Arthur Lacote’s Post

Most people think “PyTorch runs the model”. But PyTorch is just the top layer of a stack that ends at hardware-level math. What’s happening under the hood: 1) Your Python code You write: y = x @ w 2) PyTorch PyTorch mainly handles: 1️⃣ tensor metadata (shape/dtype) 2️⃣ picking the right kernel/backend 3️⃣ autograd, broadcasting, etc. 3) BLAS / LAPACK (C/Fortran libraries) This is where the real speed comes from. Matrix Multiplication and other ops often call optimized libraries (BLAS/LAPACK), built for performance. 4) Hardware (the real “acceleration”) Those libraries squeeze speed from: 1️⃣ CPU SIMD (one instruction processes multiple numbers) 2️⃣ Cache optimization (keep data in CPU cache; avoid slow RAM) 3️⃣ Careful memory access patterns Takeaway: performance isn’t just “Python vs C”. A lot of it is memory layout + SIMD + cache behavior - which is why the same model can run drastically faster/slower based on how tensors are arranged and what kernels are used. #PyTorch #DeepLearning #BLAS #SIMD #PerformanceEngineering #MLSystems

I’m excited to share my latest project — a Laptop Price Prediction System built using Machine Learning. This system predicts laptop prices based on specifications such as brand, processor, RAM, storage, GPU, display, and operating system, helping users estimate fair market value. 🔍 Key Highlights: • Data cleaning, preprocessing & feature engineering • Exploratory Data Analysis (EDA) for insights • Regression models for accurate price prediction • Model evaluation & performance optimization • User-friendly interface for real-time predictions 🛠 Tech Stack: Python | Pandas | NumPy | Scikit-learn | Matplotlib | Seaborn | Streamlit This project strengthened my understanding of regression algorithms, data analysis, feature engineering, and end-to-end ML deployment. I’d love to hear your feedback and suggestions! Feel free to connect or check out the project 🚀 https://lnkd.in/dz4857xY #MachineLearning #DataScience #PricePrediction #Python #Streamlit #MLProjects #LearningByBuilding

485: Fabric's new Forecasting Service pre-warms Spark clusters, offering near-instant starts. This eliminates the frustrating wait time often associated with Spark notebooks. By predicting demand with time series forecasting, Fabric keeps a small fleet of clusters ready. Now, testing and experimentation become seamless, just like using a Python notebook. This is a game-changer for productivity, especially for quick iterations and figuring things out on the fly. #Spark #Fabric #DataEngineering #DataScience #Productivity https://lnkd.in/eX_psnM9

Is the "Two-Language Problem" in AI finally solved? Today, the development world is reacting to the massive release of Mojo 1.0, the first stable version of the language that combines the usability of Python with the raw performance of C++. This release signifies that AI researchers no longer need to prototype in Python and rewrite in C++ for deployment. Here is the breakdown of this developer milestone: 🔥 The Speed Leap: The headline metric is staggering. Benchmark tests released with v1.0 show Mojo executing numeric algorithms up to 68,000x faster than Python 3.14. It achieves this by leveraging MLIR (Multi-Level Intermediate Representation) to unlock the full power of modern hardware. 💻 The Compatibility: Crucially, Mojo 1.0 is a superset of Python. This means you can import any existing Python library (NumPy, Pandas, PyTorch) and it just works, but you can progressively optimize the slow parts into high-performance system code. ⚡ The Hardware Shift: Along with the language, Modular announced native support for NVIDIA's new Rubin and AMD's MI400 chips, effectively giving developers "metal-level" control over AI accelerators without learning CUDA. Scan QR Code or Click Here 👇 to read the official 1.0 changelog : https://lnkd.in/dhWmrrVj #Mojo #Python #Programming #ArtificialIntelligence #SoftwareEngineering #DevNews #ModularAI #TechNews #Coding #LLM #Performance

🚗💸 Can Machine Learning predict car prices? Yes 😄 Built a Car Price Prediction system using Machine Learning + Flask that predicts car prices based on real-world data. What I learned: ✔ Cleaning messy datasets ✔ Feature engineering ✔ Training & saving ML models ✔ Deploying models using Flask This project pushed me to understand how ML actually works beyond notebooks 📊➡️🌐 On to the next challenge! 🚀 #ML #Python #DataScienceJourney #Flask #Projects

Day 343: Breaking the Speed Limit (Multiprocessing) ⚙️ Unleashing the CPU Threading is great for waiting, but what if you need to do heavy math? Image processing? Machine Learning? That's where multiprocessing comes in. Unlike threading, this creates entirely separate memory spaces and bypasses the GIL, allowing you to use all the cores on your CPU. 👉 Roasting your CPU: from multiprocessing import Pool def heavy_calculation(n): return n * n * n if __name__ == '__main__': # Utilize 4 cores at once with Pool(4) as p: print(p.map(heavy_calculation, [1, 2, 3, 4, 5])) 💡 Pro Tip: If your fan isn't spinning, you aren't using Multiprocessing yet! Use this for CPU-bound tasks. #DataScience #Python #ParallelComputing #BigData

I was recently working on industry DL project focused on vector search face identification in a group photo. The goal is to build a system that isn't just accurate, but also production-ready. 72 students just less then three Seconds . If you are keen you see the result ,You can directly jump at 4:07 min The Technical Stack: 🔹 Detection: YOLOv8-face for high-precision bounding box detection in crowded group photos. 🔹 Embedding Engine: ArcFace (Glint360k pretrained) running on ONNX Runtime for optimized inference. 🔹 Vector Database: FAISS (IndexFlatIP) for efficient L2-normalized Inner Product similarity matching. 🔹 Frontend: Streamlit for a seamless user registration experience. 🔹 Database: MongoDB for storing student metadata and high-dimensional embeddings. Challenges Overcome: Implementing Batch Inference for group photos to reduce GPU latency. Normalizing vectors to use Cosine Similarity effectively within a FAISS index. Ensuring the system handles "Unknown" faces gracefully with a fine-tuned confidence threshold. Excited to keep pushing the boundaries of what’s possible with Computer Vision! #Python #ONNX #FAISS #MongoDB #YOLOv8 #FacialRecognition #OpenCV #MachineLearningEngineering

Building the Future of GPU-Accelerated Machine Learning: Introducing AuroraML 🚀 The data science stack is evolving rapidly. While scikit-learn remains the gold standard for API design, the industry is shifting toward more efficient data structures and hardware-accelerated workflows. To bridge this gap, I am excited to announce AuroraML. AuroraML is a machine learning library designed to provide a familiar scikit-learn experience with native GPU acceleration and a focus on the modern data stack. Right, because the world needed Yet Another Machine Learning Library? Fair enough. CUML is that GPU native alternative, or so I thought. Here's where I needed a little boost. 1. Polars-Native: While many GPU libraries are still optimized for Pandas, AuroraML is built to work seamlessly with Polars. By leveraging the Apache Arrow memory format, we can minimize overhead and maximize throughput. 2. CUDA-Powered: Our current C++ implementation is already showing impressive results, maintaining feature parity with core scikit-learn estimators while unlocking the massive parallel processing power of NVIDIA GPUs. However, is C++ the best way to proceed? For academic purposes, it was sufficient but we have a much bigger goal in mind. 3. The Move to Rust: To ensure the highest levels of memory safety, performance, and lightweight deployment, we are beginning the journey of porting the core engine from C++ to Rust. A Call for Collaborators 🦀 I am passionate about this transition, but I am also transparent about the fact that I am still early in my Rust journey. I am looking for Rust enthusiasts, CUDA experts, and open-source contributors who want to help shape a high-performance ML library from the ground up. If you are interested in Polars, high-performance computing, or building the next generation of ML tools, I would love to have you involved. Check out the POC C++ repository here: https://lnkd.in/g7vU-Xe4 #MachineLearning #DataScience #RustLang #Polars #CUDA #GPUComputing #OpenSource #AuroraML #CPP #DataEngineering

🚨 STOP WASTING HOURS IN PANDAS 🐼💥 Your code feels “professional”… but it will never scale. If you’re still defaulting to Pandas for everything, you’re not being efficient—you’re being lazy with performance. Here’s the correct way to work with data: 🔹 1. Switch to Polars → Faster, lower memory, built for scale 🔹 2. Use Lazy Evaluation → Don’t compute until you must 🔹 3. Filter Early → Cut rows before heavy operations 🔹 4. Select Only What You Need → Fewer columns = faster pipelines 🔹 5. Run in Parallel → Use all your CPU, not just one core 🔹 6. Handle Data Bigger Than RAM → Polars/Dask > Pandas for real workloads ⚠️ If your dataset barely fits in memory… Pandas is already the wrong tool. You don’t need “more hardware.” You need a better workflow. #datascience #python #pandamaster #polaris #BigData #MachineLearning

485: Fabric's new Forecasting Service offers near-instant Spark notebook starts. Starting a Spark cluster typically takes minutes, slowing down testing and wasting compute. This service keeps a pre-warmed fleet ready, creating new clusters as needed. Time-series forecasting predicts demand, optimizing resource use. This allows users to treat Spark engines like Python notebooks—run, test, and iterate quickly. This is especially useful for quick tests or figuring things out without waiting. #Fabric #Spark #DataEngineering #CloudComputing #BigData https://lnkd.in/eM5uPEpK