dashai-frankenstein

A DashAI plugin that registers Frankenstein Transformer model classes as DashAI components, so end users can train, evaluate, predict, save, and load them from the DashAI UI.

Components registered

The use_dashai_dataset checkbox

Every Frankenstein model component exposes a Use DashAI dataset checkbox (default: checked) in its configuration form:

• Checked (default): training consumes the DashAI run dataset (x_train/y_train) — the dataset section of the Frankenstein JSON, if any, is overridden. Text is tokenized by the plugin's dataset adapters (masking for MLM, plain ids for causal LM, tokenized+labels for classification) and passed to the engine as a pre-built DataLoader.
• Unchecked: the Frankenstein engine itself loads the corpus from the config pasted in frankenstein_json:
  • NLP tasks (mlm, causal_lm, text_classification) use the text_dataset block (HF hub dataset_name, split, text_column, optional label_column + use_labels, data_dir with parquet/json, streaming, max_samples).
  • Vision tasks use the vision_dataset block (HF dataset_name or local dataset_dir); without either, a dummy smoke dataset is used.

MLM pretraining (Masked Language Modeling)

The FrankensteinPretrainer component runs real BERT-style MLM pre-training (task: mlm in the Frankenstein engine) on a DashAI text dataset:

1. Create a dataset with a single Text column.
2. Create a MaskedLanguageModelingTask session and select the same text column as input and output (self-supervised target).
3. Paste a Frankenstein config (one-line JSON) with tokenizer.name_or_path set — the text is tokenized + 15%-masked at collate time (80% [MASK] / 10% random / 10% keep) and consumed by the engine's TitanTrainer.compute_mlm_loss.

Every optimizer update streams the full telemetry (loss, masked-token accuracy, learning rate, gradient norms per block, GPU temp/power/mem, throughput) into the DashAI run's metric store; the trained backbone is saved via the run's artifact and can be loaded later for classification fine-tuning with FrankensteinMLMModel.

Schema (v1: passthrough JSON)

Each model exposes a minimal pydantic schema with a single user-facing field: frankenstein_json, a string containing a full Frankenstein training config as a single-line JSON. The Frankenstein JSON Schema is the source of truth — the JSON is validated against it (additionalProperties: false + enums) and Frankenstein's config loader (cross-component constraints) before any train/inference launches. Errors surface to the DashAI user as a readable ValueError.

Build your YAML with the Frankenstein YAML builder, convert it to a one-line JSON string, and paste it into the field:

python -c "import yaml,json,sys; print(json.dumps(yaml.safe_load(open(sys.argv[1]))))" my_config.yaml

Training parameters (device, batch_size, num_epochs, learning rate) are read from the config's training_runtime block and optimizer parameters — they are NOT separate DashAI form fields. Generation parameters (max_new_tokens, temperature, top_k) on the decoder component are kept as DashAI fields (they are inference-time, not training-time, and the Frankenstein schema has no home for them).

Note: The field is a single-line text input (DashAI does not yet support a multiline textarea for plugin schema fields), which is why the config is passed as a one-line JSON string rather than a multiline YAML document. A true multiline textarea is tracked as a future upstream improvement to DashAI.

Install

pip install dashai-frankenstein            # from PyPI once published
# or, from this repo:
pip install -e ./dashai-frankenstein

DashAI discovers the plugin via the dashai.plugins entry-points group on startup — no DashAI source edits required.

Architecture

See docs/dashai-plugin-audit.md in the Frankenstein repo for the full integration design (§5 component designs, §6 phased plan, §7 Frankenstein changes). This package is the Phase 1–3 adapter layer; it consumes the Frankenstein engine API (src.engine) added in Phase 0.