- Use cases
-
1. Preprocessing
- SageMaker Object Detection preprocessing
- Rekognition Object Detection preprocessing
- SageMaker Kmeans preprocessing
- Autopilot preprocessing
- DeepAR preprocessing
- Personalize preprocessing
- Select, drop or extract Columns
- Split dataset to Train and Test
- Upload to s3
- Forecast preprocessing
- Rekognition Classification preprocessing
- SageMaker Image Classification preprocessing
- Xgboost preprocessing
- Blazingtext preprocessing
- Comprehend custom preprocessing
-
2. Training
- SageMaker Object Detection training
- Rekognition Object Detection training
- Forecast training
- Personalize training
- BlazingText training
- DeepAR training
- SageMaker Kmeans training
- Comprehend custom training
- Autopilot Training
- Xgboost Training
- Autogluon training
- Rekognition Classification training
- SageMaker Image Classification training
-
3. Inference
- SageMaker Object Detection inference
- Forecast inference
- Rekognition Object Detection inference
- Comprehend custom inference
- Personalize inference
- Autopilot Inference
- BlazingText Inference
- Custom SageMaker model Inference
- DeepAR Inference
- Rekognition Classification inference
- SageMaker Image Classification inference
- SageMaker Kmeans inference
- Xgboost Inference
- Contribute a use case or contact us for help.
- Frequently Asked Questions
Autogluon training
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications.
As of today, Autogluon supports input data in tabular format, image classification, object detection, text classification and neural architecture search.
Common import statement for all task types
import autogluon as ag
Tabular Prediction
from autogluon import TabularPrediction as task
train_data = task.Dataset(file_path='path-to-your-csv-file')
label_column = 'class'
dir = 'agModels-predictClass' # specifies folder where to store trained models
predictor = task.fit(train_data=train_data, label=label_column, output_directory=dir)
Image Classification
from autogluon import ImageClassification as task
Make sure your image folders are organized as follows:
./data/train/class_A/1.jpg
./data/train/class_A/2.jpg
./data/train/class_A/3.jpg
./data/train/class_B/4.jpg
./data/train/class_B/5.jpg
./data/train/class_B/6.jpg
./data/test/class_A/100.jpg
./data/test/class_A/1024.jpg
./data/test/class_B/65535.jpg
./data/test/class_B/0.jpg
...
dataset = task.Dataset('data/train')
if ag.get_gpu_count() == 0:
dataset = task.Dataset(name='mydataset')
test_dataset = task.Dataset(name='mydataset', train=False)
classifier = task.fit(dataset,
epochs=5,
ngpus_per_trial=1,
verbose=False)
Object detection
Note, try this if you already have annotations. Otherwise, go to Rekognition custom object detection.
Annotations are xml documents that look like…
<annotation>
<folder>VOC2007</folder>
<filename>007305.jpg</filename>
<source>
<database>The VOC2007 Database</database>
<annotation>PASCAL VOC2007</annotation>
<image>flickr</image>
<flickrid>321620436</flickrid>
</source>
<owner>
<flickrid>dirfoto</flickrid>
<name>jun saitoh</name>
</owner>
<size>
<width>500</width>
<height>331</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>motorbike</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>343</xmin>
<ymin>113</ymin>
<xmax>463</xmax>
<ymax>181</ymax>
</bndbox>
</object>
.
.
.
.
.
from autogluon import ObjectDetection as task
import os
data_root = os.path.join(root, filename)
dataset_train = task.Dataset(data_root, classes=('motorbike',))
time_limits = 5*60*60 # 5 hours
epochs = 30
detector = task.fit(dataset_train,
num_trials=2,
epochs=epochs,
lr=ag.Categorical(5e-4, 1e-4),
ngpus_per_trial=1,
time_limits=time_limits)
Text Classification
from autogluon import TextClassification as task
Explore this toy dataset and use a similar format:
dataset = task.Dataset(name='ToySST')
predictor = task.fit(dataset, epochs=1, time_limits=30)
Related content:
- ☞ SageMaker Object Detection inference – 1 min read
- ☞ SageMaker Object Detection preprocessing – 2 min read
- ☞ SageMaker Object Detection training – 2 min read
- ☞ Rekognition Object Detection training – 1 min read
- ☞ Rekognition Object Detection inference – 1 min read
- ☞ Rekognition Object Detection preprocessing – 3 min read
- ☞ Comprehend custom inference – 2 min read
- ☞ BlazingText training – 1 min read
- ☞ Comprehend custom training – 1 min read
- ☞ Autopilot Inference – 1 min read