_code.py advanced_source/neural_style_tutorial.py advanced_source/neural_style_tutorial.py || true
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# TODO: Fix bugs in these tutorials to make them runnable again
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# python $DIR/remove_runnable_code.py beginner_source/audio_classifier_tutorial.py beginner_source/audio_classifier_tutorial.py || true
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# Remove runnable code from tensorboard_profiler_tutorial.py as it frequently crashes, see https://github.com/pytorch/pytorch/issues/74139
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# python $DIR/remove_runnable_code.py intermediate_source/tensorboard_profiler_tutorial.py intermediate_source/tensorboard_profiler_tutorial.py || true
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# Step 2: Keep certain tutorials based on file count, and remove runnable code in all other tutorials
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# IMPORTANT NOTE: We assume that each tutorial has a UNIQUE filename.

.jenkins/validate_tutorials_built.py

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"prototype_source/nestedtensor",
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"recipes_source/recipes/saving_and_loading_models_for_inference",
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"recipes_source/recipes/saving_multiple_models_in_one_file",
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"recipes_source/recipes/tensorboard_with_pytorch",
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"recipes_source/recipes/what_is_state_dict",
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"recipes_source/recipes/profiler_recipe",
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"recipes_source/recipes/save_load_across_devices",
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"recipes_source/recipes/amp_recipe",
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"recipes_source/recipes/Captum_Recipe",
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"intermediate_source/flask_rest_api_tutorial",
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"intermediate_source/text_to_speech_with_torchaudio",
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"intermediate_source/tensorboard_profiler_tutorial", # reenable after 2.0 release.
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"intermediate_source/torch_export_tutorial" # reenable after 2940 is fixed.
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]
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beginner_source/hyperparameter_tuning_tutorial.py

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Fortunately, there are tools that help with finding the best combination of parameters.
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`Ray Tune <https://docs.ray.io/en/latest/tune.html>`_ is an industry standard tool for
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distributed hyperparameter tuning. Ray Tune includes the latest hyperparameter search
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algorithms, integrates with TensorBoard and other analysis libraries, and natively
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algorithms, integrates with various analysis libraries, and natively
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supports distributed training through `Ray's distributed machine learning engine
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<https://ray.io/>`_.
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beginner_source/introyt/README.txt

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@@ -21,14 +21,10 @@ Introduction to PyTorch on YouTube
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Building Models with PyTorch
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https://pytorch.org/tutorials/beginner/introyt/modelsyt_tutorial.html
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6. tensorboardyt_tutorial.py
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PyTorch TensorBoard Support
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https://pytorch.org/tutorials/beginner/introyt/tensorboardyt_tutorial.html
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7. trainingyt_tutorial.py
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6. trainingyt_tutorial.py
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Training with PyTorch
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https://pytorch.org/tutorials/beginner/introyt/trainingyt_tutorial.html
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8. captumyt_tutorial.py
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7. captumyt_tutorial.py
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Model Understanding with Captum
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https://pytorch.org/tutorials/beginner/introyt/captumyt_tutorial.html

beginner_source/introyt/autogradyt_tutorial.py

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`Tensors <tensors_deeper_tutorial.html>`_ ||
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**Autograd** ||
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`Building Models <modelsyt_tutorial.html>`_ ||
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`TensorBoard Support <tensorboardyt_tutorial.html>`_ ||
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`Training Models <trainingyt.html>`_ ||
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`Model Understanding <captumyt.html>`_
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beginner_source/introyt/captumyt.py

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`Tensors <tensors_deeper_tutorial.html>`_ ||
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`Autograd <autogradyt_tutorial.html>`_ ||
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`Building Models <modelsyt_tutorial.html>`_ ||
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`TensorBoard Support <tensorboardyt_tutorial.html>`_ ||
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`Training Models <trainingyt.html>`_ ||
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**Model Understanding**
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beginner_source/introyt/introyt1_tutorial.py

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`Tensors <tensors_deeper_tutorial.html>`_ ||
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`Autograd <autogradyt_tutorial.html>`_ ||
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`Building Models <modelsyt_tutorial.html>`_ ||
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`TensorBoard Support <tensorboardyt_tutorial.html>`_ ||
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`Training Models <trainingyt.html>`_ ||
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`Model Understanding <captumyt.html>`_
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beginner_source/introyt/modelsyt_tutorial.py

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`Tensors <tensors_deeper_tutorial.html>`_ ||
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`Autograd <autogradyt_tutorial.html>`_ ||
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**Building Models** ||
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`TensorBoard Support <tensorboardyt_tutorial.html>`_ ||
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`Training Models <trainingyt.html>`_ ||
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`Model Understanding <captumyt.html>`_
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