Add Mxnet MNIST example

frameworks/mxnet/code/config.json

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+../../../config.json

frameworks/mxnet/code/inference.py

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+# coding=utf-8
+# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+# 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
+#
+#     http://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.
+
+from __future__ import print_function
+
+import logging
+
+from mxnet import gluon
+import mxnet as mx
+
+import numpy as np
+import json
+import os
+
+logging.basicConfig(level=logging.DEBUG)
+
+def model_fn(model_dir):
+    """Load the gluon model. Called once when hosting service starts.
+
+    :param: model_dir The directory where model files are stored.
+    :return: a model (in this case a Gluon network)
+    """
+    net = gluon.SymbolBlock.imports(
+            symbol_file=os.path.join(model_dir, 'model-symbol.json'),
+            input_names=['data'],
+            param_file=os.path.join(model_dir, 'model-0000.params'))
+    return net
+
+def transform_fn(net, data, input_content_type, output_content_type):
+    assert input_content_type=='application/json'
+    assert output_content_type=='application/json' 
+
+    # parsed should be a 1d array of length 728
+    parsed = json.loads(data)
+    parsed = parsed['inputs'] 
+
+    # convert to numpy array
+    arr = np.array(parsed).reshape(-1, 1, 28, 28)
+
+    # convert to mxnet ndarray
+    nda = mx.nd.array(arr)
+
+    output = net(nda)
+
+    prediction = mx.nd.argmax(output, axis=1)
+    response_body = json.dumps(prediction.asnumpy().tolist())
+
+    return response_body, output_content_type
+
+
+if __name__ == '__main__':
+    model_dir = '/home/ubuntu/models/mxnet-gluon-mnist'
+    net = model_fn(model_dir)
+
+    import json
+    import random
+    data = {'inputs': [random.random() for _ in range(784)]}
+    data = json.dumps(data)
+
+    content_type = 'application/json'
+    a, b = transform_fn(net, data, content_type, content_type)
+    print(a, b)
+
+

frameworks/mxnet/code/test_inference.py

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+# coding=utf-8
+# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+# 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
+#
+#     http://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.
+
+from inference import transform_fn, model_fn
+import os
+import json
+import shutil
+import boto3
+import botocore
+import tarfile
+import numpy as np
+import sagemaker
+
+def fetch_model(model_data):
+    """ Untar the model.tar.gz object either from local file system
+    or a S3 location
+
+    Args: 
+        model_data (str): either a path to local file system starts with 
+        file:/// that points to the `model.tar.gz` file or an S3 link
+        starts with s3:// that points to the `model.tar.gz` file
+
+    Returns:
+        model_dir (str): the directory that contains the uncompress model
+        checkpoint files
+    """
+
+    model_dir = "/tmp/model" 
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+
+    if model_data.startswith("file"):
+        _check_model(model_data)
+        shutil.copy2(os.path.join(model_dir, "model.tar.gz"),
+                os.path.join(model_dir, "model.tar.gz"))
+    elif model_data.startswith("s3"):
+        # get bucket name and object key
+        bucket_name = model_data.split("/")[2]
+        key = "/".join(model_data.split("/")[3:])
+
+        s3 = boto3.resource("s3")
+        try: 
+            s3.Bucket(bucket_name).download_file(
+                key, os.path.join(model_dir, 'model.tar.gz'))
+        except botocore.exceptions.ClientError as e:
+            if e.response['Error']['Code'] == '404':
+                print("the object does not exist.")
+            else:
+                raise
+
+    # untar the model
+    tar = tarfile.open(os.path.join(model_dir, 'model.tar.gz'))
+    tar.extractall(model_dir)
+    tar.close()
+
+    return model_dir
+
+
+def test(model_dir):
+
+    # decompress the model.tar.gz file
+    # model_dir = fetch_model(model_data)
+
+    # load the model
+    net = model_fn(model_dir)
+
+    # simulate some input data to test transform_fn
+
+    data = {
+        "inputs": np.random.rand(16, 1, 28, 28).tolist()
+        }
+
+    # encode numpy array to binary stream
+    serializer = sagemaker.serializers.JSONSerializer()
+
+    jstr = serializer.serialize(data)
+    jstr = json.dumps(data)
+
+    # "send" the bin_stream to the endpoint for inference
+    # inference container calls transform_fn to make an inference
+    # and get the response body for the caller
+
+    res, content_type = transform_fn(net, jstr, "application/json", 
+            "application/json")
+    print(res)
+    return 
+
+
+if __name__ == '__main__':
+    model_dir='/tmp/ckpt'
+    test(model_dir)
+
+

frameworks/mxnet/code/test_train.py

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+# coding=utf-8
+# Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+# 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
+#
+#     http://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.
+
+from train import train, parse_args
+
+import sys
+import os
+import boto3
+import json
+
+dirname = os.path.dirname(os.path.abspath(__file__))
+with open(os.path.join(dirname, 'config.json'), "r") as f:
+    CONFIG = json.load(f)
+
+def download_from_s3(data_dir='/tmp/data', train=True):
+    """Download MNIST dataset and convert it to numpy array
+
+    Args:
+        data_dir (str): directory to save the data
+        train (bool): download training set
+
+    Returns:
+        tuple of images and labels as numpy arrays
+    """
+
+    if not os.path.exists(data_dir):
+        os.makedirs(data_dir)
+
+
+    if train:
+        images_file = "train-images-idx3-ubyte.gz"
+        labels_file = "train-labels-idx1-ubyte.gz"
+    else:
+        images_file = "t10k-images-idx3-ubyte.gz"
+        labels_file = "t10k-labels-idx1-ubyte.gz"
+
+    # download objects
+    s3 = boto3.client('s3')
+    bucket = CONFIG["public_bucket"]
+    for obj in [images_file, labels_file]:
+        key = os.path.join("datasets/image/MNIST", obj)
+        dest = os.path.join(data_dir, obj)
+        if not os.path.exists(dest):
+            s3.download_file(bucket, key, dest)
+    return
+
+
+class Env:
+    def __init__(self):       
+        # simulate container env
+        os.environ["SM_MODEL_DIR"] = "/tmp/model"
+        os.environ["SM_CHANNEL_TRAINING"]="/tmp/data"
+        os.environ["SM_CHANNEL_TESTING"]="/tmp/data"
+        os.environ["SM_HOSTS"] = '["algo-1"]'
+        os.environ["SM_CURRENT_HOST"]="algo-1"
+        os.environ["SM_NUM_GPUS"] = "0"
+
+
+if __name__=='__main__':
+    Env()
+    args = parse_args()
+    train(args)
+