MFAI: Météo-France's AI Python package

MFAI is a Python package that provides the following features:

A variety of PyTorch Neural Network architectures (CNN, Vision Transformers, ...) adapted to our needs, tested on our projects and datasets. For each architecture, we provide the reference to the original paper and source code if applicable and also the modifications we made.
Per architecture schema validated settings using dataclasses-json
A NamedTensor class to handle multi-dimensional data with named dimensions and named features

Neural Network Architectures
- Convolutional Neural Networks:
  - deeplabv3/deeplabv3+
  - halfunet
  - unet/customunet
- Vision Transformers:
  - segformer
  - swinunetr
  - unetr++
- Graph Neural Networks:
  - HiLAM
  - GraphLAM
- Large Language Models:
  - GPT2
  - LLama2
- Multimodal Language Models:
  - A custom Fuyu inspired model
SegmentationLightningModule
NamedTensors
Metrics
- Critical Sucess Index
- False Alarm Rate
- False Negative Rate
- Precision-Recall Area Under Curve
Installation
Usage
Running tests

Neural Network Architectures

Currently we support the following neural network architectures:

Convolutional Neural Networks

Model	Research Paper	Input Shape	ONNX exportable ?	Notes	Use-Cases at MF
DeepLabV3Plus	arxiv link	(Batch, features, Height, Width)	Yes	As a very large receptive field versus U-Net, Half-Unet, ...	Front Detection, Nowcasting
HalfUNet	researchgate link	(Batch, features, Height, Width)	Yes	In prod/oper on Espresso V2 with 128 filters and standard conv blocks instead of ghost	Satellite channels to rain estimation
UNet	arxiv link	(Batch, features, Height, Width)	Yes	Vanilla U-Net	Radar image cleaning
CustomUnet	arxiv link	(Batch, features, Height, Width)	Yes	U-Net like architecture with a variety of resnet encoder choices	Radar image cleaning

Vision Transformers

Model	Research Paper	Input Shape	ONNX exportable ?	Notes	Use-Cases at MF
Segformer	arxiv link	(Batch, features, Height, Width)	Yes	On par with u-net like on Deepsyg (MF internal), added an upsampling stage. Adapted from Lucidrains' github	Segmentation tasks
SwinUNETR	arxiv link	(Batch, features, Height, Width)	No	2D Swin Unet transformer (Pangu and archweather uses customised 3D versions of Swin Transformers). Plugged in from MONAI. The decoders use Bilinear2D + Conv2d instead of Conv2dTranspose to remove artefacts/checkerboard effects	Segmentation tasks
UNETRPP	arxiv link	(Batch, features, Height, Width) or (Batch, features, Height, Width, Depth)	Yes	Vision transformer with a reduced GFLOPS footprint adapted from author's github. Modified to work both with 2d and 3d inputs. The decoders use Bilinear2D + Conv2d instead of Conv2dTranspose to remove artefacts/checkerboard effects	Front Detection, LAM Weather Forecasting

Graph Neural Networks

Model	Research Paper	Input Shape	ONNX exportable ?	Notes	Use-Cases at MF
HiLAM, GraphLAM	arxiv link	(Batch, graph_node_id, features)	No	Imported and adapted from Joel's github

Large Language Models

Model	Research Paper	Input Shape	ONNX exportable ?	Notes	Use-Cases at MF
GPT2	openai paper	(Batch, token_id)	No	Imported and adapted from Sebastian Raschka's book and github
Llama2	arxiv link	(Batch, token_id)	No	Imported and adapted from Sebastian Raschka's book and github

Multimodal Language Models

Model	Research Paper	Input Shape	ONNX exportable ?	Notes	Use-Cases at MF
Custom Fuyu Like Model	arxiv link	(Batch, token_id) for text, (Batch, Lat, Lon, Timestep, Features) for weather inputs	No	Inspired from Adept AI blog post and Sebastian Raschka's blog	Marine text product generation

Details about our models

Except for LLMs and MLLMs, each model we provide is a subclass of torch.nn.Module and can be used in a PyTorch training loop. It has multiple class attributes to facilitate model usage in a project:

settings_kls: a class that defines the settings of the model (number of filters, kernel size, ...). It is used to instanciate the model with a specific configuration.
onnx_supported: a boolean that indicates if the model can be exported to onnx. Our CI validates that the model can be exported to onnx and reloaded for inference.
supported_num_spatial_dims: a tuple that describes the spatial dimensions of the input tensor supported by the model. A model that supports 2D spatial data will have (2,) as value. A model that supports 2d or 3d spatial data will have (2, 3) as value.
num_spatial_dims: an integer that describes the number of spatial dimensions of the input/output tensor expected by the instance of the model, must be a value in supported_num_spatial_dims.
settings: a runtime property returns the settings instance used to instanciate the model.
model_type: an Enum describing the type of model: CONVOLUTIONAL, VISION_TRANSFORMER, GRAPH, LLM, MLLM.
features_last: a boolean that indicates if the features dimension is the last dimension of the input/output tensor. If False, the features dimension is the second dimension of the input/output tensor.
register: a boolean that indicates if the model should be registered in the MODELS registry. By default, it is set to False which allows the creation of intermediate subclasses not meant for direct use.

The Python interface contract for our model is enforced using Python ABC and in our case ModelABC class.

@dataclass_json
@dataclass(slots=True)
class HalfUNetSettings:
    num_filters: int = 64
    dilation: int = 1
    bias: bool = False
    use_ghost: bool = False
    last_activation: str = "Identity"
    absolute_pos_embed: bool = False

class HalfUNet(ModelABC, nn.Module):
    settings_kls = HalfUNetSettings
    onnx_supported: bool = True
    supported_num_spatial_dims = (2,)
    num_spatial_dims: int = 2
    features_last: bool = False
    model_type: int = ModelType.CONVOLUTIONAL
    register: bool = True

SegmentationLightningModule

We provide SegmentationLightningModule a lightning module adapted to supervised Deep Learning projects where the input of the neural network is made of one or multiple images and the target is also one or multiple images.

The module can be instanciated with any of the aforementioned neural networks architetures and used in 4 different modes : binary classification, multiclass classification, multilabel classification and regression.

The module provides:

customization for each stage of the training
metrics and plot logging with tensorboard
logging of configuration and hyperparameters
computation of several metrics during validation stage
test stage: compute metrics for each sample individualy and save them in CSV file

Lightning CLI

Lightning CLI is a convenient way to easily configure your DL experiments and reduce the number of lines of code in your project.

We provide an example of usage of the Lightning CLI with our lightning module and an exemple of config file to launch an experiment.

NamedTensors

PyTorch provides an experimental feature called named tensors, at this time it is subject to change so we don't use it. That's why we provide our own implementation.

NamedTensors are a way to give names to dimensions of tensors and to keep track of the names of the physical/weather parameters along the features dimension.

The NamedTensor class is a wrapper around a PyTorch tensor with additionnal attributes and methods, it allows us to pass consistent object linking data and metadata with extra utility methods (concat along features dimension, flatten in place, ...).

An example of NamedTensor usage for gridded data on a 256x256 grid:

tensor = torch.rand(4, 256, 256, 3)

nt = NamedTensor(
    tensor,
    names=["batch", "lat", "lon", "features"],
    feature_names=["u", "v", "t2m"],
)

print(nt.dim_size("lat"))
# 256

nt2 = NamedTensor(
    torch.rand(4, 256, 256, 1),
    names=["batch", "lat", "lon", "features"],
    feature_names=["q"],
)

# concat along the features dimension
nt3 = nt | nt2

# index by feature name
nt3["u"]

# Create a new NamedTensor with the same names but different data (useful for autoregressive models)
nt4 = NamedTensor.new_like(torch.rand(4, 256, 256, 4), nt3)

# Flatten in place the lat and lon dimensions and rename the new dim to 'ngrid'
# this is typically to feed our gridded data to GNNs
nt3.flatten_("ngrid", 1, 2)

# str representation of the NamedTensor yields useful statistics
>>> print(nt)
--- NamedTensor ---
Names: ['batch', 'lat', 'lon', 'features']
Tensor Shape: torch.Size([4, 256, 256, 3]))
Features:
┌────────────────┬─────────────┬──────────┐
│ Feature name   │         Min │      Max │
├────────────────┼─────────────┼──────────┤
│ u              │ 1.3113e-06  │ 0.999996 │
│ v              │ 8.9407e-07  │ 0.999997 │
│ t2m            │ 5.06639e-06 │ 0.999995 │

# rearrange in place using einops like syntax
nt3.rearrange_("batch ngrid features -> batch features ngrid")

Metrics

In addition to metrics available in torchmetrics, we implement :

Criticall Sucess Index (CSI) is given by: TP / (TP+FP+FN). This metric, usefull in meteorology, takes into account both false alarms and missed events in a neighborhood to avoid the phenomenon of double penalty.
False Alarm Rate (FAR) is given by: FP / (FP + TP).
False Negative Rate (FNR) is given by: FN / (FN + TP).
Precision-Recall Area Under the Curve (PR AUC). This metric summarize the overall performance of a model without depending on a threshold. It can be used in place of the Area Under ROC Curve when the dataset is too unbalanced.

Installation

Cloning the repository

git clone https://github.com/meteofrance/mfai
cd mfai
pip install -e .

Using pip

You can install using pip trageting the main branch:

pip install git+https://github.com/meteofrance/mfai

If you want to target a specific tag/version or branch:

pip install git+https://github.com/meteofrance/[email protected]

This syntax also work in requirements.txt. We do not provide wheel on pypi for now.

Usage

Instanciate a model

Our unit tests provides an example of how to use the models in a PyTorch training loop. Our models are instanciated with 2 mandatory positional arguments: in_channels and out_channels respectively the number of input and output channels/features of the model. A third input_shape parameter is either mandatory (UNETR++ or HalfUNet with absolute pos embedding) or optional for the other models. It describes the shape of the input tensor along its spatial dimensions.

The last parameter is an instance of the model's settings class and is a keyword argument with a default value set to the default settings.

Here is an example of how to instanciate the UNet model with a 3 channels input (like an RGB image) and 1 channel output with its default settings:

from mfai.torch.models import UNet
unet = UNet(in_channels=3, out_channels=1)

FEATURE: Once instanciated, the model (subclass of nn.Module) can be used like any standard PyTorch model.

In order to instanciate a HalfUNet model with a 2 channels inputs, 2 channels outputs and a custom settings (128 filters, ghost module):

from mfai.torch.models import HalfUNet
halfunet = HalfUNet(in_channels=2, out_channels=2, settings=HalfUNet.settings_kls(num_filters=128, use_ghost=True))

Finally, to instanciate a model with the mandatory input_shape parameter, here is an example with the UNETR++ model working on 2d spatial data (256x256) with 3 channels input and 1 channel output:

from mfai.torch.models import UNETRPP
unetrpp = UNETRPP(in_channels=3, out_channels=1, input_shape=(256, 256))

FEATURE: Each model has its settings class available under the settings_kls attribute.

You can use the load_from_settings_file function to instanciate a model with its settings from a json file:

from pathlib import Path
from mfai.torch.models import load_from_settings_file
model = load_from_settings_file(
    "HalfUNet",
    2,
    2,
    Path(".") / "mfai" / "config" / "models" / "halfunet128.json",
)

FEATURE: Use the load_from_settings_file to have the strictest validation of the settings.

Export to onnx

Our tests illustrate how to export and later reload a model to/from onnx. Here is an example of how to export a model to onnx:

from mfai.torch import export_to_onnx, onnx_load_and_infer

# Export the model to onnx assuming we have just trained a 'model'
export_to_onnx(model, "model.onnx")

# Load the onnx model and infer
output_tensor = onnx_load_and_infer("model.onnx", input_tensor)

Check the code of onnx_load_and_infer if you would like to load the model once and make multiple inferences.

SegmentationLightningModule

The lightning module can be instantiated and used in a forward pass as follows:

import torch
from mfai.torch.models import UNet
from mfai.torch.segmentation_module import SegmentationLightningModule

arch = UNet(in_channels=1, out_channels=1, input_shape=[64, 64])
loss = torch.nn.MSELoss()
model = SegmentationLightningModule(arch, "binary", loss)
x = torch.randn((1, 1, 64, 64)).float()
model(x)

The script examples/train_oxford_pets.py provides an example of how to instantiate the ligthning module and the lightning Trainer and use them to train and test a model on the Oxford-IIIT Pet Dataset.

You can also look at our unit tests in tests/test_lightning.py for example of usage.

See pytorch lightning documentation for how to configure the Trainer and customize the module to suit your needs.

Lightning CLI

Setting up lightning CLI is as easy as our examples/main_cli_dummy.py script:

from lightning.pytorch.cli import LightningCLI

from mfai.torch.dummy_dataset import DummyDataModule
from mfai.torch.segmentation_module import SegmentationLightningModule


def cli_main():
    cli = LightningCLI(SegmentationLightningModule, DummyDataModule)  # noqa: F841


if __name__ == "__main__":
    cli_main()

Then launch your experiment with: python examples/main_cli_dummy.py {fit, validate, test, predict} YOUR_MODEL_AND_TRAINER_ARGUMENTS

For instance: python examples/main_cli_dummy.py fit --model.model=Segformer --model.type_segmentation=binary --model.loss=torch.nn.BCEWithLogitsLoss --model.model.in_channels=2 --model.model.out_channels=1 --model.model.input_shape=[64, 64] --optimizer=AdamW --trainer.fast_dev_run=True

python examples/main_cli_dummy.py test --ckpt_path logs/best.ckpt

To avoid very very long command lines, you can use a config file to setup your experiment:

For instance, see mfai/config/cli_fit_test.yaml:

seed_everything: true
model:
  model:
    class_path: mfai.torch.models.Segformer
    init_args:
      in_channels: 2
      out_channels: 1
      input_shape: [64, 64]
      settings:
        num_layers: 2
        decoder_dim: 256
        num_downsampling_chans: 32
  type_segmentation: "binary"
  loss: torch.nn.BCEWithLogitsLoss
data:
  batch_size: 4
optimizer:
  class_path: torch.optim.AdamW
  init_args:
    lr: 0.001
...

Then you can pass your config file as argument to the CLI: python examples/main_cli_dummy.py fit --config mfai/config/cli_fit_test.yaml

You can override arguments of the config file or add new ones in the CLI: python examples/main_cli_dummy.py fit --config mfai/config/cli_fit_test.yaml --optimizer.lr 0.0001 --trainer.fast_dev_run True

Don't be shy about using the CLI help tool! It can save you a lot of time:

runai python examples/main_cli_dummy.py fit --help (click to expand)

usage: main.py [options] fit [-h] [-c CONFIG] [--print_config[=flags]] [--seed_everything SEED_EVERYTHING] [--trainer CONFIG]
                             [--trainer.accelerator.help CLASS_PATH_OR_NAME] [--trainer.accelerator ACCELERATOR]
                             [--trainer.strategy.help CLASS_PATH_OR_NAME] [--trainer.strategy STRATEGY] [--trainer.devices DEVICES]
                             [--trainer.num_nodes NUM_NODES] [--trainer.precision PRECISION]
                             [--trainer.logger.help CLASS_PATH_OR_NAME] [--trainer.logger LOGGER]
                             [--trainer.callbacks.help CLASS_PATH_OR_NAME] [--trainer.callbacks CALLBACKS]
                             [--trainer.fast_dev_run FAST_DEV_RUN] [--trainer.max_epochs MAX_EPOCHS]
                             [--trainer.min_epochs MIN_EPOCHS] [--trainer.max_steps MAX_STEPS] [--trainer.min_steps MIN_STEPS]
                             [--trainer.max_time MAX_TIME] [--trainer.limit_train_batches LIMIT_TRAIN_BATCHES]
                             [--trainer.limit_val_batches LIMIT_VAL_BATCHES] [--trainer.limit_test_batches LIMIT_TEST_BATCHES]
                             [--trainer.limit_predict_batches LIMIT_PREDICT_BATCHES] [--trainer.overfit_batches OVERFIT_BATCHES]
                             [--trainer.val_check_interval VAL_CHECK_INTERVAL]
                             [--trainer.check_val_every_n_epoch CHECK_VAL_EVERY_N_EPOCH]
                             [--trainer.num_sanity_val_steps NUM_SANITY_VAL_STEPS] [--trainer.log_every_n_steps LOG_EVERY_N_STEPS]
                             [--trainer.enable_checkpointing {true,false,null}] [--trainer.enable_progress_bar {true,false,null}]
                             [--trainer.enable_model_summary {true,false,null}]
                             [--trainer.accumulate_grad_batches ACCUMULATE_GRAD_BATCHES]
                             [--trainer.gradient_clip_val GRADIENT_CLIP_VAL]
                             [--trainer.gradient_clip_algorithm GRADIENT_CLIP_ALGORITHM] [--trainer.deterministic DETERMINISTIC]
                             [--trainer.benchmark {true,false,null}] [--trainer.inference_mode {true,false}]
                             [--trainer.use_distributed_sampler {true,false}] [--trainer.profiler.help CLASS_PATH_OR_NAME]
                             [--trainer.profiler PROFILER] [--trainer.detect_anomaly {true,false}]
                             [--trainer.barebones {true,false}] [--trainer.plugins.help CLASS_PATH_OR_NAME]
                             [--trainer.plugins PLUGINS] [--trainer.sync_batchnorm {true,false}]
                             [--trainer.reload_dataloaders_every_n_epochs RELOAD_DATALOADERS_EVERY_N_EPOCHS]
                             [--trainer.default_root_dir DEFAULT_ROOT_DIR] [--model CONFIG] [--model.model.help CLASS_PATH_OR_NAME]
                             --model.model MODEL --model.type_segmentation {binary,multiclass,multilabel,regression}
                             --model.loss LOSS [--data CONFIG] [--data.batch_size BATCH_SIZE]
                             [--optimizer.help CLASS_PATH_OR_NAME] [--optimizer CONFIG | CLASS_PATH_OR_NAME | .INIT_ARG_NAME VALUE]
                             [--lr_scheduler.help CLASS_PATH_OR_NAME]
                             [--lr_scheduler CONFIG | CLASS_PATH_OR_NAME | .INIT_ARG_NAME VALUE] [--ckpt_path CKPT_PATH]

Runs the full optimization routine.

options:
  -h, --help            Show this help message and exit.
  -c CONFIG, --config CONFIG
                        Path to a configuration file in json or yaml format.
  --print_config[=flags]
                        Print the configuration after applying all other arguments and exit. The optional flags customizes the
                        output and are one or more keywords separated by comma. The supported flags are: comments, skip_default,
                        skip_null.
  --seed_everything SEED_EVERYTHING
                        Set to an int to run seed_everything with this value before classes instantiation.Set to True to use a
                        random seed. (type: Union[bool, int], default: True)

Customize every aspect of training via flags:
  --trainer CONFIG      Path to a configuration file.
  --trainer.accelerator.help CLASS_PATH_OR_NAME
                        Show the help for the given subclass of Accelerator and exit.
  --trainer.accelerator ACCELERATOR
                        Supports passing different accelerator types ("cpu", "gpu", "tpu", "ipu", "hpu", "mps", "auto") as well as
                        custom accelerator instances. (type: Union[str, Accelerator], default: auto, known subclasses:
                        lightning.pytorch.accelerators.CPUAccelerator, lightning.pytorch.accelerators.CUDAAccelerator,
                        lightning.pytorch.accelerators.MPSAccelerator, lightning.pytorch.accelerators.XLAAccelerator)
  --trainer.strategy.help CLASS_PATH_OR_NAME
                        Show the help for the given subclass of Strategy and exit.

runai python examples/main_cli_dummy.py fit --model.model.help mfai.torch.models.Segformer (click to expand)

usage: main.py --model.model.in_channels IN_CHANNELS --model.model.out_channels OUT_CHANNELS
               --model.model.input_shape [ITEM,...] [--model.model.settings.dims [ITEM,...]]
               [--model.model.settings.heads [ITEM,...]] [--model.model.settings.ff_expansion [ITEM,...]]
               [--model.model.settings.reduction_ratio [ITEM,...]] [--model.model.settings.num_layers NUM_LAYERS]
               [--model.model.settings.decoder_dim DECODER_DIM]
               [--model.model.settings.num_downsampling_chans NUM_DOWNSAMPLING_CHANS]

Help for --model.model.help=mfai.torch.models.Segformer

Segformer architecture with extra:
  --model.model.in_channels IN_CHANNELS
                        (required, type: int)
  --model.model.out_channels OUT_CHANNELS
                        (required, type: int)
  --model.model.input_shape [ITEM,...]
                        (required, type: Tuple[int, int])

SegformerSettings(dims: Tuple[int, ...] = (32, 64, 160, 256), heads: Tuple[int, ...] = (1, 2, 5, 8), ff_expansion: Tuple[int, ...] = (8, 8, 4, 4), reduction_ratio: Tuple[int, ...] = (8, 4, 2, 1), num_layers: int = 2, decoder_dim: int = 256, num_downsampling_chans: int = 32):
  --model.model.settings.dims [ITEM,...]
                        (type: Tuple[int, ], default: (32, 64, 160, 256))
  --model.model.settings.heads [ITEM,...]
                        (type: Tuple[int, ], default: (1, 2, 5, 8))
  --model.model.settings.ff_expansion [ITEM,...]
                        (type: Tuple[int, ], default: (8, 8, 4, 4))
  --model.model.settings.reduction_ratio [ITEM,...]
                        (type: Tuple[int, ], default: (8, 4, 2, 1))
  --model.model.settings.num_layers NUM_LAYERS
                        (type: int, default: 2)
  --model.model.settings.decoder_dim DECODER_DIM
                        (type: int, default: 256)
  --model.model.settings.num_downsampling_chans NUM_DOWNSAMPLING_CHANS
                        (type: int, default: 32)

To help you write correctly your config file, use --print_config:

runai python examples/main_cli_dummy.py fit --model.model mfai.torch.models.Segformer --print_config (click to expand)

model:
  model:
    class_path: mfai.torch.models.Segformer
    init_args:
      in_channels: null
      out_channels: null
      input_shape: null
      settings:
        dims:
        - 32
        - 64
        - 160
        - 256
        heads:
        - 1
        - 2
        - 5
        - 8
        ff_expansion:
        - 8
        - 8
        - 4
        - 4
        reduction_ratio:
        - 8
        - 4
        - 2
        - 1
        num_layers: 2
        decoder_dim: 256
        num_downsampling_chans: 32
  type_segmentation: null
  loss: null

Metrics

As our metrics are subclasses of the torchmetrics.Metric class, you can both use them in a Pytorch or Pytorch Lightning model. Here is an example of how to set up a metric:

import torch
from mfai.torch.metrics import CSINeighborood

preds = torch.rand(2, 2).softmax(dim=-1)
target = torch.randint(2, (2, 2))

csi_metric = CSINeighborood(task="multiclass", num_classes=2, num_neighbors=0)
csi = csi_metric(preds, target)

Running Tests

Our tests are written using pytest. We check that:

The models can be instantiated with their default parameters, trained on a toy problem, onnx exported and reloaded for inference.
The NamedTensor class can be instantiated and used to manipulate data and metadata.
The SegmentationLightningModule can be instantiated and used to make a train step and a forward step.
The Ligthning CLI can be used for a "fast_dev" fit with command line arguments and a config file.

docker build . -f Dockerfile -t mfai
docker run -it --rm mfai python -m pytest tests

Contributing

We welcome contributions to this package. Our guidelines are the following:

Submit a PR with a clear description of the changes and the motivation behind them.
Make sure the current tests pass and add new tests if necessary to cover the new features. Our CI will fail with a test coverage below 80%.
Make sure the code is formatted with ruff : ruff format and ruff check

Publishing

We provide a script build_and_publish.sh to build the package and publish it to PyPI (TestPyPI by default). For now it uses Docker and our private/internal wrapper runai.

The full process is as follows (adjust the tag name and message to your needs):

git tag -a v0.1.0 -m "First release"

or

git tag v0.1.0

git push origin v0.1.0
./build_and_publish.sh

If you don't create and push a tag, the script will return an error.

In order to publish to pypi passes the argument pypi:

./build_and_publish.sh pypi

See the Python Packaging User Guide for more information on how to publish a package to PyPI.

Acknowledgements

This package is maintained by the DSM/LabIA team at Météo-France. We would like to thank the authors of the papers and codes we used to implement the models (see above links to arxiv and github) and the authors of the libraries we use to build this package (see our requirements.txt).

Name		Name	Last commit message	Last commit date
Latest commit History 100 Commits
.github/workflows		.github/workflows
examples		examples
mfai		mfai
tests		tests
.gitignore		.gitignore
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
build_and_publish.sh		build_and_publish.sh
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
requirements_dev.txt		requirements_dev.txt

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MFAI: Météo-France's AI Python package

Table of contents

Neural Network Architectures

Convolutional Neural Networks

Vision Transformers

Graph Neural Networks

Large Language Models

Multimodal Language Models

SegmentationLightningModule

Lightning CLI

NamedTensors

Metrics

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Cloning the repository

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MFAI: Météo-France's AI Python package

Table of contents

Neural Network Architectures

Convolutional Neural Networks

Vision Transformers

Graph Neural Networks

Large Language Models

Multimodal Language Models

SegmentationLightningModule

Lightning CLI

NamedTensors

Metrics

Installation

Cloning the repository

Using pip

Usage

Instanciate a model

Export to onnx

SegmentationLightningModule

Lightning CLI

Metrics

Running Tests

Contributing

Publishing

Acknowledgements

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