Add support for keras callback

hyperdash/VERSION

@@ -1 +1 @@
-0.13.1
+0.14.1

hyperdash/experiment.py

@@ -20,11 +20,13 @@
 # Python 2/3 compatibility
 __metaclass__ = type
 
-"""
-    No-op class for reusing CodeRunner architecture
-"""
+KERAS = "keras"
+
 
 class ExperimentRunner:
+    """
+        No-op class for reusing CodeRunner architecture
+    """
     def __init__(
         self,
         done=False,
@@ -81,6 +83,7 @@ def __init__(
             2) capture_io: Should save stdout/stderror to log file and upload it to Hyperdash.
         """
         self.model_name = model_name
+        self.callbacks = Callbacks(self)
         self._experiment_runner = ExperimentRunner()
         self.lock = Lock()
 
@@ -110,6 +113,7 @@ def __init__(
             self._logger,
             self._experiment_runner,
         )
+
         # Channel to update once experiment has finished running
         # Syncs with the seperate hyperdash messaging loop thread
         self.done_chan = Queue()
@@ -161,3 +165,65 @@ def end(self):
     """
     def log(self, string):
         self._logger.info(string)
+
+
+class Callbacks:
+    """Callbacks is a container class for 3rd-party library callbacks.
+
+    An instance of Experiment is injected so that the callbacks can emit
+    metrics/logs/parameters on behalf of an experiment.
+    """
+    def __init__(self, exp):
+        self._exp = exp
+        self._callbacks = {}
+
+    @property
+    def keras(self):
+        """
+        Returns an object that implements the Keras Callback interface.
+
+        This method initializes the Keras callback lazily to to prevent
+        any possible import issues from affecting users who don't use it,
+        as well as prevent it from importing Keras/tensorflow and all of
+        their accompanying baggage unnecessarily in the case that they
+        happened to be installed, but the user is not using them.
+        """
+        cb = self._callbacks.get(KERAS)
+        # Keras is not importable
+        if cb == False:
+            return None
+        # If this is the first time, try and import Keras
+        if not cb:
+            # Check if Keras is installed and fallback gracefully
+            try:
+                from keras.callbacks import Callback as KerasCallback
+                class _KerasCallback(KerasCallback):
+                    """_KerasCallback implement KerasCallback using an injected Experiment.
+
+                    # TODO: Decide if we want to handle the additional callbacks:
+                    # 1) on_epoch_begin
+                    # 2) on_batch_begin
+                    # 3) on_batch_end
+                    # 4) on_train_begin
+                    # 5) on_train_end
+                    """
+                    def __init__(self, exp):
+                        super(_KerasCallback, self).__init__()
+                        self._exp = exp
+
+                    def on_epoch_end(self, epoch, logs={}):
+                        val_acc = logs.get("val_acc")
+                        val_loss = logs.get("val_loss")
+
+                        if val_acc is not None:
+                            self._exp.metric("val_acc", val_acc)
+                        if val_loss is not None:
+                            self._exp.metric("val_loss", val_loss)
+                cb = _KerasCallback(self._exp)
+                self._callbacks[KERAS] = cb
+                return cb
+            except ImportError:
+                # Mark Keras as unimportable for future calls                
+                self._callbacks[KERAS] = False
+                return None
+        return cb

requirements_dev.txt

@@ -7,3 +7,8 @@ jupyter==1.0.0
 python-slugify==1.2.4
 twine==1.9.1
 numpy==1.13.3
+keras==2.1.1
+# Required for Keras
+np_utils==0.5.3.4
+# Required for Keras
+tensorflow==1.4.0

tests/test_sdk.py

@@ -402,6 +402,35 @@ def test_experiment(self):
                 assert_in(log, data)
         os.remove(latest_log_file)
 
+    def test_experiment_keras_callback(self):
+        with patch("sys.stdout", new=StringIO()) as faked_out:
+            exp = Experiment("MNIST")
+            keras_cb = exp.callbacks.keras
+            keras_cb.on_epoch_end(0, {"val_acc": 1, "val_loss": 2})
+            # Sleep 1 second due to client sampling
+            time.sleep(1)
+            keras_cb.on_epoch_end(1, {"val_acc": 3, "val_loss": 4})
+            exp.end()
+
+        # Test metrics match what is expected
+        metrics_messages = []
+        for msg in server_sdk_messages:
+            payload = msg["payload"]
+            if "name" in payload:
+                metrics_messages.append(payload)
+        expect_metrics = [
+            {"is_internal": False, "name": "val_acc", "value": 1},
+            {"is_internal": False, "name": "val_loss", "value": 2},
+            {"is_internal": False, "name": "val_acc", "value": 3},
+            {"is_internal": False, "name": "val_loss", "value": 4},
+        ]
+        assert len(expect_metrics) == len(metrics_messages)
+        for i, message in enumerate(metrics_messages):
+            assert message == expect_metrics[i]
+
+        captured_out = faked_out.getvalue()
+        assert "error" not in captured_out
+
     def test_experiment_handles_numpy_numbers(self):
         nums_to_test = [
             ("int_", np.int_()),