MindSpore

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 @MindSporeOSS]

Teach your model to see beyond pixels. 👀
With #MindSpore, put Vision Transformer (ViT) to work and see how it turns image patches into meaningful predictions for image classification.
See how it all comes together 👇
https://t.co/RCvr5S5ra6
#opensource #AI

[MindSpore @MindSporeOSS]

Meet #MindSpore 2.11 — less time adapting models, more time optimizing training!
Curious about what's new?
Get hands-on with MindSpore 2.11 👇
https://t.co/B0Ey6u6v7A
Or stay tuned for more on the new features!

[MindSpore @MindSporeOSS]

How can #LLM training combine the flexibility of dynamic graphs with the performance of static graph compilation?
#MindSpore Transformer PyNative + HyperParallel bring the best of both worlds, simplifying high-performance parallel training.
Try it out 👇
https://t.co/2YkubXtHIW

[MindSpore @MindSporeOSS]

For image classification with ResNet-50, why start from scratch when you can leverage a pre-trained model?
Load, adapt, train—with #MindSpore, transfer learning gets you a head start.
See it in action 👇
https://t.co/Qyf8iaOaml
#opensource #AI

[MindSpore @MindSporeOSS]

Exploring computer vision with #MindSpore? Start with a ResNet-50 image classification example — learn to build a complete workflow using CIFAR-10, from dataset preparation and model training to inference.
Dive in 👇
https://t.co/yptFIMxOGn
More CV examples coming soon!

[MindSpore @MindSporeOSS]

Failures happen during large-scale training, but recovery doesn't have to be slow. Faster writes, less storage overhead, smarter reads, and flexible resharding—#MindSpore HyperParallel DCP redesigns checkpointing for efficient recovery.
Learn more 👇
https://t.co/9RvtHPX1Au

[MindSpore @MindSporeOSS]

Hugging Face gives you the models. #MindSpore HyperParallel helps you scale training.
Instead of rebuilding your workflow, HyperParallel handles the optimization and parallelization, keeping you right in the Hugging Face ecosystem.
Learn how 👇
https://t.co/aoQShvQMcn

[MindSpore @MindSporeOSS]

As #DeepSeek-V4.1-Flash arrives, #MindSpore HyperParallel is ready for it. Powered by Model Adapter and multi-dimensional parallelism, HyperParallel now accelerates model adaptation and supports scalable training for advanced #AI workloads.
⬇️ Get started:
https://t.co/9RvtHPX1Au

[MindSpore @MindSporeOSS]

🔄 Migrating models to #MindSpore?
Then get started by preparing these...👇
Also, in case you're interested in the differences between MindSpore and PyTorch in terms of dataset packing, model building, and training workflows — explore the full tutorial:
https://t.co/M56DNDpbqc

[MindSpore @MindSporeOSS]

Deploying VLA models for embodied AI comes with challenges. With smarter execution, memory reuse, and graph optimization, #MindSpore Lite enables OpenPi π0.5 deployment and boosts inference — helping with resource-constrained scenarios.
Explore now👇
https://t.co/lXTOpUnbf4

[MindSpore @MindSporeOSS]

How can #AI models handle unseen scenarios?
Prompt-driven dynamic object-centric learning tackles domain shifts with dynamic feature selection and transferable representations, helping models adapt across diverse environments.
Try it with #MindSpore:
https://t.co/FrgIAzH0yU

[MindSpore @MindSporeOSS]

AI development starts with the right hardware and software. This time, try developing with #OrangePi AIpro — featuring pre-installed OS images and #MindSpore for a streamlined development experience.
Discover everything you need here:
https://t.co/I1BM2fd02S
#MindSporeTutorials

[MindSpore @MindSporeOSS]

📢Introducing #MindSpore 2.10 — unlocking new possibilities through hardware-software co-optimization:
🔹 MindSpore HyperParallel
🔹 MindSpore AKG
🔹 Framework evolution
🔹 Enhanced MindSpore Lite
⬇️Discover what's new in the full release note:
https://t.co/sOfc2xVmZR
#opensource

[MindSpore @MindSporeOSS]

DeepFake analysis goes beyond detecting fake or real.
#MindSpore supports the DFGC-VRA first-place solution, enabling visual realism assessment to evaluate the perceived authenticity of DeepFake videos and advance AIGC quality evaluation.
🔗Start here:
https://t.co/4kzaKclaT5

[MindSpore @MindSporeOSS]

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:
https://t.co/uVMQSw8LGo

[MindSpore @MindSporeOSS]

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:
https://t.co/vooWhNPlsf

[MindSpore @MindSporeOSS]

#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:
https://t.co/1mZrkj1fQ7

[MindSpore @MindSporeOSS]

🧐 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! https://t.co/ocISCVrBQU

[MindSpore @MindSporeOSS]

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
https://t.co/ksunarboUU

[MindSpore @MindSporeOSS]

🚀 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
https://t.co/m53GISdKGU