sync (#63)#64

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sync (#63) #64

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1 change: 1 addition & 0 deletions .gitignore
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Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~
189 changes: 110 additions & 79 deletions README.rst
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126 changes: 126 additions & 0 deletions examples/cli/host/data/model.json
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{
"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]}
}
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3 changes: 3 additions & 0 deletions examples/cli/host/run_hosting_example.sh
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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>
41 changes: 41 additions & 0 deletions examples/cli/host/script.py
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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
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10 changes: 10 additions & 0 deletions examples/cli/train/download_training_data.py
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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()
7 changes: 7 additions & 0 deletions examples/cli/train/hyperparameters.json
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{
"batch_size": 100,
"epochs": 10,
"learning_rate": 0.1,
"momentum": 0.9,
"log_interval": 100
}
4 changes: 4 additions & 0 deletions examples/cli/train/run_training_example.sh
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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>
118 changes: 118 additions & 0 deletions examples/cli/train/script.py
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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
# 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()

# retrieve the hyperparameters we set in notebook (with some defaults)
batch_size = hyperparameters.get('batch_size', 100)
epochs = hyperparameters.get('epochs', 10)
learning_rate = hyperparameters.get('learning_rate', 0.1)
momentum = hyperparameters.get('momentum', 0.9)
log_interval = hyperparameters.get('log_interval', 100)

training_data = channel_input_dirs['training']

# load training and validation data
# we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
# but point it at the location where SageMaker placed the data files, so it doesn't download them again.
train_data = get_train_data(training_data, batch_size)
val_data = get_val_data(training_data, batch_size)

# define the network
net = define_network()

# Collect all parameters from net and its children, then initialize them.
net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
# Trainer is for updating parameters with gradient.
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': learning_rate, 'momentum': momentum})
metric = mx.metric.Accuracy()
loss = gluon.loss.SoftmaxCrossEntropyLoss()

for epoch in range(epochs):
# reset data iterator and metric at begining of epoch.
metric.reset()
btic = time.time()
for i, (data, label) in enumerate(train_data):
# Copy data to ctx if necessary
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
# Start recording computation graph with record() section.
# Recorded graphs can then be differentiated with backward.
with autograd.record():
output = net(data)
L = loss(output, label)
L.backward()
# take a gradient step with batch_size equal to data.shape[0]
trainer.step(data.shape[0])
# update metric at last.
metric.update([label], [output])

if i % log_interval == 0 and i > 0:
name, acc = metric.get()
logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
(epoch, i, name, acc, batch_size / (time.time() - btic)))

btic = time.time()

name, acc = metric.get()
logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

name, val_acc = test(ctx, net, val_data)
logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

return net


def save(net, model_dir):
# save the model
y = net(mx.sym.var('data'))
y.save('%s/model.json' % model_dir)
net.collect_params().save('%s/model.params' % model_dir)


def define_network():
net = nn.Sequential()
with net.name_scope():
net.add(nn.Dense(128, activation='relu'))
net.add(nn.Dense(64, activation='relu'))
net.add(nn.Dense(10))
return net


def input_transformer(data, label):
data = data.reshape((-1,)).astype(np.float32) / 255
return data, label


def get_train_data(data_dir, batch_size):
return gluon.data.DataLoader(
gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
return gluon.data.DataLoader(
gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
metric = mx.metric.Accuracy()
for data, label in val_data:
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
output = net(data)
metric.update([label], [output])
return metric.get()
7 changes: 6 additions & 1 deletion setup.py
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@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
extras_require={
'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
'mock', 'tensorflow>=1.3.0', 'contextlib2']},

entry_points={
'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
}
)
4 changes: 2 additions & 2 deletions src/sagemaker/amazon/amazon_estimator.py
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Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
"""Base class for Amazon first-party Estimator implementations. This class isn't intended
to be instantiated directly."""

feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
"""Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
Loading
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var __m = "github.com";
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sync (#63)#64

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ragavvenkatesan
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commit into

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sync (#63)
#64
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1 change: 1 addition & 0 deletions
.gitignore

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Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~

189 changes: 110 additions & 79 deletions
README.rst

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126 changes: 126 additions & 0 deletions
examples/cli/host/data/model.json

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{
 "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]}
}
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3 changes: 3 additions & 0 deletions
examples/cli/host/run_hosting_example.sh

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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>

41 changes: 41 additions & 0 deletions
examples/cli/host/script.py

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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
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examples/cli/train/download_training_data.py

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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()

7 changes: 7 additions & 0 deletions
examples/cli/train/hyperparameters.json

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{
 "batch_size": 100,
 "epochs": 10,
 "learning_rate": 0.1,
 "momentum": 0.9,
 "log_interval": 100
}

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examples/cli/train/run_training_example.sh

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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>

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examples/cli/train/script.py

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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
 # 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()

 # retrieve the hyperparameters we set in notebook (with some defaults)
 batch_size = hyperparameters.get('batch_size', 100)
 epochs = hyperparameters.get('epochs', 10)
 learning_rate = hyperparameters.get('learning_rate', 0.1)
 momentum = hyperparameters.get('momentum', 0.9)
 log_interval = hyperparameters.get('log_interval', 100)

 training_data = channel_input_dirs['training']

 # load training and validation data
 # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
 # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
 train_data = get_train_data(training_data, batch_size)
 val_data = get_val_data(training_data, batch_size)

 # define the network
 net = define_network()

 # Collect all parameters from net and its children, then initialize them.
 net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
 # Trainer is for updating parameters with gradient.
 trainer = gluon.Trainer(net.collect_params(), 'sgd',
 {'learning_rate': learning_rate, 'momentum': momentum})
 metric = mx.metric.Accuracy()
 loss = gluon.loss.SoftmaxCrossEntropyLoss()

 for epoch in range(epochs):
 # reset data iterator and metric at begining of epoch.
 metric.reset()
 btic = time.time()
 for i, (data, label) in enumerate(train_data):
 # Copy data to ctx if necessary
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 # Start recording computation graph with record() section.
 # Recorded graphs can then be differentiated with backward.
 with autograd.record():
 output = net(data)
 L = loss(output, label)
 L.backward()
 # take a gradient step with batch_size equal to data.shape[0]
 trainer.step(data.shape[0])
 # update metric at last.
 metric.update([label], [output])

 if i % log_interval == 0 and i > 0:
 name, acc = metric.get()
 logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
 (epoch, i, name, acc, batch_size / (time.time() - btic)))

 btic = time.time()

 name, acc = metric.get()
 logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

 name, val_acc = test(ctx, net, val_data)
 logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

 return net


def save(net, model_dir):
 # save the model
 y = net(mx.sym.var('data'))
 y.save('%s/model.json' % model_dir)
 net.collect_params().save('%s/model.params' % model_dir)


def define_network():
 net = nn.Sequential()
 with net.name_scope():
 net.add(nn.Dense(128, activation='relu'))
 net.add(nn.Dense(64, activation='relu'))
 net.add(nn.Dense(10))
 return net


def input_transformer(data, label):
 data = data.reshape((-1,)).astype(np.float32) / 255
 return data, label


def get_train_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
 batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
 batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
 metric = mx.metric.Accuracy()
 for data, label in val_data:
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 output = net(data)
 metric.update([label], [output])
 return metric.get()

7 changes: 6 additions & 1 deletion
setup.py

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Original file line numberDiff line numberDiff line change
@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
 return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
 extras_require={
 'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
 'mock', 'tensorflow>=1.3.0', 'contextlib2']},

 entry_points={
 'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
 }
 )

4 changes: 2 additions & 2 deletions
src/sagemaker/amazon/amazon_estimator.py

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Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
 """Base class for Amazon first-party Estimator implementations. This class isn't intended
 to be instantiated directly."""

 feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
 mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
 feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
 mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

 def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
 """Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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sync (#63)#64

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sync (#63)
#64
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1 change: 1 addition & 0 deletions
.gitignore

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Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~

189 changes: 110 additions & 79 deletions
README.rst

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examples/cli/host/data/model.json

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{
 "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]}
}
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examples/cli/host/run_hosting_example.sh

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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>

41 changes: 41 additions & 0 deletions
examples/cli/host/script.py

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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
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examples/cli/train/download_training_data.py

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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()

7 changes: 7 additions & 0 deletions
examples/cli/train/hyperparameters.json

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

4 changes: 4 additions & 0 deletions
examples/cli/train/run_training_example.sh

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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>

118 changes: 118 additions & 0 deletions
examples/cli/train/script.py

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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
 # 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()

 # retrieve the hyperparameters we set in notebook (with some defaults)
 batch_size = hyperparameters.get('batch_size', 100)
 epochs = hyperparameters.get('epochs', 10)
 learning_rate = hyperparameters.get('learning_rate', 0.1)
 momentum = hyperparameters.get('momentum', 0.9)
 log_interval = hyperparameters.get('log_interval', 100)

 training_data = channel_input_dirs['training']

 # load training and validation data
 # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
 # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
 train_data = get_train_data(training_data, batch_size)
 val_data = get_val_data(training_data, batch_size)

 # define the network
 net = define_network()

 # Collect all parameters from net and its children, then initialize them.
 net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
 # Trainer is for updating parameters with gradient.
 trainer = gluon.Trainer(net.collect_params(), 'sgd',
 {'learning_rate': learning_rate, 'momentum': momentum})
 metric = mx.metric.Accuracy()
 loss = gluon.loss.SoftmaxCrossEntropyLoss()

 for epoch in range(epochs):
 # reset data iterator and metric at begining of epoch.
 metric.reset()
 btic = time.time()
 for i, (data, label) in enumerate(train_data):
 # Copy data to ctx if necessary
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 # Start recording computation graph with record() section.
 # Recorded graphs can then be differentiated with backward.
 with autograd.record():
 output = net(data)
 L = loss(output, label)
 L.backward()
 # take a gradient step with batch_size equal to data.shape[0]
 trainer.step(data.shape[0])
 # update metric at last.
 metric.update([label], [output])

 if i % log_interval == 0 and i > 0:
 name, acc = metric.get()
 logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
 (epoch, i, name, acc, batch_size / (time.time() - btic)))

 btic = time.time()

 name, acc = metric.get()
 logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

 name, val_acc = test(ctx, net, val_data)
 logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

 return net


def save(net, model_dir):
 # save the model
 y = net(mx.sym.var('data'))
 y.save('%s/model.json' % model_dir)
 net.collect_params().save('%s/model.params' % model_dir)


def define_network():
 net = nn.Sequential()
 with net.name_scope():
 net.add(nn.Dense(128, activation='relu'))
 net.add(nn.Dense(64, activation='relu'))
 net.add(nn.Dense(10))
 return net


def input_transformer(data, label):
 data = data.reshape((-1,)).astype(np.float32) / 255
 return data, label


def get_train_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
 batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
 batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
 metric = mx.metric.Accuracy()
 for data, label in val_data:
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 output = net(data)
 metric.update([label], [output])
 return metric.get()

7 changes: 6 additions & 1 deletion
setup.py

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Original file line numberDiff line numberDiff line change
@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
 return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
 extras_require={
 'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
 'mock', 'tensorflow>=1.3.0', 'contextlib2']},

 entry_points={
 'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
 }
 )

4 changes: 2 additions & 2 deletions
src/sagemaker/amazon/amazon_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
 """Base class for Amazon first-party Estimator implementations. This class isn't intended
 to be instantiated directly."""

 feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
 mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
 feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
 mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

 def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
 """Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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sync (#63)#64

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ragavvenkatesan
wants to merge
1
commit into

from

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sync (#63)
#64
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1 change: 1 addition & 0 deletions
.gitignore

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Original file line numberDiff line numberDiff line change
Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~

189 changes: 110 additions & 79 deletions
README.rst

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126 changes: 126 additions & 0 deletions
examples/cli/host/data/model.json

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Original file line numberDiff line numberDiff line change
@@ -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]}
}
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examples/cli/host/run_hosting_example.sh

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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>

41 changes: 41 additions & 0 deletions
examples/cli/host/script.py

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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
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examples/cli/train/download_training_data.py

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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()

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examples/cli/train/hyperparameters.json

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{
 "batch_size": 100,
 "epochs": 10,
 "learning_rate": 0.1,
 "momentum": 0.9,
 "log_interval": 100
}

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examples/cli/train/run_training_example.sh

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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>

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examples/cli/train/script.py

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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
 # 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()

 # retrieve the hyperparameters we set in notebook (with some defaults)
 batch_size = hyperparameters.get('batch_size', 100)
 epochs = hyperparameters.get('epochs', 10)
 learning_rate = hyperparameters.get('learning_rate', 0.1)
 momentum = hyperparameters.get('momentum', 0.9)
 log_interval = hyperparameters.get('log_interval', 100)

 training_data = channel_input_dirs['training']

 # load training and validation data
 # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
 # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
 train_data = get_train_data(training_data, batch_size)
 val_data = get_val_data(training_data, batch_size)

 # define the network
 net = define_network()

 # Collect all parameters from net and its children, then initialize them.
 net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
 # Trainer is for updating parameters with gradient.
 trainer = gluon.Trainer(net.collect_params(), 'sgd',
 {'learning_rate': learning_rate, 'momentum': momentum})
 metric = mx.metric.Accuracy()
 loss = gluon.loss.SoftmaxCrossEntropyLoss()

 for epoch in range(epochs):
 # reset data iterator and metric at begining of epoch.
 metric.reset()
 btic = time.time()
 for i, (data, label) in enumerate(train_data):
 # Copy data to ctx if necessary
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 # Start recording computation graph with record() section.
 # Recorded graphs can then be differentiated with backward.
 with autograd.record():
 output = net(data)
 L = loss(output, label)
 L.backward()
 # take a gradient step with batch_size equal to data.shape[0]
 trainer.step(data.shape[0])
 # update metric at last.
 metric.update([label], [output])

 if i % log_interval == 0 and i > 0:
 name, acc = metric.get()
 logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
 (epoch, i, name, acc, batch_size / (time.time() - btic)))

 btic = time.time()

 name, acc = metric.get()
 logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

 name, val_acc = test(ctx, net, val_data)
 logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

 return net


def save(net, model_dir):
 # save the model
 y = net(mx.sym.var('data'))
 y.save('%s/model.json' % model_dir)
 net.collect_params().save('%s/model.params' % model_dir)


def define_network():
 net = nn.Sequential()
 with net.name_scope():
 net.add(nn.Dense(128, activation='relu'))
 net.add(nn.Dense(64, activation='relu'))
 net.add(nn.Dense(10))
 return net


def input_transformer(data, label):
 data = data.reshape((-1,)).astype(np.float32) / 255
 return data, label


def get_train_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
 batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
 batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
 metric = mx.metric.Accuracy()
 for data, label in val_data:
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 output = net(data)
 metric.update([label], [output])
 return metric.get()

7 changes: 6 additions & 1 deletion
setup.py

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@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
 return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
 extras_require={
 'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
 'mock', 'tensorflow>=1.3.0', 'contextlib2']},

 entry_points={
 'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
 }
 )

4 changes: 2 additions & 2 deletions
src/sagemaker/amazon/amazon_estimator.py

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Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
 """Base class for Amazon first-party Estimator implementations. This class isn't intended
 to be instantiated directly."""

 feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
 mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
 feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
 mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

 def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
 """Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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1 change: 1 addition & 0 deletions
.gitignore

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Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~

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examples/cli/host/data/model.json

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{
 "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]}
}
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examples/cli/host/run_hosting_example.sh

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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>

41 changes: 41 additions & 0 deletions
examples/cli/host/script.py

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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
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examples/cli/train/download_training_data.py

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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()

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examples/cli/train/hyperparameters.json

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{
 "batch_size": 100,
 "epochs": 10,
 "learning_rate": 0.1,
 "momentum": 0.9,
 "log_interval": 100
}

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examples/cli/train/run_training_example.sh

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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>

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examples/cli/train/script.py

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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
 # 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()

 # retrieve the hyperparameters we set in notebook (with some defaults)
 batch_size = hyperparameters.get('batch_size', 100)
 epochs = hyperparameters.get('epochs', 10)
 learning_rate = hyperparameters.get('learning_rate', 0.1)
 momentum = hyperparameters.get('momentum', 0.9)
 log_interval = hyperparameters.get('log_interval', 100)

 training_data = channel_input_dirs['training']

 # load training and validation data
 # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
 # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
 train_data = get_train_data(training_data, batch_size)
 val_data = get_val_data(training_data, batch_size)

 # define the network
 net = define_network()

 # Collect all parameters from net and its children, then initialize them.
 net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
 # Trainer is for updating parameters with gradient.
 trainer = gluon.Trainer(net.collect_params(), 'sgd',
 {'learning_rate': learning_rate, 'momentum': momentum})
 metric = mx.metric.Accuracy()
 loss = gluon.loss.SoftmaxCrossEntropyLoss()

 for epoch in range(epochs):
 # reset data iterator and metric at begining of epoch.
 metric.reset()
 btic = time.time()
 for i, (data, label) in enumerate(train_data):
 # Copy data to ctx if necessary
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 # Start recording computation graph with record() section.
 # Recorded graphs can then be differentiated with backward.
 with autograd.record():
 output = net(data)
 L = loss(output, label)
 L.backward()
 # take a gradient step with batch_size equal to data.shape[0]
 trainer.step(data.shape[0])
 # update metric at last.
 metric.update([label], [output])

 if i % log_interval == 0 and i > 0:
 name, acc = metric.get()
 logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
 (epoch, i, name, acc, batch_size / (time.time() - btic)))

 btic = time.time()

 name, acc = metric.get()
 logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

 name, val_acc = test(ctx, net, val_data)
 logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

 return net


def save(net, model_dir):
 # save the model
 y = net(mx.sym.var('data'))
 y.save('%s/model.json' % model_dir)
 net.collect_params().save('%s/model.params' % model_dir)


def define_network():
 net = nn.Sequential()
 with net.name_scope():
 net.add(nn.Dense(128, activation='relu'))
 net.add(nn.Dense(64, activation='relu'))
 net.add(nn.Dense(10))
 return net


def input_transformer(data, label):
 data = data.reshape((-1,)).astype(np.float32) / 255
 return data, label


def get_train_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
 batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
 batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
 metric = mx.metric.Accuracy()
 for data, label in val_data:
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 output = net(data)
 metric.update([label], [output])
 return metric.get()

7 changes: 6 additions & 1 deletion
setup.py

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@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
 return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
 extras_require={
 'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
 'mock', 'tensorflow>=1.3.0', 'contextlib2']},

 entry_points={
 'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
 }
 )

4 changes: 2 additions & 2 deletions
src/sagemaker/amazon/amazon_estimator.py

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Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
 """Base class for Amazon first-party Estimator implementations. This class isn't intended
 to be instantiated directly."""

 feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
 mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
 feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
 mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

 def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
 """Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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1 change: 1 addition & 0 deletions
.gitignore

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Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~

189 changes: 110 additions & 79 deletions
README.rst

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126 changes: 126 additions & 0 deletions
examples/cli/host/data/model.json

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@@ -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]}
}
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examples/cli/host/run_hosting_example.sh

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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>

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examples/cli/host/script.py

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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
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examples/cli/train/download_training_data.py

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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()

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examples/cli/train/hyperparameters.json

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{
 "batch_size": 100,
 "epochs": 10,
 "learning_rate": 0.1,
 "momentum": 0.9,
 "log_interval": 100
}

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examples/cli/train/run_training_example.sh

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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>

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examples/cli/train/script.py

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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
 # 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()

 # retrieve the hyperparameters we set in notebook (with some defaults)
 batch_size = hyperparameters.get('batch_size', 100)
 epochs = hyperparameters.get('epochs', 10)
 learning_rate = hyperparameters.get('learning_rate', 0.1)
 momentum = hyperparameters.get('momentum', 0.9)
 log_interval = hyperparameters.get('log_interval', 100)

 training_data = channel_input_dirs['training']

 # load training and validation data
 # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
 # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
 train_data = get_train_data(training_data, batch_size)
 val_data = get_val_data(training_data, batch_size)

 # define the network
 net = define_network()

 # Collect all parameters from net and its children, then initialize them.
 net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
 # Trainer is for updating parameters with gradient.
 trainer = gluon.Trainer(net.collect_params(), 'sgd',
 {'learning_rate': learning_rate, 'momentum': momentum})
 metric = mx.metric.Accuracy()
 loss = gluon.loss.SoftmaxCrossEntropyLoss()

 for epoch in range(epochs):
 # reset data iterator and metric at begining of epoch.
 metric.reset()
 btic = time.time()
 for i, (data, label) in enumerate(train_data):
 # Copy data to ctx if necessary
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 # Start recording computation graph with record() section.
 # Recorded graphs can then be differentiated with backward.
 with autograd.record():
 output = net(data)
 L = loss(output, label)
 L.backward()
 # take a gradient step with batch_size equal to data.shape[0]
 trainer.step(data.shape[0])
 # update metric at last.
 metric.update([label], [output])

 if i % log_interval == 0 and i > 0:
 name, acc = metric.get()
 logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
 (epoch, i, name, acc, batch_size / (time.time() - btic)))

 btic = time.time()

 name, acc = metric.get()
 logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

 name, val_acc = test(ctx, net, val_data)
 logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

 return net


def save(net, model_dir):
 # save the model
 y = net(mx.sym.var('data'))
 y.save('%s/model.json' % model_dir)
 net.collect_params().save('%s/model.params' % model_dir)


def define_network():
 net = nn.Sequential()
 with net.name_scope():
 net.add(nn.Dense(128, activation='relu'))
 net.add(nn.Dense(64, activation='relu'))
 net.add(nn.Dense(10))
 return net


def input_transformer(data, label):
 data = data.reshape((-1,)).astype(np.float32) / 255
 return data, label


def get_train_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
 batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
 batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
 metric = mx.metric.Accuracy()
 for data, label in val_data:
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 output = net(data)
 metric.update([label], [output])
 return metric.get()

7 changes: 6 additions & 1 deletion
setup.py

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@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
 return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
 extras_require={
 'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
 'mock', 'tensorflow>=1.3.0', 'contextlib2']},

 entry_points={
 'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
 }
 )

4 changes: 2 additions & 2 deletions
src/sagemaker/amazon/amazon_estimator.py

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Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
 """Base class for Amazon first-party Estimator implementations. This class isn't intended
 to be instantiated directly."""

 feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
 mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
 feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
 mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

 def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
 """Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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sync (#63)#64

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commit into

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sync (#63)
#64
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.gitignore

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Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~

189 changes: 110 additions & 79 deletions
README.rst

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126 changes: 126 additions & 0 deletions
examples/cli/host/data/model.json

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@@ -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]}
}
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examples/cli/host/run_hosting_example.sh

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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>

41 changes: 41 additions & 0 deletions
examples/cli/host/script.py

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@@ -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
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examples/cli/train/download_training_data.py

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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()

7 changes: 7 additions & 0 deletions
examples/cli/train/hyperparameters.json

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{
 "batch_size": 100,
 "epochs": 10,
 "learning_rate": 0.1,
 "momentum": 0.9,
 "log_interval": 100
}

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examples/cli/train/run_training_example.sh

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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>

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examples/cli/train/script.py

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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
 # 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()

 # retrieve the hyperparameters we set in notebook (with some defaults)
 batch_size = hyperparameters.get('batch_size', 100)
 epochs = hyperparameters.get('epochs', 10)
 learning_rate = hyperparameters.get('learning_rate', 0.1)
 momentum = hyperparameters.get('momentum', 0.9)
 log_interval = hyperparameters.get('log_interval', 100)

 training_data = channel_input_dirs['training']

 # load training and validation data
 # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
 # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
 train_data = get_train_data(training_data, batch_size)
 val_data = get_val_data(training_data, batch_size)

 # define the network
 net = define_network()

 # Collect all parameters from net and its children, then initialize them.
 net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
 # Trainer is for updating parameters with gradient.
 trainer = gluon.Trainer(net.collect_params(), 'sgd',
 {'learning_rate': learning_rate, 'momentum': momentum})
 metric = mx.metric.Accuracy()
 loss = gluon.loss.SoftmaxCrossEntropyLoss()

 for epoch in range(epochs):
 # reset data iterator and metric at begining of epoch.
 metric.reset()
 btic = time.time()
 for i, (data, label) in enumerate(train_data):
 # Copy data to ctx if necessary
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 # Start recording computation graph with record() section.
 # Recorded graphs can then be differentiated with backward.
 with autograd.record():
 output = net(data)
 L = loss(output, label)
 L.backward()
 # take a gradient step with batch_size equal to data.shape[0]
 trainer.step(data.shape[0])
 # update metric at last.
 metric.update([label], [output])

 if i % log_interval == 0 and i > 0:
 name, acc = metric.get()
 logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
 (epoch, i, name, acc, batch_size / (time.time() - btic)))

 btic = time.time()

 name, acc = metric.get()
 logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

 name, val_acc = test(ctx, net, val_data)
 logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

 return net


def save(net, model_dir):
 # save the model
 y = net(mx.sym.var('data'))
 y.save('%s/model.json' % model_dir)
 net.collect_params().save('%s/model.params' % model_dir)


def define_network():
 net = nn.Sequential()
 with net.name_scope():
 net.add(nn.Dense(128, activation='relu'))
 net.add(nn.Dense(64, activation='relu'))
 net.add(nn.Dense(10))
 return net


def input_transformer(data, label):
 data = data.reshape((-1,)).astype(np.float32) / 255
 return data, label


def get_train_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
 batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
 batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
 metric = mx.metric.Accuracy()
 for data, label in val_data:
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 output = net(data)
 metric.update([label], [output])
 return metric.get()

7 changes: 6 additions & 1 deletion
setup.py

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Original file line numberDiff line numberDiff line change
@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
 return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
 extras_require={
 'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
 'mock', 'tensorflow>=1.3.0', 'contextlib2']},

 entry_points={
 'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
 }
 )

4 changes: 2 additions & 2 deletions
src/sagemaker/amazon/amazon_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
 """Base class for Amazon first-party Estimator implementations. This class isn't intended
 to be instantiated directly."""

 feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
 mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
 feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
 mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

 def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
 """Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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1 change: 1 addition & 0 deletions
.gitignore

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Expand Up@@ -20,3 +20,4 @@ examples/tensorflow/distributed_mnist/data
doc/_build
**/.DS_Store
venv/
*~

189 changes: 110 additions & 79 deletions
README.rst

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examples/cli/host/data/model.json

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{
 "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]}
}
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examples/cli/host/run_hosting_example.sh

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#!/bin/bash

sagemaker mxnet host --role-name <your-sagemaker-execution-role>

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examples/cli/host/script.py

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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
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examples/cli/train/download_training_data.py

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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()

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examples/cli/train/hyperparameters.json

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{
 "batch_size": 100,
 "epochs": 10,
 "learning_rate": 0.1,
 "momentum": 0.9,
 "log_interval": 100
}

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examples/cli/train/run_training_example.sh

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#!/bin/bash

python ./download_training_data.py
sagemaker mxnet train --role-name <your-sagemaker-execution-role>

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examples/cli/train/script.py

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import logging
import time

import mxnet as mx
import numpy as np
from mxnet import gluon, autograd
from mxnet.gluon import nn

logger = logging.getLogger(__name__)


def train(channel_input_dirs, hyperparameters, **kwargs):
 # 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()

 # retrieve the hyperparameters we set in notebook (with some defaults)
 batch_size = hyperparameters.get('batch_size', 100)
 epochs = hyperparameters.get('epochs', 10)
 learning_rate = hyperparameters.get('learning_rate', 0.1)
 momentum = hyperparameters.get('momentum', 0.9)
 log_interval = hyperparameters.get('log_interval', 100)

 training_data = channel_input_dirs['training']

 # load training and validation data
 # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
 # but point it at the location where SageMaker placed the data files, so it doesn't download them again.
 train_data = get_train_data(training_data, batch_size)
 val_data = get_val_data(training_data, batch_size)

 # define the network
 net = define_network()

 # Collect all parameters from net and its children, then initialize them.
 net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
 # Trainer is for updating parameters with gradient.
 trainer = gluon.Trainer(net.collect_params(), 'sgd',
 {'learning_rate': learning_rate, 'momentum': momentum})
 metric = mx.metric.Accuracy()
 loss = gluon.loss.SoftmaxCrossEntropyLoss()

 for epoch in range(epochs):
 # reset data iterator and metric at begining of epoch.
 metric.reset()
 btic = time.time()
 for i, (data, label) in enumerate(train_data):
 # Copy data to ctx if necessary
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 # Start recording computation graph with record() section.
 # Recorded graphs can then be differentiated with backward.
 with autograd.record():
 output = net(data)
 L = loss(output, label)
 L.backward()
 # take a gradient step with batch_size equal to data.shape[0]
 trainer.step(data.shape[0])
 # update metric at last.
 metric.update([label], [output])

 if i % log_interval == 0 and i > 0:
 name, acc = metric.get()
 logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
 (epoch, i, name, acc, batch_size / (time.time() - btic)))

 btic = time.time()

 name, acc = metric.get()
 logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))

 name, val_acc = test(ctx, net, val_data)
 logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))

 return net


def save(net, model_dir):
 # save the model
 y = net(mx.sym.var('data'))
 y.save('%s/model.json' % model_dir)
 net.collect_params().save('%s/model.params' % model_dir)


def define_network():
 net = nn.Sequential()
 with net.name_scope():
 net.add(nn.Dense(128, activation='relu'))
 net.add(nn.Dense(64, activation='relu'))
 net.add(nn.Dense(10))
 return net


def input_transformer(data, label):
 data = data.reshape((-1,)).astype(np.float32) / 255
 return data, label


def get_train_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
 batch_size=batch_size, shuffle=True, last_batch='discard')


def get_val_data(data_dir, batch_size):
 return gluon.data.DataLoader(
 gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
 batch_size=batch_size, shuffle=False)


def test(ctx, net, val_data):
 metric = mx.metric.Accuracy()
 for data, label in val_data:
 data = data.as_in_context(ctx)
 label = label.as_in_context(ctx)
 output = net(data)
 metric.update([label], [output])
 return metric.get()

7 changes: 6 additions & 1 deletion
setup.py

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Original file line numberDiff line numberDiff line change
@@ -1,9 +1,10 @@
import os
from setuptools import setup, find_packages
from glob import glob
from os.path import basename
from os.path import splitext

from setuptools import setup, find_packages


def read(fname):
 return open(os.path.join(os.path.dirname(__file__), fname)).read()
Expand DownExpand Up@@ -36,4 +37,8 @@ def read(fname):
 extras_require={
 'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist',
 'mock', 'tensorflow>=1.3.0', 'contextlib2']},

 entry_points={
 'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
 }
 )

4 changes: 2 additions & 2 deletions
src/sagemaker/amazon/amazon_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -28,8 +28,8 @@ class AmazonAlgorithmEstimatorBase(EstimatorBase):
 """Base class for Amazon first-party Estimator implementations. This class isn't intended
 to be instantiated directly."""

 feature_dim = hp('feature_dim', (validation.isint, validation.gt(0)))
 mini_batch_size = hp('mini_batch_size', (validation.isint, validation.gt(0)))
 feature_dim = hp('feature_dim', validation.gt(0), data_type=int)
 mini_batch_size = hp('mini_batch_size', validation.gt(0), data_type=int)

 def __init__(self, role, train_instance_count, train_instance_type, data_location=None, **kwargs):
 """Initialize an AmazonAlgorithmEstimatorBase.
Expand Down
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