docs: add training roadmap
This commit is contained in:
15
README.md
15
README.md
@@ -10,6 +10,7 @@ Docs:
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- `docs/STATUS.md`
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- `docs/STATUS.md`
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- `docs/ROADMAP.md`
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- `docs/ROADMAP.md`
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- `docs/REPO_FACTS.json`
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- `docs/REPO_FACTS.json`
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- `docs/TRAINING_ROADMAP.md`
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Quickstart:
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Quickstart:
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- `python -m afs_scawful datasets index`
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- `python -m afs_scawful datasets index`
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@@ -30,3 +31,17 @@ Example `mounts.json`:
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]
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]
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}
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}
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```
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```
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Training monitor (AFS Studio):
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- Use `training_monitor` in `mounts.json` or a separate `training_monitor.json` in the same config dirs.
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- Optional override: `AFS_TRAINING_MONITOR_CONFIG=/path/to/training_monitor.json`
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Example `training_monitor` block:
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```json
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{
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"training_monitor": {
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"windows_mount_path": "~/Mounts/windows-training",
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"windows_training_dir": "D:/afs_training"
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}
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}
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```
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@@ -1,13 +1,15 @@
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# ROADMAP
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# ROADMAP
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## Committed
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## Committed
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- Minimal module layout + package stubs
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- Plugin config helpers (training paths/resources + mounts)
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- One small utility
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- Dataset registry indexing CLI
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- Resource indexing CLI
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## Planned
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## Planned
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- Local config template
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- Validator base + initial validators (ASM/C++/KG/ASAR)
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- Example generator
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- Generator base + one small generator
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- Training monitor schema validation
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## Ideas
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## Ideas
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- Idea: Local validation hooks for training data
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- Idea: Dataset manifest + QA summary reports
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- Idea: Minimal dataset manifest generator
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- Idea: Lightweight training campaign runner (local-only)
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27
docs/TRAINING_ROADMAP.md
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27
docs/TRAINING_ROADMAP.md
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@@ -0,0 +1,27 @@
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# TRAINING ROADMAP
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Scope: AFS Scawful training data pipelines and monitoring. Research-only.
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## Committed (exists now)
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- Dataset registry indexing (local)
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- Resource indexing (local)
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- Plugin config loader for training paths/resources
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## Planned (near-term)
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- Validator base + initial validators (ASM/C++/KG/ASAR)
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- Generator base + one small generator
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- Training monitor schema validation and config docs
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## Planned (mid-term)
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- Generator runner that produces dataset manifests
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- QA summary reports (counts, sizes, validation results)
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- Repeatable dataset build scripts (local-only)
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## Ideas (later)
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- Training campaign runner (batching + resume)
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- Per-generator metrics export (JSON)
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- Optional remote sync hooks (explicit opt-in, no auto-network)
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## Unknown / needs verification
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- Which generators can be safely generalized outside local workflows
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- Which validation checks are still useful vs legacy noise
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