mnist.py

import argparse
import gzip
import json
import logging
import os
import struct

import mxnet as mx
import numpy as np
from sagemaker_mxnet_container.training_utils import scheduler_host


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(
    batch_size,
    num_epoch,
    learning_rate,
    optimizer,
    training_channel,
    testing_channel,
    hosts,
    current_host,
    model_dir,
):
    (train_labels, train_images) = load_data(training_channel)
    (test_labels, test_images) = load_data(testing_channel)

    # 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

    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())
    mlp_model.fit(
        train_iter,
        eval_data=val_iter,
        kvstore=kvstore,
        optimizer=optimizer,
        optimizer_params={"learning_rate": learning_rate},
        eval_metric="acc",
        batch_end_callback=mx.callback.Speedometer(batch_size, 100),
        num_epoch=num_epoch,
    )
    return mlp_model

    if current_host == scheduler_host(hosts):
        save(model_dir, mlp_model)


def get_train_context():
    if int(os.environ["SM_NUM_GPUS"]) > 0:
        return mx.gpu()
    return mx.cpu()


def save(model_dir, model):
    model.symbol.save(os.path.join(model_dir, "model-symbol.json"))
    model.save_params(os.path.join(model_dir, "model-0000.params"))

    signature = [
        {"name": data_desc.name, "shape": [dim for dim in data_desc.shape]}
        for data_desc in model.data_shapes
    ]
    with open(os.path.join(model_dir, "model-shapes.json"), "w") as f:
        json.dump(signature, f)


def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument("--batch_size", type=int, default=100)
    parser.add_argument("--num_epoch", type=int, default=25)
    parser.add_argument("--learning_rate", type=float, default=0.1)
    parser.add_argument("--optimizer", default="sgd")

    parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
    parser.add_argument("--train", type=str, default=os.environ["SM_CHANNEL_TRAIN"])
    parser.add_argument("--test", type=str, default=os.environ["SM_CHANNEL_TEST"])

    parser.add_argument("--current_host", type=str, default=os.environ["SM_CURRENT_HOST"])
    parser.add_argument("--hosts", type=list, default=json.loads(os.environ["SM_HOSTS"]))

    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()

    train(
        args.batch_size,
        args.num_epoch,
        args.learning_rate,
        args.optimizer,
        args.train,
        args.test,
        args.hosts,
        args.current_host,
        args.model_dir,
    )