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a6d8945
add toml description for installing
xju2 Apr 2, 2024
ce941ce
into src folder
xju2 Apr 2, 2024
6e942c2
new formatting
xju2 Apr 2, 2024
b139cc1
update examples
xju2 Apr 2, 2024
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201 changes: 201 additions & 0 deletions LICENSE
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4 changes: 2 additions & 2 deletions Makefile
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Expand Up @@ -27,7 +27,7 @@ test-full: ## Run all tests
pytest

train: ## Train the model
python src/train.py
python hadml/train.py

debug: ## Enter debugging mode with pdb
#
Expand All @@ -39,4 +39,4 @@ debug: ## Enter debugging mode with pdb
# - use "l" to print src code around current line, "ll" for full function code
# - docs: https://docs.python.org/3/library/pdb.html
#
python -m pdb src/train.py debug=default
python -m pdb hadml/train.py debug=default
33 changes: 11 additions & 22 deletions README.md
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Expand Up @@ -10,38 +10,27 @@ The original README.md is moved to [`tests/README.md`](tests/README.md).
git clone [email protected]:hep-lbdl/hadml.git
cd hadml

# [OPTIONAL] create conda environment
conda create -n herwig python=3.9
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Maybe we should still have this command, so that it's obvious which python version should have all the correct versions of dependencies available?

conda activate herwig

# install pytorch according to instructions
# https://pytorch.org/get-started/
# for example, for cuda 11.7

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

## And install pyG
conda install pyg -c pyg

# install requirements
# recommend to create .venv and activate it
# it can be easily done in VSCODE by running the command "Python: Create Environment"
pip install -r requirements.txt
pip install -e .

# IF not runnin the code in cori, you need to copy data from cori (7.1 GB), sorry for the large size.
# scp cori.nersc.gov:/global/project/projectdirs/m3246/Herwig7/StartingData/allHadrons_10M_mode4_with_quark_with_pert.npz data/Herwig/
# IF in cori, create a soft link to the data
ln -s /global/cfs/cdirs/m3246/Herwig7/StartingData/allHadrons_10M_mode4_with_quark_with_pert.npz data/Herwig/

# run the training for cluster-level generation
python hadml/train.py experiment=herwig_all_hadron
python train.py experiment=herwig_all_hadron

# or run the training for event-level generation
python hadml/train.py experiment=herwig_event
python train.py experiment=herwig_event

# to run the training for event-level generation with fitting to the nominal Herwig sample (data samples to be downloaded at https://doi.org/10.5281/zenodo.7958362 and placed at data/Herwig/raw)
python hadml/train.py experiment=herwig_event_nominal
python train.py experiment=herwig_event_nominal

# to run the training for event-level generation with fitting to the variation Herwig sample (data samples to be downloaded at https://doi.org/10.5281/zenodo.7958362 and placed at data/Herwig/raw)
python hadml/train.py experiment=herwig_event_variation
python train.py experiment=herwig_event_variation
```

# A crash course on training
Expand All @@ -66,7 +55,7 @@ The environment variable `HerwigData` is used to locate the data files.

To perform a training, run the following command:
```bash
python hadml/train.py experiment=herwig_all_hadron
python train.py experiment=herwig_all_hadron
```
This will train a model using the `configs/experiment/herwig_all_hadron.yaml`.
Results and logs will be saved in the `logs/herwigAllhadron/runs` as defined by `hydra.run.dir` in the `configs/hydra/default.yaml`. You can change the directory by changing the `task_name` in the `configs/experiment/herwig_all_hadron.yaml`.
Expand All @@ -75,19 +64,19 @@ Results and logs will be saved in the `logs/herwigAllhadron/runs` as defined by
It is optional to add a logger. For example, you can monitor the training
performance using the [Weights & Biases](https://wandb.ai/site) logger by running:
```bash
python hadml/train.py experiment=herwig_all_hadron logger=wandb
python train.py experiment=herwig_all_hadron logger=wandb
```

### Training with different training options
[Pytorch Trainer](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) provides a lot of options for training. For example, you can run the training with gradient clip by running:
```bash
python hadml/train.py experiment=herwig_all_hadron logger=wandb +trainer.gradient_clip_val=0.5
python train.py experiment=herwig_all_hadron logger=wandb +trainer.gradient_clip_val=0.5
```

## Training the "Cluster Decayer" with event level information
(not tested yet)

Similarly, the training can be performed by running:
```bash
python hadml/train.py experiment=herwig_event logger=wandb
python train.py experiment=herwig_event logger=wandb
```
2 changes: 1 addition & 1 deletion configs/datamodule/herwig.yaml
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Expand Up @@ -5,4 +5,4 @@ pin_memory: False
core_dataset:
_target_: hadml.datamodules.components.herwig.Herwig
data_dir: "${paths.data_dir}Herwig"
train_val_test_split: [0.96, 0.02, 0.02]
train_val_test_split: [80, 10, 10]
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Can we change it to [800, 100, 100]? It's not very important, but this way it would be clear those aren't percentage points

27 changes: 0 additions & 27 deletions configs/experiment/herwig_all_hadron.yaml
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Expand Up @@ -69,30 +69,3 @@ model:
discriminator:
hidden_dims: ${gen_list:1000,4} # = [1000,] * 4
dropout: 0


# criterion:
# __target__: torch.nn.BCELoss
# reduction: "mean"

# optimizer_generator:
# lr: 0.000001

# optimizer_discriminator:
# lr: 0.000005


# scheduler_generator:
# # _target_: torch.optim.lr_scheduler.ExponentialLR
# _target_: torch.optim.lr_scheduler.CosineAnnealingWarmRestarts
# _partial_: true
# T_0: 1
# T_mult: 2
# eta_min: 0.00001

# scheduler_discriminator:
# _target_: torch.optim.lr_scheduler.CosineAnnealingWarmRestarts
# _partial_: true
# T_0: 1
# T_mult: 2
# eta_min: 0.0001
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