Introducing Lightning AI Studios - A persistent GPU cloud environment. Setup once. Ready any time.
Code online. Code from your local IDE. Prototype. Train. Serve. Multi-node. All from the same place.
No credit card. 6 Free GPU hours/month.
https://t.co/fa3S0wOkKu
A robust, thoroughly tested collection of 100+ metrics with distributed synchronization for large-scale training and extensive plotting support: TorchMetrics 1.0.0 is live. 🚀
Learn more 👉 https://t.co/XwiTeD5KcD
#PyTorch#MachineLearning#DataScience
🤔 Can you guess the secret location of our meetup with @stabilityAI in NYC? 🤫
Register now to find out! 👉👉https://t.co/387bMxJ1ud
📅May 19th | 6 PM
📍NYC | Secret Location
🚀Show off your AI demos, be inspired and join the movement to democratize AI!
#KeepAIOpenSource #NYCevents
Calling the AI community of NYC 🗽
Come celebrate the power of open-source AI in our meetup with @StabilityAI!
📅May 19th | 6 PM
📍NYC | Secret Location
🚀Show off your AI demos, be inspired and join the movement to democratize AI!
RSVP here 👉https://t.co/387bMxJ1ud
#AI #OpenSource #NYC
With the help of our community, we are excited to announce PyTorch Lightning 2.0🎉🎉
Install now 👉https://t.co/Y4rxJpRkDG
Highlights Include:
⚡️ Commitment to backward compatibility in the 2.0 series
⚡️Simplified abstraction layers, removed legacy functionality, integrations out of the main repo. This improves the project's readability and debugging experience.
⚡️Introducing Fabric! Scale any PyTorch model with just a few lines of code.
#PyTorch #PyTorchLightning
@Luk8YrselfIshan@LightningAI Full training time depends on number and type of nodes as well as chosen model and dataset. We are currently working on cutting these times though!
@muchomuchacho@LightningAI Since we're just using deepspeed for this example, you can consolidate with the python file created with the checkpoint (https://t.co/0W41DK50rn)
@muchomuchacho@LightningAI We don't do so on purpose. If you need to do inference in a sharded way, you need to load the checkpoint when the model is sharded already or you might run into OOM issues (same with fine-tuning). (1/2)
@NotMiles_@LightningAI Not yet, but if you want a vision transformer, you should be able to just use the same techniques and plugin your vision model.
Vision examples are noted down for the future!
@NotMiles_@LightningAI Also checkout https://t.co/2puBFSPcEt our minimal wrapper around minGPT and nanoGPT (using their Code without any changes!). You have support for sharding models with offloading via DeepSpeed or torch native Fully Sharded Data Parallel (FSDP)
⚡ Lightning allows you to finetune LLMs with billions of parameters!
Train your own text classifier with 3B parameters on the cloud with just a few clicks - no need to worry about infrastructure management.
Try it out: https://t.co/BodTeiyxyI
With just a few days of work, you too can build an intelligent app that is:
📈 Scalable
💪 Performant & distributed
🎨 Fully customizable
Learn how 👉👉👉 https://t.co/nxr1E8Z8IQ
#Diffusion#AI#ML#BuildWithLightning
@DorGoldenberg@rasbt@yaroslavvb you would have to sample it on rank0 and then broadcast it manually. This would probably be the boilerplate @chaton_thomas meant and lightning has primitives for that.
Excited to share the official torch metrics paper was published in JOSS, nice job @PyTorchLightnin team!
Torch metrics is a package for reproducible metrics optimized for scale even with the most complex distributed @PyTorch models