diff --git a/doc/examples/parameter_server/async_parameter_server.py b/doc/examples/parameter_server/async_parameter_server.py
deleted file mode 100644
index 3e1aa67da..000000000
--- a/doc/examples/parameter_server/async_parameter_server.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import time
-
-import ray
-import model
-
-parser = argparse.ArgumentParser(description="Run the asynchronous parameter "
-                                             "server example.")
-parser.add_argument("--num-workers", default=4, type=int,
-                    help="The number of workers to use.")
-parser.add_argument("--redis-address", default=None, type=str,
-                    help="The Redis address of the cluster.")
-
-
-@ray.remote
-class ParameterServer(object):
-    def __init__(self, keys, values):
-        # These values will be mutated, so we must create a copy that is not
-        # backed by the object store.
-        values = [value.copy() for value in values]
-        self.weights = dict(zip(keys, values))
-
-    def push(self, keys, values):
-        for key, value in zip(keys, values):
-            self.weights[key] += value
-
-    def pull(self, keys):
-        return [self.weights[key] for key in keys]
-
-
-@ray.remote
-def worker_task(ps, worker_index, batch_size=50):
-    # Download MNIST.
-    mnist = model.download_mnist_retry(seed=worker_index)
-
-    # Initialize the model.
-    net = model.SimpleCNN()
-    keys = net.get_weights()[0]
-
-    while True:
-        # Get the current weights from the parameter server.
-        weights = ray.get(ps.pull.remote(keys))
-        net.set_weights(keys, weights)
-
-        # Compute an update and push it to the parameter server.
-        xs, ys = mnist.train.next_batch(batch_size)
-        gradients = net.compute_update(xs, ys)
-        ps.push.remote(keys, gradients)
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-
-    ray.init(redis_address=args.redis_address)
-
-    # Create a parameter server with some random weights.
-    net = model.SimpleCNN()
-    all_keys, all_values = net.get_weights()
-    ps = ParameterServer.remote(all_keys, all_values)
-
-    # Start some training tasks.
-    worker_tasks = [worker_task.remote(ps, i) for i in range(args.num_workers)]
-
-    # Download MNIST.
-    mnist = model.download_mnist_retry()
-
-    i = 0
-    while True:
-        # Get and evaluate the current model.
-        current_weights = ray.get(ps.pull.remote(all_keys))
-        net.set_weights(all_keys, current_weights)
-        test_xs, test_ys = mnist.test.next_batch(1000)
-        accuracy = net.compute_accuracy(test_xs, test_ys)
-        print("Iteration {}: accuracy is {}".format(i, accuracy))
-        i += 1
-        time.sleep(1)
diff --git a/doc/examples/parameter_server/model.py b/doc/examples/parameter_server/model.py
deleted file mode 100644
index dc0c15b49..000000000
--- a/doc/examples/parameter_server/model.py
+++ /dev/null
@@ -1,203 +0,0 @@
-# Most of the tensorflow code is adapted from Tensorflow's tutorial on using
-# CNNs to train MNIST
-# https://www.tensorflow.org/get_started/mnist/pros#build-a-multilayer-convolutional-network.  # noqa: E501
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import time
-
-import tensorflow as tf
-from tensorflow.examples.tutorials.mnist import input_data
-
-import ray
-import ray.experimental.tf_utils
-
-
-def download_mnist_retry(seed=0, max_num_retries=20):
-    for _ in range(max_num_retries):
-        try:
-            return input_data.read_data_sets(
-                "MNIST_data", one_hot=True, seed=seed)
-        except tf.errors.AlreadyExistsError:
-            time.sleep(1)
-    raise Exception("Failed to download MNIST.")
-
-
-class SimpleCNN(object):
-    def __init__(self, learning_rate=1e-4):
-        with tf.Graph().as_default():
-
-            # Create the model
-            self.x = tf.placeholder(tf.float32, [None, 784])
-
-            # Define loss and optimizer
-            self.y_ = tf.placeholder(tf.float32, [None, 10])
-
-            # Build the graph for the deep net
-            self.y_conv, self.keep_prob = deepnn(self.x)
-
-            with tf.name_scope("loss"):
-                cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
-                    labels=self.y_, logits=self.y_conv)
-            self.cross_entropy = tf.reduce_mean(cross_entropy)
-
-            with tf.name_scope("adam_optimizer"):
-                self.optimizer = tf.train.AdamOptimizer(learning_rate)
-                self.train_step = self.optimizer.minimize(self.cross_entropy)
-
-            with tf.name_scope("accuracy"):
-                correct_prediction = tf.equal(
-                    tf.argmax(self.y_conv, 1), tf.argmax(self.y_, 1))
-                correct_prediction = tf.cast(correct_prediction, tf.float32)
-            self.accuracy = tf.reduce_mean(correct_prediction)
-
-            self.sess = tf.Session(
-                config=tf.ConfigProto(
-                    intra_op_parallelism_threads=1,
-                    inter_op_parallelism_threads=1))
-            self.sess.run(tf.global_variables_initializer())
-
-            # Helper values.
-
-            self.variables = ray.experimental.tf_utils.TensorFlowVariables(
-                self.cross_entropy, self.sess)
-
-            self.grads = self.optimizer.compute_gradients(self.cross_entropy)
-            self.grads_placeholder = [(tf.placeholder(
-                "float", shape=grad[1].get_shape()), grad[1])
-                                      for grad in self.grads]
-            self.apply_grads_placeholder = self.optimizer.apply_gradients(
-                self.grads_placeholder)
-
-    def compute_update(self, x, y):
-        # TODO(rkn): Computing the weights before and after the training step
-        # and taking the diff is awful.
-        weights = self.get_weights()[1]
-        self.sess.run(
-            self.train_step,
-            feed_dict={
-                self.x: x,
-                self.y_: y,
-                self.keep_prob: 0.5
-            })
-        new_weights = self.get_weights()[1]
-        return [x - y for x, y in zip(new_weights, weights)]
-
-    def compute_gradients(self, x, y):
-        return self.sess.run(
-            [grad[0] for grad in self.grads],
-            feed_dict={
-                self.x: x,
-                self.y_: y,
-                self.keep_prob: 0.5
-            })
-
-    def apply_gradients(self, gradients):
-        feed_dict = {}
-        for i in range(len(self.grads_placeholder)):
-            feed_dict[self.grads_placeholder[i][0]] = gradients[i]
-        self.sess.run(self.apply_grads_placeholder, feed_dict=feed_dict)
-
-    def compute_accuracy(self, x, y):
-        return self.sess.run(
-            self.accuracy,
-            feed_dict={
-                self.x: x,
-                self.y_: y,
-                self.keep_prob: 1.0
-            })
-
-    def set_weights(self, variable_names, weights):
-        self.variables.set_weights(dict(zip(variable_names, weights)))
-
-    def get_weights(self):
-        weights = self.variables.get_weights()
-        return list(weights.keys()), list(weights.values())
-
-
-def deepnn(x):
-    """deepnn builds the graph for a deep net for classifying digits.
-
-    Args:
-        x: an input tensor with the dimensions (N_examples, 784), where 784 is
-            the number of pixels in a standard MNIST image.
-
-    Returns:
-        A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with
-            values equal to the logits of classifying the digit into one of 10
-            classes (the digits 0-9). keep_prob is a scalar placeholder for the
-            probability of dropout.
-    """
-    # Reshape to use within a convolutional neural net.
-    # Last dimension is for "features" - there is only one here, since images
-    # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
-    with tf.name_scope("reshape"):
-        x_image = tf.reshape(x, [-1, 28, 28, 1])
-
-    # First convolutional layer - maps one grayscale image to 32 feature maps.
-    with tf.name_scope("conv1"):
-        W_conv1 = weight_variable([5, 5, 1, 32])
-        b_conv1 = bias_variable([32])
-        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
-
-    # Pooling layer - downsamples by 2X.
-    with tf.name_scope("pool1"):
-        h_pool1 = max_pool_2x2(h_conv1)
-
-    # Second convolutional layer -- maps 32 feature maps to 64.
-    with tf.name_scope("conv2"):
-        W_conv2 = weight_variable([5, 5, 32, 64])
-        b_conv2 = bias_variable([64])
-        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
-
-    # Second pooling layer.
-    with tf.name_scope("pool2"):
-        h_pool2 = max_pool_2x2(h_conv2)
-
-    # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
-    # is down to 7x7x64 feature maps -- maps this to 1024 features.
-    with tf.name_scope("fc1"):
-        W_fc1 = weight_variable([7 * 7 * 64, 1024])
-        b_fc1 = bias_variable([1024])
-
-        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
-        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
-
-    # Dropout - controls the complexity of the model, prevents co-adaptation of
-    # features.
-    with tf.name_scope("dropout"):
-        keep_prob = tf.placeholder(tf.float32)
-        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
-
-    # Map the 1024 features to 10 classes, one for each digit
-    with tf.name_scope("fc2"):
-        W_fc2 = weight_variable([1024, 10])
-        b_fc2 = bias_variable([10])
-
-        y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
-    return y_conv, keep_prob
-
-
-def conv2d(x, W):
-    """conv2d returns a 2d convolution layer with full stride."""
-    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
-
-
-def max_pool_2x2(x):
-    """max_pool_2x2 downsamples a feature map by 2X."""
-    return tf.nn.max_pool(
-        x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
-
-
-def weight_variable(shape):
-    """weight_variable generates a weight variable of a given shape."""
-    initial = tf.truncated_normal(shape, stddev=0.1)
-    return tf.Variable(initial)
-
-
-def bias_variable(shape):
-    """bias_variable generates a bias variable of a given shape."""
-    initial = tf.constant(0.1, shape=shape)
-    return tf.Variable(initial)
diff --git a/doc/examples/parameter_server/ray-project/cluster.yaml b/doc/examples/parameter_server/ray-project/cluster.yaml
deleted file mode 100644
index 4a6c9f418..000000000
--- a/doc/examples/parameter_server/ray-project/cluster.yaml
+++ /dev/null
@@ -1,18 +0,0 @@
-# This file is generated by `ray project create`.
-
-# A unique identifier for the head node and workers of this cluster.
-cluster_name: ray-example-parameter-server
-
-# The maximum number of workers nodes to launch in addition to the head
-# node. This takes precedence over min_workers. min_workers defaults to 0.
-max_workers: 1
-
-# Cloud-provider specific configuration.
-provider:
-    type: aws
-    region: us-west-2
-    availability_zone: us-west-2a
-
-# How Ray will authenticate with newly launched nodes.
-auth:
-    ssh_user: ubuntu
diff --git a/doc/examples/parameter_server/ray-project/project.yaml b/doc/examples/parameter_server/ray-project/project.yaml
deleted file mode 100644
index 846170f8a..000000000
--- a/doc/examples/parameter_server/ray-project/project.yaml
+++ /dev/null
@@ -1,41 +0,0 @@
-# This file is generated by `ray project create`.
-
-name: ray-example-parameter-server
-
-description: "A simple parameter server example implemented with ray actors"
-tags: ["ray-example", "parameter-server", "machine-learning"]
-documentation: https://ray.readthedocs.io/en/latest/auto_examples/plot_parameter_server.html
-
-cluster:
-  config: ray-project/cluster.yaml
-
-environment:
-  requirements: ray-project/requirements.txt
-
-commands:
-  - name: run-sync
-    command: python sync_parameter_server.py --num-workers {{num-workers}}
-    help: "Start the synchronous parameter server."
-    params:
-      - name: num-workers
-        help: "Number of workers"
-        default: 4
-        type: int
-    config:
-      tmux: true
-
-  - name: run-async
-    command: python async_parameter_server.py --num-workers {{num-workers}}
-    help: "Start the asynchronous parameter server."
-    params:
-      - name: num-workers
-        help: "Number of workers"
-        default: 4
-        type: int
-    config:
-      tmux: true
-
-output_files: [
-  # Save the logs from the latest run in snapshots.
-  "/tmp/ray/session_latest/logs"
-]
diff --git a/doc/examples/parameter_server/ray-project/requirements.txt b/doc/examples/parameter_server/ray-project/requirements.txt
deleted file mode 100644
index 72d631c31..000000000
--- a/doc/examples/parameter_server/ray-project/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-ray[debug,rllib]
-torch
-torchvision
-filelock
diff --git a/doc/examples/parameter_server/sync_parameter_server.py b/doc/examples/parameter_server/sync_parameter_server.py
deleted file mode 100644
index 77c7a4867..000000000
--- a/doc/examples/parameter_server/sync_parameter_server.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import numpy as np
-
-import ray
-import model
-
-parser = argparse.ArgumentParser(description="Run the synchronous parameter "
-                                             "server example.")
-parser.add_argument("--num-workers", default=4, type=int,
-                    help="The number of workers to use.")
-parser.add_argument("--redis-address", default=None, type=str,
-                    help="The Redis address of the cluster.")
-
-
-@ray.remote
-class ParameterServer(object):
-    def __init__(self, learning_rate):
-        self.net = model.SimpleCNN(learning_rate=learning_rate)
-
-    def apply_gradients(self, *gradients):
-        self.net.apply_gradients(np.mean(gradients, axis=0))
-        return self.net.variables.get_flat()
-
-    def get_weights(self):
-        return self.net.variables.get_flat()
-
-
-@ray.remote
-class Worker(object):
-    def __init__(self, worker_index, batch_size=50):
-        self.worker_index = worker_index
-        self.batch_size = batch_size
-        self.mnist = model.download_mnist_retry(seed=worker_index)
-        self.net = model.SimpleCNN()
-
-    def compute_gradients(self, weights):
-        self.net.variables.set_flat(weights)
-        xs, ys = self.mnist.train.next_batch(self.batch_size)
-        return self.net.compute_gradients(xs, ys)
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-
-    ray.init(redis_address=args.redis_address)
-
-    # Create a parameter server.
-    net = model.SimpleCNN()
-    ps = ParameterServer.remote(1e-4 * args.num_workers)
-
-    # Create workers.
-    workers = [Worker.remote(worker_index)
-               for worker_index in range(args.num_workers)]
-
-    # Download MNIST.
-    mnist = model.download_mnist_retry()
-
-    i = 0
-    current_weights = ps.get_weights.remote()
-    while True:
-        # Compute and apply gradients.
-        gradients = [worker.compute_gradients.remote(current_weights)
-                     for worker in workers]
-        current_weights = ps.apply_gradients.remote(*gradients)
-
-        if i % 10 == 0:
-            # Evaluate the current model.
-            net.variables.set_flat(ray.get(current_weights))
-            test_xs, test_ys = mnist.test.next_batch(1000)
-            accuracy = net.compute_accuracy(test_xs, test_ys)
-            print("Iteration {}: accuracy is {}".format(i, accuracy))
-        i += 1