TPU with Tensorflow v1

Question

Please can anyone give me code to run TPU with Tensorflow V1 ? I'm trying this code but it works only with 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)

score 1 · Answer 1 · answered Jul 05 '20 at 09:54

Tensorflow V1.0 TPU or GPU Detection code:

# 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)

TPU with Tensorflow v1

1 Answers1