remove opt-requirements.txt

MANIFEST.in

 include requirements.txt
-include opt-requirements.txt

docs/tutorial/performance-tuning.md

@@ -2,7 +2,7 @@
 # Performance Tuning
 
 __We do not know why your training is slow__.
-Performance is different on every machine. So you need to figure out most parts by your own.
+Performance is different across machines and tasks. So you need to figure out most parts by your own.
 Here's a list of things you can do when your training is slow.
 
 If you're going to open an issue about slow training, PLEASE do them and include your findings.
@@ -14,14 +14,14 @@ If you're going to open an issue about slow training, PLEASE do them and include
 2. If you use queue-based input + dataflow, you can look for the queue size statistics in
 	 training log. Ideally the queue should be near-full (default size is 50).
  	 If the size is near-zero, data is the bottleneck.
-3. If the GPU utilization is low, it may be because of slow data, or some ops are inefficient. Also make sure GPUs are not locked in P8 state.
+3. If GPU utilization is low, it may be because of slow data, or some ops are inefficient. Also make sure GPUs are not locked in P8 state.
 
 ## Benchmark the components
 1. Use `DummyConstantInput(shapes)` as the `InputSource`.
 	so that the iterations doesn't take any data from Python side but train on a constant tensor.
 	This will help find out the slow operations you're using in the graph.
 2. Use `dataflow=FakeData(shapes, random=False)` to replace your original DataFlow by a constant DataFlow.
-  This has similar effect to (1), i.e., it eliminates the overhead of data.
+  This is almost the same as (1), i.e., it eliminates the overhead of data.
 3. If you're using a TF-based input pipeline you wrote, you can simply run it in a loop and test its speed.
 4. Use `TestDataSpeed(mydf).start()` to benchmark your DataFlow.
 
@@ -31,7 +31,7 @@ A benchmark will give you more precise information about which part you should i
 
 Understand the [Efficient DataFlow](efficient-dataflow.html) tutorial, so you know what your DataFlow is doing.
 
-Benchmark your DataFlow with modifications and you'll understand why it runs slow. Some examples
+Benchmark your DataFlow with modifications and you'll understand which part is the bottleneck. Some examples
 include:
 
 1. Remove everything except for the raw reader (and perhaps add some prefetching).

examples/FasterRCNN/README.md

@@ -4,7 +4,7 @@ Faster-RCNN / Mask-RCNN (without FPN) on COCO.
 
 ## Dependencies
 + Python 3; TensorFlow >= 1.4.0
-+ Install [pycocotools](https://github.com/pdollar/coco/tree/master/PythonAPI/pycocotools), OpenCV.
++ [pycocotools](https://github.com/pdollar/coco/tree/master/PythonAPI/pycocotools), OpenCV.
 + Pre-trained [ResNet model](https://goo.gl/6XjK9V) from tensorpack model zoo.
 + COCO data. It assumes the following directory structure:
 ```

opt-requirements.txt

-pillow
-scipy
-h5py
-lmdb>=0.92
-matplotlib
-scikit-learn

setup.py

@@ -21,24 +21,6 @@ except ImportError:
 # configure requirements
 reqfile = os.path.join(CURRENT_DIR, 'requirements.txt')
 req = [x.strip() for x in open(reqfile).readlines()]
-reqfile = os.path.join(CURRENT_DIR, 'opt-requirements.txt')
-extra_req = [x.strip() for x in open(reqfile).readlines()]
-if sys.version_info.major < 3:
-    extra_req.append('tornado')
-
-# parse scripts
-scripts = ['scripts/plot-point.py', 'scripts/dump-model-params.py']
-scripts_to_install = []
-for s in scripts:
-    dirname = os.path.dirname(s)
-    basename = os.path.basename(s)
-    if basename.endswith('.py'):
-        basename = basename[:-3]
-    newname = 'tpk-' + basename  # install scripts with a prefix to avoid name confusion
-    # setup.py could be executed the second time in the same dir
-    if not os.path.isfile(newname):
-        shutil.move(s, newname)
-    scripts_to_install.append(newname)
 
 setup(
     name='tensorpack',
@@ -48,7 +30,7 @@ setup(
     install_requires=req,
     tests_require=['flake8', 'scikit-image'],
     extras_require={
-        'all': extra_req
+        'all': ['pillow', 'scipy', 'h5py', 'lmdb>=0.92', 'matplotlib',
+                'scikit-learn', "tornado; python_version < '3.0'"]
     },
-    scripts=scripts_to_install,
 )