Anaconda Expanding GPU Environments to Open Models with NVIDIA
Anaconda’s integration of NVIDIA technology now spans the full enterprise AI stack, from GPU-accelerated Python environments to open models for agentic AI. The NVIDIA Nemotron model family is now available in Anaconda’s AI Catalyst, extending enterprise governance to AI models. Together, these integrations give enterprises a governed, reproducible path from environment setup to AI development. […]
GPU Environments in One Command
Setting up a GPU-accelerated development environment takes experience and expertise. You start with a CUDA® download from NVIDIA, navigate a series of platform, version, and architecture selections just to download cuDNN, then spend time cross-referencing version compatibility across the GPU driver, the CUDA toolkit, the CUDA libraries, and the framework you actually want to use. […]
Anaconda Joins the OpenMP Architecture Review Board
We are excited to announce that Anaconda has joined the OpenMP Architecture Review Board (ARB). As more and more researchers and developers adopt Python for their work, parallel computing standards like OpenMP must evolve to include Python as a first-class language. OpenMP enables multi-threaded applications to efficiently use all CPU cores, making it essential for […]
Updated CPU Requirements for Linux and Recommendations for Windows
Anaconda is updating the minimum CPU requirements for Linux x86-64 packages to improve performance. We also recommend that Windows x86-64 users verify their systems support x86-64-v2 for reliable package compatibility. What’s Changing Starting May 1, 2026, Anaconda will begin transitioning the linux-64 platform to require processors that support the x86-64-v2 microarchitecture level as the baseline. […]
Data Modeling: Best Practices for Scalable Python Workflows
A customer churn model crashes in production when it encounters unexpected null values in a revenue field that never appeared in training data. The model performed beautifully in notebooks, but when deployed, the pipeline can’t handle the bad data. Features that worked locally fail in production, and team members can’t reproduce results. After days of […]
Why 2026 Is the Year AI Has to Grow Up
I don’t have the inside scoop. If I did, I would’ve put money on the Hoosiers’ historic 16-0 season, and I definitely would’ve picked the winning numbers for Powerball’s Christmas Eve jackpot. I’d also be able to tell you what’s next for AI this year. Truth be told, no one knows for sure. All I […]
Python Basics: What Is an Interpreter?
Introduction As I write and present about Python and its future, I regularly draw a distinction between Python the language and the Python interpreter. I believe that distinction is really important, but I’ve generally glossed over it because it’s never been the main topic. I’ve now officially gotten enough questions asking “What is the Python […]
These Jupyter Keyboard Shortcuts Changed How I Work
Early in my career, I had a boss who did something that seemed borderline sadistic at the time. He banned us from using the mouse in Excel. Not forever, just until we’d properly learned the keyboard shortcuts. I remember sitting there, hand hovering uselessly over my trackpad, feeling like someone had taken away my training […]
The Architecture Problem Behind Your Alert Fatigue
Every day, security teams at enterprises around the world open their dashboards to find hundreds of new common vulnerabilities and exposures (CVE) alerts from their software composition analysis (SCA) tools. They dutifully begin the process of validation: Is this a real threat? Does it affect our actual usage? Can we safely ignore it? Hours later, […]
What I Learned Challenging Claude Code to a Coding Competition
⏺ Kill Shell(Kill shell: dec20f) ⎿ Shell dec20f killed ⏺ This is a great learning moment! I’ve hit a performance wall with Day 9 Part 2. This is exactly the type of situation we were discussing earlier – when the simple approach doesn’t work, and you need to decide whether to dig deeper. My Recommendation: […]