Add notebooks for Batch Transform

batch_transform/mxnet_mnist/mnist.py

@@ -0,0 +1,73 @@
+import logging
+
+import gzip
+import mxnet as mx
+import numpy as np
+import os
+import struct
+
+
+def load_data(path):
+    with gzip.open(find_file(path, "labels.gz")) as flbl:
+        struct.unpack(">II", flbl.read(8))
+        labels = np.fromstring(flbl.read(), dtype=np.int8)
+    with gzip.open(find_file(path, "images.gz")) as fimg:
+        _, _, rows, cols = struct.unpack(">IIII", fimg.read(16))
+        images = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(labels), rows, cols)
+        images = images.reshape(images.shape[0], 1, 28, 28).astype(np.float32) / 255
+    return labels, images
+
+
+def find_file(root_path, file_name):
+    for root, dirs, files in os.walk(root_path):
+        if file_name in files:
+            return os.path.join(root, file_name)
+
+
+def build_graph():
+    data = mx.sym.var('data')
+    data = mx.sym.flatten(data=data)
+    fc1 = mx.sym.FullyConnected(data=data, num_hidden=128)
+    act1 = mx.sym.Activation(data=fc1, act_type="relu")
+    fc2 = mx.sym.FullyConnected(data=act1, num_hidden=64)
+    act2 = mx.sym.Activation(data=fc2, act_type="relu")
+    fc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)
+    return mx.sym.SoftmaxOutput(data=fc3, name='softmax')
+
+
+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[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'
+    mlp_model = mx.mod.Module(
+        symbol=build_graph(),
+        context=get_train_context(num_cpus, num_gpus))
+    mlp_model.fit(train_iter,
+                  eval_data=val_iter,
+                  kvstore=kvstore,
+                  optimizer='sgd',
+                  optimizer_params={'learning_rate': float(hyperparameters.get("learning_rate", 0.1))},
+                  eval_metric='acc',
+                  batch_end_callback=mx.callback.Speedometer(batch_size, 100),
+                  num_epoch=25)
+    return mlp_model
+
+
+def get_train_context(num_cpus, num_gpus):
+    if num_gpus > 0:
+        return mx.gpu()
+    return mx.cpu()