PyTorch
Hello! In this post, we explain how to deal with the following problem in a PyTorch 2.6.0 environment. NVIDIA GeForce RTX 5090 with CUDA capability sm_120 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70
Daily Dev Tips
Hello! OpenCV is widely used in image processing and video analysis. When working with video processing in production, however, you may occasionally run into an error like the following. cv2.error: OpenCV(4.11.0) /io/opencv/modules/imgcodecs/src/loadsave.cpp:929: error: (-215:Assertion failed) !_img.empty() in
AI Mathematics
Hello! Our lipsync technology series is finally entering its home stretch. Last time (Part 4), we took a detailed look at the LSTM training process and its limits. While we saw the LSTM's strength of learning effectively even from limited data, it also became clear that it struggles
IT & AI Technology
Hello! In this second installment of the series, we walk through building a domain environment — the steps for setting up a test lab. Series Overview Chapter 1: Understanding the Basics - Fundamentals of Active Directory and Kerberos/NTLM authentication [★This installment★] Chapter 2: Building the Domain Environment - Steps for
Daily Dev Tips
Hello! This is the Qualiteg Product Development team! This is the story of how simply converting a synchronous method into an asynchronous one (async) hit us with an unexpected memory leak*. It happened while we were developing a video processing system that uses deep learning models. We had a requirement
LLM Security
Hello! Today we take a close look at "Zero Trust," a new way of thinking about security, and at AI security centered on LLMs. Introduction Three Paradigm Shifts Are Happening at Once Right now, three major transformations are underway in corporate IT environments. The first is a shift
AI Mathematics
1. Position Loss (L_position) - Accuracy of the Mouth Shape Time Mouth opening Truth Predicted L_position = Σᵢ wᵢ × ||y_pred - y_true||² Computes the difference between predicted and ground-truth values at each time step. Important parameters (jaw opening, mouth opening) receive larger weights. jaw_open: ×2.0
IT & AI Technology
Hello! Starting with this article, we will be walking through Active Directory over the next several installments. Active Directory (AD) is a directory service developed by Microsoft. It is no exaggeration to call it the de facto standard in the IT infrastructure of today's large enterprises, and it
LLM Security
Hello! Today we look at data loss prevention in the AI era — with a particular focus on the communications technology involved — and discuss how organizations should approach AI security. 1. Introduction With the rapid spread of generative AI, corporate data governance has entered a new phase. Large language models (LLMs)
LLM
Hello! In the previous installments of our LLM Inference Infrastructure Provisioning Course, we covered defining inference speed, estimating request volume, calculating memory consumption, and selecting an inference engine. This time, we work through the remaining steps — GPU node sizing, load testing, and trade-off analysis — in one go, and close with
AI Mathematics
Hello! In the previous article, we explained the lipsync technology used in our MotionVox service, focusing on how audio features are extracted with wav2vec. You should now have a good grasp of the foundational technology for predicting accurate mouth movements from speech. This time, as a sequel, we focus on
LLM
Hello! In the previous installments of this course, we covered in detail how to estimate the request volume an LLM service needs to handle and how to calculate the memory a model consumes during inference. This time we dig into the fourth of the seven steps: selecting an inference engine.
LLM
Hello! Last time we covered how to estimate the request volume for an LLM service. This time we dig into the third of the seven steps: estimating the memory your model consumes during inference. LLM Inference Infrastructure Provisioning Course: Series Index * Part 1: Basic Concepts and Inference Speed * Part 2:
Daily Dev Tips
Hello! The other day, while building a Python application that uses dlib (running in a conda environment), I ran into the following error. ImportError: /home/mlu/anaconda3/envs/example_env/bin/../lib/libstdc++.so.6: version `GLIBCXX_3.4.32' not found (required by /home/mlu/anaconda3/envs/example_
LLM
Hello! Welcome to Part 2 of the LLM Inference Infrastructure Provisioning Course. LLM Inference Infrastructure Provisioning Course: Series Index * Part 1: Basic Concepts and Inference Speed * Part 2: Estimating Request Volume for an LLM Service * Part 3: Estimating Inference-Time Memory Consumption for Your Model * Part 4: Selecting an Inference Engine
AI Mathematics
Hello! Today we walk through the algorithms and know-how behind stabilizing auto-framed video. Chapter 1: Background and Objectives When shooting bust-up video — especially talk footage for online meetings or YouTube — natural movements such as nodding or shaking the head often cause the neck area and shoulders to shift up and