diff --git a/examples/jax/train_dist.py b/examples/jax/train_dist.py
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+# Copyright 2018 The JAX Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""An MNIST example with single-program multiple-data (SPMD) data parallelism.
+
+The aim here is to illustrate how to use JAX's `pmap` to express and execute
+SPMD programs for data parallelism along a batch dimension, while also
+minimizing dependencies by avoiding the use of higher-level layers and
+optimizers libraries.
+"""
+
+
+from functools import partial
+import time
+
+import numpy as np
+import numpy.random as npr
+
+import jax
+from jax import jit, grad, pmap
+from jax.scipy.special import logsumexp
+from jax.tree_util import tree_map
+from jax import lax
+import jax.numpy as jnp
+from examples import datasets
+
+
+def init_random_params(scale, layer_sizes, rng=npr.RandomState(0)):
+  return [(scale * rng.randn(m, n), scale * rng.randn(n))
+          for m, n, in zip(layer_sizes[:-1], layer_sizes[1:])]
+
+def predict(params, inputs):
+  activations = inputs
+  for w, b in params[:-1]:
+    outputs = jnp.dot(activations, w) + b
+    activations = jnp.tanh(outputs)
+
+  final_w, final_b = params[-1]
+  logits = jnp.dot(activations, final_w) + final_b
+  return logits - logsumexp(logits, axis=1, keepdims=True)
+
+def loss(params, batch):
+  inputs, targets = batch
+  preds = predict(params, inputs)
+  return -jnp.mean(jnp.sum(preds * targets, axis=1))
+
+@jit
+def accuracy(params, batch):
+  inputs, targets = batch
+  target_class = jnp.argmax(targets, axis=1)
+  predicted_class = jnp.argmax(predict(params, inputs), axis=1)
+  return jnp.mean(predicted_class == target_class)
+
+
+if __name__ == "__main__":
+  layer_sizes = [784, 1024, 1024, 10]
+  param_scale = 0.1
+  step_size = 0.001
+  num_epochs = 10
+  batch_size = 128
+
+  train_images, train_labels, test_images, test_labels = datasets.mnist()
+  num_train = train_images.shape[0]
+  num_complete_batches, leftover = divmod(num_train, batch_size)
+  num_batches = num_complete_batches + bool(leftover)
+
+  # For this manual SPMD example, we get the number of devices (e.g. GPUs or
+  # TPU cores) that we're using, and use it to reshape data minibatches.
+  num_devices = jax.device_count()
+  def data_stream():
+    rng = npr.RandomState(0)
+    while True:
+      perm = rng.permutation(num_train)
+      for i in range(num_batches):
+        batch_idx = perm[i * batch_size:(i + 1) * batch_size]
+        images, labels = train_images[batch_idx], train_labels[batch_idx]
+        # For this SPMD example, we reshape the data batch dimension into two
+        # batch dimensions, one of which is mapped over parallel devices.
+        batch_size_per_device, ragged = divmod(images.shape[0], num_devices)
+        if ragged:
+          msg = "batch size must be divisible by device count, got {} and {}."
+          raise ValueError(msg.format(batch_size, num_devices))
+        shape_prefix = (num_devices, batch_size_per_device)
+        images = images.reshape(shape_prefix + images.shape[1:])
+        labels = labels.reshape(shape_prefix + labels.shape[1:])
+        yield images, labels
+  batches = data_stream()
+
+  @partial(pmap, axis_name='batch')
+  def spmd_update(params, batch):
+    grads = grad(loss)(params, batch)
+    # We compute the total gradients, summing across the device-mapped axis,
+    # using the `lax.psum` SPMD primitive, which does a fast all-reduce-sum.
+    grads = [(lax.psum(dw, 'batch'), lax.psum(db, 'batch')) for dw, db in grads]
+    return [(w - step_size * dw, b - step_size * db)
+            for (w, b), (dw, db) in zip(params, grads)]
+
+  # We replicate the parameters so that the constituent arrays have a leading
+  # dimension of size equal to the number of devices we're pmapping over.
+  init_params = init_random_params(param_scale, layer_sizes)
+  replicate_array = lambda x: np.broadcast_to(x, (num_devices,) + x.shape)
+  replicated_params = tree_map(replicate_array, init_params)
+
+  for epoch in range(num_epochs):
+    start_time = time.time()
+    for _ in range(num_batches):
+      replicated_params = spmd_update(replicated_params, next(batches))
+    epoch_time = time.time() - start_time
+
+    # We evaluate using the jitted `accuracy` function (not using pmap) by
+    # grabbing just one of the replicated parameter values.
+    params = tree_map(lambda x: x[0], replicated_params)
+    train_acc = accuracy(params, (train_images, train_labels))
+    test_acc = accuracy(params, (test_images, test_labels))
+    print(f"Epoch {epoch} in {epoch_time:0.2f} sec")
+    print(f"Training set accuracy {train_acc}")
+    print(f"Test set accuracy {test_acc}")
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