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PyTorch v2.0 반영, pytorch/tutorials@9efe789b (#626)
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β€Ž.build/get_files_to_run.pyβ€Ž

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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()

β€Ž.build/get_sphinx_filenames.pyβ€Ž

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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())

β€Ž.build/validate_tutorials_built.pyβ€Ž

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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])
@@ -68,6 +66,7 @@ def main() -> None:
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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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β€Ž.github/ISSUE_TEMPLATE/1_TRANSLATE_REQUEST.mdβ€Ž

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@@ -15,4 +15,4 @@ _(λ°˜λ“œμ‹œ 지킀셔야 ν•˜λŠ” 일정이 μ•„λ‹™λ‹ˆλ‹€ - 일정이 λ„ˆλ¬΄ λŠ¦μ–΄
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## κ΄€λ ¨ 이슈
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_ν˜„μž¬ λ²ˆμ—­ μš”μ²­ / 진행 내역을 보기 μœ„ν•΄ 각 λ²„μ „μ˜ 메인 이슈λ₯Ό μ°Έμ‘°ν•©λ‹ˆλ‹€._ <br />
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_(νŠΉλ³„ν•œ 일이 μ—†λ‹€λ©΄ λ³€κ²½ν•˜μ§€ μ•ŠμœΌμ…”λ„ λ©λ‹ˆλ‹€.)_
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* κ΄€λ ¨ 이슈: #615 (v1.13)
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* κ΄€λ ¨ 이슈: #660 (v2.0)

β€ŽLICENSEβ€Ž

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@@ -1,6 +1,6 @@
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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

β€ŽREADME.mdβ€Ž

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# PyTorch ν•œκ΅­μ–΄ νŠœν† λ¦¬μ–Ό
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# νŒŒμ΄ν† μΉ˜ ν•œκ΅­μ–΄ νŠœν† λ¦¬μ–Ό (PyTorch tutorials in Korean)
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## μ†Œκ°œ
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PyTorchμ—μ„œ μ œκ³΅ν•˜λŠ” νŠœν† λ¦¬μ–Όμ˜ ν•œκ΅­μ–΄ λ²ˆμ—­μ„ μœ„ν•œ μ €μž₯μ†Œμž…λ‹ˆλ‹€.\
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λ²ˆμ—­μ˜ 결과물은 [https://tutorials.pytorch.kr](https://tutorials.pytorch.kr)μ—μ„œ ν™•μΈν•˜μ‹€ 수 μžˆμŠ΅λ‹ˆλ‹€. (λ²ˆμ—­μ„ μ§„ν–‰ν•˜λ©° **λΉ„μ •κΈ°μ μœΌλ‘œ** λ°˜μ˜ν•©λ‹ˆλ‹€.)\
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ν˜„μž¬ λ²„μ „μ˜ λ²ˆμ—­ / λ³€κ²½ κ΄€λ ¨ μ΄μŠˆλŠ” [#615 이슈](https://github.com/PyTorchKorea/tutorials-kr/issues/615)λ₯Ό μ°Έκ³ ν•΄μ£Όμ„Έμš”.
7+
ν˜„μž¬ λ²„μ „μ˜ λ²ˆμ—­ / λ³€κ²½ κ΄€λ ¨ μ΄μŠˆλŠ” [#660 이슈](https://github.com/PyTorchKorea/tutorials-kr/issues/660)λ₯Ό μ°Έκ³ ν•΄μ£Όμ„Έμš”.
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99
## κΈ°μ—¬ν•˜κΈ°
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@@ -22,7 +22,7 @@ PyTorchμ—μ„œ μ œκ³΅ν•˜λŠ” νŠœν† λ¦¬μ–Όμ˜ ν•œκ΅­μ–΄ λ²ˆμ—­μ„ μœ„ν•œ μ €μž₯μ†Œ
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## 원문
2424

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ν˜„μž¬ PyTorch v1.13 νŠœν† λ¦¬μ–Ό([pytorch/tutorials@db34a77](https://github.com/pytorch/tutorials/commit/db34a779242f1a71346db4a9e5d6ac962a8d9b77) κΈ°μ€€) λ²ˆμ—­μ΄ 진행 μ€‘μž…λ‹ˆλ‹€.
25+
ν˜„μž¬ PyTorch v2.0 νŠœν† λ¦¬μ–Ό([pytorch/tutorials@9efe789b](https://github.com/pytorch/tutorials/commit/9efe789bfc3763ec359b60f12b5e6dda4e6d5db0) κΈ°μ€€) λ²ˆμ—­μ΄ 진행 μ€‘μž…λ‹ˆλ‹€.
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μ΅œμ‹  λ²„μ „μ˜ νŠœν† λ¦¬μ–Ό(곡식, μ˜μ–΄)은 [PyTorch tutorials μ‚¬μ΄νŠΈ](https://pytorch.org/tutorials) 및 [PyTorch tutorials μ €μž₯μ†Œ](https://github.com/pytorch/tutorials)λ₯Ό μ°Έκ³ ν•΄μ£Όμ„Έμš”.
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@@ -46,5 +46,5 @@ v1.0 이후 λ²ˆμ—­μ€ 별도 μ €μž₯μ†Œλ‘œ κ΄€λ¦¬ν•˜μ§€ μ•ŠμŠ΅λ‹ˆλ‹€. [이 μ €μž₯
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λΉŒλ“œ 방법은 [κΈ°μ—¬ν•˜κΈ° λ¬Έμ„œμ˜ `2-5. (λ‚΄ μ»΄ν“¨ν„°μ—μ„œ) κ²°κ³Ό ν™•μΈν•˜κΈ°`](https://github.com/PyTorchKorea/tutorials-kr/blob/master/CONTRIBUTING.md#2-5-λ‚΄-μ»΄ν“¨ν„°μ—μ„œ-κ²°κ³Ό-ν™•μΈν•˜κΈ°) 뢀뢄을 μ°Έκ³ ν•΄μ£Όμ„Έμš”.
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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).

β€Ž_static/img/invpendulum.gifβ€Ž

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β€Ž_static/img/reinforcement_learning_diagram.drawioβ€Ž

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<mxfile host="app.diagrams.net" modified="2022-10-01T16:00:40.980Z" agent="5.0 (X11)" etag="_qbqVrrm3wUvm_i0-Q9T" version="20.4.0" type="device"><diagram id="aSXDm0BvLjt-Za0vl2Tv" name="Page-1">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</diagram></mxfile>

β€Ž_static/img/reinforcement_learning_diagram.jpgβ€Ž

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β€Ž_templates/layout.htmlβ€Ž

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url="https://docs.microsoft.com/learn/modules/intro-machine-learning-pytorch/8-inference?WT.mc_id=aiml-7486-cxa";
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}
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$(".pytorch-call-to-action-links").children().first().before("<a href="+url+' data-behavior="call-to-action-event" data-response="Run in Microsoft Learn" target="_blank"><div id="microsoft-learn-link" style="padding-bottom: 0.625rem;border-bottom: 1px solid #f3f4f7;padding-right: 2.5rem;display: -webkit-box; display: -ms-flexbox; isplay: flex; -webkit-box-align: center;-ms-flex-align: center;align-items: center;"><img class="call-to-action-img" src="../../_static/images/microsoft-logo.svg"/><div class="call-to-action-desktop-view">Run in Microsoft Learn</div><div class="call-to-action-mobile-view">Learn</div></div></a>')
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$(".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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}
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</script>
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@@ -91,7 +91,7 @@
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</script>
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<script type="text/javascript">
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var collapsedSections = ['νŒŒμ΄ν† μΉ˜(PyTorch) λ ˆμ‹œν”Ό', 'νŒŒμ΄ν† μΉ˜(PyTorch) 배우기', '이미지/λΉ„λ””μ˜€', 'μ˜€λ””μ˜€', 'ν…μŠ€νŠΈ', 'κ°•ν™”ν•™μŠ΅', 'PyTorch λͺ¨λΈμ„ ν”„λ‘œλ•μ…˜ ν™˜κ²½μ— λ°°ν¬ν•˜κΈ°', 'Code Transforms with FX', 'ν”„λ‘ νŠΈμ—”λ“œ API', 'PyTorch ν™•μž₯ν•˜κΈ°', 'λͺ¨λΈ μ΅œμ ν™”', '병렬 및 λΆ„μ‚° ν•™μŠ΅', 'Mobile', 'Introduction to PyTorch on YouTube', 'Recommendation Systems'];
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var collapsedSections = ['νŒŒμ΄ν† μΉ˜(PyTorch) λ ˆμ‹œν”Ό', 'νŒŒμ΄ν† μΉ˜(PyTorch) 배우기', '이미지/λΉ„λ””μ˜€', 'μ˜€λ””μ˜€', 'ν…μŠ€νŠΈ', 'κ°•ν™”ν•™μŠ΅', 'PyTorch λͺ¨λΈμ„ ν”„λ‘œλ•μ…˜ ν™˜κ²½μ— λ°°ν¬ν•˜κΈ°', 'Code Transforms with FX', 'ν”„λ‘ νŠΈμ—”λ“œ API', 'PyTorch ν™•μž₯ν•˜κΈ°', 'λͺ¨λΈ μ΅œμ ν™”', '병렬 및 λΆ„μ‚° ν•™μŠ΅', 'λͺ¨λ°”일', 'Introduction to PyTorch on YouTube', 'μΆ”μ²œ μ‹œμŠ€ν…œ', 'Multimodality'];
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</script>
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{% endblock %}

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