Let’s break down the two concept and how to implement it. The Difference: Data Parallelism vs. Model Parallelism Think of it like a factory: PyTorch Underutilizing GPUs? Unraveling the Mystery of nn.Parallel and Single-GPU Usage… Data Parallelism & Model Parallelism
Harnessing the power of Google’s state-of-the-art generative AI models, Imagen 4 for image creation and Google Veo for video production, is now more accessible than ever through the Vertex AI platform. By leveraging the Vertex… Generate Images and Videos at Low Cost with Vertex AI
The robots.txt file is a simple text file that lives in your website’s root directory and tells search engine crawlers which parts of your site they can or cannot access. It’s like a set of… What’s Robots.txt for WordPress websites and potential issues
In Google Colab, cd changes the directory only within the current cell. However, Colab resets the working directory when a new cell runs. To make sure the directory change is persistent, try the following: Method… How to change working directory in Colab
Error detected: The error suggests that the package name isn’t correctly formatted. Try using double quotes or removing the quotes altogether: or If the issue persists, you might want to check if your pip version… Pip package installation specifications and Error fixed: pip install -U ‘accelerate>=0.26.0’ ERROR: Invalid requirement: “‘accelerate”: Expected package name at the start of dependency specifier ‘accelerate
Google Vertex AI is a comprehensive AI platform by Google Cloud aimed at streamlining the development of AI models. It unifies various machine learning services, offering features like foundation models access, a web-based Vertex AI… Google Vertex AI for Streamlining AI Development and model fine – tuning
Monitoring GPU usage within an SSH-accessed Slurm environment requires a combination of Slurm commands and GPU monitoring utilities, which are essential for optimizing performance and resource management. These tools not only help in tracking GPU… Effective GPU Monitoring in Slurm Environments
Setting things up for the first time may not be an easy task. If you are unlucky, you may encounter this error So, let’s set up AWS CLI to generate Session Token for the first… How to use AWS CLI to generate SESSION_TOKEN
GitHub’s fine-grained personal access tokens allow precise control over repository access. Here’s a step-by-step guide to generate and manage a token for a specific repository: 1. Access Developer Settings 2. Create a Fine-Grained Personal Access… Step-by-Step Guide to Clone and Write repos with GitHub Personal Access Tokens
To determine the number of GPUs your account can access in a SLURM-managed cluster, follow these steps: Check Account and Partition Access: Use the command sacctmgr show associations to view your account’s associations with partitions.… How to Check GPU Access in SLURM Clusters
OSError: You are trying to access a gated repo. Make sure to have access to it at https://huggingface.co/google/gemma-3-27b-it. 401 Client Error. (Request ID: Root=1-67da7d97-6326b5f53a96415516d2c709;7a741876-1aae-4afd-8d26-afd122bc2c2d) Cannot access gated repo for url https://huggingface.co/google/gemma-3-27b-it/resolve/main/config.json. Access to model google/gemma-3-27b-it… Fixed: OSError: You are trying to access a gated repo. Make sure to have access to it at https://huggingface.co….
How to run – basic To run a .sh file with a specific environment using SLURM, you can follow these steps: Here’s an example of a SLURM script (job.sh) that demonstrates these steps: It’s important… SSH and SLURM basic and how to send a notification email upon job completion
If you’ve submitted a job but don’t see it in the SLURM queue, there could be several reasons for this. Let’s troubleshoot: Ensure you received this confirmation. If not, your job may not have been… submitted a job but don’t see it in the SLURM queue
Google offers two libraries for generative AI: google-generativeai for complex, direct work with Gemini models, and google-genai for simplified access and easier integration in applications.
This tutorial explains creating an In-App Update feature for apps, utilizing Google Play’s AppUpdateManager to check for updates, and initiating updates if available upon app launch.
Backing up WordPress data with Jetpack is a straightforward process, as Jetpack includes a feature called Jetpack VaultPress Backup. This requires a Jetpack subscription. Here’s a step-by-step guide: Prerequisites Steps to Backup WordPress Data with… Effortless WordPress Backup and download all Files with Jetpack VaultPress
Create stunning scientific infographics, graphical abstracts, and educational visuals with Mind the Graph. Explore thousands of customizable illustrations and templates tailored for researchers, educators, and scientists.
Debugging code generated by ChatGPT by pasting the error into ChatGPT for it to resolve its own problem, most of the time, is a very bad choice for me. So, most of the time, I… How to debug codes generated by ChatGPT
This content explains how to link a Google Form to a Google Sheet and submit user email data using a C# script in Unity.
Sometimes, even though you already installed torchtext, but can’t import it, or when you import it, you receive this error: and then if you try to verify if the installation was successful by using this… Fixed: TorchText install issue: The procedure entry point?? could not be located in the dynamic link library
Leonardo AI is an innovative tool for generating images through text prompts. Leveraging advanced algorithms, it offers features like image editing, upscaling, and community sharing. Users can create unique visuals effortlessly for various projects.
In this post, I will illustrate how to use Leonardo AI to animate an image effectively and creatively. This powerful tool allows users to bring static visuals to life, providing an engaging experience for viewers.
In NumPy, you can perform element-wise operations on matrices using vectorized operations, numpy.vectorize for custom functions, and numpy.apply_along_axis for applying functions along specific axes.
The content outlines various PySpark functions used for data manipulation in DataFrames. Key functions include filtering with where(), limiting rows with limit(), returning distinct rows, dropping columns, and grouping by criteria. Each function includes a brief example, illustrating how to access, modify, and aggregate data effectively within PySpark.
This song and example code help remember PySpark data frame creation functions easier. Key functions include creating Data Frames, displaying data, printing schemas, and filtering. The document facilitates understanding how to manipulate data effectively in PySpark, making it a useful reference for users working with large datasets.
This song and code examples help us understand and remember various Pandas functions for data manipulation, including grouping, aggregating, and transforming data. Key functions include groupby(), pivot_table(), resample(), rolling(), expanding(), cumsum(), cumprod(), cut(), qcut(), aggregate(), and transform().
A cute, catchy song on various Pandas functions applied to DataFrames. Key functions include sorting values, resetting the index, dropping columns and duplicates, sampling data, and handling missing values. Example codes illustrate each function’s output, demonstrating how to manipulate and visualize data effectively with Pandas.
The provided content showcases a series of PyTorch functions with descriptions and examples. Functions like torch.abs, torch.ceil, torch.floor, torch.clamp, torch.std, torch.prod, and torch.unique are explained with their respective use cases. These functions are fundamental for manipulating tensors in PyTorch.
The provided content showcases common linear algebra operations in PyTorch, including determinant calculation, matrix inverse, LU decomposition, QR decomposition, Cholesky decomposition, SVD, eigenvalue and eigenvector computation, matrix and vector norms, trace calculation, solving linear systems, and other operations with code and output examples.
Tensor Creation: Example: Here are examples for each of the basic tensor creation functions in PyTorch: Output: Output: Output: Output: Output: Output: Output: Output: Output:
Autograd: Random Number Generation: Loss Functions: Optimization: Examples for Autograd, Random Number Generation, Loss Functions, and Optimization in PyTorch: Autograd Output: Output: Output: Output: Random Number Generation Output: Loss Functions Output: Output: Optimization This function… PyTorch function song & examples: Autograd, Random Number Generation, Loss Functions, optimization
The provided content discusses tensor reshaping and tensor type and device management in PyTorch. It covers functions such as tensor.view(), tensor.reshape(), tensor.transpose(), tensor.squeeze(), tensor.unsqueeze(), tensor.to(), tensor.type(), tensor.is_cuda, tensor.cpu(), and tensor.cuda(). Demonstrated examples showcase effective memory management and computation, especially when utilizing GPUs.