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Bu kılavuz, tek çalışanlı çoklu GPU iş akışlarının TensorFlow 1'den TensorFlow 2'ye nasıl geçirileceğini gösterir.
Tek bir makinede birden çok GPU'da eşzamanlı eğitim gerçekleştirmek için:
- TensorFlow 1'de
tf.estimator.Estimator
API'lerinitf.distribute.MirroredStrategy
ile kullanırsınız. - TensorFlow 2'de Keras Model.fit'i veya
tf.distribute.MirroredStrategy
ile özel bir eğitim döngüsünü kullanabilirsiniz. TensorFlow kılavuzu ile Dağıtılmış eğitimde daha fazla bilgi edinin.
Kurmak
Gösteri amacıyla içe aktarma ve basit bir veri kümesiyle başlayın:
import tensorflow as tf
import tensorflow.compat.v1 as tf1
tutucu1 l10n-yerfeatures = [[1., 1.5], [2., 2.5], [3., 3.5]]
labels = [[0.3], [0.5], [0.7]]
eval_features = [[4., 4.5], [5., 5.5], [6., 6.5]]
eval_labels = [[0.8], [0.9], [1.]]
TensorFlow 1: tf.estimator.Estimator ile tek çalışana dağıtılmış eğitim
Bu örnek, tek çalışanlı çoklu GPU eğitiminin TensorFlow 1 kurallı iş akışını gösterir. Dağıtım stratejisini ( tf.distribute.MirroredStrategy
) tf.estimator.Estimator
öğesinin config
parametresi aracılığıyla ayarlamanız gerekir:
def _input_fn():
return tf1.data.Dataset.from_tensor_slices((features, labels)).batch(1)
def _eval_input_fn():
return tf1.data.Dataset.from_tensor_slices(
(eval_features, eval_labels)).batch(1)
def _model_fn(features, labels, mode):
logits = tf1.layers.Dense(1)(features)
loss = tf1.losses.mean_squared_error(labels=labels, predictions=logits)
optimizer = tf1.train.AdagradOptimizer(0.05)
train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())
return tf1.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
strategy = tf1.distribute.MirroredStrategy()
config = tf1.estimator.RunConfig(
train_distribute=strategy, eval_distribute=strategy)
estimator = tf1.estimator.Estimator(model_fn=_model_fn, config=config)
train_spec = tf1.estimator.TrainSpec(input_fn=_input_fn)
eval_spec = tf1.estimator.EvalSpec(input_fn=_eval_input_fn)
tf1.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
tutucu3 l10n-yerINFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',) INFO:tensorflow:Initializing RunConfig with distribution strategies. INFO:tensorflow:Not using Distribute Coordinator. WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp5g_f_ufk INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp5g_f_ufk', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true graph_options { rewrite_options { meta_optimizer_iterations: ONE } } , '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': <tensorflow.python.distribute.mirrored_strategy.MirroredStrategyV1 object at 0x7f6853562450>, '_device_fn': None, '_protocol': None, '_eval_distribute': <tensorflow.python.distribute.mirrored_strategy.MirroredStrategyV1 object at 0x7f6853562450>, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None} INFO:tensorflow:Not using Distribute Coordinator. INFO:tensorflow:Running training and evaluation locally (non-distributed). INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600. /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py:374: UserWarning: To make it possible to preserve tf.data options across serialization boundaries, their implementation has moved to be part of the TensorFlow graph. As a consequence, the options value is in general no longer known at graph construction time. Invoking this method in graph mode retains the legacy behavior of the original implementation, but note that the returned value might not reflect the actual value of the options. warnings.warn("To make it possible to preserve tf.data options across " INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/adagrad.py:77: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/util.py:95: DistributedIteratorV1.initialize (from tensorflow.python.distribute.input_lib) is deprecated and will be removed in a future version. Instructions for updating: Use the iterator's `initializer` property instead. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp5g_f_ufk/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... 2021-09-22 20:15:43.228503: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2021-09-22 20:15:43.229960: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' INFO:tensorflow:loss = 0.14477473, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3... INFO:tensorflow:Saving checkpoints for 3 into /tmp/tmp5g_f_ufk/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3... INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Starting evaluation at 2021-09-22T20:15:43 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmp/tmp5g_f_ufk/model.ckpt-3 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Inference Time : 0.17626s INFO:tensorflow:Finished evaluation at 2021-09-22-20:15:44 INFO:tensorflow:Saving dict for global step 3: global_step = 3, loss = 1.1251448 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmp/tmp5g_f_ufk/model.ckpt-3 INFO:tensorflow:Loss for final step: 0.45722964. 2021-09-22 20:15:44.095116: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2021-09-22 20:15:44.096454: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' ({'loss': 1.1251448, 'global_step': 3}, [])
TensorFlow 2: Keras ile tek çalışan eğitimi
TensorFlow 2'ye geçiş yaparken, Keras API'lerini tf.distribute.MirroredStrategy
ile kullanabilirsiniz.
Model oluşturma için tf.keras
API'lerini ve eğitim için Model.fit
kullanırsanız, temel fark, tf.estimator için bir config
tanımlamak yerine tf.estimator.Estimator
modelini, bir optimize ediciyi ve metrikleri Strategy.scope
bağlamında örneklemektir. tf.estimator.Estimator
.
Özel bir eğitim döngüsü kullanmanız gerekiyorsa, Özel eğitim döngüleriyle tf.distribute.Strategy kullanma kılavuzuna bakın.
dataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(1)
eval_dataset = tf.data.Dataset.from_tensor_slices(
(eval_features, eval_labels)).batch(1)
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)])
optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)
model.compile(optimizer=optimizer, loss='mse')
model.fit(dataset)
model.evaluate(eval_dataset, return_dict=True)
-yer tutucu6 l10n-yerINFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',) INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',). 2021-09-22 20:15:44.265351: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:695] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: "TensorSliceDataset/_2" op: "TensorSliceDataset" input: "Placeholder/_0" input: "Placeholder/_1" attr { key: "Toutput_types" value { list { type: DT_FLOAT type: DT_FLOAT } } } attr { key: "output_shapes" value { list { shape { dim { size: 2 } } shape { dim { size: 1 } } } } } INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',). 3/3 [==============================] - 2s 3ms/step - loss: 0.2363 2021-09-22 20:15:46.836745: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:695] AUTO sharding policy will apply DATA sharding policy as it failed to apply FILE sharding policy because of the following reason: Found an unshardable source dataset: name: "TensorSliceDataset/_2" op: "TensorSliceDataset" input: "Placeholder/_0" input: "Placeholder/_1" attr { key: "Toutput_types" value { list { type: DT_FLOAT type: DT_FLOAT } } } attr { key: "output_shapes" value { list { shape { dim { size: 2 } } shape { dim { size: 1 } } } } } 3/3 [==============================] - 1s 3ms/step - loss: 0.0079 {'loss': 0.007883546873927116}
Sonraki adımlar
tf.distribute.MirroredStrategy
2'de tf.distribute.MirroredStrategy ile dağıtılmış eğitim hakkında daha fazla bilgi edinmek için aşağıdaki belgelere göz atın: