* perf: defer heavy imports to improve CLI startup time
Move expensive imports (engine, models, controllers) out of the module-level import path so that data-designer --help and other non-generation commands no longer pay the full startup cost.
Key changes:
- Defer controller imports to inside command functions
- Remove eager re-export chains from CLI package __init__ files
- Move default-settings bootstrap into load_config_builder() and DataDesigner.__init__() instead of running at import time
- Add lazy __getattr__ exports in interface/__init__.py
- Replace module-level tokenizer init with cached lazy getter
- Fix ModelProvider import to use config layer instead of engine
- Update test mock paths to match new import locations
Reduces CLI import-time from ~1.67s to ~0.46s.
* perf: defer pandas/numpy in io_helpers and add config_list benchmark
- Replace eager `from lazy_heavy_imports import pd, np` in io_helpers
with module-level __getattr__ (for backwards-compatible external
access / test mocks) and function-level imports in the 3 functions
that actually use them (read_parquet_dataset, smart_load_dataframe,
_convert_to_serializable). Importing io_helpers no longer triggers
pandas/numpy loading.
- Defer heavy imports in list and reset CLI commands into function
bodies to avoid loading repositories, Rich, and prompt_toolkit at
module import time.
- Add `config_list` (data-designer config list) measurement to the
CLI startup benchmark with isolated cold measurement in a separate
venv and a --skip-config-list-check flag.
- Update test mock paths to match new import locations.
* Refine lazy import usage and TYPE_CHECKING cleanup
* Run license header updater on PR-touched files
* fix: update sqlfluff mock target for lazy imports in test_sql
* perf: cache globals() in lazy __getattr__ to avoid repeated lookups
Add globals() caching and explanatory comment to all three lazy
__getattr__ implementations (lazy_heavy_imports, config/__init__,
interface/__init__) so subsequent attribute accesses bypass __getattr__.
* perf: lazy CLI command loading and deferred heavy import evaluations
- Add LazyTyperGroup to defer command module loading until invocation, allowing module-level imports in all CLI command files
- Split DataFrameSeedSource into seed_source_dataframe.py to isolate pandas dependency from other seed source classes
- Move TypeVar/TypeAlias definitions (DataT, NumpyArray1dT, RadomStateT, EngineT) to TYPE_CHECKING blocks with runtime fallbacks
- Wrap module-level constants in lru_cache (phone_number parquet data, jsonschema validator) to defer I/O and heavy imports to first use
- Update test mock targets to patch at usage-site for module-level imports
* refactor: use direct pandas import in seed_source_dataframe
Drop lazy-loading for pandas in DataFrameSeedSource; use direct import
for simplicity.
* update lazy import pattern
* update tests to use lazy import namespace
Switch test modules to import data_designer.lazy_heavy_imports as lazy
and reference heavy libraries through that namespace. This keeps heavy
imports deferred during module import and aligns tests with the new
lazy-import usage pattern.
* tighten import perf test thresholds
Document recent baseline timings and lower the allowed average
import time and timeout so regressions are detected sooner.
* document pandas import requirement
Clarify that Pydantic needs DataFrame resolved at module load and
that keeping the direct import preserves IDE typing support.
* increase timeout time
* use lazy pandas imports in visualization tests
- replace direct pandas usage with lazy.pd in visualization tests to avoid eager imports
- add TYPE_CHECKING pandas import and keep CLI controller imports sorted
* fix lazy pandas runtime usage and preview mocks
Switch sample-record handling to lazy pandas types so runtime paths no longer
depend on TYPE_CHECKING imports. Align preview controller tests to patch the
module-local DataDesigner symbol, preventing real engine invocation in save
results scenarios.
