Train an LSTM on one station

This tutorial trains the lstm baseline on one Swiss station for a few minutes. Use it as a smoke test after a fresh install.

Prerequisites

• Finished Quickstart step 1 (srn prepare-data has produced the CSV splits).
• A CUDA GPU.

Run

uv run srn train-isolated

The __main__ block of swissrivernetwork/benchmark/train_isolated_station.py has settings["dev_run"] = True by default, which:

• caps training data to 4 windows,
• caps validation to 4 windows,
• disables Weights & Biases.

The run should finish in under a minute.

Switch to a real training run

Open the file and set:

settings = {
    "dev_run": False,
    "enable_wandb": True,  # or False if you don't have an API key
}

config["station"]  = "2091"     # pick any station from read_stations(graph_name)
config["graph_name"] = "swiss-1990"
config["epochs"]   = 30

Then rerun uv run srn train-isolated. Checkpoints land in outputs/ray_results/<run_id>/.

Inspect the result

Open the visualize_results/visualize_results.ipynb notebook and set graph_name = "swiss-1990", method = "lstm", station = "2091".