fix multigpu tricks for tf < 2.10

delft/applications/textClassifier.py

@@ -93,7 +93,7 @@ def train(model_name,
     if fold_count == 1:
         model.train(xtr, y_, incremental=incremental, multi_gpu=multi_gpu)
     else:
-        model.train_nfold(xtr, y_)
+        model.train_nfold(xtr, y_, multi_gpu=multi_gpu)
     # saving the model
     model.save()
 

delft/sequenceLabelling/wrapper.py

@@ -151,8 +151,9 @@ def train(self, x_train, y_train, f_train=None, x_valid=None, y_valid=None, f_va
 
             # This trick avoid an exception being through when the --multi-gpu approach is used on a single GPU system.
             # It might be removed with TF 2.10 https://github.com/tensorflow/tensorflow/issues/50487
-            import atexit
-            atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
+            if version.parse(tf.__version__) < version.parse('2.10.0'):
+                import atexit
+                atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
 
             with strategy.scope():
                 self.train_(x_train, y_train, f_train, x_valid, y_valid, f_valid, incremental, callbacks)
@@ -219,8 +220,9 @@ def train_nfold(self, x_train, y_train, x_valid=None, y_valid=None, f_train=None
 
             # This trick avoid an exception being through when the --multi-gpu approach is used on a single GPU system.
             # It might be removed with TF 2.10 https://github.com/tensorflow/tensorflow/issues/50487
-            import atexit
-            atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
+            if version.parse(tf.__version__) < version.parse('2.10.0'):
+                import atexit
+                atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
 
             with strategy.scope():
                 self.train_nfold_(x_train, y_train, x_valid, y_valid, f_train, f_valid, incremental, callbacks)

delft/textClassification/wrapper.py

@@ -145,8 +145,9 @@ def train(self, x_train, y_train, vocab_init=None, incremental=False, callbacks=
 
             # This trick avoid an exception being through when the --multi-gpu approach is used on a single GPU system.
             # It might be removed with TF 2.10 https://github.com/tensorflow/tensorflow/issues/50487
-            import atexit
-            atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
+            if version.parse(tf.__version__) < version.parse('2.10.0'):
+                import atexit
+                atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
 
             with strategy.scope():
                 self.train_(x_train, y_train, vocab_init, incremental, callbacks)
@@ -217,8 +218,9 @@ def train_nfold(self, x_train, y_train, vocab_init=None, incremental=False, call
 
             # This trick avoid an exception being through when the --multi-gpu approach is used on a single GPU system.
             # It might be removed with TF 2.10 https://github.com/tensorflow/tensorflow/issues/50487
-            import atexit
-            atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
+            if version.parse(tf.__version__) < version.parse('2.10.0'):
+                import atexit
+                atexit.register(strategy._extended._collective_ops._pool.close) # type: ignore
 
             with strategy.scope():
                 self.train_nfold_(x_train, y_train,vocab_init, incremental, callbacks)