From c14b9b148a619da970823b06214a7a30655b3885 Mon Sep 17 00:00:00 2001
From: Jannis Klinkenberg <j.klinkenberg@itc.rwth-aachen.de>
Date: Sat, 16 Nov 2024 18:24:42 +0100
Subject: [PATCH] also reduced complexity on single GPU case
---
tensorflow/cifar10/train_model.py | 84 +++++++------------
tensorflow/cifar10_distributed/train_model.py | 5 +-
.../train_model_horovod.py | 4 +-
3 files changed, 31 insertions(+), 62 deletions(-)
diff --git a/tensorflow/cifar10/train_model.py b/tensorflow/cifar10/train_model.py
index b505879..05648eb 100644
--- a/tensorflow/cifar10/train_model.py
+++ b/tensorflow/cifar10/train_model.py
@@ -6,7 +6,6 @@ import argparse
import datetime
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
-from tensorflow.keras import backend as K
from tensorflow.keras.datasets import cifar10
import tensorflow.keras.applications as applications
@@ -15,82 +14,57 @@ def parse_command_line():
parser.add_argument("--device", required=False, type=str, choices=["cpu", "cuda"], default="cuda")
parser.add_argument("--num_epochs", required=False, type=int, default=5)
parser.add_argument("--batch_size", required=False, type=int, default=128)
- parser.add_argument("--verbosity", required=False, help="Keras verbosity level for training/evaluation", type=int, default=2)
- parser.add_argument("--num_intraop_threads", required=False, help="Number of intra-op threads", type=int, default=None)
- parser.add_argument("--num_interop_threads", required=False, help="Number of inter-op threads", type=int, default=None)
parser.add_argument("--tensorboard", required=False, help="Whether to use tensorboard callback", action="store_true", default=False)
parser.add_argument("--profile_batches", required=False, help='Batches to profile with for tensorboard. Format "batch_start,batch_end"', type=str, default="2,5")
args = parser.parse_args()
- # specific to cifar 10 dataset
- args.num_classes = 10
-
print("Settings:")
settings_map = vars(args)
for name in sorted(settings_map.keys()):
print("--" + str(name) + ": " + str(settings_map[name]))
print("")
- sys.stdout.flush()
return args
-def load_dataset(args):
- K.set_image_data_format("channels_last")
+def preprocess_data(images, labels):
+ images = tf.image.resize(images, (224, 224)) # Resize for ResNet-50
+ images = images / 255.0 # Normalize to [0, 1]
+ return images, labels
- # load the cifar10 data
+def load_dataset(args):
+ # load the cifar10 data and generate input pipelines
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# convert class vectors to binary class matrices.
- y_train = tf.keras.utils.to_categorical(y_train, args.num_classes)
- y_test = tf.keras.utils.to_categorical(y_test, args.num_classes)
-
- # normalize base data
- x_train = x_train.astype("float32") / 255
- x_test = x_test.astype("float32") / 255
- x_train_mean = np.mean(x_train, axis=0)
- x_train -= x_train_mean
- x_test -= x_train_mean
-
- # dimensions
- print(f"original train_shape: {x_train.shape}")
- print(f"original test_shape: {x_test.shape}")
- n_train, n_test = x_train.shape[0], x_test.shape[0]
- resize_size = 224 # use bigger images with ResNet
-
- # Generating input pipelines
- ds_train = (tf.data.Dataset.from_tensor_slices((x_train, y_train))
- .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
- .shuffle(n_train).cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE)
+ y_train = tf.keras.utils.to_categorical(y_train, 10)
+ y_test = tf.keras.utils.to_categorical(y_test, 10)
+
+ ds_train = (
+ tf.data.Dataset.from_tensor_slices((x_train, y_train))
+ .map(preprocess_data)
+ .shuffle(x_train.shape[0])
+ .cache()
+ .batch(args.batch_size)
+ .prefetch(tf.data.experimental.AUTOTUNE)
)
- ds_test = (tf.data.Dataset.from_tensor_slices((x_test, y_test))
- .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
- .shuffle(n_test).cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE)
+
+ ds_test = (
+ tf.data.Dataset.from_tensor_slices((x_test, y_test))
+ .map(preprocess_data)
+ .cache()
+ .batch(args.batch_size)
+ .prefetch(tf.data.experimental.AUTOTUNE)
)
# get updated shapes
train_shape, test_shape = ds_train.element_spec[0].shape, ds_test.element_spec[0].shape
- print(f"final train_shape: {train_shape}")
- print(f"final test_shape: {test_shape}")
+ print(f"train_shape:", x_train.shape, " -> ", train_shape)
+ print(f"test_shape:", x_test.shape, " -> ", test_shape)
return ds_train, ds_test, train_shape
def setup(args):
- if args.num_intraop_threads:
- tf.config.threading.set_intra_op_parallelism_threads(args.num_intraop_threads)
- if args.num_interop_threads:
- tf.config.threading.set_inter_op_parallelism_threads(args.num_interop_threads)
-
- gpu_devices = [] if args.device == "cpu" else tf.config.list_physical_devices("GPU")
-
- print(f"Tensorflow get_intra_op_parallelism_threads: {tf.config.threading.get_intra_op_parallelism_threads()}")
- print(f"Tensorflow get_inter_op_parallelism_threads: {tf.config.threading.get_inter_op_parallelism_threads()}")
-
-
- print("List of GPU devices found:")
- for dev in gpu_devices:
- print(str(dev.device_type) + ": " + dev.name)
- print("")
- sys.stdout.flush()
+ print(f"Number of GPU devices found on worker {args.world_rank}", len(tf.config.list_physical_devices("GPU")))
tf.keras.backend.clear_session()
tf.config.optimizer.set_jit(True)
@@ -113,9 +87,8 @@ def main():
# callbacks to register
callbacks = []
- model = applications.ResNet50(weights=None, input_shape=train_shape[1:], classes=args.num_classes)
- # model.summary() # display the model architecture
-
+ # create and compile the model
+ model = applications.ResNet50(weights=None, input_shape=train_shape[1:], classes=10)
cur_optimizer = Adam(0.001)
model.compile(loss="categorical_crossentropy", optimizer=cur_optimizer, metrics=["accuracy"])
@@ -134,7 +107,6 @@ def main():
# evaluate model
scores = model.evaluate(ds_test, verbose=args.verbosity)
print(f"Test Evaluation: Accuracy: {scores[1]}")
- sys.stdout.flush()
if __name__ == "__main__":
main()
diff --git a/tensorflow/cifar10_distributed/train_model.py b/tensorflow/cifar10_distributed/train_model.py
index 5a742e6..c80076d 100644
--- a/tensorflow/cifar10_distributed/train_model.py
+++ b/tensorflow/cifar10_distributed/train_model.py
@@ -108,11 +108,10 @@ def main():
# callbacks to register
callbacks = []
+ # create and compile the model
with strategy.scope():
model = applications.ResNet50(weights=None, input_shape=train_shape[1:], classes=10)
-
- # create optimizer and scale learning rate with number of workers
- cur_optimizer = Adam(learning_rate=0.001 * args.world_size)
+ cur_optimizer = Adam(learning_rate=0.001 * args.world_size) # scale learning rate with number of workers
model.compile(loss="categorical_crossentropy", optimizer=cur_optimizer, metrics=["accuracy"])
# callbacks to register
diff --git a/tensorflow/cifar10_distributed/train_model_horovod.py b/tensorflow/cifar10_distributed/train_model_horovod.py
index e18aff6..6cae19e 100644
--- a/tensorflow/cifar10_distributed/train_model_horovod.py
+++ b/tensorflow/cifar10_distributed/train_model_horovod.py
@@ -110,13 +110,11 @@ def main():
hvd.callbacks.BroadcastGlobalVariablesCallback(0),
]
+ # create and compile the model
model = applications.ResNet50(weights=None, input_shape=train_shape[1:], classes=10)
- # model.summary() # display the model architecture
-
# Horovod: create Horovod DistributedOptimizer and scale learning rate with number of workers
cur_optimizer = Adam(learning_rate=0.001 * hvd.size())
opt = hvd.DistributedOptimizer(cur_optimizer, compression=compression)
-
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
# callbacks to register
--
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