remove extra kwargs in TrainConfig

tensorpack/callbacks/group.py

@@ -33,7 +33,7 @@ class CallbackTimeLogger(object):
     def log(self):
 
         """ log the time of some heavy callbacks """
-        if self.tot < 3:
+        if self.tot < 2:
             return
         msgs = []
         for name, t in self.times:

tensorpack/train/config.py

@@ -61,8 +61,7 @@ class TrainConfig(object):
                  model=None,
                  callbacks=None, extra_callbacks=None, monitors=None,
                  session_creator=None, session_config=None, session_init=None,
-                 starting_epoch=1, steps_per_epoch=None, max_epoch=99999,
-                 **kwargs):
+                 starting_epoch=1, steps_per_epoch=None, max_epoch=99999):
         """
         Args:
             dataflow (DataFlow):

tensorpack/train/trainers.py

@@ -344,28 +344,19 @@ class HorovodTrainer(SingleCostTrainer):
     .. code-block:: bash
 
         # First, change trainer to HorovodTrainer(), then
-        CUDA_VISIBLE_DEVICES=0,1,2,3 NCCL_DEBUG=INFO mpirun -np 4 --output-filename mylog python train.py
+        CUDA_VISIBLE_DEVICES=0,1,2,3 NCCL_DEBUG=INFO horovodrun -np 4 --output-filename mylog python train.py
 
     To use for distributed training:
 
     .. code-block:: bash
 
         # First, change trainer to HorovodTrainer(), then
-        mpirun -np 8 -H server1:4,server2:4  \\
-            -bind-to none -map-by slot \\
-            --output-filename mylog -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH \\
+        horovodrun -np 8 -H server1:4,server2:4 --output-filename mylog \\
             python train.py
-        # Add other environment variables you need by -x, e.g. PYTHONPATH, PATH.
-        # If using all GPUs, you can always skip the `CUDA_VISIBLE_DEVICES` option.
-        # There are other MPI options that can potentially improve performance especially on special hardwares.
-
-    Horovod can also be launched without MPI. See
-    `its documentation <https://github.com/horovod/horovod#running-horovod>`_
-    for more details.
 
     Note:
         1. To reach the maximum speed in your system, there are many options to tune
-           for Horovod installation and in the MPI command line.
+           in Horovod installation, horovodrun arguments, and in the MPI command line.
            See Horovod docs for details.
 
         2. Due to a TF bug (#8136), you must not initialize CUDA context before the trainer starts training.
@@ -378,6 +369,10 @@ class HorovodTrainer(SingleCostTrainer):
             + MPI does not like `fork()`. If your code (e.g. dataflow) contains multiprocessing, it may cause problems.
             + MPI sometimes fails to kill all processes in the end. Be sure to check it afterwards.
 
+           The gloo backend is recommended though it may come with very minor slow down.
+           To use gloo backend, see
+           `horovod documentation <https://github.com/horovod/horovod#running-horovod>`_ for more details.
+
         4. Keep in mind that there is one process running the script per GPU, therefore:
 
            + Make sure your InputSource has reasonable randomness.