Python device_lib.list_local_devices方法代碼示例

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def sg_gpus():
    r""" Gets current available GPU nums

    Returns:
      A integer : total # of GPUs available
    """
    global _gpus

    if _gpus is None:
        local_device_protos = device_lib.list_local_devices()
        _gpus = len([x.name for x in local_device_protos if x.device_type == 'GPU'])

    return max(_gpus, 1)


#
# context helpers
# 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def _collect_gpu_info(run_info):
  """Collect local GPU information by TF device library."""
  gpu_info = {}
  local_device_protos = device_lib.list_local_devices()

  gpu_info["count"] = len([d for d in local_device_protos
                           if d.device_type == "GPU"])
  # The device description usually is a JSON string, which contains the GPU
  # model info, eg:
  # "device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0"
  for d in local_device_protos:
    if d.device_type == "GPU":
      gpu_info["model"] = _parse_gpu_model(d.physical_device_desc)
      # Assume all the GPU connected are same model
      break
  run_info["machine_config"]["gpu_info"] = gpu_info 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def _get_devices(self):
        available_devices = device_lib.list_local_devices()

        # Remove internal `XLA` devices, see `using JIT compilation <https://www.tensorflow.org/xla/jit>`_.
        usable_devices = [device.name for device in available_devices
                          if 'XLA' not in device.name]

        if self.config.get('device'):
            devices = self.config.get('device')
            devices = devices if isinstance(devices, list) else [devices]
            devices = [device for name in devices for device in usable_devices
                       if re.search(name.upper(), device.upper()) is not None]
            devices = [device for i, device in enumerate(devices)
                       if device not in devices[:i]]
        else:
            cpu_devices = [device for device in usable_devices
                           if 'CPU' in device]
            gpu_devices = [device for device in usable_devices
                           if 'GPU' in device]
            if gpu_devices:
                devices = [gpu_devices[0]]
            else:
                devices = [cpu_devices[0]]
        return devices 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_machine_info():
    parameter_value_map = {}
    operating_sys = sys.platform
    parameter_value_map['Operating System'] = operating_sys
    if 'linux' not in operating_sys:
        return parameter_value_map

    for i, device in enumerate(device_lib.list_local_devices()):
        if device.device_type != 'GPU':
            continue
        parameter_value_map['GPU_{}_name'.format(i + 1)] = device.name
        parameter_value_map['GPU_{}_memory_limit'.format(i + 1)] = device.memory_limit
        parameter_value_map['GPU_{}_description'.format(i + 1)] = device.physical_device_desc
    lscpu = subprocess.check_output("lscpu | grep '^CPU(s):\\|Core\\|Thread'", shell=True).strip().decode()
    lscpu = lscpu.split('\n')
    for row in lscpu:
        row = row.split(':')
        parameter_value_map[row[0]] = row[1].strip()
    return parameter_value_map 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def gather_available_device_info():
  """Gather list of devices available to TensorFlow.

  Returns:
    A list of test_log_pb2.AvailableDeviceInfo messages.
  """
  device_info_list = []
  devices = device_lib.list_local_devices()

  for d in devices:
    device_info = test_log_pb2.AvailableDeviceInfo()
    device_info.name = d.name
    device_info.type = d.device_type
    device_info.memory_limit = d.memory_limit
    device_info.physical_description = d.physical_device_desc
    device_info_list.append(device_info)

  return device_info_list 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def validate_batch_size_for_multi_gpu(batch_size):
    """For multi-gpu, batch-size must be a multiple of the number of
    available GPUs.
    Note that this should eventually be handled by replicate_model_fn
    directly. Multi-GPU support is currently experimental, however,
    so doing the work here until that feature is in place.
    """
    if FLAGS.multi_gpu:
        from tensorflow.python.client import device_lib
        local_device_protos = device_lib.list_local_devices()
        num_gpus = sum([1 for d in local_device_protos if d.device_type == 'GPU'])
        if not num_gpus:
            raise ValueError('Multi-GPU mode was specified, but no GPUs '
                             'were found. To use CPU, run --multi_gpu=False.')
        remainder = batch_size % num_gpus
        if remainder:
            err = ('When running with multiple GPUs, batch size '
                   'must be a multiple of the number of available GPUs. '
                   'Found {} GPUs with a batch size of {}; try --batch_size={} instead.'
                  ).format(num_gpus, batch_size, batch_size - remainder)
            raise ValueError(err)
        return num_gpus
    return 0 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_available_gpus():
    """
    Returns a list of string names of all available GPUs
    """
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU'] 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def available_gpus():
  """List of GPU device names detected by TensorFlow."""
  local_device_protos = device_lib.list_local_devices()
  return [x.name for x in local_device_protos if x.device_type == 'GPU'] 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_available_gpus():
    from tensorflow.python.client import device_lib
    local_device_protos = device_lib.list_local_devices()
    return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU'] 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_available_gpus():
    # recipe from here:
    # https://stackoverflow.com/questions/38559755/how-to-get-current-available-gpus-in-tensorflow?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa

    from tensorflow.python.client import device_lib
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

# ================================================================
# Saving variables
# ================================================================ 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_available_devs():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU'] 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_gpu_count():
    local_device_protos = device_lib.list_local_devices()
    return len([x.name for x in local_device_protos if x.device_type == 'GPU']) 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_tf_session():
    """ Returning a session. Set options here if desired. """
    tf.reset_default_graph()
    tf_config = tf.ConfigProto(inter_op_parallelism_threads=1,
                               intra_op_parallelism_threads=1)
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
    session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

    def get_available_gpus():
        from tensorflow.python.client import device_lib
        local_device_protos = device_lib.list_local_devices()
        return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU']

    print("AVAILABLE GPUS: ", get_available_gpus())
    return session 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_tf_session():
    """ Returning a session. Set options here (e.g. for GPUs) if desired. """
    tf.reset_default_graph()
    tf_config = tf.ConfigProto(inter_op_parallelism_threads=1,
                               intra_op_parallelism_threads=1)
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
    session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

    def get_available_gpus():
        from tensorflow.python.client import device_lib
        local_device_protos = device_lib.list_local_devices()
        return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU']

    print("AVAILABLE GPUS: ", get_available_gpus())
    return session 

# 需要導入模塊: from tensorflow.python.client import device_lib [as 別名]
# 或者: from tensorflow.python.client.device_lib import list_local_devices [as 別名]
def get_available_gpus():
    from tensorflow.python.client import device_lib
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type=='GPU']