Update README.md

README.md

@@ -8,6 +8,10 @@ license: apache-2.0
   <img src="logo.png" alt="Logo" height="300"/>
 </p>
 
+<br>
+
+> **🚨 NEW Version 0.2.0: Mantis pre-training is now available! 🚨**
+
 ## Overview
 
 **MANTIS** is an open-source python package with a pre-trained time series classification foundation model implemented by Huawei Noah's Ark Lab.
@@ -65,6 +69,20 @@ Please refer to [`getting_started/`](https://github.com/vfeofanov/mantis/tree/ma
 
 Below we summarize the basic commands needed to use the package.
 
+### Prepare Data.
+
+As an input, Mantis accepts any time series with sequence length **proportional** to 32, which corresponds to the number of tokens fixed in our model. 
+We found that resizing time series via interpolation is generally a good choice:
+``` python
+import torch
+import torch.nn.functional as F
+
+def resize(X):
+    X_scaled = F.interpolate(torch.tensor(X, dtype=torch.float), size=512, mode='linear', align_corners=False)
+    return X_scaled.numpy()
+```
+Generally speaking, the interpolation size is a hyperparameter to play with. Nevertheless, since Mantis was pre-trained on sequences of length 512, interpolating to this length looks reasonable in most of cases.
+
 ### Initialization.
 
 To load our pre-trained model from the HuggingFace, it is sufficient to run:
@@ -125,6 +143,15 @@ adapter = LinearChannelCombiner(num_channels=X.shape[1], new_num_channels=5)
 model.fit(X, y, adapter=adapter, fine_tuning_type='adapter_head')
 ```
 
+### Pre-training.
+
+The model can be pre-trained using the `pretrain` method of `MantisTrainer` that supports data parallelization. You can see a pre-training demo at `getting_started/pretrain.py`.
+For example, to pre-train the model on 4 GPUs, you can run the following commands:
+```
+cd getting_started/
+python -m torch.distributed.run --nproc_per_node=4 --nnodes=1 pretrain.py --seed 42
+```
+
 ## Citing Mantis 📚
 
 If you use Mantis in your work, please cite this technical report: