* Fix resume training crash recovery and MLX checkpoints
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* Address review: preserve interrupted stop-and-save output_dir, verify MLX checkpoint
- finish_run: add clear_output_dir flag; preserve output_dir for stopped/error
unless cancel explicitly clears it (fixes pump finalization wiping persisted path).
- training pump: pass interrupted stop-and-save context into finalize_run_in_db.
- MLX stop-and-save: verify resumable checkpoint exists before sending complete;
return bool from _write_mlx_stop_checkpoint and add regression tests.
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* Address Codex review: MLX current-step checkpoint and cancel error finalize
- Only skip MLX stop checkpoint write when checkpoint-{current_step} exists;
stale periodic checkpoints no longer mask missing stop saves.
- Pass clear_output_dir through error-event finalization so Stop-without-save
cannot leave a persisted output_dir that still offers Resume.
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* Address review
* Address more reviews
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* more reviews
* clear in-memory output_dir on interrupted cancel
* allow resuming errored runs at the final step
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* Clear persisted output_dir in cancel watchdog path
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* Write MLX stop checkpoint in stop path, keep output_dir on crash finalize
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* fix(studio): harden resumable run finalization
* fix(studio): defer safetensors checkpoint import
* fix(studio): reject stale training cancellation
* fix(studio): replay null resume targets
* fix(studio): serialize terminal cancellation
* Harden resume checkpoint validation and fix stop-save cleanup
- Reject unrecognized shard formats and keep indexed shard paths inside the checkpoint dir
- Require a non-empty tensor record when validating .pt/.bin optimizer and model state
- Always finalize TensorBoard and W&B on stop-save-failure exits
- Refuse writing an MLX stop checkpoint through a symlinked directory
- Clarify the resume rejection message to cover errored runs
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* Tighten resume/checkpoint comments
* Recover resumability when a valid stop checkpoint landed
- Re-validate the current-step checkpoint in the dead-worker and error finalization paths so a stop-and-save that actually wrote a valid checkpoint is not wrongly marked error/resume_blocked
- Accept a valid tensor-free optimizer state (e.g. SGD without momentum); the model-state check still requires real tensors
- Include errored runs in the frontend resume rejection message
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Co-authored-by: Lyxot <longyixing331@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* feat(studio): add S3 dataset configuration foundation (#4539)
Add foundational types and configuration for S3 bucket dataset loading:
- Add S3Config type to frontend training types
- Add S3Config Pydantic model to backend training models
- Add "s3" as a DatasetSource option
- Add s3Config state and setS3Config action to training config store
- Add i18n translations for S3 configuration (English and Chinese)
This provides the type definitions and UI text for S3 integration.
Full implementation requires boto3 dependency and data loading logic.
Refs: #4539
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* Wire S3 config into training pipeline and prevent secrets persistence
- Pass s3_config from request into training_kwargs so it flows to training subprocess
- Add s3Config to NON_PERSISTED_STATE_KEYS to prevent AWS secrets from being
saved to localStorage
Addresses code review feedback on PR #5951.
* Exclude S3 config from database persistence to protect secrets
Filter out s3_config (which contains secret_access_key) from the
config_json stored in training_runs table, preventing AWS credentials
from being persisted to disk.
Addresses P1 security feedback on PR #5951.
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* Re-raise HTTPException in start_training and defer s3 DatasetSource widening for PR #5951
* Redact s3_config from W&B run config and accept camelCase S3 credential aliases for PR #5951
* feat(studio): implement S3 dataset loading end-to-end
Builds the actual S3 loader on top of the hardened #5951 foundation,
turning the 501-gated scaffold into a working dataset source.
Backend:
- Add core/training/s3_dataset.py: lists and downloads supported dataset
files (parquet/json/jsonl/csv) from an S3 bucket to a temp dir, using
IAM-role or access-key credentials. boto3 is imported lazily (optional dep).
- Wire s3_config into UnslothTrainer.load_and_format_dataset (downloads then
reuses the existing local-file path) and thread it through worker.py.
- Replace the 501 "not implemented" gate with a boto3-availability guard so
S3 works when boto3 is present and fails clearly when it is not.
- Add boto3 to studio.txt requirements.
- Add tests/test_s3_dataset.py (8 tests) covering download/filtering,
collisions, missing-boto3, and S3Config camelCase/IAM validation.
Frontend:
- Widen DatasetSource to include "s3"; add s3_config to the training payload
type and mapper; add an S3 validation branch and selectS3Source store action.
- Add s3-config-form.tsx (bucket/region/prefix/keys/IAM toggle) reusing the
existing studio.dataset.s3.* i18n strings.
- Add a Hugging Face / Local / Amazon S3 source toggle in dataset-section;
the S3 config card replaces the dataset combobox when S3 is selected.
- Fix DatasetPreviewDialog to accept the widened DatasetSource type.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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* Fix S3 dataset loader for PR #6222
* Fix S3 dataset edge cases for PR #6222
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* Fix S3 IAM payload handling for PR #6222
* Block multimodal S3 datasets for PR #6222
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Nilay