update docs

docs/conf.py

@@ -375,6 +375,7 @@ def autodoc_skip_member(app, what, name, obj, skip, options):
         'DumpParamAsImage',
         'StagingInputWrapper',
         'PeriodicRunHooks',
+        'get_nr_gpu',
 
         # deprecated or renamed symbolic code
         'Deconv2D', 'LeakyReLU',

docs/tutorial/trainer.md

@@ -28,8 +28,8 @@ The tower function needs to follow some conventions:
 
 1. __It might get called multiple times__ for data-parallel training or inference.
 2. It has to respect variable collections:
-   * Only put variables __trainable by gradient descent__ into `TRAINABLE_VARIABLES`.
-   * Put variables that need to be saved into `MODEL_VARIABLES`.
+   * (Required) Only put variables __trainable by gradient descent__ into `TRAINABLE_VARIABLES`.
+   * (Recommended) Put non-trainable variables that need to be used in inference into `MODEL_VARIABLES`.
 3. It has to respect variable scopes:
    * The name of any trainable variables created in the function must be like "variable_scope_name/custom/name".
      Don't depend on name_scope's name. Don't use variable_scope's name twice.

examples/DoReFa-Net/README.md

@@ -7,7 +7,7 @@ It also contains an implementation of the following papers:
 + [Trained Ternary Quantization](https://arxiv.org/abs/1612.01064), with (W,A,G)=(t,32,32).
 + [Binarized Neural Networks](https://arxiv.org/abs/1602.02830), with (W,A,G)=(1,1,32).
 
-These different quantization techniques achieves the following accuracy in this implementation:
+These quantization techniques achieves the following ImageNet performance in this implementation:
 
 | Model              | W,A,G       | Top 1 Error |
 |:-------------------|-------------|------------:|

tensorpack/__init__.py

 # -*- coding: utf-8 -*-
 # File: __init__.py
+# flake8: noqa
 
 
 import os as _os
@@ -21,11 +22,7 @@ if STATICA_HACK:
     from tensorpack.callbacks import *
     from tensorpack.tfutils import *
 
-    # Default to v2
-    if _os.environ.get('TENSORPACK_TRAIN_API', 'v2') == 'v2':
     from tensorpack.train import *
-    else:
-        from tensorpack.trainv1 import *
     from tensorpack.graph_builder import InputDesc, ModelDesc, ModelDescBase
     from tensorpack.input_source import *
     from tensorpack.predict import *

tensorpack/train/__init__.py

 #  -*- coding: utf-8 -*-
 #  File: __init__.py
+# flake8: noqa
 
 # https://github.com/celery/kombu/blob/7d13f9b95d0b50c94393b962e6def928511bfda6/kombu/__init__.py#L34-L36
 STATICA_HACK = True

tensorpack/utils/concurrency.py

@@ -178,10 +178,12 @@ def enable_death_signal():
     in case the parent dies accidentally.
     """
     try:
-        import prctl
+        import prctl    # pip install prctl-python
     except ImportError:
         return
     else:
+        assert hasattr(prctl, 'set_pdeathsig'), \
+            "prctl.set_pdeathsig does not exist! Note that you need to install 'prctl-python' instead of 'prctl'."
         # is SIGHUP a good choice?
         prctl.set_pdeathsig(signal.SIGHUP)
 

tensorpack/utils/gpu.py

@@ -8,7 +8,7 @@ from . import logger
 from .nvml import NVMLContext
 from .concurrency import subproc_call
 
-__all__ = ['change_gpu', 'get_nr_gpu']
+__all__ = ['change_gpu', 'get_nr_gpu', 'get_num_gpu']
 
 
 def change_gpu(val):
@@ -22,7 +22,7 @@ def change_gpu(val):
     return change_env('CUDA_VISIBLE_DEVICES', val)
 
 
-def get_nr_gpu():
+def get_num_gpu():
     """
     Returns:
         int: #available GPUs in CUDA_VISIBLE_DEVICES, or in the system.
@@ -47,3 +47,6 @@ def get_nr_gpu():
             from tensorflow.python.client import device_lib
             local_device_protos = device_lib.list_local_devices()
             return len([x.name for x in local_device_protos if x.device_type == 'GPU'])
+
+
+get_nr_gpu = get_num_gpu