Ensure MXNet notebooks run in distributed mode.

sagemaker-python-sdk/mxnet_gluon_cifar10/cifar10.py

@@ -36,8 +36,17 @@ def train(current_host, hosts, num_cpus, num_gpus, channel_input_dirs, model_dir
     # load training and validation data
     # we use the gluon.data.vision.CIFAR10 class because of its built in pre-processing logic,
     # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
+
+    part_index = 0
+    for i, host in enumerate(hosts):
+        if host == current_host:
+            part_index = i
+            break
+
+
     data_dir = channel_input_dirs['training']
-    train_data = get_train_data(num_cpus, data_dir, batch_size, (3, 32, 32))
+    train_data = get_train_data(num_cpus, data_dir, batch_size, (3, 32, 32),
+                                num_parts=len(hosts), part_index=part_index)
     test_data = get_test_data(num_cpus, data_dir, batch_size, (3, 32, 32))
 
     # Collect all parameters from net and its children, then initialize them.
@@ -104,23 +113,26 @@ def save(net, model_dir):
         os.rename(os.path.join(model_dir, best), os.path.join(model_dir, 'model.params'))
 
 
-def get_data(path, augment, num_cpus, batch_size, data_shape, resize=-1):
+def get_data(path, augment, num_cpus, batch_size, data_shape, resize=-1, num_parts=1, part_index=0):
     return mx.io.ImageRecordIter(
         path_imgrec=path,
         resize=resize,
         data_shape=data_shape,
         batch_size=batch_size,
         rand_crop=augment,
         rand_mirror=augment,
-        preprocess_threads=num_cpus)
+        preprocess_threads=num_cpus,
+        num_parts=num_parts,
+        part_index=part_index)
 
 
 def get_test_data(num_cpus, data_dir, batch_size, data_shape, resize=-1):
-    return get_data(os.path.join(data_dir, "test.rec"), False, num_cpus, batch_size, data_shape, resize)
+    return get_data(os.path.join(data_dir, "test.rec"), False, num_cpus, batch_size, data_shape, resize, 1, 0)
 
 
-def get_train_data(num_cpus, data_dir, batch_size, data_shape, resize=-1):
-    return get_data(os.path.join(data_dir, "train.rec"), True, num_cpus, batch_size, data_shape, resize)
+def get_train_data(num_cpus, data_dir, batch_size, data_shape, resize=-1, num_parts=1, part_index=0):
+    return get_data(os.path.join(data_dir, "train.rec"), True, num_cpus, batch_size, data_shape, resize, num_parts,
+                    part_index)
 
 
 def test(ctx, net, test_data):

sagemaker-python-sdk/mxnet_gluon_mnist/mnist.py

@@ -16,7 +16,7 @@
 # ------------------------------------------------------------ #
 
 
-def train(channel_input_dirs, hyperparameters, hosts, num_gpus, **kwargs):
+def train(current_host, channel_input_dirs, hyperparameters, hosts, num_gpus):
     # SageMaker passes num_cpus, num_gpus and other args we can use to tailor training to
     # the current container environment, but here we just use simple cpu context.
     ctx = mx.cpu()
@@ -53,6 +53,19 @@ def train(channel_input_dirs, hyperparameters, hosts, num_gpus, **kwargs):
     metric = mx.metric.Accuracy()
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
 
+    # shard the training data in case we are doing distributed training. Alternatively to splitting in memory,
+    # the data could be pre-split in S3 and use ShardedByS3Key to do distributed training.
+    if len(hosts) > 1:
+        train_data = [x for x in train_data]
+        shard_size = len(train_data) // len(hosts)
+        for i, host in enumerate(hosts):
+            if host == current_host:
+                start = shard_size * i
+                end = start + shard_size
+                break
+
+        train_data = train_data[start:end]
+
     for epoch in range(epochs):
         # reset data iterator and metric at begining of epoch.
         metric.reset()

sagemaker-python-sdk/mxnet_gluon_sentiment/sentiment.py

@@ -46,7 +46,16 @@ def train(current_host, hosts, num_cpus, num_gpus, channel_input_dirs, model_dir
     train_sentences = [[vocab.get(token, 1) for token in line if len(line)>0] for line in train_sentences]
     val_sentences = [[vocab.get(token, 1) for token in line if len(line)>0] for line in val_sentences]
 
-    train_iterator = BucketSentenceIter(train_sentences, train_labels, batch_size)
+    # Alternatively to splitting in memory, the data could be pre-split in S3 and use ShardedByS3Key
+    # to do parallel training.
+    shard_size = len(train_sentences) // len(hosts)
+    for i, host in enumerate(hosts):
+        if host == current_host:
+            start = shard_size * i
+            end = start + shard_size
+            break
+
+    train_iterator = BucketSentenceIter(train_sentences[start:end], train_labels[start:end], batch_size)
     val_iterator = BucketSentenceIter(val_sentences, val_labels, batch_size)
 
     # define the network

sagemaker-python-sdk/mxnet_mnist/mnist.py

@@ -35,11 +35,21 @@ def build_graph():
     return mx.sym.SoftmaxOutput(data=fc3, name='softmax')
 
 
-def train(channel_input_dirs, hyperparameters, hosts, num_cpus, num_gpus, **kwargs):
+def train(current_host, channel_input_dirs, hyperparameters, hosts, num_cpus, num_gpus):
     (train_labels, train_images) = load_data(os.path.join(channel_input_dirs['train']))
     (test_labels, test_images) = load_data(os.path.join(channel_input_dirs['test']))
+
+    # Alternatively to splitting in memory, the data could be pre-split in S3 and use ShardedByS3Key
+    # to do parallel training.
+    shard_size = len(train_images) // len(hosts)
+    for i, host in enumerate(hosts):
+        if host == current_host:
+            start = shard_size * i
+            end = start + shard_size
+            break
+
     batch_size = 100
-    train_iter = mx.io.NDArrayIter(train_images, train_labels, batch_size, shuffle=True)
+    train_iter = mx.io.NDArrayIter(train_images[start:end], train_labels[start:end], batch_size, shuffle=True)
     val_iter = mx.io.NDArrayIter(test_images, test_labels, batch_size)
     logging.getLogger().setLevel(logging.DEBUG)
     kvstore = 'local' if len(hosts) == 1 else 'dist_sync'