Note
ამ გვერდზე წვდომა ავტორიზაციას მოითხოვს. შეგიძლიათ სცადოთ შესვლა ან დირექტორიების შეცვლა.
ამ გვერდზე წვდომა ავტორიზაციას მოითხოვს. შეგიძლიათ სცადოთ დირექტორიების შეცვლა.
Important
This feature is in Public Preview.
The following examples are complete, end-to-end workloads you submit from the air CLI
with air run -f train.yaml. Each shows a real multi-GPU pattern on H100 GPUs, including
the workload YAML, bootstrap commands, and code. Start with the
quickstart if you haven't submitted a run before.
| Example | Description |
|---|---|
| Multi-node LLM fine-tuning with FSDP | Supervised fine-tuning of Llama-3.1-8B across 16 H100 GPUs (2 nodes) using torchrun and PyTorch Fully Sharded Data Parallel (FSDP). Logs to MLflow and checkpoints to a Unity Catalog volume. |
| Distributed training with Ray Train | Distributed data-parallel fine-tuning with Ray Train's TorchTrainer across 8 H100 GPUs on a single node, with one worker per GPU. |
| Batch inference with Ray Data and vLLM | Offline LLM batch inference with Ray Data and vLLM across 8 H100 GPUs on a single node, running one vLLM replica per GPU and writing results to a Unity Catalog volume as Parquet. |