> ## Documentation Index > Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt > Use this file to discover all available pages before exploring further. # Save and Load Models Saving and loading trained Deep Learning models has multiple valuable uses. These models are often costly to train; storing a pre-trained model can help reduce costs as it can be loaded and reused to forecast multiple times. Moreover, it enables Transfer learning capabilities, consisting of pre-training a flexible model on a large dataset and using it later on other data with little to no training. It is one of the most outstanding 🚀 achievements in Machine Learning 🧠 and has many practical applications. In this notebook we show an example on how to save and load `NeuralForecast` models. The two methods to consider are:
1. `NeuralForecast.save`: Saves models into disk, allows save dataset and config.
2. `NeuralForecast.load`: Loads models from a given path.
> **Important** > > This Guide assumes basic knowledge on the NeuralForecast library. For > a minimal example visit the [Getting > Started](../getting-started/quickstart.html) guide. You can run these experiments using GPU with Google Colab.

## 1. Installing NeuralForecast ```python theme={null} %%capture !pip install neuralforecast ``` ## 2. Loading AirPassengers Data For this example we will use the classical [AirPassenger Data set](https://www.kaggle.com/datasets/rakannimer/air-passengers). Import the pre-processed AirPassenger from `utils`. ```python theme={null} from neuralforecast.utils import AirPassengersDF ``` ```python theme={null} Y_df = AirPassengersDF Y_df.head() ``` | | unique\_id | ds | y | | - | ---------- | ---------- | ----- | | 0 | 1.0 | 1949-01-31 | 112.0 | | 1 | 1.0 | 1949-02-28 | 118.0 | | 2 | 1.0 | 1949-03-31 | 132.0 | | 3 | 1.0 | 1949-04-30 | 129.0 | | 4 | 1.0 | 1949-05-31 | 121.0 | ## 3. Model Training Next, we instantiate and train three models: `NBEATS`, `NHITS`, and `AutoMLP`. The models with their hyperparameters are defined in the `models` list. ```python theme={null} import logging from ray import tune from neuralforecast.core import NeuralForecast from neuralforecast.auto import AutoMLP from neuralforecast.models import NBEATS, NHITS ``` ```python theme={null} logging.getLogger('pytorch_lightning').setLevel(logging.ERROR) ``` ```python theme={null} horizon = 12 models = [NBEATS(input_size=2 * horizon, h=horizon, max_steps=50), NHITS(input_size=2 * horizon, h=horizon, max_steps=50), AutoMLP(# Ray tune explore config config=dict(max_steps=100, # Operates with steps not epochs input_size=tune.choice([3*horizon]), learning_rate=tune.choice([1e-3])), h=horizon, num_samples=1, cpus=1)] ``` ```text theme={null} Seed set to 1 Seed set to 1 ``` ```python theme={null} %%capture nf = NeuralForecast(models=models, freq='ME') nf.fit(df=Y_df) ``` Produce the forecasts with the `predict` method. ```python theme={null} Y_hat_df = nf.predict() Y_hat_df.head() ``` ```text theme={null} Predicting: | … ``` ```text theme={null} Predicting: | … ``` ```text theme={null} Predicting: | … ``` | | unique\_id | ds | NBEATS | NHITS | AutoMLP | | - | ---------- | ---------- | ---------- | ---------- | ---------- | | 0 | 1.0 | 1961-01-31 | 446.882172 | 447.219238 | 454.914154 | | 1 | 1.0 | 1961-02-28 | 465.145813 | 464.558014 | 430.188446 | | 2 | 1.0 | 1961-03-31 | 469.978424 | 474.637238 | 458.478577 | | 3 | 1.0 | 1961-04-30 | 493.650665 | 502.670349 | 477.244507 | | 4 | 1.0 | 1961-05-31 | 537.569275 | 559.405212 | 522.252991 | We plot the forecasts for each model. ```python theme={null} from utilsforecast.plotting import plot_series ``` ```python theme={null} plot_series(Y_df, Y_hat_df) ```

## 4. Save models To save all the trained models use the `save` method. This method will save both the hyperparameters and the learnable weights (parameters). The `save` method has the following inputs: * `path`: directory where models will be saved. * `model_index`: optional list to specify which models to save. For example, to only save the `NHITS` model use `model_index=[2]`. * `overwrite`: boolean to overwrite existing files in `path`. When True, the method will only overwrite models with conflicting names. * `save_dataset`: boolean to save `Dataset` object with the dataset. ```python theme={null} nf.save(path='./checkpoints/test_run/', model_index=None, overwrite=True, save_dataset=True) ``` For each model, two files are created and stored: * `[model_name]_[suffix].ckpt`: Pytorch Lightning checkpoint file with the model parameters and hyperparameters. * `[model_name]_[suffix].pkl`: Dictionary with configuration attributes. Where `model_name` corresponds to the name of the model in lowercase (eg. `nhits`). We use a numerical suffix to distinguish multiple models of each class. In this example the names will be `automlp_0`, `nbeats_0`, and `nhits_0`. > **Important** > > The `Auto` models will be stored as their base model. For example, the > `AutoMLP` trained above is stored as an `MLP` model, with the best > hyparparameters found during tuning. ## 5. Load models Load the saved models with the `load` method, specifying the `path`, and use the new `nf2` object to produce forecasts. ```python theme={null} nf2 = NeuralForecast.load(path='./checkpoints/test_run/') Y_hat_df2 = nf2.predict() Y_hat_df2.head() ``` ```text theme={null} Seed set to 1 Seed set to 1 Seed set to 1 ``` ```text theme={null} Predicting: | … ``` ```text theme={null} Predicting: | … ``` ```text theme={null} Predicting: | … ``` | | unique\_id | ds | NHITS | NBEATS | AutoMLP | | - | ---------- | ---------- | ---------- | ---------- | ---------- | | 0 | 1.0 | 1961-01-31 | 447.219238 | 446.882172 | 454.914154 | | 1 | 1.0 | 1961-02-28 | 464.558014 | 465.145813 | 430.188446 | | 2 | 1.0 | 1961-03-31 | 474.637238 | 469.978424 | 458.478577 | | 3 | 1.0 | 1961-04-30 | 502.670349 | 493.650665 | 477.244507 | | 4 | 1.0 | 1961-05-31 | 559.405212 | 537.569275 | 522.252991 | Finally, plot the forecasts to confirm they are identical to the original forecasts. ```python theme={null} plot_series(Y_df, Y_hat_df2) ```

## References [https://pytorch-lightning.readthedocs.io/en/stable/common/checkpointing\_basic.html](https://pytorch-lightning.readthedocs.io/en/stable/common/checkpointing_basic.html) [Oreshkin, B. N., Carpov, D., Chapados, N., & Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. ICLR 2020](https://arxiv.org/abs/1905.10437) [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)