start using pip to install

.travis.yml

@@ -6,19 +6,15 @@ cache:
   pip: true
   apt: true
 
-addons:
-    apt:
-        sources:
-            ubuntu-toolchain-r-test
 matrix:
     fast_finish: true
     include:
         - os: linux
           python: 2.7
-          env: TF_VERSION=1.0.0rc0 TF_TYPE=release
+          env: TF_VERSION=1.0.0rc1 TF_TYPE=release
         - os: linux
           python: 3.5
-          env: TF_VERSION=1.0.0rc0 TF_TYPE=release
+          env: TF_VERSION=1.0.0rc1 TF_TYPE=release
         - os: linux
           python: 2.7
           env: TF_VERSION=0.12.1 TF_TYPE=nightly
@@ -34,8 +30,9 @@ install:
     # Here we use opencv-python to overcome some travis probelms.
     # Users are not recommended to use opencv-python because it brings issues working on GPU
     # together with tensorflow.
-    - if [ -e requirements.txt ]; then pip install -r requirements.txt;else pip install -r requirements.pip;fi
+    # - if [ -e requirements.txt ]; then pip install -r requirements.txt;else pip install -r requirements.pip;fi
     - ./tests/install-tensorflow.sh
+    - pip install .
 
 before_script:
     - flake8 --version
@@ -45,7 +42,7 @@ before_script:
 script:
     - flake8 .
     - cd examples && flake8 .
-    - export PYTHONPATH=$PYTHONPATH:$TRAVIS_BUILD_DIR
+    # - export PYTHONPATH=$PYTHONPATH:$TRAVIS_BUILD_DIR
     - cd $TRAVIS_BUILD_DIR && python tests/test_examples.py
 
 notifications:

README.md

@@ -54,20 +54,17 @@ With the above components defined, tensorpack trainer will run the training iter
 Multi-GPU training is off-the-shelf by simply switching the trainer.
 You can also define your own trainer for non-standard training (e.g. GAN).
 
-## Dependencies:
+## Install:
+
+Dependencies:
 
 + Python 2 or 3
 + TensorFlow >= 1.0.0rc0
 + Python bindings for OpenCV
-+ other requirements:
-```
-pip install --user -r requirements.txt
-pip install --user -r opt-requirements.txt # (some optional dependencies required by certain submodule, you can install later if needed)
-```
-+ Enable `import tensorpack`:
++ (optional) use tcmalloc if running with large data
+
 ```
-export PYTHONPATH=$PYTHONPATH:`readlink -f path/to/tensorpack`
+git clone https://github.com/ppwwyyxx/tensorpack/ && cd tensorpack
+pip install --user --upgrade .
+pip install --user -r opt-requirements.txt # (some optional dependencies required by certain submodules, you can install later if prompted)
 ```
-(or use `greadlink` from `coreutils` brew package if you're on OSX)
-
-+ Use tcmalloc if running with large data

docs/requirements.txt

 termcolor
 numpy
 tqdm
-nltk
 decorator
 Sphinx==1.5.1
 recommonmark==0.4.0

examples/HED/README.md

@@ -39,5 +39,5 @@ To view the loss curve:
 ```bash
 cat train_log/hed/stat.json | jq '.[] |
 "\(.xentropy1)\t\(.xentropy2)\t\(.xentropy3)\t\(.xentropy4)\t\(.xentropy5)\t\(.xentropy6)"' -r | \
-				../../scripts/plot-point.py --legend 1,2,3,4,5,final --decay 0.8
+				tpk-plot-point --legend 1,2,3,4,5,final --decay 0.8
 ```

setup.py

@@ -48,6 +48,7 @@ for s in scripts:
 setup(
     version=__version__,
     install_requires=req,
+    tests_require=['flake8']
     extras_require={
         'all': extra_req
     },

tensorpack/tfutils/symbolic_functions.py

@@ -7,6 +7,7 @@ from contextlib import contextmanager
 import numpy as np
 
 
+# this function exists for backwards-compatibilty
 def prediction_incorrect(logits, label, topk=1, name='incorrect_vector'):
     """
     Args:

tensorpack/train/base.py

@@ -155,9 +155,12 @@ class Trainer(object):
 
     @property
     def global_step(self):
-        return self._starting_step + \
-                self.config.steps_per_epoch * (self.epoch_num - 1) + \
-                self.local_step + 1
+        try:
+            return self._starting_step + \
+                    self.config.steps_per_epoch * (self.epoch_num - 1) + \
+                    self.local_step + 1
+        except AttributeError:
+            return get_global_step_value()
 
     def main_loop(self):
         """