remove Trainer.train_with_config

docs/tutorial/trainer.md

@@ -50,7 +50,7 @@ These trainers will build the graph by itself, with the following arguments:
 3. A function which takes input tensors and returns the cost.
 4. A function which returns an optimizer.
 
-See [SingleCostTrainer.setup_graph](http://localhost:8000/modules/train.html#tensorpack.train.SingleCostTrainer.setup_graph)
+See [SingleCostTrainer.setup_graph](http://tensorpack.readthedocs.io/en/latest/modules/train.html#tensorpack.train.SingleCostTrainer.setup_graph)
 for details.
 
 Existing multi-GPU trainers include the logic of data-parallel training.

docs/tutorial/training-interface.md

@@ -5,10 +5,22 @@ Tensorpack trainers have an interface for maximum flexibility.
 There are also interfaces built on top of trainers to simplify the use,
 when you don't want to customize too much.
 
+### Raw Trainer Interface
+
+For general trainer, build the graph by yourself.
+For single-cost trainer, build the graph by
+[SingleCostTrainer.setup_graph](http://tensorpack.readthedocs.io/en/latest/modules/train.html#tensorpack.train.SingleCostTrainer.setup_graph).
+
+Then, call
+[Trainer.train()](http://tensorpack.readthedocs.io/en/latest/modules/train.html#tensorpack.train.Trainer.train)
+or
+[Trainer.train_with_defaults()](http://tensorpack.readthedocs.io/en/latest/modules/train.html#tensorpack.train.Trainer.train_with_defaults)
+which applies some defaults options for normal use cases.
+
 ### With ModelDesc and TrainConfig
 
 [SingleCost trainers](trainer.html#single-cost-trainers)
-expects 4 arguments to build the graph: `InputDesc`, `InputSource`, get_cost function, and optimizer.
+expects 4 arguments in `setup_graph`: `InputDesc`, `InputSource`, get_cost function, and an optimizer.
 `ModelDesc` describes a model by packing 3 of them together into one object:
 
 ```python

tensorpack/train/base.py

@@ -25,7 +25,7 @@ import tensorpack.trainv1 as old_train    # noqa
 from ..trainv1.base import StopTraining, TrainLoop
 from ..trainv1.config import TrainConfig
 
-__all__ = ['TrainConfig', 'Trainer']
+__all__ = ['TrainConfig', 'Trainer', 'DEFAULT_MONITORS', 'DEFAULT_CALLBACKS']
 
 
 def DEFAULT_CALLBACKS():
@@ -243,28 +243,6 @@ class Trainer(object):
         self.initialize(session_creator, session_init)
         self.main_loop(steps_per_epoch, starting_epoch, max_epoch)
 
-    def train_with_config(self, config):
-        """
-        An alias to simplify the use of `TrainConfig` with `Trainer`.
-        This method is literally the following:
-
-        .. code-block:: python
-
-            self.train(
-                config.callbacks, config.monitors,
-                config.session_creator, config.session_init,
-                config.steps_per_epoch, config.starting_epoch, config.max_epoch)
-        """
-        if config.data or config.dataflow or config.model:
-            logger.warn(
-                "data/dataflow/model in TrainConfig will not be used "
-                "in `Trainer.train_with_config`")
-            logger.warn("To build the graph from config, use `launch_train_with_config`!")
-        self.train(
-            config.callbacks, config.monitors,
-            config.session_creator, config.session_init,
-            config.steps_per_epoch, config.starting_epoch, config.max_epoch)
-
     def train_with_defaults(
             self, callbacks=None, monitors=None,
             session_creator=None, session_init=None,

tensorpack/train/interface.py

@@ -86,5 +86,7 @@ def launch_train_with_config(config, trainer):
     trainer.setup_graph(
         inputs_desc, input,
         model._build_graph_get_cost, model.get_optimizer)
-    config.data = config.dataflow = config.model = None
-    trainer.train_with_config(config)
+    trainer.train(
+        config.callbacks, config.monitors,
+        config.session_creator, config.session_init,
+        config.steps_per_epoch, config.starting_epoch, config.max_epoch)