GPU and TPU usage guide (#826)

* GPU and TPU usage guide

* GPU and TPU usage guide

Co-authored-by: Julien Herzen <julien@unit8.co>

darts/timeseries.py

@@ -1060,7 +1060,7 @@ def quantiles_df(self, quantiles: Tuple[float] = (0.1, 0.5, 0.9)) -> pd.DataFram
 
     def astype(self, dtype: Union[str, np.dtype]) -> "TimeSeries":
         """
-        Converts this series to a new seroes with desired dtype.
+        Converts this series to a new series with desired dtype.
 
         Parameters
         ----------

docs/userguide/gpu_and_tpu_usage.md

@@ -0,0 +1,166 @@
+# Use Torch Forecasting Models with GPU and TPU
+This document was written for darts version 0.17.0.
+
+We assume that you already know about Torch Forecasting Models in Darts. If you're new to the topic we recommend you to read our [guide on Torch Forecasting Models](https://unit8co.github.io/darts/userguide/torch_forecasting_models.html) first. Here is also an [Recurrent Neural Network (RNN) Model example](https://unit8co.github.io/darts/examples/04-RNN-examples.html), in which this guide is going to be based on.
+
+## Use CPU
+
+By default all models will run on CPU. As shown in the RNN example above, we'll import the Air Passenger dataset, as well as other necessary modules.
+```
+import numpy as np
+import pandas as pd
+
+import matplotlib.pyplot as plt
+
+from darts.dataprocessing.transformers import Scaler
+from darts.models import RNNModel
+from darts.metrics import mape
+from darts.datasets import AirPassengersDataset
+```
+
+Now we read and scale the data like this:
+
+```
+# Read data:
+series = AirPassengersDataset().load()
+series = series.astype(np.float32)
+
+# Create training and validation sets:
+train, val = series.split_after(pd.Timestamp("19590101"))
+
+# Normalize the time series (note: we avoid fitting the transformer on the validation set)
+transformer = Scaler()
+train_transformed = transformer.fit_transform(train)
+val_transformed = transformer.transform(val)
+series_transformed = transformer.transform(series)
+```
+
+Next we will create our RNN like this:
+```
+my_model = RNNModel(
+    model="RNN",
+    hidden_dim=20,
+    dropout=0,
+    batch_size=16,
+    n_epochs=300,
+    optimizer_kwargs={"lr": 1e-3},
+    model_name="Air_RNN",
+    log_tensorboard=True,
+    random_state=42,
+    training_length=20,
+    input_chunk_length=14,
+    force_reset=True,
+)
+```
+and fit it to the data:
+```
+my_model.fit(train_transformed, val_series=val_transformed)
+```
+where in the output we can see that no other processing unit is used to train our model:
+```
+GPU available: False, used: False
+TPU available: False, using: 0 TPU cores
+IPU available: False, using: 0 IPUs
+
+  | Name      | Type    | Params
+--------------------------------------
+0 | criterion | MSELoss | 0     
+1 | rnn       | RNN     | 460   
+2 | V         | Linear  | 21    
+--------------------------------------
+481       Trainable params
+0         Non-trainable params
+481       Total params
+0.004     Total estimated model params size (MB)
+
+Epoch 299: 100% 8/8 [00:00<00:00, 42.49it/s, loss=0.00285, v_num=logs]
+<darts.models.forecasting.rnn_model.RNNModel at 0x7fc75901cb10>
+```
+
+Now the model is ready to start predicting, which won't be shown here since it's included in the example linked in the start of this guide.
+
+## Use a GPU
+GPUs can dramatically improve the performance of your model in terms of processing time. By using an Accelarator in the [Pytorch Lightning Trainer](https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#accelerator), we can enjoy the benefits of a GPU. We only need to instruct our model to use our machine's GPU through PyTorch Lightning Trainer parameters, which are expressed as the `pl_trainer_kwargs` dictionary, like this:
+```
+my_model = RNNModel(
+    model="RNN",
+    ...
+    force_reset=True,
+    pl_trainer_kwargs={
+      "accelerator": "gpu",
+      "gpus": [0]
+    },
+)
+```
+which now outputs:
+```
+GPU available: True, used: True
+TPU available: False, using: 0 TPU cores
+IPU available: False, using: 0 IPUs
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
+
+  | Name      | Type    | Params
+--------------------------------------
+0 | criterion | MSELoss | 0     
+1 | rnn       | RNN     | 460   
+2 | V         | Linear  | 21    
+--------------------------------------
+481       Trainable params
+0         Non-trainable params
+481       Total params
+0.004     Total estimated model params size (MB)
+
+Epoch 299: 100% 8/8 [00:00<00:00, 39.81it/s, loss=0.00285, v_num=logs]
+<darts.models.forecasting.rnn_model.RNNModel at 0x7ff1b5e4d4d0>
+```
+From the output we can see that the GPU is both available and used. The rest of the code doesn't require any change, i.e. it's irrelevant if we are using a GPU or CPU.
+
+## Use a TPU
+
+Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning.
+
+There are three main ways to get access to a TPU:
+
+* Google Colab
+* Google Cloud (GCP)
+* Kaggle
+
+If you are using a TPU in the Google Colab kind of notebook, then you should first install these:
+```
+!pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl
+!pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchtext==0.10.0 -f https://download.pytorch.org/whl/cu111/torch_stable.html
+!pip install pyyaml==5.4.1
+```
+and then instruct our model to use a TPU or more. In our example we are using four TPUs, like this:
+```
+my_model = RNNModel(
+    model="RNN",
+    ...
+    force_reset=True,
+    pl_trainer_kwargs={
+      "accelerator": "tpu",
+      "tpu_cores": [4]
+    },
+)
+```
+which outputs:
+```
+WARNING:root:TPU has started up successfully with version pytorch-1.9
+GPU available: False, used: False
+TPU available: True, using: [4] TPU cores
+IPU available: False, using: 0 IPUs
+
+  | Name      | Type    | Params
+--------------------------------------
+0 | criterion | MSELoss | 0     
+1 | rnn       | RNN     | 460   
+2 | V         | Linear  | 21    
+--------------------------------------
+481       Trainable params
+0         Non-trainable params
+481       Total params
+0.002     Total estimated model params size (MB)
+Epoch 299: 100% 8/8 [00:00<00:00, 8.52it/s, loss=0.00285, v_num=logs]
+<darts.models.forecasting.rnn_model.RNNModel at 0x7ff1b5e4d4d0>
+```
+From the output we can see that our model is using 4 TPUs.