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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

High-Performance, Scalable Array Storage with TensorStore

Drishti
Drishti Last Updated : 19 Apr, 2023
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Various contemporary computer science and machine learning applications use multidimensional datasets encompassing a single expansive coordinate system. Two examples are utilizing air measurements over a geographical grid to predict the weather or making medical imaging predictions using multi-channel image intensity values from a 2D or 3D scan. These datasets can be difficult because users may receive and write data at unpredictable intervals and on varied scales. They frequently want to run studies on several workstations simultaneously. Under these conditions, even a single dataset might require petabytes of storage.

In light of this, Google AI has introduced TensorStore, an open-source Python and C++ software library designed to store and manipulate n-dimensional data.

TensorStore

As we briefly discussed in the Introduction section, TensorStore is an open-source Python and C++ software library designed to store and manipulate n-dimensional data. Numerous storage systems, including local and network filesystems, Google Cloud Storage, etc., are supported by this module. It provides a uniform API that can read and write various array types. The library offers read/writeback caching, transactions, robust atomicity, isolation, consistency, and durability (ACID) guarantee. Optimistic concurrency ensures secure access to various programs and systems.

Safe and Performant Scaling

Large numerical datasets require extensive processing power to process and analyze. Typically, this is done by parallelizing activities among numerous CPU or accelerator cores dispersed across multiple devices. TensorStore’s primary objective has been to make it possible for individual datasets to be processed in parallel in a way that is both secure (i.e., prevents corruption or inconsistencies caused by parallel access patterns) and performant (i.e., reading and writing to TensorStore is not a bottleneck during computation). In fact, in a test conducted within Google’s data centers, it was noted that nearly linear scaling of reading and write performance as the number of CPUs was raised:

TensorStore

Fig 1: Read/write performance for a TensorStore dataset in Zarr format located on Google Cloud Storage (GCS)

Performance is achieved by implementing fundamental operations in C++, heavily utilizing multithreading for tasks like encoding/decoding and network I/O, and chunking enormous datasets to quickly read and write portions of the full dataset. TensorStore also has an asynchronous API that lets a read or writes operation continue in the background. At the same time, a program completes other tasks, as well as customizable in-memory caching (which reduces slower storage system interactions for the data which is often accessed).

Optimistic concurrency, which maintains compatibility with various underlying storage layers (including Cloud storage platforms, such as GCS, as well as local filesystems), the safety of the parallel operations is achieved when multiple machines are accessing the same dataset. This is done without significantly affecting performance. TensorStore further offers strong ACID guarantees for each action running within a single runtime.

Additionally, researchers combined TensorStore with parallel computing frameworks like Apache Beam (sample code) and Dask to make distributed computing with TensorStore compatible with many already used data processing workflows (example code).

Use Case 1: Language Models

Introducing more complex language models, like PaLM, is an exciting new advancement in machine learning. These neural networks display excellent natural language processing and generation abilities with hundreds of billions of parameters. These models also strain available computational resources; for example, training a language model like PaLM necessitates thousands of concurrent TPUs.

One challenge during this training process is reading and writing the model parameters efficiently. Although training is spread across numerous machines, parameters must be periodically recorded to a single object (referred to as a “checkpoint”) on a permanent storage system without slowing down the training process. Individual training jobs must be able to read the specific set of parameters they are concerned with to eliminate the overhead required to load the whole set of model parameters (which could be 100s of gigabytes).

These issues have already been addressed using TensorStore. It has been integrated with frameworks like T5X and Pathways and has been used to handle checkpoints connected to large-scale (“multipod“) models trained with JAX (code sample). The entire set of parameters, which can take up more than a terabyte of memory, is divided among hundreds of TPUs using model parallelism. TensorStore stores checkpoints in Zarr format, with a chunk structure that enables independent parallel reading and writing of the partition for each TPU.

Use Case 2: 3D Brain Mapping

Synapse-resolution connectomics aims to trace the intricate network of individual synapses in animal and human brains. This calls for petabyte-sized datasets to be produced by imaging the brain at extremely high resolution (nanometers) over fields of view of up to millimeters or more. These databases could eventually reach exabyte size as scientists consider mapping complete mouse or monkey brains. In contrast, even a single brain sample may require millions of gigabytes with a coordinate system (pixel space) of hundreds of thousands of pixels in each dimension, posing major storage, manipulation, and processing issues.

The researchers used TensorStore to overcome the computational difficulties presented by large-scale connectomic datasets. Using Google Cloud Storage as the underlying object storage technology, TensorStore has managed some of the largest and most popular connectomic datasets. It has been used, for instance, to analyze the human cortex “h01” dataset, which is a three-dimensional image of brain tissue with a nanometer-level resolution. The raw image data, which is 1.4 petabytes in size (or around 500,000 * 350,000 * 5,000 pixels), is also coupled with other content, such as 3D segmentations and annotations, which are stored in the same coordinate system. The “Neuroglancer precomputed” format, ideal for web-based interactive viewing and readily manipulable from TensorStore, is used to store the raw data, which is separated into discrete chunks 128x128x16 pixels in size.

TensorStore

A fly brain reconstruction with freely accessible and manipulatable underlying data using TensorStore (Source: Google AI)

Conclusion

To summarize, in this article, we learned that the TensorStore, an open-source C++ and Python software library, is designed for storage and manipulation of n-dimensional data that:

  • Enables the reading and writing of numerous array formats, such as Zarr and N5, using a unified API.
  • Natively supports multiple storage systems, including local and network filesystems, Google Cloud Storage, HTTP servers, and in-memory storage.
  • Supports read/writeback caching and transactions with consistency, strong atomicity, isolation, and durability (ACID) guarantees.
  • Supports efficient and safe access from multiple processes and machines via optimistic concurrency.
  • An asynchronous API provides high-throughput access to external storage even with high latency.
  • Provides fully composable and advanced indexing operations and virtual views.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

blogathonC/C++googlepythonTensorStore
Drishti
Drishti

I'm a Researcher who works primarily on various Acoustic DL, NLP, and RL tasks. Here, my writing predominantly revolves around topics related to Acoustic DL, NLP, and RL, as well as new emerging technologies. In addition to all of this, I also contribute to open-source projects @Hugging Face.
For work-related queries please contact: [email protected]

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