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Configuration & Validation

Pairing OpenMedConfig with the validation helpers lets you reproduce experiments, keep cache paths predictable, and guard APIs against malformed inputs.

OpenMedConfig sources

Construct OpenMedConfig directly for explicit in-process settings, or load a flat TOML file with load_config_from_file(). When no path is passed, the loader checks OPENMED_CONFIG and then ~/.config/openmed/config.toml (or the matching XDG config directory).

Environment controls are field-specific rather than a generic mapping from every OPENMED_* name. For example, HF_TOKEN, OPENMED_OFFLINE, OPENMED_PROFILE, and OPENMED_TORCH_ATTENTION_BACKEND are supported. When the device preference is unset or "auto", OPENMED_TORCH_DEVICE (or the legacy OPENMED_DEVICE) is checked before automatic MPS → CUDA → CPU selection. Set the model cache with cache_dir= or the TOML cache_dir key; OPENMED_CACHE_DIR is used by selected deployment and data tooling and is not a generic OpenMedConfig override.

from pathlib import Path
from openmed.core import ModelLoader
from openmed.core.config import load_config_from_file

config = load_config_from_file(Path.home() / ".config/openmed/config.toml")
loader = ModelLoader(config=config)
ner = loader.create_pipeline("disease_detection_superclinical", aggregation_strategy="simple")
entities = ner("Dapagliflozin added for HFpEF symptom relief.")

Minimal TOML file

~/.config/openmed/config.toml
default_org = "OpenMed"
device = "cuda"
cache_dir = "/mnt/cache/openmed"
torch_attention_backend = "auto"
cjk_width_convention = "cjk"
transliteration_aware_name_matching = false
indic_name_similarity_threshold = 0.80

Runtime environment controls can select the config path, provide Hub credentials, or choose a device when the loaded config leaves it automatic:

export OPENMED_CONFIG=/etc/openmed/config.toml
export HF_TOKEN=hf_xxx
export OPENMED_TORCH_DEVICE=cuda:1

For Indic personal-name pseudonymization, enable transliteration_aware_name_matching and reuse the same setting when reopening a file-backed surrogate vault. The collision threshold and optional local transliterator adapter are described in Transliteration-aware Indic name matching.

CJK width normalization

cjk_width_convention="cjk" is the default. It converts full-width Latin letters, digits, punctuation, and U+3000 ideographic spaces before PII detection while preserving offsets into the original text. This lets existing phone, date, and identifier patterns match full-width input without changing the returned surface text.

Set cjk_width_convention="nfkc" to apply strict per-character NFKC normalization instead. Both modes retain an explicit source map so expanded compatibility characters still remap to their original code-point spans.

Chinese script normalization

Install the optional Apache-2.0 OpenCC integration when clinical text can mix Simplified and Traditional Chinese:

pip install "openmed[zh]"

Script conversion is disabled by default. Set chinese_target_script to "simplified" or "traditional" to canonicalize Chinese variants before PII detection and segmentation:

from openmed.core import OpenMedConfig

config = OpenMedConfig(chinese_target_script="simplified")

OpenMed keeps a code-point alignment from the converted text to the source, so detected PHI spans are projected back to the exact original characters before redaction. Context-dependent phrase rewrites map conservatively to their full source phrase rather than guessing partial offsets. If OpenCC is absent, the pre-pass returns the input unchanged with identity alignment and emits one optional-dependency warning.

Chinese numeral helpers do not require the optional script-conversion package. They parse everyday and financial forms, return exact source code-point spans, and recognize valid year/month/day expressions:

from openmed.processing import (
    find_chinese_numbers,
    normalize_chinese_dates,
    parse_chinese_numeral,
)

assert parse_chinese_numeral("一百零一") == 101
numbers = find_chinese_numbers("剂量三千五百毫升")
dates = normalize_chinese_dates("出生于一九八五年十二月三日")

When PII detection uses lang="zh", contextual patterns also recognize valid Chinese-numeral dates, medical-record identifiers, and clinical quantities. Invalid unit sequences and impossible calendar dates are rejected.

PyTorch attention backends

torch_attention_backend="auto" is the default. In OpenMed 1.8.1 and later, automatic mode leaves backend selection to Transformers so it can choose an implementation supported by both the installed PyTorch runtime and the model architecture.

Set an explicit backend only when you have verified that the model supports it:

from openmed.core import OpenMedConfig

config = OpenMedConfig(torch_attention_backend="eager")

The equivalent environment override is:

export OPENMED_TORCH_ATTENTION_BACKEND=eager

Supported values are auto, eager, sdpa, and flash_attention_2. The eager implementation is the compatibility fallback. sdpa and flash_attention_2 require support from the selected Transformers model in addition to compatible PyTorch and hardware.

Local-only offline mode

Set OPENMED_OFFLINE=1 or instantiate OpenMedConfig(local_only=True) when model files are already present in the configured cache or passed as a local model path. Offline mode sets the standard cache-only loader flags (HF_HUB_OFFLINE=1, TRANSFORMERS_OFFLINE=1, and HF_DATASETS_OFFLINE=1) and passes local_files_only=True to Hub-backed model loaders.

export OPENMED_OFFLINE=1

from openmed.core import OpenMedConfig

config = OpenMedConfig(local_only=True, cache_dir="~/.cache/openmed")

Download or warm the model cache before enabling this mode. Once active, OpenMed blocks outbound socket connections during inference and de-identification. A disallowed connection raises OfflineModeError with this message prefix:

OPENMED_OFFLINE/local_only=True blocks outbound network access after model loading.

For HF_ENDPOINT, pip mirror, proxy, retry, and metered-connection setup, see the low-bandwidth, mirror, and proxy installation guide. It includes a cache-warming checklist and the openmed doctor fields used to confirm the active network environment before switching offline.

Validation helpers

from openmed.utils.validation import (
    validate_input,
    validate_model_name,
)

text = validate_input(
    user_supplied_text,
    max_length=2000,
    allow_empty=False,
)
model_id = validate_model_name("disease_detection_superclinical")
  • validate_input trims whitespace, enforces max lengths, and raises informative errors for API clients.
  • validate_model_name trims the value and accepts an existing local path, a bare model name, or one org/model identifier. It validates syntax; it does not enforce a model allowlist or resolve a registry alias.

Logging and tracing

from openmed.utils import setup_logging
from openmed.core import ModelLoader

setup_logging(level="INFO", include_timestamp=True)
loader = ModelLoader()
  • setup_logging configures standard Python logging. Add structured logging in the host application when needed; OpenMed does not expose a json= option.
  • Keep raw clinical text, entity values, model outputs, access tokens, and reversible mappings out of log records and tracing attributes.

Cache & device tips

  • CPU-only teams: keep device="cpu", install a CPU-compatible PyTorch runtime for Hugging Face inference, and rely on the configured model cache.
  • GPU nodes: set device="cuda" (or a concrete index such as "cuda:1"). Pass backend-specific model-loading options to ModelLoader.create_pipeline only after verifying the selected model and hardware support them; OpenMedConfig has no pipeline field.
  • Shared runners: point cache_dir at an ephemeral volume per job to avoid artifacts leaking between builds.