> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/autorope/donkeycar/llms.txt > Use this file to discover all available pages before exploring further. # donkey models > View the model training database and history The `donkey models` command displays a database of your trained models, showing training history, tub data used, and performance metrics. ## Usage ```bash theme={null} cd ~/mycar donkey models [--config ] [--group] ``` ## Arguments Path to your config.py file Group tubs and plot them separately in the output ## Description Donkeycar maintains a database of your training history in the `models/` directory. The `models` command displays: * **Pilot information** - Model names, types, timestamps * **Training tubs** - Which data was used to train each model * **Performance metrics** - Loss values, training duration * **Model relationships** - Transfer learning lineage ## Example Output ```bash theme={null} cd ~/mycar donkey models ``` **Output:** ``` ┌──────────────────────┬─────────────┬────────────┬──────────────┬─────────┐ │ Name │ Type │ Tubs │ Date │ Loss │ ├──────────────────────┼─────────────┼────────────┼──────────────┼─────────┤ │ mypilot_22-01-15.h5 │ linear │ 1,2,3 │ 2022-01-15 │ 0.0345 │ │ fast_22-01-16.h5 │ categorical │ 1,2,3,4 │ 2022-01-16 │ 0.0289 │ │ track2_22-01-17.h5 │ linear │ 5,6 │ 2022-01-17 │ 0.0412 │ └──────────────────────┴─────────────┴────────────┴──────────────┴─────────┘ ┌──────────────────────┬────────────┬──────────┬─────────────┐ │ Tub │ Records │ Date │ Size │ ├──────────────────────┼────────────┼──────────┼─────────────┤ │ tub_1_22-01-10 │ 2,341 │ 01-10 │ 1.2 GB │ │ tub_2_22-01-11 │ 1,892 │ 01-11 │ 890 MB │ │ tub_3_22-01-12 │ 3,105 │ 01-12 │ 1.5 GB │ └──────────────────────┴────────────┴──────────┴─────────────┘ ``` ## Use Cases ### 1. Track Training Progress See which models you've trained and when: ```bash theme={null} donkey models ``` Helps you: * Remember which model performed best * Track training experiments * See which data was used for each model ### 2. Audit Data Usage Verify which tubs contributed to each model: ```bash theme={null} donkey models --group ``` Useful for: * Ensuring balanced training data * Tracking down data quality issues * Understanding model behavior based on training data ### 3. Model Management Identify old models to delete: ```bash theme={null} donkey models # Review output, then: rm models/old_model_22-01-10.h5 ``` ## Database Location The model database is stored in: ``` ~/mycar/models/ ├── database.json # Training history ├── mypilot_22-01-15.h5 # Model files └── ... ``` ## Information Displayed ### Pilot Table * **Name** - Model filename * **Type** - Model architecture (linear, categorical, lstm, etc.) * **Tubs** - Comma-separated list of tub indices used in training * **Date** - Training date * **Loss** - Final validation loss (lower is better) * **Val Loss** - Validation set loss * **Transfer** - Parent model if using transfer learning * **Comment** - User comment added during training ### Tub Table * **Tub** - Tub directory name * **Records** - Number of data records * **Date** - Collection date * **Size** - Disk space used * **Sessions** - Number of recording sessions ## Using --group Flag ```bash theme={null} donkey models --group ``` The `--group` flag organizes the output by grouping tubs together that were used in the same training runs. This makes it easier to see training patterns and data relationships. ## Model Database Schema The `database.json` file stores training metadata: ```json theme={null} { "pilots": [ { "name": "mypilot_22-01-15.h5", "type": "linear", "tubs": ["tub_1", "tub_2", "tub_3"], "date": "2022-01-15T10:30:00", "loss": 0.0345, "val_loss": 0.0389, "transfer": null, "comment": "First working model" } ], "tubs": [ { "path": "./data/tub_1_22-01-10", "records": 2341, "date": "2022-01-10" } ] } ``` ## Adding Comments to Models When training, add comments to track experiments: ```bash theme={null} donkey train --tub ./data --model ./models/mypilot.h5 \ --comment "Trained on straight sections only" ``` Comments appear in the database and help you remember model purposes. ## Troubleshooting If the command shows no models: * Ensure you're in your car directory: `cd ~/mycar` * Check models directory exists: `ls models/` * Train a model first with [`donkey train`](/cli/train) * Database might be corrupted, check `models/database.json` If `database.json` is missing: * The database is created during training * Train a new model to recreate it * Or create empty database: `echo '{"pilots": [], "tubs": []}' > models/database.json` If database information is wrong: * The database is updated during training, not retroactively * Old models trained before database feature won't appear * You can manually edit `models/database.json` (valid JSON required) ## Related Commands * [`donkey train`](/cli/train) - Train models (creates database entries) * [`donkey tubplot`](/cli/tubplot) - Compare model predictions * [`donkey makemovie`](/cli/makemovie) - Visualize model performance ## Tips **Name models descriptively** - Use meaningful names that indicate track, date, or purpose: ```bash theme={null} donkey train --tub ./data --model models/track2_fast_22-01-15.h5 ``` **Clean old models regularly** - Delete underperforming models to save space: ```bash theme={null} donkey models # review performance rm models/bad_model.h5 # delete poor performers ``` **Track experiments** - Always add comments when trying new architectures or training strategies: ```bash theme={null} --comment "LSTM with 128 units, dropout 0.2" ``` ## Source Code Implemented in: * `donkeycar/management/base.py:573-592` - CLI command * `donkeycar/pipeline/database.py` - PilotDatabase class ## Further Reading * [Training Documentation](/training/deep-learning) * [Model Architectures](/parts/keras-models) * [Data Management](/training/data-collection)