MindSpore @MindSporeOSS
Build a future-ready AI framework to create infinite possibilities over device, edge, and cloud scenarios. Join us to unleash the power of AI with MindSpore. mindspore.cn/en Joined December 2021-
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Unexpected failures can disrupt long-running model training. With #MindSpore, training process graceful exit proactively saves checkpoints, supporting efficient training continuation while reducing resource waste. ⬇️ Make your training more resilient: mindspore.cn/tutorials/en/r…
How can image restoration models handle unknown degradations? With #MindSpore, the Out-of-Sample Restoration (OSR) task uses model reprogramming to translate unknown degradations into recognizable patterns for generalized restoration. Discover the paper: arxiv.org/abs/2403.05886
#AI is no longer limited to text. Training MLLMs introduces new challenges. #MindSpore HyperParallel-Mpipe brings an algebra-driven approach to multimodal PP, organizing heterogeneous computations into efficient execution schedules. 🚀 Boost MLLM training: atomgit.com/mindspore/hype…
🧐 Worried about costly failures during model training? Don't start from scratch! Learn how #MindSpore's fault recovery uses checkpoints to save parameters periodically, allowing quick recovery & seamless continuation. ⬇️ Keep your training on track! mindspore.cn/tutorials/en/r…
In model training, try .𝐚𝐠𝐞𝐧𝐭/ for higher efficiency and less manual work. Here, it is not just configs — it's a structured #AI collaboration framework that streamlines development, from design to access control. ⬇️Discover more with #MindSpore gitcode.com/mindspore/hype…
🚀 Train #AI models across devices and organizations more efficiently with ALI-DPFL. Built on #MindSpore, it leverages adaptive local iterations to improve model performance — same privacy and communication constraints, better results. ⬇️Details here arxiv.org/pdf/2308.10457
When it comes to offline models and on-device inference, don't miss #MindSpore Lite. ➡️ Unified model IR (MindSpore / ONNX / TF) ➡️ Feature-rich, efficient runtime ➡️ High-performance CPU operator libraries Build and deploy #AI models with ease👇 mindspore.cn/tutorials/en/r…
⚡ Meet DVM — making dynamic compilation faster and lighter. With bytecode instead of machine code, DVM describes computations at runtime for efficient execution, now on #MindSpore. ☑️ Higher performance ☑️ Lower overhead Dive into the paper ↓ arxiv.org/abs/2603.24239
Tired of choosing between memory efficiency and training speed? 🤔 With #MindSpore #HyperParallel × Llama Factory, #FSDP is now faster, easier, and ready for large-scale training. Try it now 👇 github.com/hiyouga/llamaf… #opensource #AI #GrowWithMindSpore #LlamaFactory
Tell your autoregressive tracker where to look and how to describe with ARTrackV2 👀 A unified generative framework for target localization and appearance modeling — less complexity, more efficiency. Explore ARTrackV2 on #MindSpore 🚀 github.com/MIV-XJTU/ARTra… #MindSporeCaseStudy
What happens between pure noise and a generated image? This #MindSpore Case Study walks through the core ideas behind #DDPM diffusion models — from theory to implementation👇mindspore.cn/tutorials/en/r…
#MindSpore 2.9 is bringing something innovative to automatic operator fusion ⚡ Meet Graph-Free Fusion ⬇️ Fewer constraints, higher performance, and easier optimization Plus, to better support it, DVM is now fully open-sourced. Dive in 👉 mindspore.cn/version-update…
Curious about #MindSpore 2.9? What's new for developers: ⚡ Faster dynamic graph execution with Graph-Free Fusion 🧩 More flexible custom ops and execution control 🖥️ Better large-scale training stability 🧪 New MindSpore Science agent system Get started: mindspore.cn/en
👀 If you've been exploring quantum computing workflows... Scaling quantum simulation, exploring richer algorithms, and connecting to real hardware — #MindSpore Quantum 0.12.0 is here to make everything smoother. ⚛️ See what's new 👇 atomgit.com/mindspore/mind… #opensource
Behind every growing mind is someone who always believed in it. 💙 𝓗𝓪𝓹𝓹𝔂 𝓜𝓸𝓽𝓱𝓮𝓻'𝓼 𝓓𝓪𝔂 to all the amazing moms, from #MindSpore.
How can foundation models tackle multi-scale and multi-physics scientific computing? OmniArch comes with: ⚡ 1D-2D-3D joint pretraining ⚡ A novel PDE-Aligner ⚡ Strong in-context and zero-shot capabilities 👇Try it on #MindSpore openi.pcl.ac.cn/cty315/OmniArc… #MindSporeCaseStudy
🚀 A new chapter begins with #MindSpore 2.9 Not just an #AI framework upgrade — we're making model training faster ⚡ operator development easier 🛠️ and scientific workflows smarter 🧠 👉 Start exploring here mindspore.cn/install/en 📌 More feature deep dives coming soon!
Scientific busywork shouldn't drain your creativity. 🔬 #MindSpore Science introduces: ☑️𝐒𝐤𝐢𝐥𝐥𝐬 0.1: ready-to-use capabilities gitcode.com/mindspore-lab/… ☑️𝐀𝐠𝐞𝐧𝐭 preview: autonomous task orchestration gitcode.com/mindspore-lab/… 👉Towards agent-driven scientific workflows.
Struggling with Host-side lag or CPU chaos? 😵💫 See how far #MindSpore has come with CPU affinity. From scattered tricks → a complete runtime optimization system ⚙️ Built for real Host-bound workloads. Start optimizing👇 mindspore.cn/tutorials/en/m… More to come in MindSpore 2.9 ✨
From symmetric graphs to structural inclusion — the structure-inclusive similarity (SIS) improves GNN-based prediction of drug-target affinity. 💊 ➡️ More accurate, higher performance 👇Discover more and try it on #MindSpore academic.oup.com/bioinformatics… #MindSporeCaseStudy
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