Distributed training with Core APIs and DTensor

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Introduction

This notebook uses the TensorFlow Core low-level APIs and DTensor to demonstrate a data parallel distributed training example. Visit the Core APIs overview to learn more about TensorFlow Core and its intended use cases. Refer to the DTensor Overview guide and Distributed Training with DTensors tutorial to learn more about DTensor.

This example uses the same model and optimizer shown in the multilayer perceptrons tutorial. See this tutorial first to get comfortable with writing an end-to-end machine learning workflow with the Core APIs.

Overview of data parallel training with DTensor

Before building an MLP that supports distribution, take a moment to explore the fundamentals of DTensor for data parallel training.

DTensor allows you to run distributed training across devices to improve efficiency, reliability and scalability. DTensor distributes the program and tensors according to the sharding directives through a procedure called Single program, multiple data (SPMD) expansion. A variable of a DTensor aware layer is created as dtensor.DVariable, and the constructors of DTensor aware layer objects take additional Layout inputs in addition to the usual layer parameters.

The main ideas for data parallel training are as follows:

  • Model variables are replicated on N devices each.
  • A global batch is split into N per-replica batches.
  • Each per-replica batch is trained on the replica device.
  • The gradient is reduced before weight up data is collectively performed on all replicas.
  • Data parallel training provides nearly linear speed with respect to the number of devices

Setup

DTensor is part of TensorFlow 2.9.0 release.

#!pip install --quiet --upgrade --pre tensorflow

importmatplotlib
frommatplotlibimport pyplot as plt
# Preset Matplotlib figure sizes.
matplotlib.rcParams['figure.figsize'] = [9, 6]

importtensorflowastf
importtensorflow_datasetsastfds
fromtensorflow.experimentalimport dtensor
print(tf.__version__)
# Set random seed for reproducible results 
tf.random.set_seed(22)

2024年08月15日 02:49:40.914029: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024年08月15日 02:49:40.935518: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024年08月15日 02:49:40.941702: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2.17.0

Configure 8 virtual CPUs for this experiment. DTensor can also be used with GPU or TPU devices. Given that this notebook uses virtual devices, the speedup gained from distributed training is not noticeable. 

defconfigure_virtual_cpus(ncpu):
 phy_devices = tf.config.list_physical_devices('CPU')
 tf.config.set_logical_device_configuration(phy_devices[0], [
 tf.config.LogicalDeviceConfiguration(),
 ] * ncpu)
configure_virtual_cpus(8)
DEVICES = [f'CPU:{i}' for i in range(8)]
devices = tf.config.list_logical_devices('CPU')
device_names = [d.name for d in devices]
device_names

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1723690183.661893 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.665603 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.669301 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.672556 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.683679 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.687589 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.691101 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.694059 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.696961 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.700515 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.704018 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690183.706976 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.934382 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.936519 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.938569 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.940700 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.942765 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.944750 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.946705 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.948674 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.950629 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.952626 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.954710 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.956738 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.995780 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.997864 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690184.999851 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.001859 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See mo
['/device:CPU:0',
 '/device:CPU:1',
 '/device:CPU:2',
 '/device:CPU:3',
 '/device:CPU:4',
 '/device:CPU:5',
 '/device:CPU:6',
 '/device:CPU:7']
re at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.003740 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.005715 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.007659 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.009659 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.011546 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.014055 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.016445 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723690185.018866 157397 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355

The MNIST Dataset

The dataset is available from TensorFlow Datasets. Split the data into training and testing sets. Only use 5000 examples for training and testing to save time.

train_data, test_data = tfds.load("mnist", split=['train[:5000]', 'test[:5000]'], batch_size=128, as_supervised=True)

Preprocessing the data

Preprocess the data by reshaping it to be 2-dimensional and by rescaling it to fit into the unit interval, [0,1].

defpreprocess(x, y):
 # Reshaping the data
 x = tf.reshape(x, shape=[-1, 784])
 # Rescaling the data
 x = x/255
 return x, y
train_data, test_data = train_data.map(preprocess), test_data.map(preprocess)

Build the MLP

Build an MLP model with DTensor aware layers.

The dense layer

Start by creating a dense layer module that supports DTensor. The dtensor.call_with_layout function can be used to call a function that takes in a DTensor input and produces a DTensor output. This is useful for initializing a DTensor variable, dtensor.DVariable, with a TensorFlow supported function.

classDenseLayer(tf.Module):
 def__init__(self, in_dim, out_dim, weight_layout, activation=tf.identity):
 super().__init__()
 # Initialize dimensions and the activation function
 self.in_dim, self.out_dim = in_dim, out_dim
 self.activation = activation
 # Initialize the DTensor weights using the Xavier scheme
 uniform_initializer = tf.function(tf.random.stateless_uniform)
 xavier_lim = tf.sqrt(6.)/tf.sqrt(tf.cast(self.in_dim + self.out_dim, tf.float32))
 self.w = dtensor.DVariable(
 dtensor.call_with_layout(
 uniform_initializer, weight_layout,
 shape=(self.in_dim, self.out_dim), seed=(22, 23),
 minval=-xavier_lim, maxval=xavier_lim))
 # Initialize the bias with the zeros
 bias_layout = weight_layout.delete([0])
 self.b = dtensor.DVariable(
 dtensor.call_with_layout(tf.zeros, bias_layout, shape=[out_dim]))
 def__call__(self, x):
 # Compute the forward pass
 z = tf.add(tf.matmul(x, self.w), self.b)
 return self.activation(z)

The MLP sequential model

Now create an MLP module that executes the dense layers sequentially.

classMLP(tf.Module):
 def__init__(self, layers):
 self.layers = layers
 def__call__(self, x, preds=False): 
 # Execute the model's layers sequentially
 for layer in self.layers:
 x = layer(x)
 return x

Performing "data-parallel" training with DTensor is equivalent to tf.distribute.MirroredStrategy. To do this each device will run the same model on a shard of the data batch. So you'll need the following:

Create a DTensor mesh that consists of a single batch dimension, where each device becomes a replica that receives a shard from the global batch. Use this mesh to instantiate an MLP mode with the following architecture:

Forward Pass: ReLU(784 x 700) x ReLU(700 x 500) x Softmax(500 x 10)

mesh = dtensor.create_mesh([("batch", 8)], devices=DEVICES)
weight_layout = dtensor.Layout([dtensor.UNSHARDED, dtensor.UNSHARDED], mesh)
input_size = 784
hidden_layer_1_size = 700
hidden_layer_2_size = 500
hidden_layer_2_size = 10
mlp_model = MLP([
 DenseLayer(in_dim=input_size, out_dim=hidden_layer_1_size, 
 weight_layout=weight_layout,
 activation=tf.nn.relu),
 DenseLayer(in_dim=hidden_layer_1_size , out_dim=hidden_layer_2_size,
 weight_layout=weight_layout,
 activation=tf.nn.relu),
 DenseLayer(in_dim=hidden_layer_2_size, out_dim=hidden_layer_2_size, 
 weight_layout=weight_layout)])

Training metrics

Use the cross-entropy loss function and accuracy metric for training.

defcross_entropy_loss(y_pred, y):
 # Compute cross entropy loss with a sparse operation
 sparse_ce = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=y_pred)
 return tf.reduce_mean(sparse_ce)
defaccuracy(y_pred, y):
 # Compute accuracy after extracting class predictions
 class_preds = tf.argmax(y_pred, axis=1)
 is_equal = tf.equal(y, class_preds)
 return tf.reduce_mean(tf.cast(is_equal, tf.float32))

Optimizer

Using an optimizer can result in significantly faster convergence compared to standard gradient descent. The Adam optimizer is implemented below and has been configured to be compatible with DTensor. In order to use Keras optimizers with DTensor, refer to the experimentaltf.keras.dtensor.experimental.optimizers module.

classAdam(tf.Module):
 def__init__(self, model_vars, learning_rate=1e-3, beta_1=0.9, beta_2=0.999, ep=1e-7):
 # Initialize optimizer parameters and variable slots
 self.model_vars = model_vars
 self.beta_1 = beta_1
 self.beta_2 = beta_2
 self.learning_rate = learning_rate
 self.ep = ep
 self.t = 1.
 self.v_dvar, self.s_dvar = [], []
 # Initialize optimizer variable slots
 for var in model_vars:
 v = dtensor.DVariable(dtensor.call_with_layout(tf.zeros, var.layout, shape=var.shape))
 s = dtensor.DVariable(dtensor.call_with_layout(tf.zeros, var.layout, shape=var.shape))
 self.v_dvar.append(v)
 self.s_dvar.append(s)
 defapply_gradients(self, grads):
 # Update the model variables given their gradients
 for i, (d_var, var) in enumerate(zip(grads, self.model_vars)):
 self.v_dvar[i].assign(self.beta_1*self.v_dvar[i] + (1-self.beta_1)*d_var)
 self.s_dvar[i].assign(self.beta_2*self.s_dvar[i] + (1-self.beta_2)*tf.square(d_var))
 v_dvar_bc = self.v_dvar[i]/(1-(self.beta_1**self.t))
 s_dvar_bc = self.s_dvar[i]/(1-(self.beta_2**self.t))
 var.assign_sub(self.learning_rate*(v_dvar_bc/(tf.sqrt(s_dvar_bc) + self.ep)))
 self.t += 1.
 return

Data packing

Start by writing a helper function for transferring data to the device. This function should use dtensor.pack to send (and only send) the shard of the global batch that is intended for a replica to the device backing the replica. For simplicity, assume a single-client application.

Next, write a function that uses this helper function to pack the training data batches into DTensors sharded along the batch (first) axis. This ensures that DTensor evenly distributes the training data to the 'batch' mesh dimension. Note that in DTensor, the batch size always refers to the global batch size; therefore, the batch size should be chosen such that it can be divided evenly by the size of the batch mesh dimension. Additional DTensor APIs to simplify tf.data integration are planned, so please stay tuned.

defrepack_local_tensor(x, layout):
 # Repacks a local Tensor-like to a DTensor with layout
 # This function assumes a single-client application
 x = tf.convert_to_tensor(x)
 sharded_dims = []
 # For every sharded dimension, use tf.split to split the along the dimension.
 # The result is a nested list of split-tensors in queue[0].
 queue = [x]
 for axis, dim in enumerate(layout.sharding_specs):
 if dim == dtensor.UNSHARDED:
 continue
 num_splits = layout.shape[axis]
 queue = tf.nest.map_structure(lambda x: tf.split(x, num_splits, axis=axis), queue)
 sharded_dims.append(dim)
 # Now you can build the list of component tensors by looking up the location in
 # the nested list of split-tensors created in queue[0].
 components = []
 for locations in layout.mesh.local_device_locations():
 t = queue[0]
 for dim in sharded_dims:
 split_index = locations[dim] # Only valid on single-client mesh.
 t = t[split_index]
 components.append(t)
 return dtensor.pack(components, layout)
defrepack_batch(x, y, mesh):
 # Pack training data batches into DTensors along the batch axis
 x = repack_local_tensor(x, layout=dtensor.Layout(['batch', dtensor.UNSHARDED], mesh))
 y = repack_local_tensor(y, layout=dtensor.Layout(['batch'], mesh))
 return x, y

Training

Write a traceable function that executes a single training step given a batch of data. This function does not require any special DTensor annotations. Also write a function that executes a test step and returns the appropriate performance metrics.

@tf.function
deftrain_step(model, x_batch, y_batch, loss, metric, optimizer):
 # Execute a single training step
 with tf.GradientTape() as tape:
 y_pred = model(x_batch)
 batch_loss = loss(y_pred, y_batch)
 # Compute gradients and update the model's parameters
 grads = tape.gradient(batch_loss, model.trainable_variables)
 optimizer.apply_gradients(grads)
 # Return batch loss and accuracy
 batch_acc = metric(y_pred, y_batch)
 return batch_loss, batch_acc
@tf.function
deftest_step(model, x_batch, y_batch, loss, metric):
 # Execute a single testing step
 y_pred = model(x_batch)
 batch_loss = loss(y_pred, y_batch)
 batch_acc = metric(y_pred, y_batch)
 return batch_loss, batch_acc

Now, train the MLP model for 3 epochs with a batch size of 128.

# Initialize the training loop parameters and structures
epochs = 3
batch_size = 128
train_losses, test_losses = [], []
train_accs, test_accs = [], []
optimizer = Adam(mlp_model.trainable_variables)
# Format training loop
for epoch in range(epochs):
 batch_losses_train, batch_accs_train = [], []
 batch_losses_test, batch_accs_test = [], []
 # Iterate through training data
 for x_batch, y_batch in train_data:
 x_batch, y_batch = repack_batch(x_batch, y_batch, mesh)
 batch_loss, batch_acc = train_step(mlp_model, x_batch, y_batch, cross_entropy_loss, accuracy, optimizer)
 # Keep track of batch-level training performance
 batch_losses_train.append(batch_loss)
 batch_accs_train.append(batch_acc)
 # Iterate through testing data
 for x_batch, y_batch in test_data:
 x_batch, y_batch = repack_batch(x_batch, y_batch, mesh)
 batch_loss, batch_acc = test_step(mlp_model, x_batch, y_batch, cross_entropy_loss, accuracy)
 # Keep track of batch-level testing
 batch_losses_test.append(batch_loss)
 batch_accs_test.append(batch_acc)
# Keep track of epoch-level model performance
 train_loss, train_acc = tf.reduce_mean(batch_losses_train), tf.reduce_mean(batch_accs_train)
 test_loss, test_acc = tf.reduce_mean(batch_losses_test), tf.reduce_mean(batch_accs_test)
 train_losses.append(train_loss)
 train_accs.append(train_acc)
 test_losses.append(test_loss)
 test_accs.append(test_acc)
 print(f"Epoch: {epoch}")
 print(f"Training loss: {train_loss.numpy():.3f}, Training accuracy: {train_acc.numpy():.3f}")
 print(f"Testing loss: {test_loss.numpy():.3f}, Testing accuracy: {test_acc.numpy():.3f}")

Epoch: 0
Training loss: 1.850, Training accuracy: 0.343
Testing loss: 1.375, Testing accuracy: 0.504
Epoch: 1
Training loss: 1.028, Training accuracy: 0.674
Testing loss: 0.744, Testing accuracy: 0.782
Epoch: 2
Training loss: 0.578, Training accuracy: 0.839
Testing loss: 0.486, Testing accuracy: 0.869

Performance evaluation

Start by writing a plotting function to visualize the model's loss and accuracy during training. 

defplot_metrics(train_metric, test_metric, metric_type):
 # Visualize metrics vs training Epochs
 plt.figure()
 plt.plot(range(len(train_metric)), train_metric, label = f"Training {metric_type}")
 plt.plot(range(len(test_metric)), test_metric, label = f"Testing {metric_type}")
 plt.xlabel("Epochs")
 plt.ylabel(metric_type)
 plt.legend()
 plt.title(f"{metric_type} vs Training Epochs");

plot_metrics(train_losses, test_losses, "Cross entropy loss")

png

plot_metrics(train_accs, test_accs, "Accuracy")

png

Saving your model

The integration of tf.saved_model and DTensor is still under development. As of TensorFlow 2.9.0, tf.saved_model only accepts DTensor models with fully replicated variables. As a workaround, you can convert a DTensor model to a fully replicated one by reloading a checkpoint. However, after a model is saved, all DTensor annotations are lost and the saved signatures can only be used with regular Tensors. This tutorial will be updated to showcase the integration once it is solidified.

Conclusion

This notebook provided an overview of distributed training with DTensor and the TensorFlow Core APIs. Here are a few more tips that may help:

For more examples of using the TensorFlow Core APIs, check out the guide. If you want to learn more about loading and preparing data, see the tutorials on image data loading or CSV data loading.

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Last updated 2024年08月15日 UTC.