update execution docs

Author: matbun

README.md

@@ -1,37 +1,48 @@
 # xtclim
+
 ## ML-based extreme events detection and characterization (CERFACS)
 
 The code is adapted from CERFACS' [repository](https://github.com/cerfacs-globc/xtclim/tree/master).
-The implementation of a pipeline with itwinai framework is shown below. 
+The implementation of a pipeline with itwinai framework is shown below.
+
+## Method
 
-## Method 
 Convolutional Variational AutoEncoder.
 
 ## Input
+
 "3D daily images", daily screenshots of Europe for three climate variables (maximum temperature, precipitation, wind).
 
-## Output 
+## Output
+
 Error between original and reconstructed image: postprocessed for analysis in the `scenario_season_comparison.ipynb` file.
 
-## Idea 
+## Idea
+
 The more unusual an image (anomaly), the higher error.
 
 ## Information on files
 
-In the preprocessing folder, the `preprocess_functions_2d_ssp.py` class loads NetCDF files from a `data` folder, which has to be specified in `dataset_root` in the config file `pipeline.yaml` (please change the location). The data can be found [here](https://b2drop.eudat.eu/s/rtAadDNYDWBkxjJ). The given class normalizes and adjusts the data for the network. The function `preprocess_2d_seasons.py` splits the data into seasonal files. Preprocessed data is stored in the `input` folder.
+In the preprocessing folder, the `preprocess_functions_2d_ssp.py` class loads NetCDF files from a `data` folder,
+which has to be specified in `dataset_root` in the config file `config.yaml` (please change the location).
+The data can be found [here](https://b2drop.eudat.eu/s/rtAadDNYDWBkxjJ). The given class normalizes
+and adjusts the data for the network. The function `preprocess_2d_seasons.py` splits the data into
+seasonal files. Preprocessed data is stored in the `input` folder.
 
-The file `train.py` trains the network. Caution: It will overwrite the weights of the network already saved in outputs (unless you change the path name `outputs/cvae_model_3d.pth` in the script).
+The file `train.py` trains the network. Caution: It will overwrite the weights of the network already
+saved in outputs (unless you change the path name `outputs/cvae_model_3d.pth` in the script).
 
 The `anomaly.py` file evaluates the network on the available datasets - train, test, and projection.
 
-## How to launch pipeline
+## How to launch training workflow
 
-The config file `pipeline.yaml` contains all the steps to execute the workflow. You can launch it from the root of the repository with:
+The config file `config.yaml` contains all the steps to execute the workflow.
+You can launch it from the root of the repository with:
 
 ```bash
-python train.py -p pipeline.yaml
-
+itwinai exec-pipeline --config config.yaml
 ```
 
 ## TODOs
+
 Integration of post-processing step + distributed strategies