start_time moved to just training

docker/pyeddl/eddl_compss_distributed.py

@@ -16,7 +16,7 @@ class Eddl_Compss_Distributed:
     def __init__(self):
         self.model = None
 
-    #@constraint(computing_units="${OMP_NUM_THREADS}")
+    @constraint(computing_units="${OMP_NUM_THREADS}")
     @task(serialized_model=IN, optimizer=IN, losses=IN, metrics=IN, compserv=IN, is_replicated=True)
     def build(self, serialized_model, optimizer, losses, metrics, compserv):
 
@@ -42,7 +42,7 @@ class Eddl_Compss_Distributed:
 
         #print("Finaliza build task en worker")
 
-    #@constraint(computing_units="${OMP_NUM_THREADS}")
+    @constraint(computing_units="${OMP_NUM_THREADS}")
     @task(
         x_train={Type: COLLECTION_IN, Depth: 2},
         y_train={Type: COLLECTION_IN, Depth: 2},
@@ -195,7 +195,7 @@ class Eddl_Compss_Distributed:
 
         return final_parameters
 
-    #@constraint(computing_units="${OMP_NUM_THREADS}")
+    @constraint(computing_units="${OMP_NUM_THREADS}")
     @task(accumulated_parameters=COMMUTATIVE, parameters_to_aggregate=IN, mult_factor=IN, target_direction=IN)
     def aggregate_parameters_async(self, accumulated_parameters, parameters_to_aggregate, mult_factor):
 
@@ -207,7 +207,7 @@ class Eddl_Compss_Distributed:
 
         return accumulated_parameters
 
-    #@constraint(computing_units="${OMP_NUM_THREADS}")
+    @constraint(computing_units="${OMP_NUM_THREADS}")
     @task(initial_parameters=IN, train_test_flag=IN, target_direction=IN)
     def evaluate(self, initial_parameters, train_test_flag):
 
@@ -249,7 +249,7 @@ class Eddl_Compss_Distributed:
         return 1
 
 
-    #@constraint(computing_units="${OMP_NUM_THREADS}")
+    @constraint(computing_units="${OMP_NUM_THREADS}")
     @task(initial_parameters=IN, x_test={Type: COLLECTION_IN, Depth: 2}, y_test={Type: COLLECTION_IN, Depth: 2}, num_images_per_worker=IN, workers_batch_size=IN, target_direction=IN)
     def eval_batch(self, x_test, y_test, initial_parameters, num_images_per_worker, workers_batch_size):
 

docker/pyeddl/eddl_train_batch_compss_mnist_async.py

@@ -20,7 +20,6 @@ def main(args):
 
     #eddl.download_mnist()
 
-    start_time = timer()
 
     num_workers = args.num_workers
     num_epochs = args.num_epochs
@@ -68,6 +67,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("Model training...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/eddl_train_batch_compss_mnist_sync.py

@@ -20,7 +20,6 @@ def main(args):
 
     #eddl.download_mnist()
 
-    start_time = timer()
 
     num_workers = args.num_workers
     num_epochs = args.num_epochs
@@ -65,6 +64,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("MODEL TRAINING...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_cifar100_lenet_async.py

@@ -24,8 +24,6 @@ def main(args):
 
     #download_cifar100_npy()
 
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -62,6 +60,7 @@ def main(args):
     x_text = apply(eddlT.div_, x_test, 255.0)
 
     # Model training
+    start_time = timer()
     print("Model training...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_cifar100_lenet_sync.py

@@ -27,8 +27,6 @@ def main(args):
 
     #download_cifar100_npy()
 
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -65,6 +63,7 @@ def main(args):
     x_text = apply(eddlT.div_, x_test, 255.0)
 
     # Model training
+    start_time = timer()
     print("MODEL TRAINING...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_cifar10_lenet_async.py

@@ -22,8 +22,6 @@ def main(args):
 
     #eddl.download_cifar10()
 
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -63,6 +61,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("Model training...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_cifar10_lenet_sync.py

@@ -24,9 +24,6 @@ def main(args):
     #print("E: ", platform.uname())
 
     #eddl.download_cifar10()
-
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -66,6 +63,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("MODEL TRAINING...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_cifar10_lenet_sync_test.py

@@ -21,8 +21,6 @@ def main(args):
 
     #eddl.download_cifar10()
 
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -62,6 +60,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("MODEL TRAINING...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_cifar10_vgg16_async.py

@@ -23,8 +23,6 @@ def main(args):
 
     #eddl.download_cifar10()
 
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -63,6 +61,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("Model training...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_cifar10_vgg16_seq.py

@@ -19,8 +19,6 @@ def main(args):
 
     #eddl.download_cifar10()
 
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -54,6 +52,7 @@ def main(args):
     eddlT.div_(x_test, 255.0)
 
     # Model training
+    start_time = timer()
     print("Model training...")
     print("Number of epochs: ", num_epochs)
     print("Number of epochs for parameter syncronization: ", num_epochs_for_param_sync)

docker/pyeddl/examples/eddl_train_batch_compss_cifar10_vgg16_sync.py

@@ -21,8 +21,6 @@ def main(args):
 
     #eddl.download_cifar10()
 
-    start_time = timer()
-
     num_workers = args.num_workers
     num_epochs = args.num_epochs
     workers_batch_size = args.workers_batch_size
@@ -61,6 +59,7 @@ def main(args):
     eddlT.div_(x_test, 255.0)
 
     # Model training
+    start_time = timer()
     print("MODEL TRAINING...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_mnist_async.py

@@ -20,7 +20,6 @@ def main(args):
 
     #eddl.download_mnist()
 
-    start_time = timer()
 
     num_workers = args.num_workers
     num_epochs = args.num_epochs
@@ -68,6 +67,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("Model training...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)

docker/pyeddl/examples/eddl_train_batch_compss_mnist_sync.py

@@ -20,7 +20,6 @@ def main(args):
 
     #eddl.download_mnist()
 
-    start_time = timer()
 
     num_workers = args.num_workers
     num_epochs = args.num_epochs
@@ -45,7 +44,7 @@ def main(args):
         eddl.sgd(CVAR_SGD1, CVAR_SGD2),
         ["soft_cross_entropy"],
         ["categorical_accuracy"],
-        eddl.CS_CPU(),
+        eddl.CS_GPU(),
         True
     )
 
@@ -65,6 +64,7 @@ def main(args):
     y_train_dist = array(y_train, train_images_per_worker)
 
     # Model training
+    start_time = timer()
     print("MODEL TRAINING...")
     print("Num workers: ", num_workers)
     print("Number of epochs: ", num_epochs)