Both CPUs and GPUs have a play in the world of LLMs. GPUs are the choice for training large models and performing fast inference on high-dimensional data, while CPUs can be more cost-effective and sufficient for smaller-scale or lightweight models. Ultimately, the right choice depends on your specific use case, budget, and performance requirements. As ..read more

Scenario 1 You created a new feature branch from the master branch and continued to add new code to that branch for the feature At the same time, new functionalities or changes were made in the master branch — commits c2 and c3. Now you want those changes to be included in the feature branch as well. You can do this with either git ..read more

Choosing between COPY and ADD in your Dockerfile is not just a matter of syntax; it’s about selecting the right tool for the job. For most scenarios, COPY is the recommended choice due to its simplicity and security. Use ADD when you need to leverage its advanced features like auto-extraction of archives or downloading files ..read more

I recently stumbled upon this question: How do you swap two numbers without using a temporary variable? This took me back to my college days when I memorized this program without fully understanding the logic. Later on, I learned how it works, and today I met someone with the same question, so I thought of ..read more

Running docker stats without any arguments gives you a live stream of resource consumption data for all your running containers. This includes: CPU Usage: See the percentage of your host machine’s CPU each container is consuming. Memory Usage: Monitor how much memory each container is using, both as an absolute value and a percentage of the allocated memory limit. Network I/O: Track incoming and outgoing network traffic for each container. Block I/O: Monitor disk reads and writes for each container. PIDs: Get a quick glimpse of how many processes each container is running. If you want to s ..read more

Temperature is a setting in LLM model, which determines the randomness of the model’s output, determining whether the output is more random or more predictable. Lower value of temperature means, the output of the model will be more deterministic or repetitive Lowe temperature value makes the model focus on the most likely and predictable continuations. This results in factual and reliable outputs, but may lack originality. Higher temperature value typically makes the output more diverse and creative but might also increase its likelihood of straying from the context. Default temperature se ..read more

Here’s the breakdown of the key differences between OpenAI and Azure OpenAI: OpenAI is an independent research organization focused on developing and deploying artificial intelligence Azure OpenAI is a collaboration between Microsoft and OpenAI, integrating OpenAI’s models into Microsoft’s Azure cloud platform. OpenAI models can be accessed through its APIs. This open approach makes it a good choice for individual developers and researchers who want flexibility and control over their projects. Azure OpenAI access, on the other hand, is achieved through Microsoft’s Azure cloud platform. Thi ..read more

Thrilled and honored to share that our blog has made it to the list of top Kubernetes blogs, securing the 33rd position on Feedspot’s curated selection. This achievement is a testament to the vibrant community we’ve built and the shared enthusiasm for all things Kubernetes. https://blog.feedspot.com/kubernetes_blogs/ Kubernetes, being at the forefront of container orchestration and cloud-native technologies, has been a focal point of our blog. I extend my heartfelt thanks to each and every one of you who has been a part of this journey. Your feedback, comments, and shares have fueled the pas ..read more

Training a model means, teaching it to perform a specific task or make predictions by learning from examples. The goal of training is to adjust the models weights and biases in a way that minimizes the error between the model’s predictions and the actual outputs for the training data. The model is typically built using a deep learning architecture, such as a recurrent neural network (RNN) or transformer. During training, the model is exposed to a large dataset and their corresponding functionalities. For instance, if the task of the model to generate java code for a function that calculates ..read more

Large Language Models (LLM) : A computer program designed to understand and generate human-like text. They are trained on massive amounts of text data, which allows them to learn the patterns and rules of human language. These models are designed to process and comprehend natural language, making them capable of tasks such as text generation, language translation, summarization, and answering questions. It is the Brain of Language Processing Temperature : Temperature is a parameter that controls the randomness of the model’s output. Think of temperature as a control knob that adjusts how cre ..read more