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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

Memory Profiling in Python

Yana Khare
Yana Khare Last Updated : 03 Jun, 2024
5 min read

Introduction

Creating high-performing and efficient apps requires optimizing memory utilization in the present software development environment. Memory profiling is an efficient technique for accomplishing this. Memory profiling examines a program’s memory usage and finds memory-intensive code segments, possible memory leaks, and optimization possibilities. This procedure aids programmers in making sure their apps utilize memory effectively, resulting in quicker and more scalable processes. This article is an introductory guide for memory profiling in Python.

Memory Profiling in Python

Table of contents

What is Memory Profiling?

Memory profiling is the technique of examining software to determine how much memory it utilizes. It helps identify memory-intensive code segments, potential memory leaks, and potential optimization targets. By profiling memory, developers may ensure that their applications use memory efficiently and achieve faster and more scalable operations.

Importance of Memory Profiling

  • Performance Improvement: Developers can enhance an application’s speed and responsiveness by identifying memory-intensive tasks and optimizing the code.
  • Resource Management: When memory is used efficiently, applications become more robust and scalable, lessening the strain on system resources.
  • Debugging Memory Leaks: Memory leaks, which can eventually cause crashes or sluggish performance, can be found and fixed using memory profiling.

Advantages of Memory Profiling in Python

  • Identifying Bottlenecks: Memory profiling aids in locating code segments that use excessive amounts of memory. Thus, allowing programmers to modify such sections for increased efficiency.
  • Leak Detection: Memory profiling tools can identify memory leaks, which happen when memory no longer required is kept in reserve. Hence eventually increasing memory utilization.
  • Predictable Behavior: Memory profiling contributes to steady memory utilization, stabilizing and producing more predictable application behavior.
  • Long-term Reliability: Continuous memory profiling can ensure that long-running applications remain reliable and efficient over time.
  • Code Refactoring: Profiling can highlight inefficient code. Therefore, prompting developers to refactor and improve the overall code quality.

Disadvantages of Memory Profiling in Python

  • Increased Runtime: Memory profiling may cause the application to execute with additional overhead. Therefore, resulting in a slower profile during the profiling process.
  • Code Modification: Some profiling tools require adding decorators or certain function calls to the code. Thus, it can be invasive and impact the codebase.
  • Limited Scope: Some memory profiling tools may provide only a partial view of memory usage, potentially missing issues that manifest under specific conditions or workloads.
  • Snapshot Limitations: Tools that rely on snapshots might not capture transient memory usage spikes, leading to incomplete analysis.
  • Inaccurate Reports: Profiling tools might sometimes report false positives or negatives, especially in complex applications, leading to misdirected optimization efforts.

Tools for Memory Profiling in Python

Several tools are available for Python memory profiling, each with unique capabilities and applications. We look at a few of the more well-known ones here.

1. memory_profiler

One common tool for tracking memory consumption is memory_profiler. It provides line-by-line information on memory use. Thus simplifying the identification of memory-intensive portions of the code.

Features

  • Line-by-Line Analysis: Allows detailed inspection of memory usage at each line of code.
  • Ease of Use: Simple to integrate with Python code using decorators.
  • Integration with IPython: This can be used with IPython for interactive profiling.

Installation

pip install memory-profiler

Usage

To use memory_profiler, decorate the function you want to profile with @profile And run the script with the -m memory_profiler Flag.

from memory_profiler import profile

@profile
def my_function():
a = [1] * (10**6)
b = [2] * (2 * 10**7)
del b
return a

if __name__ == "__main__":
my_function()

Output

Running this script will produce a detailed memory usage report for each line in the decorated function.

2. guppy3

guppy3 is a comprehensive Python programming environment with a heap analysis toolset. It includes a heap analysis tool for finding memory leaks and analyzing memory usage.

Features

  • Heap Analysis: Detailed heap usage reports and analysis.
  • Debugging Memory Leaks: Effective in identifying memory leaks.
  • Interactive Use: This can be used interactively for in-depth analysis.

Installation

pip install guppy3

Usage

from guppy import hpy

hp = hpy()
heap = hp.heap()
print(heap)

Output

3. tracemalloc

tracemalloc is a built-in module in Python that tracks memory allocations. It provides statistical memory usage analysis. Additionally, it can be useful for tracking memory leaks.

Features

  • Built-in: No need for additional installations.
  • Snapshot Comparison: Compare memory usage snapshots to identify changes.
  • Detailed Reports: Provides detailed reports on memory allocation.

Usage

import tracemalloc

tracemalloc.start()

# Your code here (replace with actual code whose memory usage you want to monitor)
# For example, creating a large list:
data = [x ** 2 for x in range(100000)]

snapshot1 = tracemalloc.take_snapshot()

# Your code here (replace with actual code whose memory usage you want to monitor)
# For example, modifying the list:
data = [x ** 3 for x in range(100000)]


snapshot2 = tracemalloc.take_snapshot()

top_stats = snapshot2.compare_to(snapshot1, 'lineno')

for stat in top_stats[:10]:
print(stat)

Output

4. objgraph

objgraph is a module that lets you visually explore Python object graphs. It can help find memory leaks by identifying objects that are taking up memory. Furthermore, it can help in tracking down why they are not being garbage collected.

Features

  • Visual Representation: Generates visual representations of object graphs.
  • Finding Leaks: Helps identify objects that are causing memory leaks.
  • Interactive: Using it interactively to explore object relationships.

Installation

pip install objgraph

Usage

import objgraph

# Your code here
# For example,
#  Define a sample class
class MyClass:
    def __init__(self, value):
        self.value = value

# Create instances of the class
obj1 = MyClass(10)
obj2 = MyClass(20)
obj3 = MyClass(30)

# Assign one of the objects to a variable for inspection
some_object = obj1


objgraph.show_most_common_types()
objgraph.show_backrefs([some_object], max_depth=10)

Output

5. pympler

pympler is another powerful tool for tracking memory usage in Python programs. It provides detailed reports, and moreover, it can help analyze the lifetime of Python objects.

Features

  • Comprehensive Reports: Provides detailed memory usage reports.
  • Tracking Object Lifetimes: Tracks the lifetime of Python objects.
  • Integrates with Existing Code: This can be integrated for continuous monitoring.

Installation

pip install pympler

Usage

from pympler import summary, muppy

all_objects = muppy.get_objects()
sum1 = summary.summarize(all_objects)
summary.print_(sum1)

Output

Best Practices for Memory Profiling

  1. Profile Regularly: Integrate memory profiling into your development workflow to catch memory issues early.
  2. Optimize Data Structures: Choose the right data structures that balance memory usage and performance.
  3. Monitor Long-Running Applications: Continuously monitor memory usage in applications that run for extended periods.
  4. Use Memory Pools: Implement memory pools to manage memory allocation and deallocation efficiently.

Also Read: Generate Reports Using Pandas Profiling

Conclusion

Therefore, memory profiling is essential for developing efficient and robust Python applications. By using tools like memory_profiler, guppy3, tracemalloc, objgraph, and pympler, developers can learn about their applications’ memory consumption patterns and adjust them accordingly. Frequent memory profiling enhances efficiency, guards against memory leaks, and guarantees that apps function properly.

If you want to learn more about Python, do Analytics Vidhya’s Python course.

Memory ProfilingMemory Profiling in Pythonpython
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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