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PyTorch v2.0 ๋ฐ˜์˜, pytorch/tutorials@9efe789b (#626)
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โ€Ž.build/get_files_to_run.py

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from typing import Any, Dict, List, Optional, Tuple
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import json
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import os
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from pathlib import Path
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# from remove_runnable_code import remove_runnable_code
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# Calculate repo base dir
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REPO_BASE_DIR = Path(__file__).absolute().parent.parent
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def get_all_files() -> List[str]:
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sources = [x.relative_to(REPO_BASE_DIR) for x in REPO_BASE_DIR.glob("*_source/**/*.py") if 'data' not in x.parts]
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return [str(x) for x in sources]
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def read_metadata() -> Dict[str, Any]:
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with (REPO_BASE_DIR / ".jenkins" / "metadata.json").open() as fp:
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return json.load(fp)
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def calculate_shards(all_files: List[str], num_shards: int = 20) -> List[List[str]]:
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sharded_files: List[Tuple[float, List[str]]] = [(0.0, []) for _ in range(num_shards)]
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metadata = read_metadata()
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def get_duration(file: str) -> int:
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# tutorials not listed in the metadata.json file usually take
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# <3min to run, so we'll default to 1min if it's not listed
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return metadata.get(file, {}).get("duration", 60)
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def get_needs_machine(file: str) -> Optional[str]:
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return metadata.get(file, {}).get("needs", None)
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def add_to_shard(i, filename):
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shard_time, shard_jobs = sharded_files[i]
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shard_jobs.append(filename)
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sharded_files[i] = (
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shard_time + get_duration(filename),
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shard_jobs,
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)
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all_other_files = all_files.copy()
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needs_gpu_nvidia_small_multi = list(
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filter(lambda x: get_needs_machine(x) == "gpu.nvidia.small.multi", all_files,)
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)
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needs_gpu_nvidia_medium = list(
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filter(lambda x: get_needs_machine(x) == "gpu.nvidia.large", all_files,)
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)
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for filename in needs_gpu_nvidia_small_multi:
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# currently, the only job that uses gpu.nvidia.small.multi is the 0th worker,
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# so we'll add all the jobs that need this machine to the 0th worker
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add_to_shard(0, filename)
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all_other_files.remove(filename)
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for filename in needs_gpu_nvidia_medium:
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# currently, the only job that uses gpu.nvidia.large is the 1st worker,
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# so we'll add all the jobs that need this machine to the 1st worker
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add_to_shard(1, filename)
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all_other_files.remove(filename)
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sorted_files = sorted(all_other_files, key=get_duration, reverse=True,)
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for filename in sorted_files:
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min_shard_index = sorted(range(num_shards), key=lambda i: sharded_files[i][0])[
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0
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]
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add_to_shard(min_shard_index, filename)
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return [x[1] for x in sharded_files]
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def compute_files_to_keep(files_to_run: List[str]) -> List[str]:
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metadata = read_metadata()
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files_to_keep = list(files_to_run)
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for file in files_to_run:
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extra_files = metadata.get(file, {}).get("extra_files", [])
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files_to_keep.extend(extra_files)
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return files_to_keep
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def remove_other_files(all_files, files_to_keep) -> None:
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for file in all_files:
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if file not in files_to_keep:
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remove_runnable_code(file, file)
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def parse_args() -> Any:
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from argparse import ArgumentParser
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parser = ArgumentParser("Select files to run")
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parser.add_argument("--dry-run", action="store_true")
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parser.add_argument("--num-shards", type=int, default=int(os.environ.get("NUM_WORKERS", 20)))
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parser.add_argument("--shard-num", type=int, default=int(os.environ.get("WORKER_ID", 0)))
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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all_files = get_all_files()
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files_to_run = calculate_shards(all_files, num_shards=args.num_shards)[args.shard_num]
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if not args.dry_run:
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remove_other_files(all_files, compute_files_to_keep(files_to_run))
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stripped_file_names = [Path(x).stem for x in files_to_run]
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print(" ".join(stripped_file_names))
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if __name__ == "__main__":
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main()

โ€Ž.build/get_sphinx_filenames.py

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from pathlib import Path
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from typing import List
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from get_files_to_run import get_all_files
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from validate_tutorials_built import NOT_RUN
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def get_files_for_sphinx() -> List[str]:
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all_py_files = get_all_files()
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return [x for x in all_py_files if all(y not in x for y in NOT_RUN)]
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SPHINX_SHOULD_RUN = "|".join(get_files_for_sphinx())

โ€Ž.build/validate_tutorials_built.py

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# the file name to explain why, like intro.html), or fix the tutorial and remove it from this list).
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NOT_RUN = [
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"basics/intro", # no code
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"translation_transformer",
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"profiler",
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"saving_loading_models",
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"introyt/captumyt",
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"introyt/trainingyt",
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"examples_nn/polynomial_module",
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"examples_nn/dynamic_net",
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"examples_nn/polynomial_optim",
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"former_torchies/autograd_tutorial_old",
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"former_torchies/tensor_tutorial_old",
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"examples_autograd/polynomial_autograd",
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"examples_autograd/polynomial_custom_function",
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"parametrizations",
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"mnist_train_nas", # used by ax_multiobjective_nas_tutorial.py
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"fx_conv_bn_fuser",
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"super_resolution_with_onnxruntime",
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"ddp_pipeline", # requires 4 gpus
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"fx_graph_mode_ptq_dynamic",
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"vmap_recipe",
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"torchscript_freezing",
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"nestedtensor",
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"recipes/saving_and_loading_models_for_inference",
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"recipes/saving_multiple_models_in_one_file",
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"recipes/loading_data_recipe",
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"recipes/tensorboard_with_pytorch",
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"recipes/what_is_state_dict",
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"recipes/profiler_recipe",
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"recipes/save_load_across_devices",
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"recipes/warmstarting_model_using_parameters_from_a_different_model",
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"recipes/dynamic_quantization",
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"recipes/saving_and_loading_a_general_checkpoint",
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"recipes/benchmark",
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"recipes/tuning_guide",
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"recipes/zeroing_out_gradients",
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"recipes/defining_a_neural_network",
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"recipes/timer_quick_start",
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"recipes/amp_recipe",
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"recipes/Captum_Recipe",
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"hyperparameter_tuning_tutorial",
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"flask_rest_api_tutorial",
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"text_to_speech_with_torchaudio",
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"beginner_source/basics/intro", # no code
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"beginner_source/translation_transformer",
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"beginner_source/profiler",
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"beginner_source/saving_loading_models",
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"beginner_source/introyt/captumyt",
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"beginner_source/examples_nn/polynomial_module",
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"beginner_source/examples_nn/dynamic_net",
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"beginner_source/examples_nn/polynomial_optim",
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"beginner_source/former_torchies/autograd_tutorial_old",
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"beginner_source/former_torchies/tensor_tutorial_old",
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"beginner_source/examples_autograd/polynomial_autograd",
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"beginner_source/examples_autograd/polynomial_custom_function",
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"intermediate_source/parametrizations",
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"intermediate_source/mnist_train_nas", # used by ax_multiobjective_nas_tutorial.py
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"intermediate_source/fx_conv_bn_fuser",
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"advanced_source/super_resolution_with_onnxruntime",
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"advanced_source/ddp_pipeline", # requires 4 gpus
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"prototype_source/fx_graph_mode_ptq_dynamic",
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"prototype_source/vmap_recipe",
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"prototype_source/torchscript_freezing",
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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/loading_data_recipe",
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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/warmstarting_model_using_parameters_from_a_different_model",
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"recipes_source/recipes/dynamic_quantization",
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"recipes_source/recipes/saving_and_loading_a_general_checkpoint",
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"recipes_source/recipes/benchmark",
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"recipes_source/recipes/tuning_guide",
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"recipes_source/recipes/zeroing_out_gradients",
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"recipes_source/recipes/defining_a_neural_network",
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"recipes_source/recipes/timer_quick_start",
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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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]
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def tutorial_source_dirs() -> List[Path]:
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return [
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p.relative_to(REPO_ROOT).with_name(p.stem[:-7])
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glob_path = f"{tutorial_source_dir}/**/*.html"
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html_file_paths += docs_dir.glob(glob_path)
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should_not_run = [f'{x.replace("_source", "")}.html' for x in NOT_RUN]
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did_not_run = []
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for html_file_path in html_file_paths:
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with open(html_file_path, "r", encoding="utf-8") as html_file:
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if (
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"Total running time of the script: ( 0 minutes 0.000 seconds)"
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in elem.text
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and not any(
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html_file_path.match(file) for file in NOT_RUN
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)
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and not any(html_file_path.match(file) for file in should_not_run)
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):
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did_not_run.append(html_file_path.as_posix())
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โ€Ž.github/ISSUE_TEMPLATE/1_TRANSLATE_REQUEST.md

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@@ -15,4 +15,4 @@ _(๋ฐ˜๋“œ์‹œ ์ง€ํ‚ค์…”์•ผ ํ•˜๋Š” ์ผ์ •์ด ์•„๋‹™๋‹ˆ๋‹ค - ์ผ์ •์ด ๋„ˆ๋ฌด ๋Šฆ์–ด
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## ๊ด€๋ จ ์ด์Šˆ
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_ํ˜„์žฌ ๋ฒˆ์—ญ ์š”์ฒญ / ์ง„ํ–‰ ๋‚ด์—ญ์„ ๋ณด๊ธฐ ์œ„ํ•ด ๊ฐ ๋ฒ„์ „์˜ ๋ฉ”์ธ ์ด์Šˆ๋ฅผ ์ฐธ์กฐํ•ฉ๋‹ˆ๋‹ค._ <br />
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_(ํŠน๋ณ„ํ•œ ์ผ์ด ์—†๋‹ค๋ฉด ๋ณ€๊ฒฝํ•˜์ง€ ์•Š์œผ์…”๋„ ๋ฉ๋‹ˆ๋‹ค.)_
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* ๊ด€๋ จ ์ด์Šˆ: #615 (v1.13)
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* ๊ด€๋ จ ์ด์Šˆ: #660 (v2.0)

โ€ŽLICENSE

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BSD 3-Clause License
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3-
Copyright (c) 2017, Pytorch contributors
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Copyright (c) 2017, PyTorch contributors
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All rights reserved.
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Redistribution and use in source and binary forms, with or without

โ€ŽREADME.md

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# PyTorch ํ•œ๊ตญ์–ด ํŠœํ† ๋ฆฌ์–ผ
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# ํŒŒ์ดํ† ์น˜ ํ•œ๊ตญ์–ด ํŠœํ† ๋ฆฌ์–ผ (PyTorch tutorials in Korean)
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## ์†Œ๊ฐœ
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PyTorch์—์„œ ์ œ๊ณตํ•˜๋Š” ํŠœํ† ๋ฆฌ์–ผ์˜ ํ•œ๊ตญ์–ด ๋ฒˆ์—ญ์„ ์œ„ํ•œ ์ €์žฅ์†Œ์ž…๋‹ˆ๋‹ค.\
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๋ฒˆ์—ญ์˜ ๊ฒฐ๊ณผ๋ฌผ์€ [https://tutorials.pytorch.kr](https://tutorials.pytorch.kr)์—์„œ ํ™•์ธํ•˜์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. (๋ฒˆ์—ญ์„ ์ง„ํ–‰ํ•˜๋ฉฐ **๋น„์ •๊ธฐ์ ์œผ๋กœ** ๋ฐ˜์˜ํ•ฉ๋‹ˆ๋‹ค.)\
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ํ˜„์žฌ ๋ฒ„์ „์˜ ๋ฒˆ์—ญ / ๋ณ€๊ฒฝ ๊ด€๋ จ ์ด์Šˆ๋Š” [#615 ์ด์Šˆ](https://github.com/PyTorchKorea/tutorials-kr/issues/615)๋ฅผ ์ฐธ๊ณ ํ•ด์ฃผ์„ธ์š”.
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ํ˜„์žฌ ๋ฒ„์ „์˜ ๋ฒˆ์—ญ / ๋ณ€๊ฒฝ ๊ด€๋ จ ์ด์Šˆ๋Š” [#660 ์ด์Šˆ](https://github.com/PyTorchKorea/tutorials-kr/issues/660)๋ฅผ ์ฐธ๊ณ ํ•ด์ฃผ์„ธ์š”.
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## ๊ธฐ์—ฌํ•˜๊ธฐ
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@@ -22,7 +22,7 @@ PyTorch์—์„œ ์ œ๊ณตํ•˜๋Š” ํŠœํ† ๋ฆฌ์–ผ์˜ ํ•œ๊ตญ์–ด ๋ฒˆ์—ญ์„ ์œ„ํ•œ ์ €์žฅ์†Œ
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## ์›๋ฌธ
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ํ˜„์žฌ PyTorch v1.13 ํŠœํ† ๋ฆฌ์–ผ([pytorch/tutorials@db34a77](https://github.com/pytorch/tutorials/commit/db34a779242f1a71346db4a9e5d6ac962a8d9b77) ๊ธฐ์ค€) ๋ฒˆ์—ญ์ด ์ง„ํ–‰ ์ค‘์ž…๋‹ˆ๋‹ค.
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ํ˜„์žฌ PyTorch v2.0 ํŠœํ† ๋ฆฌ์–ผ([pytorch/tutorials@9efe789b](https://github.com/pytorch/tutorials/commit/9efe789bfc3763ec359b60f12b5e6dda4e6d5db0) ๊ธฐ์ค€) ๋ฒˆ์—ญ์ด ์ง„ํ–‰ ์ค‘์ž…๋‹ˆ๋‹ค.
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์ตœ์‹  ๋ฒ„์ „์˜ ํŠœํ† ๋ฆฌ์–ผ(๊ณต์‹, ์˜์–ด)์€ [PyTorch tutorials ์‚ฌ์ดํŠธ](https://pytorch.org/tutorials) ๋ฐ [PyTorch tutorials ์ €์žฅ์†Œ](https://github.com/pytorch/tutorials)๋ฅผ ์ฐธ๊ณ ํ•ด์ฃผ์„ธ์š”.
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@@ -46,5 +46,5 @@ v1.0 ์ดํ›„ ๋ฒˆ์—ญ์€ ๋ณ„๋„ ์ €์žฅ์†Œ๋กœ ๊ด€๋ฆฌํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. [์ด ์ €์žฅ
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๋นŒ๋“œ ๋ฐฉ๋ฒ•์€ [๊ธฐ์—ฌํ•˜๊ธฐ ๋ฌธ์„œ์˜ `2-5. (๋‚ด ์ปดํ“จํ„ฐ์—์„œ) ๊ฒฐ๊ณผ ํ™•์ธํ•˜๊ธฐ`](https://github.com/PyTorchKorea/tutorials-kr/blob/master/CONTRIBUTING.md#2-5-๋‚ด-์ปดํ“จํ„ฐ์—์„œ-๊ฒฐ๊ณผ-ํ™•์ธํ•˜๊ธฐ) ๋ถ€๋ถ„์„ ์ฐธ๊ณ ํ•ด์ฃผ์„ธ์š”.
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---
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This is a project to translate [pytorch/tutorials@db34a77](https://github.com/pytorch/tutorials/commit/db34a779242f1a71346db4a9e5d6ac962a8d9b77) into Korean.
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This is a project to translate [pytorch/tutorials@9efe789b](https://github.com/pytorch/tutorials/commit/9efe789bfc3763ec359b60f12b5e6dda4e6d5db0) into Korean.
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For the latest version, please visit to the [official PyTorch tutorials repo](https://github.com/pytorch/tutorials).

โ€Ž_static/img/invpendulum.gif

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โ€Ž_static/img/reinforcement_learning_diagram.drawio

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$(".pytorch-call-to-action-links").children().first().before("<a href="+url+' data-behavior="call-to-action-event" data-response="Run in Microsoft Learn" target="_blank"><div id="microsoft-learn-link" style="padding-bottom: 0.625rem;border-bottom: 1px solid #f3f4f7;padding-right: 2.5rem;display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-align: center;-ms-flex-align: center;align-items: center;"><img class="call-to-action-img" src="../../_static/images/microsoft-logo.svg"/><div class="call-to-action-desktop-view">Run in Microsoft Learn</div><div class="call-to-action-mobile-view">Learn</div></div></a>')
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