bvandeusen
4daa3f2790
Make the SigLIP embedder an operator choice (drop-in to SigLIP 2:
google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no
schema change, better small-cue fidelity). A swap = set model + re-embed +
retrain, all operator-driven; the GPU agent does the re-embed so it's fast.
- settings: embedder_model_name is now a setting (migration 0065) alongside the
existing embedder_model_version; both editable + validated (non-empty) in the
ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model,
model-agnostic), preferring the pre-downloaded local dir for the default so
existing deploys don't re-download; rebuilds on a name change.
- agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames)
under the lease-announced model → POST /jobs/submit_embedding writes
image_record.siglip_embedding + siglip_model_version. The lease now announces
the model FROM THE SETTING (not a constant).
- re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale-
version images; 'siglip' now re-embeds concept crops whose version != current
(so a swap re-triggers crops, not just the never-embedded back-catalogue). The
CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't
churn the library at 512px) — the GPU agent owns version re-embeds. Daily
'embed' + 'siglip' beats self-heal.
- scoring: score_image only bags embeddings in the CURRENT model's space (whole-
image gated by siglip_model_version, concept regions by embedding_version) so a
mid-swap stale vector isn't scored by new-space heads; legacy NULL = current.
- UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and
"Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2.
Tests: lease announces model + submit_embedding round-trip; enqueue 'embed'
selects stale/unembedded; stale-version excluded from scoring; embedder model
settable + empty rejected; siglip gate updated to current-version concept.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
"""ml_settings: embedder_model_name (#1190 operator model swap)
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The embedder MODEL VERSION was already a setting (and stamps image_record.
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siglip_model_version); the HF model NAME was env-only, so an operator couldn't
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actually point the pipeline at a different embedder. Storing the name as a
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setting makes the model an operator choice: set name + version → re-embed (the
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GPU agent) → retrain heads. Default = the current SigLIP so400m.
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Revision ID: 0065
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Revises: 0064
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Create Date: 2026-06-30
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"""
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from typing import Sequence, Union
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import sqlalchemy as sa
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from alembic import op
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revision: str = "0065"
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down_revision: Union[str, None] = "0064"
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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op.add_column(
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"ml_settings",
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sa.Column(
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"embedder_model_name", sa.String(length=128), nullable=False,
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server_default="google/siglip-so400m-patch14-384",
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),
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)
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def downgrade() -> None:
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op.drop_column("ml_settings", "embedder_model_name")
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