prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Clarify the `num_workers` in `ThreadDataLoader`
  **Is your feature request related to a problem? Please describe.**
  When I was introducing GPU transforms and the associated `ThreadDataLoader` to users, got several times feedback about the `num_workers` arg, which is confusing that users think it means the multi-threads in `ThreadDataLoader`, but actually it's the multi-processing workers of PyTorch DataLoader.
  Would be nice to clarify this arg and the use cases.
  
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `monai/data/thread_buffer.py`
  Content:
  ```
  1 # Copyright (c) MONAI Consortium
  2 # Licensed under the Apache License, Version 2.0 (the "License");
  3 # you may not use this file except in compliance with the License.
  4 # You may obtain a copy of the License at
  5 #     http://www.apache.org/licenses/LICENSE-2.0
  6 # Unless required by applicable law or agreed to in writing, software
  7 # distributed under the License is distributed on an "AS IS" BASIS,
  8 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  9 # See the License for the specific language governing permissions and
  10 # limitations under the License.
  11 
  12 
  13 from queue import Empty, Full, Queue
  14 from threading import Thread
  15 
  16 from monai.data import DataLoader, Dataset
  17 
  18 
  19 class ThreadBuffer:
  20     """
  21     Iterates over values from self.src in a separate thread but yielding them in the current thread. This allows values
  22     to be queued up asynchronously. The internal thread will continue running so long as the source has values or until
  23     the stop() method is called.
  24 
  25     One issue raised by using a thread in this way is that during the lifetime of the thread the source object is being
  26     iterated over, so if the thread hasn't finished another attempt to iterate over it will raise an exception or yield
  27     unexpected results. To ensure the thread releases the iteration and proper cleanup is done the stop() method must
  28     be called which will join with the thread.
  29 
  30     Args:
  31         src: Source data iterable
  32         buffer_size: Number of items to buffer from the source
  33         timeout: Time to wait for an item from the buffer, or to wait while the buffer is full when adding items
  34     """
  35 
  36     def __init__(self, src, buffer_size: int = 1, timeout: float = 0.01):
  37         self.src = src
  38         self.buffer_size = buffer_size
  39         self.timeout = timeout
  40         self.buffer: Queue = Queue(self.buffer_size)
  41         self.gen_thread = None
  42         self.is_running = False
  43 
  44     def enqueue_values(self):
  45         for src_val in self.src:
  46             while self.is_running:
  47                 try:
  48                     self.buffer.put(src_val, timeout=self.timeout)
  49                 except Full:
  50                     pass  # try to add the item again
  51                 else:
  52                     break  # successfully added the item, quit trying
  53             else:  # quit the thread cleanly when requested to stop
  54                 break
  55 
  56     def stop(self):
  57         self.is_running = False  # signal the thread to exit
  58 
  59         if self.gen_thread is not None:
  60             self.gen_thread.join()
  61 
  62         self.gen_thread = None
  63 
  64     def __iter__(self):
  65 
  66         self.is_running = True
  67         self.gen_thread = Thread(target=self.enqueue_values, daemon=True)
  68         self.gen_thread.start()
  69 
  70         try:
  71             while self.is_running and (self.gen_thread.is_alive() or not self.buffer.empty()):
  72                 try:
  73                     yield self.buffer.get(timeout=self.timeout)
  74                 except Empty:
  75                     pass  # queue was empty this time, try again
  76         finally:
  77             self.stop()  # ensure thread completion
  78 
  79 
  80 class ThreadDataLoader(DataLoader):
  81     """
  82     Subclass of `DataLoader` using a `ThreadBuffer` object to implement `__iter__` method asynchronously. This will
  83     iterate over data from the loader as expected however the data is generated on a separate thread. Use this class
  84     where a `DataLoader` instance is required and not just an iterable object.
  85 
  86     The default behaviour with `repeats` set to 1 is to yield each batch as it is generated, however with a higher
  87     value the generated batch is yielded that many times while underlying dataset asynchronously generates the next.
  88     Typically not all relevant information is learned from a batch in a single iteration so training multiple times
  89     on the same batch will still produce good training with minimal short-term overfitting while allowing a slow batch
  90     generation process more time to produce a result.
  91 
  92     See:
  93         * Fischetti et al. "Faster SGD training by minibatch persistency." ArXiv (2018) https://arxiv.org/abs/1806.07353
  94         * Dami et al., "Faster Neural Network Training with Data Echoing" ArXiv (2020) https://arxiv.org/abs/1907.05550
  95         * Ramezani et al. "GCN meets GPU: Decoupling "When to Sample" from "How to Sample"." NeurIPS (2020).
  96           https://proceedings.neurips.cc/paper/2020/file/d714d2c5a796d5814c565d78dd16188d-Paper.pdf
  97 
  98     Args:
  99         dataset: input dataset.
  100         buffer_size: number of items to buffer from the data source.
  101         buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.
  102         num_workers: number of the multi-processing workers in PyTorch DataLoader.
  103         repeats: number of times to yield the same batch
  104     """
  105 
  106     def __init__(
  107         self,
  108         dataset: Dataset,
  109         buffer_size: int = 1,
  110         buffer_timeout: float = 0.01,
  111         num_workers: int = 0,
  112         repeats: int = 1,
  113         **kwargs,
  114     ):
  115         super().__init__(dataset, num_workers, **kwargs)
  116         self.buffer_size = buffer_size
  117         self.buffer_timeout = buffer_timeout
  118         self.repeats = repeats
  119 
  120     def __iter__(self):
  121         buffer = ThreadBuffer(src=super().__iter__(), buffer_size=self.buffer_size, timeout=self.buffer_timeout)
  122 
  123         for batch in buffer:
  124             for _ in range(self.repeats):
  125                 yield batch
  126
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/monai/data/thread_buffer.py b/monai/data/thread_buffer.py
  --- a/monai/data/thread_buffer.py
  +++ b/monai/data/thread_buffer.py
  @@ -89,6 +89,12 @@
       on the same batch will still produce good training with minimal short-term overfitting while allowing a slow batch
       generation process more time to produce a result.
   
  +    Another typical usage is to accelerate light-weight preprocessing (usually cached all the deterministic transforms
  +    and no IO operations), because it leverages the separate thread to execute preprocessing to avoid unnecessary IPC
  +    between multiple workers of DataLoader. And as CUDA may not work well with the multi-processing of DataLoader,
  +    `ThreadDataLoader` can be useful for GPU transforms. For more details:
  +    https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_model_training_guide.md.
  +
       See:
           * Fischetti et al. "Faster SGD training by minibatch persistency." ArXiv (2018) https://arxiv.org/abs/1806.07353
           * Dami et al., "Faster Neural Network Training with Data Echoing" ArXiv (2020) https://arxiv.org/abs/1907.05550
  @@ -99,20 +105,15 @@
           dataset: input dataset.
           buffer_size: number of items to buffer from the data source.
           buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.
  -        num_workers: number of the multi-processing workers in PyTorch DataLoader.
  -        repeats: number of times to yield the same batch
  +        repeats: number of times to yield the same batch.
  +        kwargs: other arguments for `DataLoader` except for `dataset`.
  +
       """
   
       def __init__(
  -        self,
  -        dataset: Dataset,
  -        buffer_size: int = 1,
  -        buffer_timeout: float = 0.01,
  -        num_workers: int = 0,
  -        repeats: int = 1,
  -        **kwargs,
  +        self, dataset: Dataset, buffer_size: int = 1, buffer_timeout: float = 0.01, repeats: int = 1, **kwargs
       ):
  -        super().__init__(dataset, num_workers, **kwargs)
  +        super().__init__(dataset, **kwargs)
           self.buffer_size = buffer_size
           self.buffer_timeout = buffer_timeout
           self.repeats = repeats
  
verification_info: {"golden_diff": "diff --git a/monai/data/thread_buffer.py b/monai/data/thread_buffer.py\n--- a/monai/data/thread_buffer.py\n+++ b/monai/data/thread_buffer.py\n@@ -89,6 +89,12 @@\n     on the same batch will still produce good training with minimal short-term overfitting while allowing a slow batch\n     generation process more time to produce a result.\n \n+    Another typical usage is to accelerate light-weight preprocessing (usually cached all the deterministic transforms\n+    and no IO operations), because it leverages the separate thread to execute preprocessing to avoid unnecessary IPC\n+    between multiple workers of DataLoader. And as CUDA may not work well with the multi-processing of DataLoader,\n+    `ThreadDataLoader` can be useful for GPU transforms. For more details:\n+    https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_model_training_guide.md.\n+\n     See:\n         * Fischetti et al. \"Faster SGD training by minibatch persistency.\" ArXiv (2018) https://arxiv.org/abs/1806.07353\n         * Dami et al., \"Faster Neural Network Training with Data Echoing\" ArXiv (2020) https://arxiv.org/abs/1907.05550\n@@ -99,20 +105,15 @@\n         dataset: input dataset.\n         buffer_size: number of items to buffer from the data source.\n         buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.\n-        num_workers: number of the multi-processing workers in PyTorch DataLoader.\n-        repeats: number of times to yield the same batch\n+        repeats: number of times to yield the same batch.\n+        kwargs: other arguments for `DataLoader` except for `dataset`.\n+\n     \"\"\"\n \n     def __init__(\n-        self,\n-        dataset: Dataset,\n-        buffer_size: int = 1,\n-        buffer_timeout: float = 0.01,\n-        num_workers: int = 0,\n-        repeats: int = 1,\n-        **kwargs,\n+        self, dataset: Dataset, buffer_size: int = 1, buffer_timeout: float = 0.01, repeats: int = 1, **kwargs\n     ):\n-        super().__init__(dataset, num_workers, **kwargs)\n+        super().__init__(dataset, **kwargs)\n         self.buffer_size = buffer_size\n         self.buffer_timeout = buffer_timeout\n         self.repeats = repeats\n", "issue": "Clarify the `num_workers` in `ThreadDataLoader`\n**Is your feature request related to a problem? Please describe.**\r\nWhen I was introducing GPU transforms and the associated `ThreadDataLoader` to users, got several times feedback about the `num_workers` arg, which is confusing that users think it means the multi-threads in `ThreadDataLoader`, but actually it's the multi-processing workers of PyTorch DataLoader.\r\nWould be nice to clarify this arg and the use cases.\r\n\r\n\n", "before_files": [{"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom queue import Empty, Full, Queue\nfrom threading import Thread\n\nfrom monai.data import DataLoader, Dataset\n\n\nclass ThreadBuffer:\n    \"\"\"\n    Iterates over values from self.src in a separate thread but yielding them in the current thread. This allows values\n    to be queued up asynchronously. The internal thread will continue running so long as the source has values or until\n    the stop() method is called.\n\n    One issue raised by using a thread in this way is that during the lifetime of the thread the source object is being\n    iterated over, so if the thread hasn't finished another attempt to iterate over it will raise an exception or yield\n    unexpected results. To ensure the thread releases the iteration and proper cleanup is done the stop() method must\n    be called which will join with the thread.\n\n    Args:\n        src: Source data iterable\n        buffer_size: Number of items to buffer from the source\n        timeout: Time to wait for an item from the buffer, or to wait while the buffer is full when adding items\n    \"\"\"\n\n    def __init__(self, src, buffer_size: int = 1, timeout: float = 0.01):\n        self.src = src\n        self.buffer_size = buffer_size\n        self.timeout = timeout\n        self.buffer: Queue = Queue(self.buffer_size)\n        self.gen_thread = None\n        self.is_running = False\n\n    def enqueue_values(self):\n        for src_val in self.src:\n            while self.is_running:\n                try:\n                    self.buffer.put(src_val, timeout=self.timeout)\n                except Full:\n                    pass  # try to add the item again\n                else:\n                    break  # successfully added the item, quit trying\n            else:  # quit the thread cleanly when requested to stop\n                break\n\n    def stop(self):\n        self.is_running = False  # signal the thread to exit\n\n        if self.gen_thread is not None:\n            self.gen_thread.join()\n\n        self.gen_thread = None\n\n    def __iter__(self):\n\n        self.is_running = True\n        self.gen_thread = Thread(target=self.enqueue_values, daemon=True)\n        self.gen_thread.start()\n\n        try:\n            while self.is_running and (self.gen_thread.is_alive() or not self.buffer.empty()):\n                try:\n                    yield self.buffer.get(timeout=self.timeout)\n                except Empty:\n                    pass  # queue was empty this time, try again\n        finally:\n            self.stop()  # ensure thread completion\n\n\nclass ThreadDataLoader(DataLoader):\n    \"\"\"\n    Subclass of `DataLoader` using a `ThreadBuffer` object to implement `__iter__` method asynchronously. This will\n    iterate over data from the loader as expected however the data is generated on a separate thread. Use this class\n    where a `DataLoader` instance is required and not just an iterable object.\n\n    The default behaviour with `repeats` set to 1 is to yield each batch as it is generated, however with a higher\n    value the generated batch is yielded that many times while underlying dataset asynchronously generates the next.\n    Typically not all relevant information is learned from a batch in a single iteration so training multiple times\n    on the same batch will still produce good training with minimal short-term overfitting while allowing a slow batch\n    generation process more time to produce a result.\n\n    See:\n        * Fischetti et al. \"Faster SGD training by minibatch persistency.\" ArXiv (2018) https://arxiv.org/abs/1806.07353\n        * Dami et al., \"Faster Neural Network Training with Data Echoing\" ArXiv (2020) https://arxiv.org/abs/1907.05550\n        * Ramezani et al. \"GCN meets GPU: Decoupling \"When to Sample\" from \"How to Sample\".\" NeurIPS (2020).\n          https://proceedings.neurips.cc/paper/2020/file/d714d2c5a796d5814c565d78dd16188d-Paper.pdf\n\n    Args:\n        dataset: input dataset.\n        buffer_size: number of items to buffer from the data source.\n        buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.\n        num_workers: number of the multi-processing workers in PyTorch DataLoader.\n        repeats: number of times to yield the same batch\n    \"\"\"\n\n    def __init__(\n        self,\n        dataset: Dataset,\n        buffer_size: int = 1,\n        buffer_timeout: float = 0.01,\n        num_workers: int = 0,\n        repeats: int = 1,\n        **kwargs,\n    ):\n        super().__init__(dataset, num_workers, **kwargs)\n        self.buffer_size = buffer_size\n        self.buffer_timeout = buffer_timeout\n        self.repeats = repeats\n\n    def __iter__(self):\n        buffer = ThreadBuffer(src=super().__iter__(), buffer_size=self.buffer_size, timeout=self.buffer_timeout)\n\n        for batch in buffer:\n            for _ in range(self.repeats):\n                yield batch\n", "path": "monai/data/thread_buffer.py"}], "after_files": [{"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom queue import Empty, Full, Queue\nfrom threading import Thread\n\nfrom monai.data import DataLoader, Dataset\n\n\nclass ThreadBuffer:\n    \"\"\"\n    Iterates over values from self.src in a separate thread but yielding them in the current thread. This allows values\n    to be queued up asynchronously. The internal thread will continue running so long as the source has values or until\n    the stop() method is called.\n\n    One issue raised by using a thread in this way is that during the lifetime of the thread the source object is being\n    iterated over, so if the thread hasn't finished another attempt to iterate over it will raise an exception or yield\n    unexpected results. To ensure the thread releases the iteration and proper cleanup is done the stop() method must\n    be called which will join with the thread.\n\n    Args:\n        src: Source data iterable\n        buffer_size: Number of items to buffer from the source\n        timeout: Time to wait for an item from the buffer, or to wait while the buffer is full when adding items\n    \"\"\"\n\n    def __init__(self, src, buffer_size: int = 1, timeout: float = 0.01):\n        self.src = src\n        self.buffer_size = buffer_size\n        self.timeout = timeout\n        self.buffer: Queue = Queue(self.buffer_size)\n        self.gen_thread = None\n        self.is_running = False\n\n    def enqueue_values(self):\n        for src_val in self.src:\n            while self.is_running:\n                try:\n                    self.buffer.put(src_val, timeout=self.timeout)\n                except Full:\n                    pass  # try to add the item again\n                else:\n                    break  # successfully added the item, quit trying\n            else:  # quit the thread cleanly when requested to stop\n                break\n\n    def stop(self):\n        self.is_running = False  # signal the thread to exit\n\n        if self.gen_thread is not None:\n            self.gen_thread.join()\n\n        self.gen_thread = None\n\n    def __iter__(self):\n\n        self.is_running = True\n        self.gen_thread = Thread(target=self.enqueue_values, daemon=True)\n        self.gen_thread.start()\n\n        try:\n            while self.is_running and (self.gen_thread.is_alive() or not self.buffer.empty()):\n                try:\n                    yield self.buffer.get(timeout=self.timeout)\n                except Empty:\n                    pass  # queue was empty this time, try again\n        finally:\n            self.stop()  # ensure thread completion\n\n\nclass ThreadDataLoader(DataLoader):\n    \"\"\"\n    Subclass of `DataLoader` using a `ThreadBuffer` object to implement `__iter__` method asynchronously. This will\n    iterate over data from the loader as expected however the data is generated on a separate thread. Use this class\n    where a `DataLoader` instance is required and not just an iterable object.\n\n    The default behaviour with `repeats` set to 1 is to yield each batch as it is generated, however with a higher\n    value the generated batch is yielded that many times while underlying dataset asynchronously generates the next.\n    Typically not all relevant information is learned from a batch in a single iteration so training multiple times\n    on the same batch will still produce good training with minimal short-term overfitting while allowing a slow batch\n    generation process more time to produce a result.\n\n    Another typical usage is to accelerate light-weight preprocessing (usually cached all the deterministic transforms\n    and no IO operations), because it leverages the separate thread to execute preprocessing to avoid unnecessary IPC\n    between multiple workers of DataLoader. And as CUDA may not work well with the multi-processing of DataLoader,\n    `ThreadDataLoader` can be useful for GPU transforms. For more details:\n    https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_model_training_guide.md.\n\n    See:\n        * Fischetti et al. \"Faster SGD training by minibatch persistency.\" ArXiv (2018) https://arxiv.org/abs/1806.07353\n        * Dami et al., \"Faster Neural Network Training with Data Echoing\" ArXiv (2020) https://arxiv.org/abs/1907.05550\n        * Ramezani et al. \"GCN meets GPU: Decoupling \"When to Sample\" from \"How to Sample\".\" NeurIPS (2020).\n          https://proceedings.neurips.cc/paper/2020/file/d714d2c5a796d5814c565d78dd16188d-Paper.pdf\n\n    Args:\n        dataset: input dataset.\n        buffer_size: number of items to buffer from the data source.\n        buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.\n        repeats: number of times to yield the same batch.\n        kwargs: other arguments for `DataLoader` except for `dataset`.\n\n    \"\"\"\n\n    def __init__(\n        self, dataset: Dataset, buffer_size: int = 1, buffer_timeout: float = 0.01, repeats: int = 1, **kwargs\n    ):\n        super().__init__(dataset, **kwargs)\n        self.buffer_size = buffer_size\n        self.buffer_timeout = buffer_timeout\n        self.repeats = repeats\n\n    def __iter__(self):\n        buffer = ThreadBuffer(src=super().__iter__(), buffer_size=self.buffer_size, timeout=self.buffer_timeout)\n\n        for batch in buffer:\n            for _ in range(self.repeats):\n                yield batch\n", "path": "monai/data/thread_buffer.py"}]}