Bug fixes and addition of CERFACS use-case (#151)

* Update train.py

* Update generic_tf.sh

* Update pyproject.toml

* Update train.py

* Fix: head problems with MacOS

* Fixes for MacOS support

* Fix: Update basic_components.py

* Addition of cerfacs use-case

* Update README.md

* Update train.py

env-files/itwinai-installer.sh

@@ -25,14 +25,14 @@ fi
 if [ "$ML_FRAMEWORK" == "pytorch" ]; then
     echo "Installing itwinai with PyTorch support..."
     # Skip last line (head -n -1) because it contains the istallation of itwinai
-    curl -fsSL https://github.com/interTwin-eu/itwinai/raw/main/env-files/torch/generic_torch.sh | head -n -1 | bash
+    curl -fsSL https://github.com/interTwin-eu/itwinai/raw/main/env-files/torch/generic_torch.sh | sed '$d' | bash
     # cat ../env-files/torch/generic_torch.sh | head -n -1 | bash
     # Install from PyPI
     pip install itwinai[torch]
 elif [ "$ML_FRAMEWORK" == "tensorflow" ]; then
     echo "Installing itwinai with TensorFlow support..."
     # Skip last line (head -n -1) because it contains the istallation of itwinai
-    curl -fsSL https://github.com/interTwin-eu/itwinai/raw/main/env-files/tensorflow/generic_tf.sh | head -n -1 | bash
+    curl -fsSL https://github.com/interTwin-eu/itwinai/raw/main/env-files/tensorflow/generic_tf.sh | sed '$d' | bash
     # cat ../env-files/tensorflow/generic_tf.sh | head -n -1 | bash
     # Install from PyPI
     pip install itwinai

env-files/tensorflow/generic_tf.sh

@@ -52,10 +52,10 @@ if [ -f "${cDir}/$ENV_NAME/bin/tensorboard" ]; then
   echo
 else
   if [ -z "$NO_CUDA" ]; then
-    pip3 install tensorflow[and-cuda]==2.15 --no-cache-dir
+    pip3 install tensorflow[and-cuda] --no-cache-dir
   else
     # CPU only installation
-    pip3 install tensorflow==2.15 --no-cache-dir
+    pip3 install tensorflow --no-cache-dir
   fi
 fi
 
@@ -84,7 +84,7 @@ fi
 # # Since TF 2.16, keras updated to 3.3,
 # # which leads to an error when more than 1 node is used
 # # https://keras.io/getting_started/
-# pip3 install tf_keras
+pip3 install tf_keras
 
 # itwinai
 pip3 install -e .[dev]

env-files/torch/generic_torch.sh

@@ -50,21 +50,31 @@ pip3 install --no-cache-dir wheel
 if [ -f "${cDir}/$ENV_NAME/bin/torchrun" ]; then
   echo 'Torch already installed'
 else
-  if [ -z "$NO_CUDA" ]; then
+  if [ -z "$NO_CUDA" ] ; then
     pip3 install --no-cache-dir \
-      torch==2.1.* torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+    torch==2.1.* torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
   else
-    # CPU only installation 
-    pip3 install --no-cache-dir \
-      torch==2.1.* torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+    # CPU only installation for MacOS
+    if [[ "$OSTYPE" =~ ^darwin ]] ; then
+      pip3 install --no-cache-dir \
+        torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0
+    else
+    # CPU only installation for other OSs
+      pip3 install --no-cache-dir \
+         torch==2.1.* torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+    fi
   fi
 fi
 
 # HPO - RayTune
 if [ -f "${cDir}/$ENV_NAME/bin/ray" ]; then
   echo 'Ray already installed'
 else
-  pip3 install --no-cache-dir ray ray[tune]
+  if [[ "$OSTYPE" =~ ^darwin ]] ; then
+    echo 'Installation issues: Skipping Ray installation for MacOS'
+  else
+    pip3 install --no-cache-dir ray ray[tune]
+  fi
 fi
 
 # install deepspeed

pyproject.toml

@@ -61,7 +61,7 @@ dev = [
     "pytest-cov>=4.1.0",
     "ipykernel",
     "ipython",
-    "tensorflow==2.15",    # needed by tests on tensorboard
+#    "tensorflow==2.15",    # needed by tests on tensorboard
 ]
 
 [project.urls]

tutorials/distributed-ml/tf-tutorial-0-basics/train.py

@@ -5,7 +5,6 @@
 >>> sbatch tfmirrored_slurm.sh
 
 """
-import os
 from typing import Any
 import argparse
 import tensorflow as tf
@@ -33,11 +32,11 @@ def parse_args() -> argparse.Namespace:
     return args
 
 
-def tf_rnd_dataset():
+def tf_rnd_dataset(args):
     """Dummy TF dataset."""
     (x_train, y_train), (x_test, y_test) = \
         tf.keras.datasets.mnist.load_data(
-            path=os.getcwd()+'/.keras/datasets/mnist.npz')
+            path='p/scratch/intertwin/datasets/.keras/datasets/mnist.npz')
 
     train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
     train_dataset = train_dataset.batch(args.batch_size)

tutorials/distributed-ml/tf-tutorial-1-imagenet/train.py

@@ -151,7 +151,7 @@ def main():
     et = timer()
 
     # trains the model
-    model.fit(dist_train, epochs=args.epochs, steps_per_epoch=2000, verbose=10)
+    model.fit(dist_train, epochs=args.epochs, steps_per_epoch=500, verbose=10)
 
     print('TIMER: total epoch time:',
           timer() - et, ' s')

tutorials/ml-workflows/basic_components.py

@@ -10,6 +10,7 @@
 class MyDataGetter(DataGetter):
     def __init__(self, data_size: int, name: Optional[str] = None) -> None:
         super().__init__(name)
+        self.data_size = data_size
         self.save_parameters(data_size=data_size)
 
     @monitor_exec

use-cases/xtclim/LICENSE

@@ -0,0 +1,201 @@
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use-cases/xtclim/README.md

@@ -0,0 +1,37 @@
+# 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. 
+
+## Method 
+Convolutional Variational AutoEncoder.
+
+## Input
+"3D daily images", daily screenshots of Europe for three climate variables (maximum temperature, precipitation, wind).
+
+## Output 
+Error between original and reconstructed image: postprocessed for analysis in the `scenario_season_comparison.ipynb` file.
+
+## 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.
+
+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
+
+The config file `pipeline.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
+
+```
+
+## TODOs
+Integration of post-processing step + distributed strategies

use-cases/xtclim/Requirements.txt

@@ -0,0 +1,17 @@
+cartopy
+cftime
+codecarbon
+dask
+datetime
+imageio
+ipykernel
+matplotlib
+numpy
+pandas
+torch
+torchvision
+tqdm
+urllib3==1.26.13
+xarray
+netCDF4
+h5netcdf