add sagemaker cli (#32)

* add sagemaker cli

* remove unnecessary close

* address PR comments

* tidy up imports

* fix imports, flake8 errors

* improve help message for bucket-name

* remove default role name

* fix log-level and py3 tests, add copyright

* update cli example scripts

Author: jesterhazy

examples/cli/host/data/model.json

@@ -0,0 +1,126 @@
+{
+  "nodes": [
+    {
+      "op": "null", 
+      "name": "data", 
+      "inputs": []
+    }, 
+    {
+      "op": "null", 
+      "name": "sequential0_dense0_weight", 
+      "attr": {
+        "__dtype__": "0", 
+        "__lr_mult__": "1.0", 
+        "__shape__": "(128, 0)", 
+        "__wd_mult__": "1.0"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "null", 
+      "name": "sequential0_dense0_bias", 
+      "attr": {
+        "__dtype__": "0", 
+        "__init__": "zeros", 
+        "__lr_mult__": "1.0", 
+        "__shape__": "(128,)", 
+        "__wd_mult__": "1.0"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "FullyConnected", 
+      "name": "sequential0_dense0_fwd", 
+      "attr": {"num_hidden": "128"}, 
+      "inputs": [[0, 0, 0], [1, 0, 0], [2, 0, 0]]
+    }, 
+    {
+      "op": "Activation", 
+      "name": "sequential0_dense0_relu_fwd", 
+      "attr": {"act_type": "relu"}, 
+      "inputs": [[3, 0, 0]]
+    }, 
+    {
+      "op": "null", 
+      "name": "sequential0_dense1_weight", 
+      "attr": {
+        "__dtype__": "0", 
+        "__lr_mult__": "1.0", 
+        "__shape__": "(64, 0)", 
+        "__wd_mult__": "1.0"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "null", 
+      "name": "sequential0_dense1_bias", 
+      "attr": {
+        "__dtype__": "0", 
+        "__init__": "zeros", 
+        "__lr_mult__": "1.0", 
+        "__shape__": "(64,)", 
+        "__wd_mult__": "1.0"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "FullyConnected", 
+      "name": "sequential0_dense1_fwd", 
+      "attr": {"num_hidden": "64"}, 
+      "inputs": [[4, 0, 0], [5, 0, 0], [6, 0, 0]]
+    }, 
+    {
+      "op": "Activation", 
+      "name": "sequential0_dense1_relu_fwd", 
+      "attr": {"act_type": "relu"}, 
+      "inputs": [[7, 0, 0]]
+    }, 
+    {
+      "op": "null", 
+      "name": "sequential0_dense2_weight", 
+      "attr": {
+        "__dtype__": "0", 
+        "__lr_mult__": "1.0", 
+        "__shape__": "(10, 0)", 
+        "__wd_mult__": "1.0"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "null", 
+      "name": "sequential0_dense2_bias", 
+      "attr": {
+        "__dtype__": "0", 
+        "__init__": "zeros", 
+        "__lr_mult__": "1.0", 
+        "__shape__": "(10,)", 
+        "__wd_mult__": "1.0"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "FullyConnected", 
+      "name": "sequential0_dense2_fwd", 
+      "attr": {"num_hidden": "10"}, 
+      "inputs": [[8, 0, 0], [9, 0, 0], [10, 0, 0]]
+    }
+  ], 
+  "arg_nodes": [0, 1, 2, 5, 6, 9, 10], 
+  "node_row_ptr": [
+    0, 
+    1, 
+    2, 
+    3, 
+    4, 
+    5, 
+    6, 
+    7, 
+    8, 
+    9, 
+    10, 
+    11, 
+    12
+  ], 
+  "heads": [[11, 0, 0]], 
+  "attrs": {"mxnet_version": ["int", 1100]}
+}

examples/cli/host/data/model.params

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+sagemaker mxnet host --role-name <your-sagemaker-execution-role>

examples/cli/host/run_hosting_example.sh

@@ -0,0 +1,41 @@
+from __future__ import print_function
+
+import json
+import mxnet as mx
+from mxnet import gluon
+
+
+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)
+    """
+    symbol = mx.sym.load('%s/model.json' % model_dir)
+    outputs = mx.symbol.softmax(data=symbol, name='softmax_label')
+    inputs = mx.sym.var('data')
+    param_dict = gluon.ParameterDict('model_')
+    net = gluon.SymbolBlock(outputs, inputs, param_dict)
+    net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
+    return net
+
+
+def transform_fn(net, data, input_content_type, output_content_type):
+    """
+    Transform a request using the Gluon model. Called once per request.
+
+    :param net: The Gluon model.
+    :param data: The request payload.
+    :param input_content_type: The request content type.
+    :param output_content_type: The (desired) response content type.
+    :return: response payload and content type.
+    """
+    # we can use content types to vary input/output handling, but
+    # here we just assume json for both
+    parsed = json.loads(data)
+    nda = mx.nd.array(parsed)
+    output = net(nda)
+    prediction = mx.nd.argmax(output, axis=1)
+    response_body = json.dumps(prediction.asnumpy().tolist())
+    return response_body, output_content_type

examples/cli/host/script.py

@@ -0,0 +1,10 @@
+from mxnet import gluon
+
+
+def download_training_data():
+    gluon.data.vision.MNIST('./data/training', train=True)
+    gluon.data.vision.MNIST('./data/training', train=False)
+
+
+if __name__ == "__main__":
+    download_training_data()

examples/cli/train/data/training/t10k-images-idx3-ubyte.gz

@@ -0,0 +1,7 @@
+{
+  "batch_size": 100,
+  "epochs": 10,
+  "learning_rate": 0.1,
+  "momentum": 0.9,
+  "log_interval": 100
+}