update docs

docs/tutorial/index.rst

@@ -37,7 +37,6 @@ User Tutorials
   callback
   summary
   faq
-  performance-tuning
 
 Extend Tensorpack
 =================
@@ -50,3 +49,10 @@ Extend Tensorpack
   extend/model
   extend/callback
   extend/trainer
+
+Notes
+======
+.. toctree::
+  :maxdepth: 1
+
+  performance-tuning

docs/tutorial/summary.md

@@ -39,7 +39,7 @@ are likely to have too much variance. To address this issue, you can:
 ### Other Data
 
 Besides TensorFlow summaries,
-a callback can also write other data to the monitor backend anytime after the training has started.
+a callback can also write other data to the monitor backend anytime once the training has started.
 As long as the type of data is supported, the data will be dispatched to and logged to the same place.
 
 As a result, tensorboard will show not only summaries in the graph, but also your custom data.

examples/DisturbLabel/mnist-disturb.py

@@ -59,13 +59,10 @@ class Model(mnist_example.Model):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
-    parser.add_argument('--load', help='load model')
     parser.add_argument('--prob', help='disturb prob', type=float, required=True)
     args = parser.parse_args()
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
     config = get_config()
-    if args.load:
-        config.session_init = SaverRestore(args.load)
     QueueInputTrainer(config).train()

examples/DisturbLabel/svhn-disturb.py

@@ -46,7 +46,6 @@ svhn_example.get_data = get_data
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', help='a gpu to use')
-    parser.add_argument('--load', help='load model')
     parser.add_argument('--prob', help='disturb prob',
                         type=float, required=True)
     args = parser.parse_args()
@@ -57,6 +56,4 @@ if __name__ == '__main__':
         os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
     config = get_config(args.prob)
-    if args.load:
-        config.session_init = SaverRestore(args.load)
     QueueInputTrainer(config).train()

examples/DoReFa-Net/svhn-digit-dorefa.py

@@ -176,19 +176,11 @@ def get_config():
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--gpu', help='the GPU to use')
-    parser.add_argument('--load', help='load a checkpoint')
     parser.add_argument('--dorefa',
                         help='number of bits for W,A,G, separated by comma. Defaults to \'1,2,4\'',
                         default='1,2,4')
     args = parser.parse_args()
 
     BITW, BITA, BITG = map(int, args.dorefa.split(','))
-
-    if args.gpu:
-        os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-
     config = get_config()
-    if args.load:
-        config.session_init = SaverRestore(args.load)
     QueueInputTrainer(config).train()

examples/mnist-keras.py

@@ -123,14 +123,11 @@ def get_config():
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
-    parser.add_argument('--load', help='load model')
     args = parser.parse_args()
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
     config = get_config()
-    if args.load:
-        config.session_init = SaverRestore(args.load)
     if args.gpu:
         config.nr_tower = len(args.gpu.split(','))
     if config.nr_tower > 1:

examples/mnist-tfslim.py

@@ -96,12 +96,9 @@ def get_config():
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
-    parser.add_argument('--load', help='load model')
     args = parser.parse_args()
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
     config = get_config()
-    if args.load:
-        config.session_init = SaverRestore(args.load)
     SimpleTrainer(config).train()

tensorpack/train/config.py

@@ -62,7 +62,6 @@ class TrainConfig(object):
 
             nr_tower (int): number of training towers, used by multigpu trainers.
             tower ([int]): list of training towers in relative GPU id.
-            predict_tower ([int]): list of prediction towers in their relative gpu id. Use -1 for cpu.
         """
 
         # TODO type checker decorator