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Building AI Solutions with Azure Machine Learning

Building AI Solutions with Azure Machine Learning

Presentation at Education Webinar Series


Dmitri Soshnikov

April 13, 2021


  1. Building AI Solutions with Azure Machine Learning Dmitry Soshnikov, Ph.D.

    Cloud Developer Advocate, Microsoft Associate Professor, MIPT/HSE/MAI – @shwars
  2. AI / Machine Learning on Azure Domain specific pretrained models

    To reduce time to market Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlow Pytorch Onnx Azure Machine Learning Language Speech … Search Vision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn PyCharm Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
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  4. Typical Usage of DSVM for Deep Learnong Data Science Virtual

    Machine GPU Disk ssh Problems: + resource management + cost optimization VS Code Remote Jupyter Hint: When creating GPU DSVM always set it to auto-shutdown at midnight!
  5. Typical ML Process Data Prep Training Deployment > > 70%

    time Low-intensive CPU compute 25% time GPU VM / Cluster 5% time Scalable VM/ Kubernetes Hyperparameter optimization Experiment / Model tracking
  6. Sharing Data between DSVMs DataPrep DSVM CPU Cloud Storage (e.g.

    fuse) Problems: + resource management + tracking experiments / models + collaboration in teams + distributed training Training DSVM GPU
  7. Real-Life Complex ML Setup DataPrep DSVM CPU Data+Models Training DSVM

    GPU Training DSVM GPU Registry Deployment Cluster
  8. Azure Machine Learning Workspace Dataset Datastore Compute Compute Cluster Experiments

    Notebooks Model Deployment Designer AutoML
  9. Different “Styles” of Using Azure ML • Using Jupyter Notebooks

    with switchable compute • Collecting Experiment Statistics (Logging) and Model Catalog • Scheduling Experiments to Run on the Cluster • Hyperparameter Optimization • Parallel Training • Model Deployment • Pipelines / ML Ops Lifecycle • Using Auto ML / Designer
  10. The Workshop

  11. Azure ML Workspace: A container for Everything Azure ML Workspace

    encapsulates it all: 1. Storage 2. Datasets 3. Compute 4. Notebooks 5. Experiment Results 6. Models 7. Deployments az extension add -n azure-cli-ml az group create -n ml -l westus2 az ml workspace create -w AzML -g ml az ml folder attach -w AzML -g ml Create Workspace using Azure CLI: az ml computetarget create amlcompute -n cpu --min-nodes 0 --max-nodes 2 -s STANDARD_DS3_V2 Create Cluster using Azure CLI: MS Docs: HERE
  12. Azure ML service Workspace Taxonomy

  13. Tools for Simplified ML: AutoML, Designer Automatic ML Designer Run

    the experiment to automatically try different models and select the one that performs best Can do some feature optimization (data balancing, irrelevant feature elimination) Similar to Azure ML Studio Classic Perform ML experiments without coding, by composing pre-build blocks Defines pipelines in a graphical way
  14. Task 1: Auto ML on Titanic Dataset 1. Create Titanic

    dataset in Azure ML • Use “Tabular -> From the web”: 2. Create AutoML Experiment • Select “Classification” as task • Make sure to change featurization options to include only useful fields • You can optionally enable deep learning 3. After the experiment has finished, see accuracy and the best model
  15. Task 2: Play with Designer 1. Open Designer 2. Select

    pre-build sample “Multi-Class Classification – Letter Recognition” 3. Look at the experiment structure 4. Submit the experiment
  16. Using Cluster to Train Model in Python Azure ML for

    VS Code Portal
  17. How to Start with Azure ML: Read my blog series:

    • The best way to start with Azure ML using VS Code • Using Azure ML for Hyperparameter Optimization • Training GAN to Produce Art • Training BERT Question Answering with DeepPavlov ❶ ❷ Try it out:
  18. Submit and Track Experiments Experiment is represented by a Python

    Script + Environment that run on Compute (Local Compute, Azure ML Cluster or Databricks) 1. Auto-package code 2. Keep track of results 3. Store models 4. Queue runs 5. Programmatically spawn many runs with different parameters az ml run submit-script -c sklearn –e MyExp Submit Experiment using CLI: Log Metrics in the script: from import Run run = Run.get_submitted_run() run.log('accuracy', acc)
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  20. My Computer Data Store Azure ML Workspace Compute Target Docker

    Image How Azure ML Experimentation Works Experiment
  21. Azure ML Currently Supported Compute Targets Compute target GPU acceleration

    Hyperdrive Automated model selection Can be used in pipelines Local computer Maybe ✓ Data Science Virtual Machine (DSVM) ✓ ✓ ✓ ✓ Azure ML compute ✓ ✓ ✓ ✓ Azure Databricks ✓ ✓ ✓ Azure Data Lake Analytics ✓ Azure HDInsight ✓
  22. Run Notebooks and Create Datasets When you do a lot

    of training, it makes sense to store data inside the workspace. To run Python code inside the workspace – use Notebooks! You need to create separate compute (not cluster) to do that!
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  24. Submitting Experiments / Training Script Use args to pass different

    parameters, including data path parser = argparse.ArgumentParser(description='MNIST Train') parser.add_argument(‘--data_folder', type=str, dest='data_folder', help='data folder mount point') parser.add_argument('--epochs', type=int, default=3) parser.add_argument('--batch_size', type=int, default=128) parser.add_argument('--hidden', type=int, default=100) parser.add_argument('--dropout', type=float) Store model into outputs directory os.makedirs('outputs',exist_ok=True)'outputs/mnist_model.hdf5') Load data as files fn = os.path.join(args.data_folder, 'mnist_data/mnist.pkl') with open(fn,'rb') as f: X,y = pickle.load(f) run = Run.get_context() run.log('Test Loss', score[0]) run.log('Accuracy', score[1]) Log Result
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  26. Hyperparameter Optimization Define Parameter Sampling Strategy Submit the Experiment param_sampling

    = RandomParameterSampling({ '--hidden': choice([50,100,200,300]), '--batch_size': choice([64,128]), '--epochs': choice([5,10,50]), '--dropout': choice([0.5,0.8,1]) }) Define Hyperdrive Configuration hd_config = HyperDriveConfig(estimator=est, experiment = Experiment(workspace=ws, name='keras-hyperdrive') hyperdrive_run = experiment.submit(hd_config) Strategies: Grid, Random, Bayesian Distribution: choice, uniform, normal hd_config = HyperDriveConfig(estimator=est, hyperparameter_sampling=param_sampling, policy=early_termination_policy, primary_metric_name='Accuracy', primary_metric_goal=MAXIMIZE, max_total_runs=16, max_concurrent_runs=4)
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  28. Model Deployment

  29. Case Study: Generative Adversarial Networks on AML

  30. How Generative Adversarial Networks Work Random Vector Generator (Neural Net)

    Discriminator (Neural Net) ✓ ✗
  31. GAN Library: keragan Generator Discriminator Random Noise (dim=100) Conv

    Matrix Conv Matrix Reshape DeConv DeConv Conv Matrix Conv Matrix Feature Vector Classifier
  32. GAN Library: keragan Generator Discriminator discriminator = Sequential() for

    x in [16,32,64]: # number of filters on next layer discriminator.add(Conv2D(x, (3,3), strides=1, padding="same")) discriminator.add(AveragePooling2D()) discriminator.addBatchNormalization(momentum=0.8)) discriminator.add(LeakyReLU(alpha=0.2)) discriminator.add(Dropout(0.3)) discriminator.add(Flatten()) discriminator.add(Dense(1, activation='sigmoid')) generator = Sequential() generator.add(Dense(8 * 8 * 2 * size, activation="relu", input_dim=latent_dim)) generator.add(Reshape((8, 8, 2 * size))) for x in [64;32;16]: generator.add(UpSampling2D()) generator.add(Conv2D(x, kernel_size=(3,3),strides=1, padding="same")) generator.add(BatchNormalization()) generator.add(Activation("relu")) generator.add(Conv2D(3, kernel_size=3, padding="same")) generator.add(Activation("tanh"))
  33. Model Training discriminator.trainable = False noise = np.random.normal(0, 1, (batch_size,

    latent_dim)) gen_imgs = generator.predict(noise) imgs = get_batch(batch_size) d_loss_r = discriminator.train_on_batch(imgs, ones) d_loss_f = discriminator.train_on_batch(gen_imgs, zeros) d_loss = np.add(d_loss_r , d_loss_f)*0.5 g_loss = combined.train_on_batch(noise, ones) res = generator.predict(np.random.normal(3,latent_dim)) fig,ax = plt.subplots(1,len(res)) for i,v in enumerate(res): ax[i].imshow(v[0]) run.log_image("Sample",plot=plt) # Generate Noise Vector & Images # Train Discriminator # Train Generator (by training combined model) # Log Sample Images through Azure ML Code Sample on GitHub Code Sample on GitHub
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  35. Getting and Using the Model fnames = list(filter(lambda x :

    x.startswith('outputs/models/gen_’), run.get_file_names())) no = max(map(lambda x: int(x[19:x.find('.')]), fnames)) fname = 'outputs/models/gen_{}.h5'.format(no) run.download_file(fname) # Get the Latest Model File model = keras.models.load_model(fname) latent_dim=model.layers[0].input.shape[1].value vec = np.random.normal(0,1,(10,latent_dim)) res = model.predict(vec)) res = (res+1.0)/2 # Predict 10 images Code Sample on GitHub
  36. Case Study: Open Domain Question Answering Common tasks for NLP:

    • Intent Classification • Named Entity Recognition (NER) • Keyword Extraction • Text Summarization • Question Answering Open Domain Question Answering – a task, when a model is able to give specific answers contained in a large volume of text (e.g. Wikipedia) - Where did guinea pigs originate? - Andes of South America - When did the Lynmouth floods happen? - 1804 Neural Language Models: • Recurrent Neural Network (RNN) • LSTM, GRU • Transformers • GPT-2 • BERT • Microsoft Turing-NLG
  37. How BERT Works (Simplified) Masked Language Model + Next Sentence

    Prediction During holidays, I like to ______ with my dog. It is so cute. 0.85 Play 0.05 Sleep 0.09 Fight 0.80 YES 0.20 NO BERT contains 345 million parameters => very difficult to train from scratch! In most of the cases it makes sense to use pre-trained language model.
  38. Text Processing Pipelines BERT for Classification Input Text BERT Features

    Classifier BERT for Entity Extraction Input Text BERT Features Mask Generator Class Prob Vector Entity Masks BERT for Question Answering Input Text BERT Features Bounds Generator Answer Bounds 0.85 Insult 0.15 Neutral I live in France My age is 21 LOC
  39. DeepPavlov: “Keras” for NLP $ pip install deeppavlov python

    -m deeppavlov install config.json python -m deeppavlov download config.json python -m deeppavlov train config .json Text processing pipeline is defined in JSON config: • Processing steps, their inputs and outputs • Weight location for pre-trained models • Data shape and location • Training parameters
  40. Example: Open Domain Question Answering for COVID

  41. Using Azure ML to Train the ODQA Model We will

    use the following features of Azure ML: • Define file dataset that points to data location • Create cheap non-GPU compute for data exploration and preparation • Use GPU-enabled compute on the same data to train the model • All code would be in the form of Jupyter Notebooks We do not use training on Azure ML Cluster in this case to have better control on the environment. DeepPavlov downloads large amounts of pre-trained data from the network, and for simple cases it is better to use single node. Link to the non-commercial CORD-19 dataset: here (.tar.gz)
  42. Run Notebooks and Create Datasets Datasets Notebook Directory Compute GPU

    Compute Logging
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  44. Getting Wikipedia ODQA Up and Running import sys !{sys.executable} -m

    pip install deeppavlov !{sys.executable} -m deeppavlov install en_odqa_infer_wiki !{sys.executable} -m deeppavlov download en_odqa_infer_wiki from deeppavlov import configs from deeppavlov.core.commands.infer import build_model odqa = build_model(configs.odqa.en_odqa_infer_wiki) answers = odqa([ "Where did guinea pigs originate?", "When did the Lynmouth floods happen?" ]) # Get the Library and Required Models # Build Model from Config and Run Inference ['Andes of South America', '1804']
  45. ODQA Configs Ranker en_ranker_tdifd_wiki SQuAD multi_squad_noans_infer (R-NET) Config on GitHub

    en_odqa_infer_wiki question question document answer TRAIN Replace with BERT
  46. Train the Ranker from deeppavlov.core.common.file import read_json model_config = read_json(configs.doc_retrieval.en_ranker_tfidf_wiki)

    model_config["dataset_reader"]["data_path"] = os.path.join(os.getcwd(),"text") model_config["dataset_reader"]["dataset_format"] = "txt" model_config["train"]["batch_size"] = 1000 # Specify Data Path & Format doc_retrieval = train_model(model_config) doc_retrieval(['hydroxychloroquine']) # Train the Model and See the Results "dataset_reader": { "class_name": "odqa_reader", "data_path": "{DOWNLOADS_PATH}/odqa/enwiki", "save_path": "{DOWNLOADS_PATH}/odqa/enwiki.db", "dataset_format": "wiki" } Part of en_ranker_tfidf_wiki config
  47. Results with R-NET Question Answering # Download R-NET SQuAD model

    squad = build_model(configs.squad.multi_squad_noans_infer, download = True) # Do not download the ranker model, we've just trained it odqa = build_model(configs.odqa.en_odqa_infer_wiki, download = False) odqa(["what is coronavirus?","is hydroxychloroquine suitable?"]) ['an imperfect gold standard for identifying King County influenza admissions', 'viral hepatitis']
  48. Use BERT for QA # Download Pre-trained BERT Q&A Model

    # Replace Q&A Model in the Master Config Part of en_odqa_infer_wiki config !{sys.executable} -m deeppavlov install squad_bert_infer bsquad = build_model(configs.squad.squad_bert_infer, download = True) odqa_config = read_json(configs.odqa.en_odqa_infer_wiki) odqa_config['chainer']['pipe'][-1]['squad_model']['config_path'] = '{CONFIGS_PATH}/squad/squad_bert_infer.json' odqa = build_model(odqa_config, download = False) odqa(["what is coronavirus?", "is hydroxychloroquine suitable?", "which drugs should be used?"]) # Build and Use Model { "class_name": "logit_ranker", "squad_model": {"config_path": ".../multi_squad_noans_infer.json"} "in": ["chunks","questions"], "out": ["best_answer","best_answer_score"] }
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  50. Question Answer what is coronavirus? respiratory tract infection is hydroxychloroquine

    suitable? well tolerated which drugs should be used? antibiotics, lactulose, probiotics what is incubation period? 3-5 days how to contaminate virus? helper-cell-based rescue system cells what is coronavirus type? enveloped single stranded RNA viruses what are covid symptoms? insomnia, poor appetite, fatigue, and attention deficit what is reproductive number? 5.2 what is the lethality? 10% where did covid-19 originate? uveal melanocytes is antibiotics therapy effective? less effective what are effective drugs? M2, neuraminidase, polymerase, attachment and signal-transduction inhibitors what is effective against covid? Neuraminidase inhibitors is covid similar to sars? All coronaviruses share a very similar organization in their functional and structural genes what is covid similar to? thrombogenesis Results
  51. Conclusions Azure ML enhances your ML experience by: • Grouping

    everything together in workspace • Journaling all experiment results automatically • Helping with hyperparameter optimization and scalable compute • Supporting distributed training ❶ ❷ You should try it out: • • - Blog Post
  52. Further Reading  How to train your own neural network

    to generate paintings  Can AI be creative  Creating interactive exhibit based on cognitive portraits  Training COVID ODQA on Azure ML:

  54. Mastering Azure Machine Learning Code:

  55. AutoML with Azure Book Code:

  56. © Copyright Microsoft Corporation. All rights reserved. @art_of_artificial