TPU 与 TensorFlow v1答案

【问题标题】：TPU with Tensorflow v1TPU 与 TensorFlow v1
【发布时间】：2020-07-02 16:30:04
【问题描述】：

请任何人都可以给我代码以使用 Tensorflow V1 运行 TPU 吗？我正在尝试这段代码，但它仅适用于 Tensorflow 2.0：

 try:
    # TPU detection. No parameters necessary if TPU_NAME environment variable is
    # set: this is always the case on Kaggle.
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
    print('Running on TPU ', tpu.master())
 except ValueError:
    tpu = None

 if tpu:
    tf.config.experimental_connect_to_cluster(tpu)
    tf.tpu.experimental.initialize_tpu_system(tpu)
    strategy = tf.distribute.experimental.TPUStrategy(tpu)
 else:
    # Default distribution strategy in Tensorflow. Works on CPU and single GPU.
    strategy = tf.distribute.get_strategy()

 print("REPLICAS: ", strategy.num_replicas_in_sync)

【问题讨论】：

标签： tensorflow tpu

【解决方案1】：

Tensorflow V1.0 TPU 或 GPU 检测代码：

# Detect hardware
try:
  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection
except ValueError:
  tpu = None
  gpus = tf.config.experimental.list_logical_devices("GPU")
    
# Select appropriate distribution strategy
if tpu:
  tf.tpu.experimental.initialize_tpu_system(tpu)
# Going back and forth between TPU and host is expensive. Better to run 128 batches on the TPU before reporting back.
  strategy = tf.distribute.experimental.TPUStrategy(tpu, steps_per_run=128) 
  print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])  
elif len(gpus) > 1:
  strategy = tf.distribute.MirroredStrategy([gpu.name for gpu in gpus])
  print('Running on multiple GPUs ', [gpu.name for gpu in gpus])
elif len(gpus) == 1:
  strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU
  print('Running on single GPU ', gpus[0].name)
else:
  strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU
  print('Running on CPU')
print("Number of accelerators: ", strategy.num_replicas_in_sync)

【讨论】：