Files
qlib/docs/advanced/serial.rst
XianBWandCopilot 7c95268c0c fix(security)!: harden artifact serialization and loading (#2339)
* security: harden artifact serialization and loading

* ci: restore dependency and title lint compatibility

* fix: address artifact test and lint failures

* ci: handle current MLflow and Windows OSQP changes

* ci: use lxml release with cross-platform wheels

* fix: defer optional CVXPY solver imports

* docs: mark notebook model artifacts as trusted

* docs: mark position artifacts as trusted

* style: document intentional lazy import

* ci: skip broken released OSQP on Windows Python 3.8

* fix: mark online model artifacts as trusted

* test: make MLflow client timing check robust

* ci: retry nondeterministic benchmark smoke test

* fix: cap filelock before fork safety regression

* fix: use filelock release compatible with multiprocessing

* fix: preserve artifact loading compatibility after hardening

* fix(security): validate derived artifact paths and extend regressions

* fix(security): preserve extended data artifact round trips

* fix(security): retain legacy NumPy and pandas artifact classes

Allow the exact MaskedArray and SparseDtype module paths emitted by the older dependency versions in CI. Preserve restricted loading and cover both old and new paths.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(data): avoid unused trading calendar lookup in PIT normalization

PIT normalization uses report dates and calendar-day offsets, so do not request an unused Baostock calendar. Cover annual and quarterly normalization without online calendar access.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(security): require explicit trust in artifact workflows

Expose default-off artifact trust at online, delayed-training and meta-workflow entry points. Keep data loading restricted, preserve legacy default recorder calls and restored workflow defaults, and document migration and trust boundaries.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* test(workflow): cover complete DDG-DA and HIST artifact flows

Exercise real training, cache and recorder restoration, saved-task replay, signals and portfolio backtests. Fix the cache trust, grouping, relative-path, LightGBM early-stopping and HIST CPU blockers exposed by full execution.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(security)!: unify artifact trust and publish upgrade guidance

Use trusted consistently across public APIs and CLI options. Migrate pre-release saved consent only at serialization boundaries, reject invalid or conflicting settings, and preserve restricted data reads. Add a canonical upgrade guide with README, installation, changelog and runtime-error entry points.

BREAKING CHANGE: executable artifact restoration requires explicit trusted=True. HIST mappings use JSON and high-frequency cache paths must remain within artifact_root. See docs/start/artifact_migration.rst for upgrading existing workflows.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(security): remove trial-only trust compatibility

Target upgrades from pre-PR Qlib rather than artifacts generated by intermediate PR revisions. Retain restricted defaults for saved objects without consent, unify current APIs, and remove state/keyword migration branches and documentation.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* ci: avoid failed dataset probes and space download retries

Download the same existing v2 archives used by successful jobs without probing nonexistent package-version assets. Allow five download attempts 60 seconds apart; preserve the full matrix, datasets, noninteractive setup and failure propagation. Cover the exact archives and retry policy in offline CI tests.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* docs: add practical artifact migration examples

Show explicit consent for restored online components while preserving the original manager, schema-aware Arrow conversions, timezone-preserving ZoneInfo migration, and restricted round trips into a separate recorder. Explain shared-component scope, staging artifacts and trusted-source prerequisites.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(workflow): preserve fresh artifact workflows with scoped trust

Keep immutable cache references intact at open rolling tails. Add explicit per-load record-template consent and per-artifact collector loading options without widening other reads. Restore the original template class after parent-load failures, and support audited business-frequency and NumPy record representations through exact pickle globals.

Document the scoped APIs and cover fresh trained-model reads, cache generation, numerical round trips and refusal paths.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-10-08 17:49:08 +08:00

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.. _serial:
=============
Serialization
=============
.. currentmodule:: qlib
Introduction
============
``Qlib`` supports dumping the state of ``DataHandler``, ``DataSet``, ``Processor`` and ``Model``, etc. into a disk and reloading them.
Serializable Class
==================
``Qlib`` provides a base class ``qlib.utils.serial.Serializable``, whose state can be dumped into or loaded from disk in `pickle` format.
When users dump the state of a ``Serializable`` instance, the attributes of the instance whose name **does not** start with `_` will be saved on the disk.
However, users can use ``config`` method or override ``default_dump_all`` attribute to prevent this feature.
Users can also override ``pickle_backend`` attribute to choose a pickle backend. The supported value is "pickle" (default and common) and "dill" (dump more things such as function, more information in `here <https://pypi.org/project/dill/>`_).
Example
=======
``Qlib``'s serializable class includes ``DataHandler``, ``DataSet``, ``Processor`` and ``Model``, etc., which are subclass of ``qlib.utils.serial.Serializable``.
Specifically, ``qlib.data.dataset.DatasetH`` is one of them. Users can serialize ``DatasetH`` as follows.
.. warning::
The direct local ``pickle.load`` below can execute code and must only read a
file from an independently trusted source and store. Recorder/workflow
``trusted`` settings do not authorize or protect this API. For the separate
recorder loading policy and upgrade steps, see :ref:`artifact_loading_migration`.
.. code-block:: Python
##=============dump dataset=============
dataset.to_pickle(path="dataset.pkl") # dataset is an instance of qlib.data.dataset.DatasetH
##=============reload dataset=============
with open("dataset.pkl", "rb") as file_dataset:
dataset = pickle.load(file_dataset)
.. note::
Only state of ``DatasetH`` should be saved on the disk, such as some `mean` and `variance` used for data normalization, etc.
After reloading the ``DatasetH``, users need to reinitialize it. It means that users can reset some states of ``DatasetH`` or ``QlibDataHandler`` such as `instruments`, `start_time`, `end_time` and `segments`, etc., and generate new data according to the states (data is not state and should not be saved on the disk).
A more detailed example is in this `link <https://github.com/microsoft/qlib/tree/main/examples/highfreq>`_.
API
===
Please refer to `Serializable API <../reference/api.html#module-qlib.utils.serial.Serializable>`_.