Download - TensorFlow Dev Summit 2017 요약
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tf.Session()
agenda = tf.summary.merge(dev.summit, 2017)
sess.run(agenda)
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KeynoteDistBelief -> TensorFlow
제품 뿐 아니라 연구 레벨에서까지 광범위하게 사용 가능
RNN 등의 복잡한 모델까지 커버 가능하다
CPU, GPU, TPU, Android, iOS, Raspberry Pi 등 다양한 플랫폼 지원
구Ú 클라우드에서도 사용 가능
Python, C++, Java, Go, Haskell, R ...
텐서보드 짱짱맨
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Keynote
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Keynote
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Keynote
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Keynote - TensorFlow 1.058배 빨라짐
XLA (Accelerated Linear Algebra), JIT
하이레벨 API 제공
Layers
Keras
Estimator
Canned Estimators
API 안정화
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Keynote - TensorFlow 1.0
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Keynote - TensorFlow 1.0
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TensorBoardScalars
Images
Audio
Graphs
Distributions
Histogram
Embeddings
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TensorBoard
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Future for TensorBoardTensorFlow Debugger Integration
Plugins
Org-scale TensorBoard
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TensorFlow at DeepMindChoosing a Platform
Flexibility
Usability
Scalability
Performance
Production Readiness
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TensorFlow at DeepMindData Center Cooling
Gorila (DistBelief -> TensorFlow)
AlphaGo
WaveNet
Text to Speech
Music Generation
Learning to Learn
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TensorFlow at DeepMind
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TensorFlow at DeepMind
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TensorFlow at DeepMind
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Mobile and EmbeddedARM
CEVA
Movidius
IBM Power Systems
Intel
Qualcomm
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Mobile and EmbeddedAndroid
tensor�ow/examples/android
TF Classify, TF Detect, TF Stylize
iOS
tensor�ow/contrib/ios_examples
Inception image labeling
Raspberry Pi
tensor�ow/contrib/pi_examples/label_image
tensor�ow/contrib/pi_examples/camera
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Mobile and EmbeddedInception v3 is 93 MB!
Inception v1 quantized is just 7 MB
Exporting Models
Freeze graph
Graph Transform Tool
Quantize weights
Quantize calculations
Memory mapping
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Mobile and Embedded12 MB increase, before tuning
2 MB increase, after tuning for Inception v3
Selective registration -DSELECTIVE_REGISTRATION
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Distributed TensorFlow
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Distributed TensorFlowwith tf.device("/job:ps/task:0/cpu:0"): W = tf.Variable(...) b = tf.Variable(...) with tf.device("/job:worker/task:0/gpu:0"): output = tf.matmul(input, W) + b loss = f(output)
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Distributed TensorFlowcluster = tf.train.ClusterSpec({ "worker": ["192.168.0.1:2222", ...], "ps": ["192.168.1.1:2222", ...]}) server = tf.train.Server( cluster, job_name="worker", task_index=0) with tf.Session(server.target) as sess: ...
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TensorFlow EcosystemData Processing (TFRecords)
Apache Beam : Native support
Hadoop MR and Spark
Cluster Manager
Kubernetes, Hadoop, MESOS, Slurm
Distributed Storage
Hadoop HDFS, Google Cloud Storage, AWS EFS
Serving
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TensorFlow EcosystemPython -> Training
C
C++
Go
Haskell
Rust
Java
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Serving Models
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Serving Models
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Serving ModelsOnline, low latency
Mutiple models in a single process
Mutiple versions of a model loaded over time
Compute cost varies in real-time to meet productdemand
auto-scale with CloudML, Docker & K8s
Aim for the ef�ciency of mini-batching at trainingtime ...
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ML Toolkitsmodel = KMeansClustering(num_clusters=1000) model.fit( input_fn=numpy_input_fn(points, num_epochs=None), steps=1000) clusters = model.clusters() assignments = model.predict_cluster_idx( input_fn=numpy_input_fn(test_points))
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ML ToolkitsLinearClassi�er
LinearRegressor
LogisticRegressor
KMeansClustering
WALSModel
SVM
TensorForestEstimator
DNN, RNN, LSTM, Wide & Deep, ...
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ML Toolkitstensor�ow/contrib/learn/python/learn
classifier = tf.contrib.learn.DNNClassifier( feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3, model_dir="/tmp/iris_model") classifier.fit(x=training_set.data, y=training_set.target, steps=2000) accuracy_score = classifier.evaluate( x=test_set.data, y=test_set.target)["accuracy"]
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ETCXLA: TensorFlow, Compiled!
Skin Cancer Image Classi�cation
Sequence Models and the RNN API
TensorFlow in Medicine
Wide & Deep Learning
Magenta: Music and Art Generation
Fast, Flexible, TensorBoard, Community
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Science -> Engineering
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sess.close()