CLI Reference

CLI Reference

After installing epftoolbox2, an epf command is available on your PATH.

pip install epftoolbox2
epf --help

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Interactive Mode

Running epf with no arguments launches a wizard-style interactive terminal where you can build and run pipelines without writing any YAML.

epf

You’ll see a menu with arrow-key navigation:

  Run experiment from YAML
  Run data pipeline from YAML → CSV
  Run model pipeline from YAML
  ──────────────────────────────────
  Build & run data pipeline interactively
  Build & run model pipeline interactively
  ──────────────────────────────────
  Exit

Build & run data pipeline interactively walks you through:

1. Adding sources (EntsoeSource, OpenMeteoSource, CsvSource, CalendarSource) with prompted config
2. Adding transformers (optional)
3. Adding validators (optional)
4. Setting date range and caching
5. Choosing an output CSV path
6. Saving a reusable data_pipeline.yaml
7. Running immediately

Build & run model pipeline interactively walks you through:

1. Loading an input CSV (from a previous data run)
2. Adding models — available columns are shown as a checkbox list for predictor selection
3. Adding evaluators and exporters
4. Setting the test period, target column, and horizon
5. Saving a reusable model_pipeline.yaml
6. Running immediately

Tip

Both wizards offer to save a YAML at the end. Run that YAML later with epf run or epf data / epf model without going through the wizard again.

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epf run — Full workflow

Run a complete experiment from a Workflow YAML.

epf run experiment.yaml

Options:

Flag	Description
--model-index N	Run only models[N]
--processes N	Override max_processes
--threads N	Override threads_per_process
--dry-run	Build both pipelines without executing — validates config

Examples:

# Basic run
epf run experiment.yaml

epf run experiment.yaml --model-index 2

# Validate config without running
epf run experiment.yaml --dry-run

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epf data — Data pipeline only

Run a DataPipeline YAML and save the result as a CSV.

epf data data_pipeline.yaml --start 2022-01-01 --end 2024-01-01 --output data.csv

Required flags:

Flag	Description
--start DATE	Fetch start date (YYYY-MM-DD)
--end DATE	Fetch end date (YYYY-MM-DD)
--output FILE	Output CSV path

Optional flags:

Flag	Description
--cache PATH	Cache directory or CSV file for source data

Example:

epf data data_pipeline.yaml \
    --start 2020-01-01 \
    --end 2024-01-01 \
    --output data.csv \
    --cache .cache/

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epf model — Model pipeline only

Run a ModelPipeline YAML on an existing CSV dataset (e.g. produced by epf data).

epf model model_pipeline.yaml \
    --data data.csv \
    --test-start 2023-01-01 \
    --test-end 2024-01-01

Required flags:

Flag	Description
--data FILE	Input CSV file
--test-start DATE	Test period start
--test-end DATE	Test period end

Optional flags:

Flag	Description
--target COL	Target column name (default: price)
--horizon N	Forecast horizon in days (default: 7)
--save-dir DIR	Save per-model prediction JSONL files to this directory
--forecast-only	Skip evaluators and exporters

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epf validate — Validate any pipeline YAML

Load and parse a YAML file, instantiate all components, and report errors — without running anything.

epf validate data_pipeline.yaml
epf validate model_pipeline.yaml
epf validate experiment.yaml

The file type is auto-detected from its top-level keys. Exits with code 1 if validation fails.

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epf init — Scaffold template files

Create experiment.yaml, data_pipeline.yaml, and model_pipeline.yaml with placeholder values in the current directory.

epf init
epf init --force   # overwrite existing files