Sync Ml Reports

Sync ML Reports

Copy reports with the skore CLI. Switch the default destination only when the user asked to. Do not evaluate, audit, or invent Project.sync Python.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Ask where reports live (disk, Hub, MLflow) in those words. Do not name G-SKORE-MODE, skill ids, or the wrapper CLI in the question. skore sync output may appear in the close as the sync table.

Procedure

1. Run python -m skore_skills status. Read policy.skore_mode and skills. Open experiments/ and audit/ for the skore.Project(...) init block: name=, Hub workspace=, MLflow tracking_uri=. Local store is always the workspace reports/ directory (absolute path). Never omit --from-workspace / --to-workspace on a local endpoint.

2. If policy.skore_mode is unset: STOP. First pick is G-SKORE-MODE in evaluate-ml-pipeline. Do not ask local / hub / mlflow here. Load that skill only if status.skills.evaluate-ml-pipeline is true and the user asked to evaluate; else one-line skip. The close is only this stop: the destination is not chosen yet and is picked when a report is stored. Do not list local, Hub, or MLflow, a workspace name, or a tracking URI. Do not use the source-to-destination close below.

3. AskUserQuestion for any answer not already in the request. Ahead of each question, state in 2–4 lines what the answer authorizes — which reports move where, whether skore_mode and the Project init lines get rewritten — and the facts it rests on: the current mode, the discovered report count, the endpoint. A file link is an addition, never the context.

   • Intent. Switch default destination (sync, then persist skore_mode and rewrite every Project init) vs copy only (sync, leave policy and experiment files).
   • Destination — same three options as G-SKORE-MODE: local (disk, no account), hub (https://skore.probabl.ai), mlflow (tracking server). Hub: ask the workspace name; it MUST NOT contain /. MLflow: ask tracking_uri; confirm a bare host:port as http://host:port. Do not default the URI.
   • Project name if name= is missing or disagrees across files.
   • Dry-run first vs transfer now.

   If intent is switch and destination equals policy.skore_mode, stop in one line.

4. Destination extras: load add-python-package only if status.skills.add-python-package is true, for Skore at the destination mode (env add-skore --mode <dest>). If that skill is missing, name Skore for the destination and stop. Do not splice pip install / skore[...] here.

5. If Hub is source or destination: require SKORE_HUB_API_KEY in the environment. Missing → name it and stop. Do not open a browser login. Do not read .skore for the key.

6. Build skore sync. If skore is not on PATH, name skore-cli and stop. Do not call skore.Project.sync in Python.

   bashCopy code
   skore sync <project> --from=<source_mode> --to=<dest_mode>
   

   Source mode is policy.skore_mode. Add:

   Endpoint	Flags
   local	--from-workspace or --to-workspace = resolved reports/
   hub	--from-workspace or --to-workspace = Hub workspace name (required)
   mlflow	--tracking-uri=...; never *-workspace

   --to-project only if the destination name differs. --hub-url only when SKORE_HUB_URI (or the user) names a non-default Hub. --both only if the user asked to copy missing reports both ways. Otherwise one-way.

   If the user picked dry-run first, run with --dry-run, show the plan, then ask to transfer. Live run omits --dry-run.

   Usage/auth/backend errors: name stdout/stderr and stop. Do not invent a Python fallback. Empty output No reports to synchronize. is success (nothing to copy; a switch may still continue).

7. Switch intent only, after a successful (or empty) sync: python -m skore_skills policy set skore_mode <dest>. Rewrite every Project init in experiments/ and audit/ to the destination form in evaluate-ml-pipeline/references/g_skore_mode.md (audit must match the paired experiment, byte-for-byte modulo formatting). If dest is local, mkdir reports (exist_ok); no README. If hub or mlflow, do not create reports/. If journal/JOURNAL.md exists, insert a --- under History and one line skore_mode: <old> → <dest> (sync-ml-reports). Missing journal → skip in one line; do not paste a JOURNAL body.

   Copy-only: do not policy set, rewrite init, mkdir, or edit JOURNAL.

Stop conditions

• Do not steal first G-SKORE-MODE when skore_mode is unset.
• Do not silently change a recorded mode; switch requires the switch intent (or an explicit user request to switch).
• Do not git commit.
• Do not evaluate, project.put, or audit.
• Do not pass *-workspace on an MLflow endpoint.
• Do not omit *-workspace on a local or Hub endpoint.

End of turn

User-facing close

This close applies only after a sync. An unset policy.skore_mode uses the step-2 stop instead.

Short story: source → destination, whether policy changed, and the skore sync table or No reports to synchronize. Do not dump experiment files.

Then python -m skore_skills git end-turn --stage evaluate (the persist bucket for this work; this is not a CV run). If JSON action is invoke, load persist-ml-git only if status.skills.persist-ml-git is true and stop; that skill returns to triage. If persist is missing, name the pending staged paths and stop. Otherwise load triage-ml-task only if status.skills.triage-ml-task is true; else stop. No git commit.