[Trainerv2] Swap trainer directory. change two examples.

CHANGES.md

@@ -8,6 +8,14 @@ so you won't need to look at here very often.
 Here are a list of things that were changed, starting from an early version.
 TensorFlow itself also changed APIs before 1.0 and those are not listed here.
 
++ [2017/10/21]
+	tensorpack is gradually switching to a new Trainer API.
+	Compatibility is kept in most ways but not guaranteed.
+	To switch to new API, the easiest way is to:
+
+	1. `export TENSORPACK_TRAIN_API=v2`	(will be default in the future).
+	2. Replace `SomeTrainer(config, ...).train()` with `launch_train_with_config(config, SomeTrainer(...))`.
+
 + [2017/10/18]
 	`TrainConfig(predict_tower)` was deprecated. You can set the inference device directly when creating the `InferenceRunner` callback.
 + [2017/10/12](https://github.com/ppwwyyxx/tensorpack/commit/7e963996f615b85f7459455596b4ee9bbd0bce8e).

docs/tutorial/trainer.md

@@ -22,9 +22,11 @@ In other words, an "epoch" in tensorpack is the __default period to run callback
 
 ### Common Trainers
 
-Most neural network training tasks are single-cost optimization.
-Tensorpack provides some trainer implementations for such tasks.
-These trainers will build the graph based on the given `ModelDesc`, and minimizes `ModelDesc.cost`.
+<!--
+   -Most neural network training tasks are single-cost optimization.
+   -Tensorpack provides some trainer implementations for such tasks.
+   -These trainers will build the graph based on the given `ModelDesc`, and minimizes `ModelDesc.cost`.
+	 -->
 
 <!--
    -To use trainers, pass a `TrainConfig` to configure them:
@@ -49,7 +51,7 @@ These trainers will build the graph based on the given `ModelDesc`, and minimize
    -in the [Input Pipeline](input-source.html) tutorial.
    -You can set the InputSource instead, to customize this behavior.
 	 -->
-Trainers are being redesigned, so the recommended API will likely be changed soon.
+Trainers are being redesigned, this page will be updated soon.
 
 Existing multi-GPU trainers include the logic of data-parallel training.
 You can enable them by just one line, and all the necessary logic to achieve the best performance was baked into the trainers already.

examples/cifar-convnet.py

@@ -2,12 +2,13 @@
 # -*- coding: UTF-8 -*-
 # File: cifar-convnet.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
-from tensorpack import *
 import tensorflow as tf
 import argparse
 import numpy as np
 import os
 
+os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
+from tensorpack import *
 import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.tfutils.summary import *
 from tensorpack.dataflow import dataset
@@ -151,8 +152,7 @@ if __name__ == '__main__':
         if args.load:
             config.session_init = SaverRestore(args.load)
 
-        config.nr_tower = max(len(args.gpu.split(',')), 1)
-        if config.nr_tower <= 1:
-            QueueInputTrainer(config).train()
-        else:
-            SyncMultiGPUTrainerParameterServer(config).train()
+        nr_gpu = len(args.gpu.split(','))
+        trainer = QueueInputTrainer() if nr_gpu <= 1 \
+            else SyncMultiGPUTrainerParameterServer(list(range(nr_gpu)))
+        launch_train_with_config(config, trainer)

examples/mnist-convnet.py

@@ -12,6 +12,7 @@ MNIST ConvNet example.
 about 0.6% validation error after 30 epochs.
 """
 
+os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 # Just import everything into current namespace
 from tensorpack import *
 from tensorpack.tfutils import summary
@@ -142,4 +143,4 @@ if __name__ == '__main__':
         config.session_init = SaverRestore(args.load)
     # SimpleTrainer is slow, this is just a demo.
     # You can use QueueInputTrainer instead
-    SimpleTrainer(config).train()
+    launch_train_with_config(config, SimpleTrainer())

examples/tox.ini

 [flake8]
 max-line-length = 120
-ignore = F403,F401,F405,F841,E401
+ignore = F403,F401,F405,F841,E401,E402
 exclude = private,
 				  FasterRCNN/utils

tensorpack/__init__.py

@@ -18,9 +18,9 @@ if _HAS_TF:
 
     # In development. Default to v1
     if _os.environ.get('TENSORPACK_TRAIN_API', 'v1') == 'v2':
-        from tensorpack.trainv2 import *
-    else:
         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/trainv2/interface.py → tensorpack/train/interface.py

@@ -7,11 +7,11 @@ import tensorflow as tf
 from ..input_source import (
     InputSource, FeedInput, QueueInput, StagingInputWrapper, DummyConstantInput)
 
-from ..train.config import TrainConfig
+from ..trainv1.config import TrainConfig
 from .base import SingleCostTrainer
 from .trainers import SimpleTrainer, DistributedTrainerReplicated
 
-__all__ = ['launch_train_with_config', 'TrainConfig', 'apply_default_prefetch']
+__all__ = ['launch_train_with_config', 'apply_default_prefetch']
 
 
 def apply_default_prefetch(input_source_or_dataflow, trainer, towers):

tensorpack/trainv2/trainers.py → tensorpack/train/trainers.py

@@ -24,6 +24,7 @@ from .base import SingleCostTrainer
 
 __all__ = ['SimpleTrainer',
            'QueueInputTrainer',
+           'SyncMultiGPUTrainer',
            'SyncMultiGPUTrainerReplicated',
            'SyncMultiGPUTrainerParameterServer',
            'AsyncMultiGPUTrainer',
@@ -68,6 +69,17 @@ class SyncMultiGPUTrainerParameterServer(SingleCostTrainer):
         return []
 
 
+def SyncMultiGPUTrainer(towers):
+    """
+    Return a default multi-GPU trainer, if you don't care about the details.
+    It may not be the most efficient one for your task.
+
+    Args:
+        towers (list[int]): list of GPU ids.
+    """
+    return SyncMultiGPUTrainerParameterServer(towers, ps_device='gpu')
+
+
 class AsyncMultiGPUTrainer(SingleCostTrainer):
 
     __doc__ = AsyncMultiGPUBuilder.__doc__

tensorpack/trainv2/__init__.py → tensorpack/trainv1/__init__.py

@@ -19,7 +19,7 @@ def global_import(name):
 
 
 _CURR_DIR = os.path.dirname(__file__)
-_SKIP = []
+_SKIP = ['utility']
 for _, module_name, _ in iter_modules(
         [_CURR_DIR]):
     srcpath = os.path.join(_CURR_DIR, module_name + '.py')

tensorpack/trainv1/interface.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# File: interface.py
+
+
+__all__ = ['launch_train_with_config']
+
+
+def launch_train_with_config(config, trainer):
+    from ..train.interface import launch_train_with_config as old_launch
+    old_launch(config, trainer)

tensorpack/trainv1/utility.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# File: utility.py
+
+# for backwards-compatibility
+from ..graph_builder.utils import (  # noqa
+    OverrideToLocalVariable,
+    override_to_local_variable, LeastLoadedDeviceSetter)