Orca spark estimator

pyzoo/test/zoo/orca/learn/jep/test_pytorch_estimator_for_dataloader.py

@@ -61,7 +61,7 @@ def forward(self, x):
         model = SimpleModel()
 
         estimator = Estimator.from_torch(model=model, loss=nn.BCELoss(),
-                                         optimizer=Adam(), backend="bigdl")
+                                         optimizer=Adam())
 
         inputs = torch.Tensor([[1, 2], [1, 3], [3, 2], [5, 6], [8, 9], [1, 9]])
         targets = torch.Tensor([[0], [0], [0], [1], [1], [1]])

pyzoo/test/zoo/orca/learn/spark/test_estimator_keras_for_spark.py

@@ -22,7 +22,6 @@
 from zoo.orca.learn.trigger import SeveralIteration
 from zoo.orca.learn.tf.estimator import Estimator
 from zoo.common.nncontext import *
-from zoo.orca.learn.tf.estimator import Estimator
 from zoo.orca.learn.tf.utils import convert_predict_to_dataframe
 
 

pyzoo/test/zoo/orca/learn/spark/test_estimator_openvino.py

@@ -21,7 +21,7 @@
 import numpy as np
 from zoo import init_nncontext
 from zoo.orca.data import SparkXShards
-from zoo.orca.learn.openvino.estimator import Estimator
+from zoo.orca.learn.openvino import Estimator
 from bigdl.dataset.base import maybe_download
 
 property_path = os.path.join(os.path.split(__file__)[0],

pyzoo/zoo/examples/orca/learn/pytorch/mnist/lenet_mnist.py

@@ -101,8 +101,7 @@ def main():
     criterion = nn.NLLLoss()
 
     adam = torch.optim.Adam(model.parameters(), args.lr)
-    zoo_estimator = Estimator.from_torch(model=model, optimizer=adam, loss=criterion,
-                                         backend="bigdl")
+    zoo_estimator = Estimator.from_torch(model=model, optimizer=adam, loss=criterion)
     zoo_estimator.fit(data=train_loader, epochs=args.epochs, validation_data=test_loader,
                       validation_methods=[Accuracy()], checkpoint_trigger=EveryEpoch())
     zoo_estimator.evaluate(data=test_loader, validation_methods=[Accuracy()])

pyzoo/zoo/orca/learn/base_estimator.py

@@ -0,0 +1,43 @@
+#
+# Copyright 2018 Analytics Zoo Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from abc import ABC, abstractmethod
+
+
+class BaseEstimator(ABC):
+    @abstractmethod
+    def fit(self, data, epochs, **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def predict(self, data, **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def evaluate(self, data, **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_model(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def save(self, model_path):
+        raise NotImplementedError
+
+    @abstractmethod
+    def load(self, checkpoint, **kwargs):
+        raise NotImplementedError

pyzoo/zoo/orca/learn/bigdl/estimator.py

@@ -15,61 +15,14 @@
 #
 from zoo.pipeline.nnframes import NNEstimator, NNModel
 from zoo.pipeline.estimator import Estimator as SparkEstimator
+from zoo.orca.learn.spark_estimator import Estimator as OrcaSparkEstimator
 from zoo.orca.data import SparkXShards
 from bigdl.optim.optimizer import MaxEpoch
 from zoo.feature.common import FeatureSet
 from pyspark.sql.dataframe import DataFrame
 
 
 class Estimator(object):
-    def fit(self, data, epochs, **kwargs):
-        pass
-
-    def predict(self, data, **kwargs):
-        pass
-
-    def evaluate(self, data, **kwargs):
-        pass
-
-    def get_model(self):
-        pass
-
-    def save(self, model_path):
-        pass
-
-    def load(self, checkpoint):
-        pass
-
-    def set_tensorboard(self, log_dir, app_name):
-        """
-        Set summary information during the training process for visualization purposes.
-        Saved summary can be viewed via TensorBoard.
-        In order to take effect, it needs to be called before fit.
-
-        Training summary will be saved to 'log_dir/app_name/train'
-        and validation summary (if any) will be saved to 'log_dir/app_name/validation'.
-
-        # Arguments
-        :param log_dir: The base directory path to store training and validation logs.
-        :param app_name: The name of the application.
-        """
-        pass
-
-    def clear_gradient_clipping(self):
-        pass
-
-    def set_constant_gradient_clipping(self, min, max):
-        pass
-
-    def set_l2_norm_gradient_clipping(self, clip_norm):
-        pass
-
-    def get_train_summary(self, tag=None):
-        pass
-
-    def get_validation_summary(self, tag=None):
-        pass
-
     @staticmethod
     def from_bigdl(*, model, loss=None, optimizer=None, feature_preprocessing=None,
                    label_preprocessing=None, model_dir=None):
@@ -107,7 +60,7 @@ def from_bigdl(*, model, loss=None, optimizer=None, feature_preprocessing=None,
                                      label_preprocessing=label_preprocessing, model_dir=model_dir)
 
 
-class BigDLEstimatorWrapper(Estimator):
+class BigDLEstimatorWrapper(OrcaSparkEstimator):
     def __init__(self, *, model, loss, optimizer=None, feature_preprocessing=None,
                  label_preprocessing=None, model_dir=None):
         self.loss = loss
@@ -262,29 +215,7 @@ def load(self, checkpoint, optimizer=None, loss=None, feature_preprocessing=None
             self.model_dir = model_dir
 
         if is_checkpoint:
-            from zoo.orca.learn.utils import find_latest_checkpoint
-            from zoo.pipeline.api.net import Net
-            from bigdl.nn.layer import Model, Container
-            from bigdl.optim.optimizer import OptimMethod
-            import os
-            path, prefix, version = find_latest_checkpoint(checkpoint, model_type="bigdl")
-            if path is None:
-                raise ValueError("Cannot find BigDL checkpoint, please check your checkpoint path.")
-            try:
-                self.model = Model.load(os.path.join(path, "model.{}".format(version)))
-                assert isinstance(self.model, Container), \
-                    "The loaded model should be a Container, please check your checkpoint type."
-                self.optimizer = OptimMethod.load(os.path.join(path,
-                                                               "{}.{}".format(prefix, version)))
-            except Exception:
-                raise ValueError("Cannot load BigDL checkpoint, please check your checkpoint path "
-                                 "and checkpoint type.")
-            self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir)
-            self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing,
-                                            self.label_preprocessing)
-            if self.optimizer is not None:
-                self.nn_estimator.setOptimMethod(self.optimizer)
-            self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing)
+            self.load_latest_orca_checkpoint(checkpoint)
         else:
             from zoo.pipeline.api.net import Net
             self.model = Net.load_bigdl(checkpoint + ".bigdl", checkpoint + ".bin")
@@ -299,6 +230,33 @@ def load(self, checkpoint, optimizer=None, loss=None, feature_preprocessing=None
             self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing)
         return self
 
+    def load_orca_checkpoint(self, path, version, prefix=None):
+        from bigdl.nn.layer import Model, Container
+        from bigdl.optim.optimizer import OptimMethod
+        import os
+        try:
+            self.model = Model.load(os.path.join(path, "model.{}".format(version)))
+            assert isinstance(self.model, Container), \
+                "The loaded model should be a Container, please check your checkpoint type."
+            self.optimizer = OptimMethod.load(os.path.join(path,
+                                                           "{}.{}".format(prefix, version)))
+        except Exception:
+            raise ValueError("Cannot load BigDL checkpoint, please check your checkpoint path "
+                             "and checkpoint type.")
+        self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir)
+        self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing,
+                                        self.label_preprocessing)
+        if self.optimizer is not None:
+            self.nn_estimator.setOptimMethod(self.optimizer)
+        self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing)
+
+    def load_latest_orca_checkpoint(self, path):
+        from zoo.orca.learn.utils import find_latest_checkpoint
+        path, prefix, version = find_latest_checkpoint(path, model_type="bigdl")
+        if path is None:
+            raise ValueError("Cannot find BigDL checkpoint, please check your checkpoint path.")
+        self.load_orca_checkpoint(path=path, version=version, prefix=prefix)
+
     def clear_gradient_clipping(self):
         self.nn_estimator.clearGradientClipping()
         self.estimator.clear_gradient_clipping()

pyzoo/zoo/orca/learn/openvino/estimator.py

@@ -15,6 +15,7 @@
 #
 from zoo.pipeline.inference import InferenceModel
 from zoo.orca.data import SparkXShards
+from zoo.orca.learn.spark_estimator import Estimator as SparkEstimator
 from zoo import get_node_and_core_number
 from zoo.util import nest
 from zoo.common.nncontext import init_nncontext
@@ -23,24 +24,6 @@
 
 
 class Estimator(object):
-    def fit(self, data, epochs, **kwargs):
-        pass
-
-    def predict(self, data, **kwargs):
-        pass
-
-    def evaluate(self, data, **kwargs):
-        pass
-
-    def get_model(self):
-        pass
-
-    def save(self, model_path):
-        pass
-
-    def load(self, model_path, **kwargs):
-        pass
-
     @staticmethod
     def from_openvino(*, model_path, batch_size=0):
         """
@@ -52,7 +35,7 @@ def from_openvino(*, model_path, batch_size=0):
         return OpenvinoEstimatorWrapper(model_path=model_path, batch_size=batch_size)
 
 
-class OpenvinoEstimatorWrapper(Estimator):
+class OpenvinoEstimatorWrapper(SparkEstimator):
     def __init__(self,
                  *,
                  model_path,
@@ -183,3 +166,27 @@ def load(self, model_path, batch_size=0):
         self.model.load_openvino(model_path=model_path,
                                  weight_path=model_path[:model_path.rindex(".")] + ".bin",
                                  batch_size=batch_size)
+
+    def set_tensorboard(self, log_dir, app_name):
+        raise NotImplementedError
+
+    def clear_gradient_clipping(self):
+        raise NotImplementedError
+
+    def set_constant_gradient_clipping(self, min, max):
+        raise NotImplementedError
+
+    def set_l2_norm_gradient_clipping(self, clip_norm):
+        raise NotImplementedError
+
+    def get_train_summary(self, tag=None):
+        raise NotImplementedError
+
+    def get_validation_summary(self, tag=None):
+        raise NotImplementedError
+
+    def load_orca_checkpoint(self, path, version):
+        raise NotImplementedError
+
+    def load_latest_orca_checkpoint(self, path):
+        raise NotImplementedError

pyzoo/zoo/orca/learn/pytorch/estimator.py

@@ -15,11 +15,10 @@
 #
 from zoo.pipeline.estimator.estimator import Estimator as SparkEstimator
 from zoo.orca.learn.pytorch.training_operator import TrainingOperator
+from zoo.orca.learn.spark_estimator import Estimator as OrcaSparkEstimator
 from zoo.orca.data import SparkXShards
 from bigdl.optim.optimizer import MaxEpoch
 from zoo.feature.common import FeatureSet
-
-import torch
 from torch.optim.optimizer import Optimizer as TorchOptimizer
 from torch.utils.data import DataLoader
 
@@ -195,7 +194,7 @@ def shutdown(self, force=False):
         return self.estimator.shutdown(force=force)
 
 
-class PytorchSparkEstimatorWrapper(Estimator):
+class PytorchSparkEstimatorWrapper(OrcaSparkEstimator):
     def __init__(self, model, loss, optimizer, model_dir=None, bigdl_type="float"):
         from zoo.pipeline.api.torch import TorchModel, TorchLoss, TorchOptim
         self.loss = loss
@@ -208,6 +207,8 @@ def __init__(self, model, loss, optimizer, model_dir=None, bigdl_type="float"):
             optimizer = SGD()
         elif isinstance(optimizer, TorchOptimizer):
             optimizer = TorchOptim.from_pytorch(optimizer)
+        self.log_dir = None
+        self.app_name = None
         self.model_dir = model_dir
         self.model = TorchModel.from_pytorch(model)
         self.estimator = SparkEstimator(self.model, optimizer, model_dir, bigdl_type=bigdl_type)
@@ -223,6 +224,9 @@ def fit(self, data, epochs=1, batch_size=32, validation_data=None, validation_me
         validation_methods = Metrics.convert_metrics_list(validation_methods)
         checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
 
+        if self.log_dir is not None and self.app_name is not None:
+            self.estimator.set_tensorboad(self.log_dir, self.app_name)
+
         if isinstance(data, SparkXShards):
             train_rdd = data.rdd.flatMap(to_sample)
             train_feature_set = FeatureSet.sample_rdd(train_rdd)
@@ -274,20 +278,25 @@ def evaluate(self, data, validation_methods=None, batch_size=32):
     def get_model(self):
         return self.model.to_pytorch()
 
-    def save(self, checkpoint):
+    def save(self, model_path):
         pass
 
     def load(self, checkpoint, loss=None):
         from zoo.orca.learn.utils import find_latest_checkpoint
-        from bigdl.nn.layer import Model
-        from bigdl.optim.optimizer import OptimMethod
-        import os
         if loss is not None:
             from zoo.pipeline.api.torch import TorchLoss
             self.loss = TorchLoss.from_pytorch(loss)
         path, prefix, version = find_latest_checkpoint(checkpoint, model_type="pytorch")
         if path is None:
             raise ValueError("Cannot find PyTorch checkpoint, please check your checkpoint path.")
+        self.load_orca_checkpoint(path, version=version, prefix=prefix)
+
+    def load_orca_checkpoint(self, path, version, prefix=None):
+        import os
+        from bigdl.nn.layer import Model
+        from bigdl.optim.optimizer import OptimMethod
+        assert prefix is not None, "You should provide optimMethod prefix, " \
+                                   "for example 'optimMethod-TorchModelf53bddcc'"
         try:
             self.model = Model.load(os.path.join(path, "model.{}".format(version)))
             optimizer = OptimMethod.load(os.path.join(path, "{}.{}".format(prefix, version)))
@@ -296,8 +305,14 @@ def load(self, checkpoint, loss=None):
                              "and checkpoint type.")
         self.estimator = SparkEstimator(self.model, optimizer, self.model_dir)
 
-    def shutdown(self, force=False):
-        pass
+    def load_latest_orca_checkpoint(self, path):
+        self.load(checkpoint=path)
+
+    def get_train_summary(self, tag=None):
+        return self.estimator.get_train_summary(tag=tag)
+
+    def get_validation_summary(self, tag=None):
+        return self.estimator.get_validation_summary(tag=tag)
 
     def clear_gradient_clipping(self):
         """