They are billions trainer for v0.9.21/18/2024 Click the floating blue colored button in the lower right corner.Ĥ. Inside the web app, click on a stage you like. Make sure it's the stage you want to upload in the dialog that appears.ġ. Inside the web app, click on the floating pink button in the lower right corner.ĥ. Copy the binary file (which is named a la stage_XXXX.bin, where XXXX could be any number) to a safe place if you want a backup.Ĥ. If you don't see this folder, make sure you had at least one Stage made in the Stage Editor inside the game, before exporting.ģ. After exporting, you should find in the path of your save in your micro SD a folder named stage. Using a save data manager tool on your Switch (I recommend Checkpoint), export your SSBU save files.Ģ. Just upload stage binary files and get their name, date of creation, name of maker and image.Fixed some visual glitches when stages had long names. I use Firebase for saving the data of the stages. The web app was developed used the Polymer framework, which allows a faster way to make a Material Design themed app. This way, people with banned switches or no access to online can play stages shared by other people. And AMD's claims that HBM meant you could just swap everything around and not have to worry about framebuffer size were never true, the PCIe bus itself is not fast enough for that.I made a web app for sharing binary files of SSBU stages for importing them with a save tool such as Checkpoint. But it wasn't what AMD trumpeted it as, as the LTT video describes, it was a very specific reaction to the question of 'workstation GPUs are using a lot of memory, HBM can't be scaled as high, how do we put more memory on a Fiji/Vega GPU for workstation users". To be absolutely fair, Fiji/Vega is a good design for that since it doesn't have a bunch of memory packages around it. The SSG is just a "combo GPU+SSD card" in the same way QNAP makes those "combo network+SSD cards", but with a lot of fanfare/marketing around it. So technically yes you can map those SSDs in "as GPU memory" via GPUDirect RDMA block storage (or DirectStorage), but you can do that with a regular NVMe SSD in an adapter card too. That's why there's the whole thing about "resizable BAR" etc - that's the aperture window in CPU memory that gets mapped in from the GPU memory. Now technically - it all depends on what you mean by "as GPU memory" because PCIe is all RDMA anyway, even CPU-to-GPU is a RDMA operation. But it's not going to be presented as GPU memory ever, it's going to work like a block storage device you can do RDMA requests against, most likely. In principle they could be used with an API like DirectStorage RDMA or CUDA GPUDirect RDMA (which dates back to Kepler) and in this case they would never need to talk to the CPU, given appropriate software support. PCIe switch with NVMe drives and the GPU behind it, they actually just are presented straight to windows even. For LLMs, we see a lot of interest in Ray + Jax or Ray + TPUs relative to what we see in other use cases. The original post is about training, but we actually see even more interest in fine-tuning and serving with LLMs, in part because there are good pre-trained models.ģ. There is a lot more we want to do to make Ray better for working with large language models and for making training, serving, and batch inference work well out of the box.Ģ. Cohere on their architecture for training LLMs ġ. OpenAI fireside chat on the evolution of their infrastructure and usage of Ray for training Another HN thread on training LLMs with Ray (on TPUs in this case) Alpa does training and serving with 175B parameter models If you're curious about how Ray is used for LLMs, here are some interesting examples of LLM projects using Ray! I'm one of the Ray developers, thanks for the shoutout :)
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