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feat: add opt-in MLflow tracking for inference runs - #1320
Introduced hssm.track() to record inference workflows as MLflow runs.
Added tracking documentation in tracking.md and how_to/track_with_mlflow.md.
Updated changelog.md to reflect new tracking feature.
Integrated tracking into the HSSM model fitting process.
Implemented network provenance tracking for models downloaded from HuggingFace.
Added tests for tracking functionality and network provenance.
Summary by CodeRabbit
New Features
Added opt-in MLflow tracking for inference runs, covering model setup, sampling or variational inference, and saved models.
Tracked runs can include model and data details, metrics, artifacts, user-added parameters, and network provenance.
Tracking is available as an optional installation extra. When unconfigured, runs use a local store; tracking errors are logged without interrupting inference.
Documentation
Added a setup guide and API references for tracking, including configuration, recorded metadata, and lineage.
Added links to the tracking guide in installation and other documentation.
- Introduced `hssm.track()` to record inference workflows as MLflow runs.
- Added tracking documentation in `tracking.md` and `how_to/track_with_mlflow.md`.
- Updated `changelog.md` to reflect new tracking feature.
- Integrated tracking into the HSSM model fitting process.
- Implemented network provenance tracking for models downloaded from HuggingFace.
- Added tests for tracking functionality and network provenance.
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📝 Walkthrough
Walkthrough
Adds opt-in MLflow tracking for HSSM inference. Tracking records model metadata, provenance, inference metrics, and optional artifacts. Runtime hooks cover network downloads, sampling, variational inference, and saved models. The pull request also adds tests and tracking documentation.
Changes
MLflow inference tracking
Layer / File(s)
Summary
Tracking API and provenance src/hssm/tracking.py, pyproject.toml, src/hssm/__init__.py, src/hssm/distribution_utils/onnx_utils/model.py, tests/test_tracking.py, tests/test_tracking_optional.py
Adds tracking metadata and lineage helpers, the optional MLflow dependency, a public track export, and network download provenance recording. Tests cover provenance helpers, revisions, and manifest lookup.
Run lifecycle and inference logging src/hssm/tracking.py, tests/test_tracking.py
Adds the Tracker and track() context manager. Records model and inference data, user parameters, metrics, figures, and optional artifacts. Tests cover run lifecycle, logged values, artifact modes, datasets, and context isolation.
HSSM runtime integration src/hssm/base.py
Connects model construction, sampling, variational inference, and model saving to the active tracker.
Tests importing HSSM without MLflow and the error raised when tracking is invoked without the optional dependency. Documents installation, configuration, run contents, lineage, and API usage. Updates navigation and ignores local MLflow storage files. Adds static-analysis comments without runtime behavior changes.
sequenceDiagram
participant User
participant track
participant HSSMBase
participant Tracker
participant MLflow
User->>track: Open tracking context
track->>MLflow: Start run and set tags and parameters
HSSMBase->>Tracker: Report model and inference events
Tracker->>MLflow: Log parameters, metrics, and artifacts
track->>MLflow: End run with status
Loading
Suggested reviewers:alexanderfengler
Merge Risk:🔵 Low · up to 5cf50
Tracking a second model in one block can produce an incomplete run. This is a narrow opt-in case; reject it explicitly before merge or accept the limitation with owner awareness.
Security Architecture Review
Security architecture risk:🟡 Moderate · up to 5cf50
Tracking is off by default, but a tracking destination can persist beyond a run, and recording failures can leave a run appearing complete when its provenance or results are incomplete.
Retained concerns
Medium · security · inferred: An explicit tracking URI or experiment is not restored on exit. Subsequent runs without an explicit destination can inherit the earlier configuration, potentially sending inference records to the wrong project or store in a shared process.
Medium · security · inferred: Best-effort logging can produce an apparently completed run with missing model provenance or fit results. Model logging is marked complete before its writes succeed, while a logging exception is swallowed; repeated fits in one run can also retain the first model's metadata.
Security review details
Security Blast Radius
inferred — Exposure is limited to callers that enter a tracking block, but can extend from a single local run to the configured MLflow store and its artifact readers. The maximum tenant or service scope depends on deployment controls not shown here.
Security Findings and Attack Paths
inferred — In a shared process, a prior caller's explicit destination can be inherited by a later call without a destination; that later run may send its records and enabled artifacts to the prior store. This is a potential isolation failure, not evidence of an observed cross-tenant transfer.
Trust Boundaries and Controls
observed — Callers select the tracking destination and may supply lineage explicitly. Reserved schema tags are protected, user-supplied lineage is identified as such, and model provenance uses the model's network record rather than the latest session record.
Resilience and Maintainability Implications
inferred — A normal inference result does not establish a complete tracking record: guarded writes can fail independently, and normal exit does not check their outcome before attempting run completion.
Hardening Proposals
proposed — Bind destination configuration to a run or restore the previous configuration on exit, and make the expected destination and artifact audience explicit for shared deployments.
proposed — Define whether one block permits multiple fits, and expose incomplete tracking or retryable write state so consumers do not mistake a partial record for complete provenance.
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Title check
✅ Passed
The title clearly and concisely describes the main change: adding opt-in MLflow tracking for inference runs.
Docstring Coverage
✅ Passed
Docstring coverage is 84.27% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 89 functions across 8 files. (10 skipped: 1…
Linked Issues check
✅ Passed
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
✅ Passed
Check skipped because no linked issues were found for this pull request.
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Actionable comments posted: 6
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Inline comments:
In `@docs/how_to/track_with_mlflow.md`:
- Line 60: Add the missing `import arviz as az` before the MLflow tracking block
containing `run.log_metric` so the existing `az.loo` call uses a defined alias.
In `@src/hssm/tracking.py`:
- Around line 75-78: Update the network-loading boundary around record_network()
so provenance is stored on the specific model or model configuration that loaded
the network, rather than only in the process-wide _LAST_NETWORK record. Change
_log_model() to read that model-associated provenance and ensure model A
continues reporting network A after model B loads network B; retain
last_network() only for non-model-specific use.
- Line 93: Update the manifest-loading flow around _manifest_entry_for() so
network’s hf_revision is passed as the revision argument to hf_hub_download,
preserving default behavior when no revision is recorded. Add a regression test
that uses distinct manifests at two revisions and verifies the recorded
revision’s manifest is selected.
- Around line 208-210: Update data_sha256 to hash canonical DataFrame schema
metadata alongside the existing row and index values, including column labels,
dtypes, and index metadata so schema changes alter the digest. Add or update
tests covering a column rename with unchanged values and verify it produces a
different digest.
- Line 53: Replace the process-wide _ACTIVE tracker state with
execution-context-local state, such as a ContextVar, so untracked sample() calls
in other threads cannot mutate a tracked run’s _sample_started or _model_logged
flags. Update the tracker access paths in the relevant tracking methods to use
the isolated state, and add a test covering overlapping tracked and untracked
threads that verifies duration and model metadata remain correct.
In `@tests/test_tracking.py`:
- Line 33: Update the test fixture around the mlruns cleanup to use a test-owned
artifact location under tmp_path, configure tracking to write there, and remove
only that location during cleanup. Eliminate the Path.cwd() / "mlruns" deletion
while preserving the fixture’s existing setup and teardown behavior.
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docs/api/tracking.md
docs/changelog.md
docs/how_to/track_with_mlflow.md
mkdocs.yml
pyproject.toml
src/hssm/__init__.py
src/hssm/base.py
src/hssm/distribution_utils/onnx_utils/model.py
src/hssm/tracking.py
tests/test_tracking.py
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Line 3 skips the expected heading level and triggers markdownlint MD001. Change ### 0.5.0 to ## 0.5.0, or update the surrounding heading hierarchy consistently.
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In `@docs/changelog.md` at line 3, Update the 0.5.0 release heading in the
changelog to use the expected H2 level, changing the current third-level heading
while preserving the surrounding heading hierarchy.
Source: Linters/SAST tools
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Inline comments:
In `@docs/changelog.md`:
- Line 73: Renumber the MLflow tracking entry in the Dependency changes
subsection from item 13 to item 2, preserving the existing entry content and
numbering sequence.
---
Outside diff comments:
In `@docs/changelog.md`:
- Line 3: Update the 0.5.0 release heading in the changelog to use the expected
H2 level, changing the current third-level heading while preserving the
surrounding heading hierarchy.
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Inline comments:
Review comments at @src/hssm/base.py:
- Around line 411-414: Reset tracking’s process-wide network record immediately
before `HSSM._make_model_distribution()`, then capture the resulting record
afterward so `_tracking_network` reflects only downloads made while constructing
this model. In `_log_model`, remove the `last_network()` fallback and preserve
an empty `_tracking_network` snapshot as empty.
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docs/api/tracking.md
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docs/ecosystem/index.md
docs/how_to/index.md
docs/how_to/track_with_mlflow.md
src/hssm/base.py
src/hssm/tracking.py
tests/test_tracking.py
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Actionable comments posted: 1
🪄 Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @src/hssm/base.py:
- Line 416: Replace the shared module-level download record used by model
construction with construction-local state, so concurrent constructions cannot
associate another model’s downloaded network with its tracking provenance.
Update the download-record flow around `tracking.reset_network_record()` and
ensure `Tracker._log_model()` resolves the network from the corresponding
construction.
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Inline comments:
Review comments at @src/hssm/tracking.py:
- Around line 417-419: Update the MLflow input logging code in the visible
tracking method to import from_pandas from mlflow.data.pandas_dataset and call
it directly when passing the dataset to self._mlflow.log_input.
- Around line 157-159: Pass revision directly to hf_hub_download instead of
constructing and expanding the conditional kwargs dictionary, preserving the
default-branch behavior when revision is None. Update the hf_hub_download stub
in the tracking tests to accept an optional revision argument.
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.gitignore
docs/api/tracking.md
docs/changelog.md
docs/ecosystem/index.md
docs/getting_started/installation.md
docs/how_to/index.md
docs/how_to/track_with_mlflow.md
docs/index.md
mkdocs.yml
pyproject.toml
src/hssm/__init__.py
src/hssm/base.py
src/hssm/distribution_utils/onnx_utils/model.py
src/hssm/tracking.py
tests/test_tracking.py
tests/test_tracking_optional.py
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@cpaniaguam I’ll review all changes in #1320 again.
🧠 Learnings used
Learnt from: cpaniaguam
Repo: lnccbrown/HSSM PR: 1320
File: src/hssm/base.py:416-416
Timestamp: 2026-09-28T19:04:09.950Z
Learning: For PR #1320 in lnccbrown/HSSM, the opt-in MLflow tracking feature has no threaded model-construction path. The maintainer considers concurrent construction outside the scope of that feature.
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Review comments at @src/hssm/tracking.py:
- Around line 430-432: Update Tracking.log_model to retain the first model
identity and raise an explicit exception if a different model is passed later;
keep repeated calls for the same model idempotent so second-fit settings are
never logged against the first model.
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Nested repository paths omit the Hugging Face revision
src/hssm/tracking.py:129
hf_hub_download also supports repository filenames in subdirectories, whose cache path is .../snapshots/<sha>/subdir/model.onnx. Checking only path.parent.parent misses that layout, so hf_revision is omitted and lineage is looked up against the moving default branch rather than the network's commit. Walk the ancestors until finding the directory whose parent is snapshots.
Callable and set specifications produce unstable hashes
src/hssm/tracking.py:296
This fallback makes spec_sha256 unstable for supported callable specifications such as loglik= or extra_namespace=: a function's repr contains its process-specific memory address, so identical models in separate processes receive different hashes. Sets are also converted in iteration order. Canonicalize these values (for example, stable callable identity/code metadata and sorted set elements) before hashing so the documented cross-run grouping works for custom models.
Changing tracking URI leaves a stale active experiment
src/hssm/tracking.py:736
Changing tracking_uri without also passing experiment leaves MLflow's process-wide active experiment ID untouched. As the test fixture notes, that ID may belong to the previous store; start_run() can then fail because the experiment does not exist in the new backend. When the URI actually changes and no experiment was requested, reset the active experiment to Default before starting the run.
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Copilot review overview
🔵 Needs a closer look
Provenance can be missing or falsely attributed, specification hashes are not stable across processes, and URI switching can retain an invalid experiment.
Capture RLSSM provenance before cached ONNX resolution
src/hssm/base.py:416
Resetting provenance here is too late for RLSSM: its public constructor resolves the decision-process ONNX before entering HSSMBase (rl/rlssm.py:529-540), and rl/registry.py:338-345 then caches the resulting callable. This line clears the download record, while RLSSM._make_model_distribution() uses the prebuilt Op and never reloads it, so tracked RLSSM fits consistently omit the network revision and manifest lineage. Capture/carry provenance when the RL config is built (including cached entries), or move the reset ahead of that resolution.
Make spec_sha256 serialization deterministic across processes
src/hssm/tracking.py:296
This serialization makes the advertised stable spec_sha256 process-dependent. Sets are emitted in hash-dependent iteration order, and repr() for accepted constructor values such as custom loglik or extra_namespace callables commonly embeds a memory address. Consequently, the same model specification in separate processes can receive different hashes. Canonicalize unordered containers and encode supported callable/config values structurally rather than with raw repr().
Do not assign Hub lineage to local ONNX files
src/hssm/tracking.py:435
Falling back to model_config.loglik falsely attributes local ONNX files to HuggingFace. load_onnx_model() deliberately records only a Hub download, so a local ddm.onnx leaves _tracking_network empty; this fallback nevertheless names it ddm.onnx, looks it up in the Hub manifest, and assigns the official network's lineage to unrelated local weights. Only manifest-resolve networks for which the loader captured Hub provenance.
Reset MLflow experiment when switching tracking URIs
src/hssm/tracking.py:736
Switching tracking_uri without also specifying an experiment can reuse MLflow's process-wide active experiment ID from the previous store. That ID may not exist in the new backend, causing start_run() to fail instead of using the new store's Default experiment; the test fixture explicitly works around this state at tests/test_tracking.py:31-33. Reset/select an experiment in the newly selected store whenever the URI changes and no experiment name is supplied.
@copilot On the three "previously missed" findings from review 5420873175, all fixed in 6403e87 (tests in 5caa78f, TestPreviouslyMissedFindings):
Nested repository paths:hf_revision_from_path now walks up to the folder directly under snapshots/, so files in repository subfolders get their revision.
Unstable spec hashes:_jsonable drops memory addresses from repr text and writes sets in sorted order, so identical specifications hash the same across processes.
Stale active experiment: when tracking_uri switches to a different store and no experiment is named, track() uses that store's Default; staying on the same store keeps the active experiment.
pyrefly 1.3.0 (2026-09-11) reports 5 new errors on an unchanged main,
turning the lint gate red for drift and every PR, because HSSM has no lockfile.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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hssm.track()to record inference workflows as MLflow runs.tracking.mdandhow_to/track_with_mlflow.md.changelog.mdto reflect new tracking feature.Summary by CodeRabbit
New Features
Documentation