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Author: yanncaniouoracle

cloud-infrastructure/ai-infra-gpu/ai-infrastructure/dstack/README.md

@@ -121,6 +121,8 @@ fsdp_config:
 * `distributed_type`: `FSDP` indicates the use of Fully Sharded Data Parallel (FSDP), a technique that enables training large models that would otherwise not fit on a single GPU.
 * `fsdp_config`: These set up how FSDP operates, such as how the model is sharded (`fsdp_sharding_strategy`) and whether parameters are offloaded to CPU (`fsdp_offload_params`).
 
+![Hybrid shards](/assets/images/image2.jpg "Hybrid shards")
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 With the `FSDP` of `distributed_type` and `FULL_SHARD` of `fsdp_config`’s `fsdp_sharding_strategy`, a model will be sharded across multiple GPUs in a single machine. When dealing with multiple compute nodes, each node will host an identical copy of the model, which is itself split across multiple GPUs within that node. This means each partitioned model instance on each node processes different sections or batches of your dataset. To distribute a single model across multiple GPUs spanning across multiple nodes, configure the parameter `fsdp_sharding_strategy` as `HYBRID_SHARD`.
 
 Additional parameters like "machine_rank," "num_machines," and "num_processes" are important for coordination. However, it's recommended to set these values dynamically at runtime, as this provides flexibility when switching between different infrastructure setups.
@@ -206,6 +208,8 @@ curl -X POST https://black-octopus-1.mycustomdomain.com/generate \
 
 Additionally, for a deployed model, dstack automatically provides a user interface to directly interact with the model:
 
+![User interface](/assets/images/image1.jpg "User interface")
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 ## Conclusion
 
 By following the steps outlined in this article, you've unlocked a powerful approach to fine-tuning and deploying LLMs using the combined capabilities of dstack, OCI, and Hugging Face's ecosystem. You can now leverage dstack's user-friendly interface to manage your OCI resources effectively, streamlining the process of setting up distributed training environments for your LLM projects.