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Machine Learning at Scale TensorFlow in the Cloud Yufeng Guo - PowerPoint PPT Presentation

Oct 04, 2023 •117 likes •655 views

Machine Learning at Scale TensorFlow in the Cloud Yufeng Guo Developer Advocate @YufengG yufengg.com @YufengG Machine Learning is using many examples to answer questions @YufengG @YufengG Training Prediction many answer examples

  1. Machine Learning at Scale TensorFlow in the Cloud Yufeng Guo Developer Advocate @YufengG yufengg.com @YufengG

  2. Machine Learning is using many examples to answer questions @YufengG @YufengG

  3. Training Prediction many answer examples questions @YufengG @YufengG

  4. Training Prediction many answer examples questions @YufengG @YufengG

  5. ● Fast, flexible, and scalable open-source machine learning library ● For research and production ● Distributed training and serving predictions ● Apache 2.0 license https://research.googleblog.com/2016/11/celebrating-tensorflows-first-year.html @YufengG @YufengG

  6. A multidimensional array. 3 + 5 A graph of operations. 2 @YufengG @YufengG

  7. TensorFlow Supports Many Platforms... CPU GPU Raspberry Android iOS Pi TPU @YufengG @YufengG

  8. … more Python Frontend C++ Frontend coming TensorFlow Distributed Execution Engine CPU GPU Android iOS ... @YufengG @YufengG

  9. Build models Layers Python Frontend C++ Frontend ... TensorFlow Distributed Execution Engine CPU GPU Android iOS ... @YufengG @YufengG

  10. Train and evaluate Keras Estimator models Model Build models Layers Python Frontend C++ Frontend ... TensorFlow Distributed Execution Engine CPU GPU Android iOS ... @YufengG @YufengG

  11. Models in a box. Takes care of Canned Estimators optimizer, training loop, learning rate, etc Train and evaluate Keras Estimator models Model Build models Layers Python Frontend C++ Frontend ... TensorFlow Distributed Execution Engine CPU GPU Android iOS ... @YufengG @YufengG

  12. area = real_valued_column("square_foot"), rooms = real_valued_column("num_rooms"), zip_code = sparse_column_with_integerized_feature("zip_code", 10000) classifier = DNNClassifier ( feature_columns=[area, rooms, embedding_column(zip_code, 8)], hidden_units=[1024, 512, 256, 128]) classifier.fit(train_input_fn) results = classifier.evaluate(eval_input_fn) print(results) @YufengG @YufengG

  13. classifier = DNNLinearCombinedRegressor( linear_feature_columns=[area, rooms, embedding_column(zip_code, 8)], linear_optimizer=tf.train.FtrlOptimizer(learning_rate=0.01, l2_regularization_strength=0.1), dnn_feature_columns=[real_valued_column(area), real_valued_column(rooms)] dnn_optimizer=tf.train.AdagradOptimizer(learning_rate=0.01, initial_accumulator_value=0.1), dnn_activation_fn=tf.nn.relu, dnn_dropout = 0.5, gradient_clip_norm=0.1, hidden_units=[1024, 512, 256, 128]) classifier.fit(train_input_fn) classifier.evaluate(eval_input_fn) @YufengG @YufengG

  14. <Storytime> @YufengG

  15. Motivation - a "magical" food app @YufengG @YufengG

  16. Just Launch and Iterate ● Naive character matching ● Say "Fried chicken" ● Get "Chicken Fried Rice" ● Oops. Now what? ● Machine learning to the rescue! @YufengG @YufengG

  17. v2.0: memorize all the things! ● Train a linear TF model ● Your app is gaining traction! @YufengG @YufengG

  18. Problem : Your users are bored! ● Too many � & waffles ● Show me similar, but different food ● Your users are picky � @YufengG @YufengG

  19. v3.0: More generalized recommendations for all @YufengG @YufengG

  20. No good deed goes unpunished ● Some recommendations are "too general" ○ Irrelevant dishes are being sent ● Your users are still picky � @YufengG @YufengG

  21. No good deed goes unpunished ● 2 types of requests: specific and general ● "iced decaf latte with nonfat milk" != "hot latte with whole milk" ● “seafood” or “italian food” or “fast food” ● How to balance this? @YufengG @YufengG

  22. v4.0: Why not both? @YufengG @YufengG

  23. Wide & Deep generalization memorization diversity relevance @YufengG @YufengG

  24. Wide and Deep @YufengG @YufengG

  25. </Storytime> @YufengG

  26. Meet our dataset: US Census Data ● Task : predict whether the household has an annual income of over $50K ● Over 32k training examples ● Extracted from the 1994 US Census by Barry Becker. @YufengG @YufengG

  27. Meet our dataset: US Census Data Column Name Type Description age Continuous The age of the individual workclass Categorical The type of employer the individual has (government, military, private, etc.). fnlwgt Continuous The number of people the census takers believe that observation represents (sample weight). Not used. education Categorical The highest level of education achieved for that individual. education_num Continuous The highest level of education in numerical form. marital_status Categorical Marital status of the individual. @YufengG @YufengG

  28. Meet our dataset: US Census Data Column Name Type Description occupation Categorical The occupation of the individual. relationship Categorical Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. race Categorical White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. gender Categorical Female, Male. capital_gain Continuous Capital gains recorded. capital_loss Continuous Capital Losses recorded. @YufengG @YufengG

  29. Meet our dataset: US Census Data Column Name Type Description hours_per_week Continuous Hours worked per week. native_country Categorical Country of origin of the individual. income_bracket Categorical ">50K" or "<=50K", meaning whether the person makes more than $50,000 annually. @YufengG @YufengG

  30. Wide & Deep generalization memorization diversity relevance Dense/Real Sparse Continuous Categorical @YufengG @YufengG

  31. To the code! bit.ly/widendeep-census @YufengG @YufengG

  32. tensorboard --logdir=models/ @YufengG @YufengG

  33. Training Prediction many answer examples questions @YufengG @YufengG

  34. What is Serving? RPC Server App TensorFlow Model Data Data Scientist

  35. What is TensorFlow Serving? ● C++ Libraries ○ TensorFlow model save / export formats ○ Generic core platform ● Binaries Best practices out of the box ○ ○ Docker containers, K8s tutorial Hosted Service across ● ○ Google Cloud ML Engine ○ Internal service

  36. Model Creation @YufengG

  37. @YufengG @YufengG

  38. @YufengG @YufengG

  39. @YufengG @YufengG

  40. @YufengG @YufengG

  41. @YufengG @YufengG

  42. @YufengG @YufengG

  43. @YufengG @YufengG

  44. @YufengG @YufengG

  45. Instance Prediction @YufengG

  46. { { "age": 25, "age": 42, "workclass": " Private", "workclass": " "education": " 11th", Self-emp-inc", "education_num": 7, "education": " HS-grad", "marital_status": " "education_num": 9, Never-married", "marital_status": " "occupation": " Married-civ-spouse", Machine-op-inspct", "occupation": " "relationship": " Exec-managerial", Own-child", "relationship": " Husband", "race": " Black", "race": " White", "gender": " Male", "gender": " Male", "capital_gain": 0, "capital_gain": 5178, "capital_loss": 0, "capital_loss": 0, "hours_per_week": 40, "hours_per_week": 50, "native_country": " "native_country": " United-States" United-States" } } @YufengG @YufengG

  47. @YufengG @YufengG

  48. { { "probabilities": [ "probabilities": [ 0.9948562383651733, 0.11601490527391434, 0.005143760237842798 0.8839850425720215 ], ], "logits": [ "logits": [ -5.2648138999938965 2.030721426010132 ], ], "classes": 0, "classes": 1, "logistic": [ "logistic": [ 0.005143760237842798 0.8839850425720215 ] ] } } @YufengG @YufengG

  49. Training Prediction many answer examples questions @YufengG @YufengG

  50. Training Prediction many answer examples questions @YufengG @YufengG

  51. Training Prediction many answer examples questions @YufengG @YufengG

  52. Resources: Thank you! Cloud Machine Learning Engine @ YufengG yufengg.com cloud.google.com/ml-engine TensorFlow tensorflow.org To the code! bit.ly/widendeep-census bit.ly/widendeep-code @YufengG @YufengG

  53. The End @YufengG

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