GitHub user aicam edited a comment on the discussion: Proposal - Supporting user-provided ML models in workflows
## Design Update ### Key Decisions 1. **Isolate mounts per computing unit (CU)** * **Context:** We currently support read-only operations for model inference, which allows using shared volumes and mounting points across CUs. * **Decision:** We are opting for dedicated per-CU dataset mounts. * **Rationale:** This architecture prepares the system for upcoming write support (model training), ensuring two users working with the same base model can modify their mounts independently without conflict. 2. **Offload mounting from CU pods to a privileged per-node DaemonSet** * **Context:** GeeseFS requires elevated privileges to perform mounts. Granting these directly to CU pods introduces security risks, as users can execute arbitrary code that could compromise adjacent pods on the node. * **Decision:** CU pods will no longer run with mount privileges. Instead, a dedicated per-node DaemonSet handles the mounts and attaches the resulting volumes to the CU pods. 3. **Defer dataset resolution and mounting from compile time to runtime** * **Decision:** Shift mount execution entirely to runtime. * **Rationale:** Mounts are only required while a workflow is actively executing so we do not want to perform mounting in compilation. 4. **Integrate model selection into the operator property panel** * **Decision:** Use a specialized UI parameter kind built on @carloea2's implementation (#5912) rather than introducing a parallel selection mechanism: * **Details:** The parameter resolves directly to the local directory where the specific model version is mounted. 5. **Provide contextual sample code based on model and storage types** * **Decision:** Auto-generate tailored Python UDF snippets based on the selected model type and storage backing to accelerate user implementation. --- ### Under Research *Next area of focus—design still in progress:* * **Hardware Constraints (GPU & VRAM):** CUs currently enforce GPU allocation limits. We are defining model-side metadata requirements and validation checks to prevent GPU-dependent models from running on unallocated CUs. * **OS-Level Portability:** Investigating how tightly coupled trained models are to their original host OS environment and defining cross-platform execution boundaries. GitHub link: https://github.com/apache/texera/discussions/6616#discussioncomment-17909726 ---- This is an automatically sent email for [email protected]. To unsubscribe, please send an email to: [email protected]
