update docs

examples/FasterRCNN/data.py

@@ -369,11 +369,12 @@ def get_train_dataflow():
         return ret
 
     if cfg.DATA.NUM_WORKERS > 0:
-        buffer_size = cfg.DATA.NUM_WORKERS * 20
         if cfg.TRAINER == 'horovod':
+            buffer_size = cfg.DATA.NUM_WORKERS * 10  # one dataflow for each process, therefore don't need large buffer
             ds = MultiThreadMapData(ds, cfg.DATA.NUM_WORKERS, preprocess, buffer_size=buffer_size)
             # MPI does not like fork()
         else:
+            buffer_size = cfg.DATA.NUM_WORKERS * 20
             ds = MultiProcessMapDataZMQ(ds, cfg.DATA.NUM_WORKERS, preprocess, buffer_size=buffer_size)
     else:
         ds = MapData(ds, preprocess)

tensorpack/dataflow/parallel.py

@@ -144,7 +144,8 @@ class MultiProcessPrefetchData(ProxyDataFlow):
               `Birthday Paradox <https://en.wikipedia.org/wiki/Birthday_problem>`_
               and know that you'll likely see duplicates.
 
-           To utilize parallelism with stricter data integrity, you can use the parallel versions of `MapData`.
+           To utilize parallelism with more strict data integrity, you can use
+           the parallel versions of :class:`MapData`: :class:`MultiThreadMapData`, :class:`MultiProcessMapData`.
         2. This has more serialization overhead than :class:`PrefetchDataZMQ` when data is large.
         3. You can nest like this: ``PrefetchDataZMQ(PrefetchData(df, nr_proc=a), nr_proc=b)``.
            A total of ``a`` instances of ``df`` worker processes will be created.
@@ -241,7 +242,8 @@ class PrefetchDataZMQ(_MultiProcessZMQDataFlow):
               `Birthday Paradox <https://en.wikipedia.org/wiki/Birthday_problem>`_
               and know that you'll likely see duplicates.
 
-           To utilize parallelism with stricter data integrity, you can use the parallel versions of `MapData`.
+           To utilize parallelism with more strict data integrity, you can use
+           the parallel versions of :class:`MapData`: :class:`MultiThreadMapData`, :class:`MultiProcessMapData`.
         2. `reset_state()` of the given dataflow will be called **once and only once** in the worker processes.
         3. The fork of processes happened in this dataflow's `reset_state()` method.
            Please note that forking a TensorFlow GPU session may be unsafe.
@@ -365,7 +367,8 @@ class MultiThreadPrefetchData(DataFlow):
               `Birthday Paradox <https://en.wikipedia.org/wiki/Birthday_problem>`_
               and know that you'll likely see duplicates.
 
-           To utilize parallelism with stricter data integrity, you can use the parallel versions of `MapData`.
+           To utilize parallelism with more strict data integrity, you can use
+           the parallel versions of :class:`MapData`: :class:`MultiThreadMapData`, :class:`MultiProcessMapData`.
     """
 
     class _Worker(StoppableThread):

tensorpack/train/config.py

@@ -94,6 +94,9 @@ class TrainConfig(object):
             starting_epoch (int): The index of the first epoch.
             steps_per_epoch (int): the number of steps (defined by :meth:`Trainer.run_step`) to run in each epoch.
                 Defaults to the input data size. You may want to divide it by the #GPUs in multi-GPU training.
+
+                Number of steps per epoch only affects the schedule of callbacks.
+                It does not affect the sequence of input data seen by the model.
             max_epoch (int): maximum number of epoch to run training.
         """