DLLab 2018 - Azure Machine Learning update
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Azure Batch AI の行く先は、Azure Machine Learning への統合です。特に、Automated Machine Learning と Hyperparameter Explorer に焦点を当ててます。
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DLLab 2018 - Azure Machine Learning update
- 2. トレーニング済みのモデル ビジネスロジックにMLをアタッチ Azure Databricks VMs Deep Learning Framework Deep Learning モデルの作成 TensorFlow KerasPytorch Azure Machine Learning LanguageSpeech … SearchVision On-premises Cloud Edge 生産性の高いサービス データサイエンティストと開発チームの生産性を上げる 強力なインフラストラクチャー Deep Learning の学習と推論の加速 柔軟な推論環境の選択肢 Cloud と Edge へのモデル展開と管理 Chainer
- 4. Purpose Graphics VM Family NV v1 GPU NVIDIA M60 Sizes 1, 2 or 4 GPU Interconnect PCIe (dual root) 2nd Network VM CPU Haswell VM RAM 56-224 GB Local SSD ~380-1500 GB Storage Std Storage Driver Quadro/Grid PC Compute Compute Compute NC v1 NC v2 NC v3 NVIDIA K80 NVIDIA P100 NVIDIA V100 1, 2 or 4 GPU 1, 2 or 4 GPU 1, 2 or 4 GPU PCIe (dual root) PCIe (dual root) PCIe (dual root) FDR InfiniBand FDR InfiniBand FDR InfiniBand Haswell Broadwell Broadwell 56-224 GB 112-448 GB 112-448 GB ~380-1500 GB ~700-3000 GB ~700-3000 GB Std Storage Prem Storage Prem Storage Tesla Tesla Tesla Deep Learning ND v1 NVIDIA P40 1, 2 or 4 GPU PCIe (dual root) FDR InfiniBand Broadwell 112-448 GB ~700-3000 GB Prem Storage Tesla
- 5. Purpose Graphics VM Family NV v2 GPU NVIDIA M60 Sizes 1, 2 or 4 GPU Interconnect PCIe (dual root) 2nd Network VM CPU Broadwell VM RAM 112-448 GB Local SSD ~700-3000 GB Storage Prem Storage Driver Quadro/Grid PC Deep Learning ND v2 NVIDIA V100 8 GPU NVLink Skylake 672 GB ~1300 GB Prem Storage
- 6. 1. 課題の特定 2. データの取得と加工 3. モデルの設計 4. モデルの学 習 5. モデルの テストと評価 a. 初期化 b. データセットからミニバッチ データ取得 c. 損失(差分)を計算d. 最適化: 損失(差分)の最小 化 e. 重みづけの更新 y =Wx + b loss = |desired – actual outcome|δ 6. 展開と推論
- 7. My Computer Experiment Docker Image Data Store Compute Target Azure ML Workspace
- 10. Mileage Condition Car brand Year of make Regulations … Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Mileage Gradient Boosted Criterion Loss Min Samples Split Min Samples Leaf Others Model Which algorithm? Which parameters?Which features? Car brand Year of make
- 11. Criterion Loss Min Samples Split Min Samples Leaf Others N Neighbors Weights Metric P Others Which algorithm? Which parameters?Which features? Mileage Condition Car brand Year of make Regulations … Gradient Boosted Nearest Neighbors SVM Bayesian Regression LGBM … Nearest Neighbors Model 繰り返し Gradient BoostedMileage Car brand Year of make Car brand Year of make Condition
- 12. Which algorithm? Which parameters?Which features? 繰り返し
- 16. Number_of_ hidden_layers Number_of_ nodes_in_layer Input layer Hidden layer 1 Hidden layer 2 Output layer E.g. Learning_rate Batch_size
- 17. Challenges • 探索箇所が広大 • 低密度の中での、最適な値の組み合 わせ • 評価までのリソースが膨大(時間、人、 カネ) 例えば.. • Number_of_layers と learning_rate の最適値 • Number_of_layers – [2, 4, 8] • learning_rate – 0 から 1 の間の あらゆる値
- 18. #1 #2 #𝑝 𝑟𝑢𝑛1 𝑟𝑢𝑛2 𝑟𝑢𝑛 𝑝 … Hyperparameter Tuning runs in Azure ML 𝑟𝑢𝑛𝑗 𝑟𝑢𝑛𝑖 𝑟𝑢𝑛 𝑘 (B) 起動中のジョブの管理 • どれくらいの間実行してい るのか? ??? (A) 新規ジョブ起動 • 探索する、Parameter を設 定 𝒓𝒖𝒏 𝟏= {learning rate=0.2, #layers=2, …} 𝒓𝒖𝒏 𝟐= {learning rate=0.5, #layers=4, …} 𝒓𝒖𝒏 𝒑= {learning rate=0.9, #layers=8, …}
- 19. Early terminate poorly performing runs
- 20. ## エスティメーターの作成 from azureml.train.dnn import TensorFlow script_params = { '--data-folder': ws.get_default_datastore().as_mount(), '--batch-size': 50, '--first-layer-neurons': 300, '--second-layer-neurons': 100, '--learning-rate': 0.01 } estimator = TensorFlow(source_directory=script_folder, script_params=script_params, compute_target=compute_target, entry_script='tf_mnist.py', use_gpu=True)
- 21. ## 探索空間の設定 from azureml.train.hyperdrive import GridParameterSampling param_sampling = GridParameterSampling( { "num_hidden_layers": choice(1, 2, 3), "batch_size": choice(16, 32) } ) ## 探索空間の設定 from azureml.train.hyperdrive import MedianStoppingPolicy early_termination_policy = MedianStoppingPolicy(evaluation_interval=1, delay_evaluation=5) ## Hyperparameter Search 設定 from azureml.train.hyperdrive import HyperDriveRunConfig hyperdrive_run_config = HyperDriveRunConfig(estimator=estimator, hyperparameter_sampling=param_sampling, policy=early_termination_policy, primary_metric_name=’accuracy’, primary_metric_goal=PrimaryMetricGoal.MAXIMIZE, max_total_runs=100, max_concurrent_runs=4)
Editor's Notes
- Microsoft Envision 2016
- Microsoft Envision 2016
- Microsoft Envision 2016
- Microsoft Envision 2016
- Microsoft Envision 2016