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The diagrams package is a powerful Python library that allows you to create diagrams and visualizations easily. In this post, we will explore different types of nodes and edges that can be used to build various diagrams. Installation Before we start, make sure you have the diagrams package installed. You can install it using pip : pip install diagrams Getting Started First, let's import the necessary modules from the diagrams package: from diagrams import Diagram, Cluster, Edge from diagrams .onprem .compute import Server from diagrams .onprem .database import PostgreSQL from diagrams .onprem .network import Nginx Simple Diagram Let's start with a simple example of a web application architecture: with Diagram( "Web App Architecture" , show=False): client = Server( "Client" ) web_server = Nginx( "Web Server" ) db_server = PostgreSQL( "Database Server" ) client >> web_server >> db_server I...

In this tutorial, we'll explore how to use PyTorch's ImageFolder dataset to load and preprocess image data efficiently. The ImageFolder dataset is a handy utility for handling image datasets organized in a specific folder structure. Table of Contents Introduction to ImageFolder Prerequisites Setting up the Dataset Data Augmentation (Optional) DataLoader Model and Training (Brief Overview) Let's get started! 1. Introduction to ImageFolder ImageFolder is a PyTorch dataset class designed to work with image data organized in folders. Each folder corresponds to a specific class, and images within those folders belong to that class. This structure makes it easy to load data for tasks like image classification. 2. Prerequisites Before we start, make sure you have the following installed: Python (>= 3.6) PyTorch (>= 1.8.0) torchvision (>= 0.9.0) You can install PyTorch and torchvision using pip: pip install torch torchvision 3. Setting up the Da...

In this tutorial, we'll explore how to use TensorFlow's tf.data API to efficiently load and process TFRecord data for training deep learning models. The tf.data API provides a powerful and flexible way to build high-performance data input pipelines for TensorFlow. Code Examples 1. Importing Libraries import tensorflow as tf 2. Reading TFRecord Files # Define the list of TFRecord files tfrecord_files = [ "file1.tfrecord" , "file2.tfrecord" , "file3.tfrecord" ] # Define the feature description for parsing feature_description = { "image" : tf.io.FixedLenFeature([], tf.string), "label" : tf.io.FixedLenFeature([], tf.int64), } # Define a function to parse the TFRecord def parse_tfrecord(example_proto): return tf.io.parse_single_example(example_proto, feature_description) # Create a dataset from the TFRecord files dataset = tf.data.TFRecordDataset(tfrecord_files) # Map the parsing function to the datas...

In this tutorial, we'll explore how to use NVIDIA DALI (Data Loading Library) with PyTorch to efficiently load and preprocess NPZ (NumPy Archive) data for training deep learning models. DALI is a powerful library that accelerates data loading and preprocessing, making it ideal for handling large datasets. Installation Before we begin, make sure to install the required libraries: pip install torch torchvision pip install --extra- index -url http s: //developer.download.nvidia. com /compute/redist nvidia-dali-cudaXX (Replace XX with your CUDA version , e .g., nvidia-dali-cuda110 for CUDA 11.0 ) Code Examples 1. Importing Libraries import torch import torchvision.transforms as transforms from nvidia.dali.pipeline import Pipeline import nvidia.dali.fn as fn import nvidia.dali.types as types 2. Defining the DALI Pipeline class NPZPipeline(Pipeline): def __init__( self , batch_size, num_threads, device_id, data_path): super ().__init__(batch_size, num_threa...

In this tutorial, we'll explore how to use NVIDIA DALI (Data Loading Library) with PyTorch to efficiently load and preprocess TFRecord data for training deep learning models. DALI is an optimized data pipeline library designed for high-throughput data loading and preprocessing, making it particularly useful when dealing with large datasets. Installation Before we begin, make sure to install the required libraries: pip install torch torchvision pip install --extra- index -url http s: //developer.download.nvidia. com /compute/redist nvidia-dali-cudaXX (Replace XX with your CUDA version , e .g., nvidia-dali-cuda110 for CUDA 11.0 ) Code Examples 1. Importing Libraries import torch import torchvision.transforms as transforms from nvidia.dali.pipeline import Pipeline import nvidia.dali.fn as fn import nvidia.dali.types as types 2. Defining the DALI Pipeline class TFRecordPipeline(Pipeline): def __init__( self , batch_size, num_threads, device_id, data_path): ...

Introduction In software development, the Factory pattern is a creational design pattern that provides an interface for creating objects without specifying their exact class. This pattern allows the client code to create objects based on certain conditions, encapsulating the object creation process and promoting loose coupling between the client and the concrete classes. In this blog post, we will explore the Factory pattern and provide a detailed C++ example to illustrate its implementation. Understanding the Factory Pattern The Factory pattern follows the concept of "separation of concerns" by providing a separate factory class responsible for object creation. It enables the client to interact with the factory to obtain objects, without having to know the specific class being instantiated. Key components of the Factory pattern: Abstract Product: Interface representing the product classes. Concrete Products: Classes that implement the Abstract Product interface. ...

Introduction In software design, the Singleton pattern is a creational design pattern that ensures a class has only one instance and provides a global point of access to that instance. This pattern is useful when you want to control the number of instances of a class and ensure that there is a single, shared instance across the entire application. In this blog post, we will explore the Singleton pattern and provide a C++ example to illustrate its implementation. Understanding the Singleton Pattern The Singleton pattern is characterized by the following key points: Private Constructor: The class has a private constructor, preventing direct instantiation of objects from outside the class. Static Instance: The class maintains a static member variable that holds the single instance of the class. Global Access: The class provides a static method to access the single instance, ensuring that all parts of the application can access the same instance. Implementing the Singleton Pa...

Chrome Tracing JSON format, also known as "Chrome trace events" or "Chrome performance trace," is a widely used data format for recording and analyzing performance data in web applications. It provides a detailed and structured representation of various events occurring during the execution of a web application, making it valuable for performance optimization and debugging. In this post, we'll delve into the Chrome Tracing JSON format, its structure, and how to interpret it with examples. JSON Format Overview Chrome Tracing JSON format consists of an array of trace events, where each event represents a specific point in time during the application's execution. Each event contains mandatory fields, such as "name" and "ts" (timestamp), along with optional fields like "args" (additional event-specific data) and "pid" (process ID). Example Chrome Tracing JSON: [ { "name" : "Event1" , ...

Introduction: When it comes to managing high-performance computing (HPC) workloads, different schedulers offer varying sets of commands and functionalities. In this tutorial, we will explore the command differences between four popular HPC schedulers: Slurm, LSF (Load Sharing Facility), Cobalt, and Flux. Understanding these differences will help users efficiently navigate and leverage the capabilities of each scheduler. Slurm Scheduler: a. Job Submission: Slurm: sbatch my_script.sh b. Job Status: Slurm: squeue -u username c. Job Cancellation: Slurm: scancel JOB_ID d. Job Details: Slurm: scontrol show job JOB_ID Advantages: Robust and widely used scheduler in HPC environments. Provides extensive features for job scheduling and resource allocation. Supports complex job dependencies and accounting mechanisms. Disadvantages: Command syntax might appear complex for beginners. Lack of native graphical interface may require CLI expertise for advanced usage. LS...

Design patterns are reusable solutions to common software design problems. They help improve code readability, maintainability, and scalability. In this tutorial, we'll cover 10 design patterns with Python code examples and discuss when to use them. 1. Singleton Pattern Use the Singleton pattern when you want only one instance of a class throughout the application. class Singleton : _instance = None @classmethod def get_instance (cls) : if not cls._instance: cls._instance = cls() return cls._instance 2. Factory Pattern Use the Factory pattern when you need to create objects without specifying the exact class. class Dog : def speak ( self ) : return "Woof!" class Cat : def speak ( self ) : return "Meow!" class AnimalFactory : def create_animal ( self , animal_type) : if animal_type == "dog" : return Dog() elif animal_type == ...

Introduction IBM Spectrum LSF (Load Sharing Facility) Scheduler offers a wide range of powerful features to efficiently manage high-performance computing workloads. The bsub command is a fundamental tool for submitting jobs to the scheduler, and it comes with various options to customize job requirements and behavior. In this blog, we will explore examples of all the essential options available with the bsub command, empowering you to make the most of IBM LSF's capabilities. Job Name ( -J ) The -J option allows you to specify a name for your job, providing a meaningful identifier in the queue. For instance: bsub - J my_job_name < my_script. sh Number of CPU Cores ( -n ) You can request a specific number of CPU cores for your job using the -n option. For example: bsub -n 8 < my_script.sh CPU Resource Requirements ( -R ) With the -R option, you can define complex resource requirements. For example, to request 2 CPU cores per host: bsub -R "span[ptil...

Introduction to IBM LSF Scheduler: IBM Spectrum LSF (Load Sharing Facility) is a powerful workload management and job scheduling system designed to optimize the utilization of computing resources in high-performance computing (HPC) environments. LSF provides a robust set of features that enable efficient resource allocation, job scheduling, and management, making it an essential tool for large-scale parallel computing. In this tutorial, we will explore the basics of using IBM LSF Scheduler, covering key concepts and command-line examples to help you get started with managing your HPC workload effectively. LSF Terminology: Host: A computing resource available in the cluster, which can be a physical machine or a virtual machine. Queue: A group of hosts sharing similar characteristics, such as hardware configuration or software environment. Job: A unit of work submitted to the scheduler, which consists of executable code and its resource requirements. Job ID: A unique identifie...

Introduction Welcome to this intermediate-level PyTorch tutorial, where we will explore how to leverage the power of Google Cloud TPUs (Tensor Processing Units) for accelerating PyTorch computations. TPUs are specialized hardware accelerators developed by Google, designed to speed up machine learning workloads. By utilizing TPUs with PyTorch, you can significantly enhance the training and inference performance of your deep learning models. Prerequisites Before diving into this tutorial, make sure you have the following prerequisites: Basic understanding of PyTorch and neural networks. Familiarity with Google Cloud Platform and setting up a GCP account. PyTorch and relevant dependencies installed in your development environment. Setting up Google Cloud TPU Sign in to your Google Cloud Console ( https://console.cloud.google.com/ ). Create a new project or select an existing one. In the left navigation pane, go to "Compute Engine" > "TPUs." Click on ...

Introduction TensorFlow, a leading deep learning library, offers great flexibility and performance for training machine learning models. To further enhance the training speed and efficiency, TensorFlow provides support for Tensor Processing Units (TPUs), specialized hardware accelerators developed by Google. In this blog post, we will explore how to run TensorFlow on TPUs with custom datasets, complete with code examples. Table of Contents: Introduction to Tensor Processing Units (TPUs) Setting Up TensorFlow with TPU Support Preparing the Custom Dataset Building TensorFlow Data Input Pipeline Constructing a Convolutional Neural Network (CNN) Model Training the Model on TPU Evaluating the Model Conclusion Introduction to Tensor Processing Units (TPUs) Tensor Processing Units are custom-developed AI accelerators by Google designed for neural network workloads. TPUs are specifically optimized for TensorFlow and can significantly speed up training times, making them an exce...

Introduction TensorFlow, an open-source deep learning library developed by Google, is widely used for building and training machine learning models. When working on real-world problems, you often need to use custom datasets tailored to your specific task. In this blog post, we will guide you through the process of running TensorFlow with a custom dataset, using a simple image classification example. Table of Contents: Understanding Custom Datasets in TensorFlow Preparing the Data Creating a TensorFlow Dataset Building a Convolutional Neural Network (CNN) Model Training the Model Evaluating the Model Conclusion Understanding Custom Datasets in TensorFlow Custom datasets in TensorFlow allow you to work with unique data formats and pre-processing steps essential for your machine learning task. TensorFlow provides a Dataset API that streamlines data loading, batching, and shuffling, making it efficient for training large models with large datasets. Preparing the Data For...