โยกย้ายการฝึกอบรม GPU หลายตัวสำหรับคนงานคนเดียว

ดูบน TensorFlow.org ทำงานใน Google Colab ดูแหล่งที่มาบน GitHub ดาวน์โหลดโน๊ตบุ๊ค

คู่มือนี้สาธิตวิธีการโยกย้ายเวิร์กโฟลว์ GPU หลายตัวของผู้ปฏิบัติงานคนเดียวจาก TensorFlow 1 เป็น TensorFlow 2

ในการดำเนินการฝึกอบรมแบบซิงโครนัสใน GPU หลายตัวในเครื่องเดียว:

ติดตั้ง

เริ่มต้นด้วยการนำเข้าและชุดข้อมูลอย่างง่ายเพื่อการสาธิต:

import tensorflow as tf
import tensorflow.compat.v1 as tf1

features = [[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: ผู้ปฏิบัติงานคนเดียวกระจายการฝึกอบรมด้วย tf.estimator.Estimator

ตัวอย่างนี้สาธิตเวิร์กโฟลว์ตามรูปแบบบัญญัติ TensorFlow 1 ของการฝึกอบรม GPU หลายตัวสำหรับคนงานเดี่ยว คุณต้องตั้งค่ากลยุทธ์การกระจาย ( tf.distribute.MirroredStrategy ) ผ่านพารามิเตอร์ config ของ tf.estimator.Estimator :

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)

INFO: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: การฝึกอบรมพนักงานคนเดียวกับ Keras

เมื่อย้ายไปยัง TensorFlow 2 คุณสามารถใช้ Keras API กับ tf.distribute.MirroredStrategy

หากคุณใช้ tf.keras API สำหรับการสร้างแบบจำลองและ Keras Model.fit สำหรับการฝึกอบรม ข้อแตกต่างที่สำคัญคือการสร้างโมเดล Keras เครื่องมือเพิ่มประสิทธิภาพ และตัววัดในบริบทของ Strategy.scope แทนที่จะกำหนดการกำหนด config สำหรับ tf.estimator.Estimator . tf.estimator.Estimator

หากคุณต้องการใช้ลูปการฝึกแบบกำหนดเอง โปรดดูที่ การ ใช้ tf.distribute.Strategy พร้อมคำแนะนำลูปการฝึกแบบกำหนดเอง

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)

INFO: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"
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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"
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3/3 [==============================] - 1s 3ms/step - loss: 0.0079
{'loss': 0.007883546873927116}

ขั้นตอนถัดไป

หากต้องการเรียนรู้เพิ่มเติมเกี่ยวกับการฝึกอบรมแบบกระจายด้วย tf.distribute.MirroredStrategy ใน TensorFlow 2 โปรดดูเอกสารประกอบต่อไปนี้: