update docs

docs/tutorial/efficient-dataflow.md

@@ -140,7 +140,7 @@ We can also dump the dataset into one single LMDB file and read it sequentially.
 
 ```python
 from tensorpack.dataflow import *
-class BinaryILSVRC12(ILSVRCFiles):
+class BinaryILSVRC12(dataset.ILSVRCFiles):
     def get_data(self):
         for fname, label in super(BinaryILSVRC12, self).get_data():
             with open(fname, 'rb') as f:
@@ -155,7 +155,7 @@ The above script builds a DataFlow which produces jpeg-encoded ImageNet data.
 We store the jpeg string as a numpy array because the function `cv2.imdecode` later expect this format.
 Please note we can only use 1 prefetch process to speed up. If `nr_proc>1`, `ds1` will take data
 from several forks of `ds0`, then neither the content nor the order of `ds1` will be the same as `ds0`.
-See [documentation](http://localhost:8000/modules/dataflow.html#tensorpack.dataflow.PrefetchDataZMQ)
+See [documentation](http://tensorpack.readthedocs.io/en/latest/modules/dataflow.html#tensorpack.dataflow.PrefetchDataZMQ)
 about caveats of `PrefetchDataZMQ`.
 
 It will generate a database file of 140G. We build a DataFlow to read this LMDB file sequentially:

tensorpack/dataflow/prefetch.py

@@ -126,9 +126,9 @@ class PrefetchDataZMQ(ProxyDataFlow):
     collect datapoints from `ds` in each process by ZeroMQ IPC pipe.
 
     Note:
-        1. An iterator cannot run faster automatically -- the underlying dataflow worker
-           will be forked ``nr_proc`` times. As a result, we have the following
-           guarantee on the dataflow correctness:
+        1. An iterator cannot run faster automatically -- what's happenning is
+           that the underlying dataflow will be forked ``nr_proc`` times.
+           As a result, we have the following guarantee on the dataflow correctness:
 
            a. When ``nr_proc=1``, the dataflow produces the same data as ``ds`` in the same order.
            b. When ``nr_proc>1``, the dataflow produces the same distribution