Python context.num_gpus函数代码示例

  def decorated(self, **kwargs):
    """A wrapped test method that treats some arguments in a special way."""
    mode = kwargs.pop("mode", "graph")

    distribution = kwargs.get("distribution", None)
    required_tpu = kwargs.pop("required_tpu", False)
    required_gpus = kwargs.pop("required_gpus", None)

    if distribution:
      assert required_gpus is None, (
          "Do not use `required_gpus` and `distribution` together.")
      assert required_tpu is False, (
          "Do not use `required_tpu` and `distribution` together.")
      required_gpus = distribution.required_gpus
      required_tpu = distribution.required_tpu

    if required_tpu and not TPU_TEST:
      self.skipTest("Test requires a TPU, but it's not available.")
    if not required_tpu and TPU_TEST:
      self.skipTest("Test that doesn't require a TPU.")

    if not required_gpus:
      if GPU_TEST:
        self.skipTest("Test that doesn't require GPUs.")
    elif context.num_gpus() < required_gpus:
      self.skipTest(
          "{} GPUs are not available for this test. {} GPUs are available".
          format(required_gpus, context.num_gpus()))

    # At this point, `kwargs` doesn't have `required_gpus` or `required_tpu`
    # that the user might have specified.  `kwargs` still has `mode`, which
    # the test is allowed to accept or ignore.
    requested_arguments = tf_inspect.getfullargspec(test_method).args
    missing_arguments = set(list(kwargs.keys()) + ["self"]).difference(
        set(requested_arguments + ["mode"]))
    if missing_arguments:
      raise ValueError("The test is missing arguments {} .".format(
          missing_arguments))

    kwargs_to_pass = {}
    for arg in requested_arguments:
      if arg == "self":
        kwargs_to_pass[arg] = self
      else:
        kwargs_to_pass[arg] = kwargs[arg]

    if mode == "eager":
      with ops.Graph().as_default(), context.eager_mode():
        if distribution:
          kwargs_to_pass["distribution"] = distribution.strategy
        test_method(**kwargs_to_pass)
    elif mode == "graph":
      with ops.Graph().as_default(), context.graph_mode():
        if distribution:
          kwargs_to_pass["distribution"] = distribution.strategy
        test_method(**kwargs_to_pass)
    else:
      raise ValueError(
          "'mode' has to be either 'eager' or 'graph' and not {}".format(
              mode))

 def _get_distribution_strategy(self):
   devices = ["/device:CPU:0", "/device:GPU:0"]
   if GPU_TEST:
     self.assertGreater(context.num_gpus(), 0)
     if context.num_gpus() > 1:
       devices = ["/device:GPU:0", "/device:GPU:1"]
   print(self.id().split(".")[-1], "devices:", ", ".join(devices))
   return mirrored_strategy.MirroredStrategy(devices)

  def test_end_to_end_keras_2_gpu(self):
    if context.num_gpus() < 2:
      self.skipTest(
          "{} GPUs are not available for this test. {} GPUs are available".
          format(2, context.num_gpus()))

    integration.run_synthetic(
        ncf_keras_main.main, tmp_root=self.get_temp_dir(), max_train=None,
        extra_flags=self._BASE_END_TO_END_FLAGS + ['-num_gpus', '2'])

def maybe_skip_test(test_case, is_tpu_required, num_gpus_required):
  if is_tpu_required and not TPU_TEST:
    test_case.skipTest("Test requires a TPU, but it's not available.")
  if not is_tpu_required and TPU_TEST:
    test_case.skipTest("Test that doesn't require a TPU.")

  if not num_gpus_required:
    if GPU_TEST:
      test_case.skipTest("Test that doesn't require GPUs.")
  elif context.num_gpus() < num_gpus_required:
    # TODO(priyag): Consider allowing tests in graph mode using soft
    # placement.
    test_case.skipTest(
        "{} GPUs are not available for this test. {} GPUs are available".format(
            num_gpus_required, context.num_gpus()))

def _all_devices():
  devices = []
  tfconfig = TFConfigClusterResolver()
  if tfconfig.cluster_spec().as_dict():
    devices = _cluster_spec_to_device_list(tfconfig.cluster_spec(),
                                           context.num_gpus())
  return devices if devices else all_local_devices()

  def __init__(self,
               devices=None,
               num_gpus=None,
               cross_tower_ops=None,
               prefetch_on_device=None):
    super(MirroredStrategy, self).__init__()
    # Convert `num_gpus` into `devices`, shouldn't specify both.
    if devices is None:
      if num_gpus is None:
        num_gpus = context.num_gpus()
      devices = ["/device:GPU:%d" % d for d in range(num_gpus)]
    elif num_gpus is not None:
      raise ValueError("Must only specify one of `devices` and `num_gpus`.")

    assert devices, "Must specify at least one device."
    assert len(set(devices)) == len(devices), (
        "No duplicates allowed in `devices` argument.")
    # TODO(josh11b): Require at least 2 devices?
    self._devices = devices
    self._canonical_device_set = set(
        [device_util.canonicalize(d) for d in devices])
    self._device_index = values.PerDevice(
        dict((d, i) for i, d in enumerate(devices)))
    self._cross_tower_ops = cross_tower_ops
    self._prefetch_on_device = prefetch_on_device

 def _get_strategy_object(self, strategy_cls, eval_strategy=False):
   if strategy_cls == mirrored_strategy.CoreMirroredStrategy:
     if eval_strategy:
       return strategy_cls()
     else:
       return strategy_cls(
           cross_device_ops=self._make_cross_device_ops(
               num_gpus_per_worker=context.num_gpus()))
   elif (strategy_cls == mirrored_strategy.MirroredStrategy and
         not eval_strategy):
     return strategy_cls(
         num_gpus_per_worker=context.num_gpus(),
         cross_device_ops=self._make_cross_device_ops(
             num_gpus_per_worker=context.num_gpus()))
   else:
     return strategy_cls(num_gpus_per_worker=context.num_gpus())

  def testSaveAndRestoreMirroredOneGraph(self):
    if context.num_gpus() < 1 and context.executing_eagerly():
      # Graph mode can work without GPU because the Placer "moves" the
      # variable to a CPU. In other words, if there is no GPU available, but
      # user requested to create a variable on GPU, Placer will ignore the
      # user request and assign the VarHandleOp to CPU. This requires
      # soft_placement, which is on by default.
      self.skipTest("A GPU is not available for this test in eager mode.")

    with self.cached_session(config=self.config) as sess:
      v, device_map, mirrored = _make_mirrored()
      devices = device_map.all_devices

      # Overwrite the initial values.
      self._assign_mirrored(devices, v, [3., 4.])

      # Saves the current value of v[0], 3.
      save_path, saver = self._save_return_saver(sess, mirrored)

      # Change the values between save and restore.
      self._assign_mirrored(devices, v, [5., 6.])

      # Restores the saved value of 3. to both variables.
      saver.restore(sess, save_path)
      self.assertEqual([3., 3.], self.evaluate([v[0], v[1]]))

 def _get_distribution_strategy(self):
   cluster_spec = server_lib.ClusterSpec({
       "worker": ["/job:worker/task:0", "/job:worker/task:1"]
   })
   strategy = mirrored_strategy.MirroredStrategy(num_gpus=context.num_gpus())
   strategy.configure(cluster_spec=cluster_spec)
   return strategy

  def testMakeInputFnIteratorDistributed(self, num_gpus, use_core_strategy,
                                         use_dataset):
    if context.num_gpus() < num_gpus:
      self.skipTest('Not enough GPUs')
    if use_dataset:
      fn = lambda: dataset_ops.Dataset.range(100)
    else:
      def fn():
        dataset = dataset_ops.Dataset.range(100)
        it = dataset.make_one_shot_iterator()
        return it.get_next
    expected_values = [[i+j for j in range(num_gpus)]
                       for i in range(0, 100, num_gpus)]

    input_fn = self._input_fn_to_test_input_context(
        fn,
        expected_num_replicas_in_sync=num_gpus,
        expected_num_input_pipelines=3,
        expected_input_pipeline_id=1)  # because task_id = 1
    self._test_input_fn_iterator(
        'worker',
        1,
        num_gpus,
        input_fn,
        expected_values,
        test_reinitialize=use_dataset,
        use_core_strategy=use_core_strategy)

  def DISABLED_testMakeInputFnIteratorLocal(self, num_gpus, use_core_strategy,
                                            use_dataset):
    if context.num_gpus() < num_gpus:
      self.skipTest('Not enough GPUs')
    if use_dataset:
      fn = lambda: dataset_ops.Dataset.range(100)
    else:
      def fn():
        dataset = dataset_ops.Dataset.range(100)
        it = dataset.make_one_shot_iterator()
        return it.get_next
    expected_values = [[i+j for j in range(num_gpus)]
                       for i in range(0, 100, num_gpus)]

    input_fn = self._input_fn_to_test_input_context(
        fn,
        expected_num_replicas_in_sync=num_gpus,
        expected_num_input_pipelines=1,
        expected_input_pipeline_id=0)  # only one worker and pipeline for local.
    self._test_input_fn_iterator(
        None,
        None,
        num_gpus,
        input_fn,
        expected_values,
        test_reinitialize=use_dataset,
        use_core_strategy=use_core_strategy)

  def DISABLED_testMakeInputFnIterator(self, num_gpus, use_dataset,
                                       use_core_strategy):
    if context.num_gpus() < num_gpus:
      self.skipTest('Not enough GPUs')
    if use_dataset:
      fn = lambda: dataset_ops.Dataset.range(100)
    else:
      def fn():
        dataset = dataset_ops.Dataset.range(100)
        it = dataset.make_one_shot_iterator()
        return it.get_next
    # We use CPU as the device when num_gpus = 0
    devices_per_worker = max(1, num_gpus)
    expected_values = [[i+j for j in range(devices_per_worker)]
                       for i in range(0, 100, devices_per_worker)]

    input_fn = self._input_fn_to_test_input_context(
        fn,
        expected_num_replicas_in_sync=3*devices_per_worker,
        expected_num_input_pipelines=3,
        expected_input_pipeline_id=1)  # because task_id = 1
    self._test_input_fn_iterator(
        'worker',
        1,
        num_gpus,
        input_fn,
        expected_values,
        test_reinitialize=use_dataset,
        use_core_strategy=use_core_strategy)

    def _independent_worker_fn(*args, **kwargs):  # pylint: disable=unused-argument
      """Simulates an Independent Worker inside of a thread."""
      # TODO(rchao/yuefengz): The following is run by both worker and ps
      # threads. The distribute coordinator should run std server immediately
      # without configuring the session (or building the graph) on PS.
      with test.mock.patch.object(dc, '_run_std_server',
                                  self._make_mock_run_std_server()):
        batch_size = 64
        steps = 10
        strategy = strategy_cls(num_gpus_per_worker=context.num_gpus())
        verification_callback.is_between_graph = \
            strategy.extended.experimental_between_graph

        train_ds, _ = _mnist_synthetic_dataset(batch_size, steps)
        val_ds, _ = _mnist_synthetic_dataset(batch_size, steps)
        with strategy.scope():
          model = _get_model((28, 28, 1))

          # TODO(b/123868066): Verify callback for model.evaluate().
          callbacks_for_fit = nest.flatten(
              kwargs.get('verification_callback', []))
          history = model.fit(
              x=train_ds,
              epochs=num_epoch,
              steps_per_epoch=steps,
              validation_data=val_ds,
              validation_steps=steps,
              callbacks=callbacks_for_fit)
        self.assertIsInstance(history, keras.callbacks.History)

 def benchmark_defun_matmul_100_by_784_GPU(self):
   if not context.num_gpus():
     return
   with context.device(GPU):
     m = self._m_100_by_784.gpu()
     self._benchmark_defun_matmul(
         m, transpose_b=True, num_iters=self._num_iters_100_by_784)

 def benchmark_defun_matmul_2_by_2_GPU(self):
   if not context.num_gpus():
     return
   with context.device(GPU):
     m = self._m_2_by_2.gpu()
     self._benchmark_defun_matmul(
         m, transpose_b=False, num_iters=self._num_iters_2_by_2)