While it's possible to use third-party monitoring tools with dstack, it is often more convenient to debug your run and
track metrics out of the box. That's why, with the latest release, dstack introduced dstack stats, a new CLI (and API)
for monitoring container metrics, including GPU usage for NVIDIA, AMD, and other accelerators.
If you’re using or planning to use TPUs with Google Cloud, you can now do so via dstack. Just specify the TPU version and the number of cores
(separated by a dash), in the gpu property under resources.
Read below to find out how to use TPUs with dstack for fine-tuning and deploying
LLMs, leveraging open-source tools like Hugging Face’s
Optimum TPU
and vLLM.
While dstack helps streamline the orchestration of containers for AI, its primary goal is to offer vendor independence
and portability, ensuring compatibility across different hardware and cloud providers.
Inspired by the recent MI300X benchmarks, we are pleased to announce that RunPod is the first cloud provider to offer
AMD GPUs through dstack, with support for other cloud providers and on-prem servers to follow.
Deploying custom models in the cloud often faces the challenge of cold start times, including the time to provision a new instance and download the model. This is especially relevant for services with autoscaling when new model replicas need to be provisioned quickly.
Let's explore how dstack optimizes this process using volumes, with an example of
deploying a model on RunPod.
dstack Sky enables you to access GPUs from the global marketplace at the most competitive rates. However, sometimes you may want to use your own cloud accounts. With today's release, both options are now supported.
Today we're previewing dstack Sky, a service built on top of
dstack that enables you to get GPUs at competitive rates from a wide pool of providers.
