Without this, /v1/chat/completions requests in local dev are served by
Vite instead of being proxied to the FastAPI backend.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add 200-sample parallel probe using ThreadPoolExecutor + safe_num_proc
to estimate download speed and failure rate before full conversion
- Abort with clear error if >=30% of probe images fail to download
- Show estimated download time in the training overlay modal
- Parallel batch conversion for URL-based datasets (vs sequential for local)
- Add warning field to /check-format response for URL-based image datasets
- Display URL warning in dataset preview dialog (amber banner)
- Thread progress_callback from trainer through format_and_template_dataset
to convert_to_vlm_format for real-time status updates
Place Train Split Start / End inputs inside the Advanced collapsible
with descriptive tooltips clarifying they slice the training split.
Revert the selectors component to its original eval-split-only layout.
Place Slice Start and Slice End inputs alongside the Eval Split
selector in a single row (grid-cols-3) so the dataset card stays
compact. Remove the duplicate controls from the Advanced section.
Add Start/End index inputs under Advanced in the dataset card,
allowing users to slice a dataset by row range before training.
Wired end-to-end: frontend store, API payload, backend Pydantic
model, and trainer dataset loading (inclusive on both ends).
Place Train Split Start / End inputs inside the Advanced collapsible
with descriptive tooltips clarifying they slice the training split.
Revert the selectors component to its original eval-split-only layout.
Place Slice Start and Slice End inputs alongside the Eval Split
selector in a single row (grid-cols-3) so the dataset card stays
compact. Remove the duplicate controls from the Advanced section.
Add Start/End index inputs under Advanced in the dataset card,
allowing users to slice a dataset by row range before training.
Wired end-to-end: frontend store, API payload, backend Pydantic
model, and trainer dataset loading (inclusive on both ends).
