Building AI Solutions with Azure Machine Learning

Presentation at Education Webinar Series

Transcript

1. Building AI Solutions with Azure Machine Learning Dmitry Soshnikov, Ph.D. Cloud Developer Advocate, Microsoft Associate Professor, MIPT/HSE/MAI http://soshnikov.com – @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
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 http://eazify.net/2dsvm
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 http://github.com/CloudAdvocacy/AzureMLStarter
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”: http://www.soshnikov.com/temp/titanic.csv 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 http://portal.azure.com
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: http://github.com/CloudAdvocacy/AzureMLStarter
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 train.py Submit Experiment using CLI: Log Metrics in the script: from azureml.core.run import Run run = Run.get_submitted_run() run.log('accuracy', acc)
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 ✓ https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#supported-compute-targets
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!
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) model.save('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
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)
28. Model Deployment
29. Case Study: Generative Adversarial Networks on AML http://aka.ms/azml_gan
30. How Generative Adversarial Networks Work Random Vector Generator (Neural Net) Discriminator (Neural Net) ✓ ✗
31. GAN Library: keragan http://github.com/shwars/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 http://github.com/shwars/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
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 http://deeppavlov.ai $ 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 http://aka.ms/deeppavlov
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
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"] }
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: • http://github.com/CloudAdvocacy/AzureMLStarter • http://aka.ms/azmlstarter - Blog Post
52. Further Reading  How to train your own neural network to generate paintings http://aka.ms/azml_gan  Can AI be creative http://aka.ms/creative_ai  Creating interactive exhibit based on cognitive portraits http://aka.ms/cognitive_portrait_exhibit  Training COVID ODQA on Azure ML: http://aka.ms/deeppavlov
53. https://docs.microsoft.com/en-us/learn/paths/build-ai-solutions-with-azure-ml-service/
54. Mastering Azure Machine Learning https://www.packtpub.com/product/mastering-azure-machine-learning/9781789807554 Code: https://github.com/PacktPublishing/Mastering-Azure-Machine-Learning
55. AutoML with Azure Book https://www.oreilly.com/library/view/practical-automated-machine/9781492055587/ Code: https://github.com/PracticalAutomatedMachineLearning/Azure
56. © Copyright Microsoft Corporation. All rights reserved. @art_of_artificial