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Bosch solution to CHAMPS Kaggle competition
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Below you can find a outline of how to reproduce our solution for the CHAMPS competition. If you run into any trouble with the setup/code or have any questions please contact us at

Copyright 2019 Robert Bosch GmbH

Code authors: Zico Kolter, Shaojie Bai, Devin Wilmott, Mordechai Kornbluth, Jonathan Mailoa, part of Bosch Research (CR).

Archive Contents

  • config/ : Configuration files
  • data/ : Raw data
  • models/ : Saved models
  • processed/ : Processed data
  • src/ : Source code for preprocessing, training, and predicting.
  • submission/ : Directory for the actual predictions

Hardware (The following specs were used to create the original solution)

The variety of models were trained on different machines, each running a Linux OS:

  • 5 machines had 4 GPUs, each a NVIDIA GeForce RTX 2080 Ti
  • 2 machines had 1 GPU NVIDIA Tesla V100 with 32 GB memory
  • 6 machines had 1 GPU NVIDIA Tesla V100 with 16 GB memory


  • Python 3.5+
  • CUDA 10.1
  • NVIDIA APEX (Only available through the repo at this phase)

Python packages are detailed separately in requirements.txt.

Note: Though listed in requirements.txt, rdkit is not available with pip. We strongly suggest installing rdkit via conda:

conda install -c rdkit rdkit

Data Setup

We use only the train.csv, test.csv, and structures.csv files of the competition. They should be (unzipped and) placed in the data/ directory. All of the commands below are executed from the src/ directory.

Data Processing

  1. cd src/
  2. python 1 (This could take 1-2 hours)
  3. python 2

(You may need to change the permission to the .csv files to use the two scripts above via chmod.)

Model Build - There are three options to produce the solution.

While in src/:

  1. Very fast prediction: fast to use the precomputed results for ensembling.
  2. Ordinary prediction: to use the precomputed checkpoints for predicting and ensembling.
  3. Re-train models: to train a new model from scratch. See -h for allowed arguments, and config files for each model for the arguments used.

The config/models.json file contains the following important keys:

  • names: List of the names we will ensemble
  • output file: The name of the ensembled output file
  • num atom types, bond types, triplet types, quad types: These are arguments to pass to the GraphTransformer instantiator. Note that in the default setting, quadruplet information is not used by GTs.
  • model_dir: The directory in models/ associated with each model. Each directory must have
    1. with a GraphTransformer class (and any modules it needs);
    2. config file with the kwargs to instantiate the GraphTransformer class;
    3. [MODEL_NAME].ckpt that can be loaded via load_state_dict(torch.load('[MODEL_NAME].ckpt').state_dict()) (to avoid PyTorch version conflict).

Notes on (Pre-trained) Model Loading

All pretrained models are stored in models/. However, different models may have slightly different architecture (e.g., some GT models are followed by a 2-layer grouped residual network, while some others only have one residual block). The training script (, when initiated without the --debug flag, will automatically create a log folder in CHAMPS-GT/ that contains the code for the GT used. When loading the model, use the in that log folder (instead of the default one in src/).

Notes on Model Training

When trained from scratch, the default parameters should lead to a model achieving a score of around -3.06 to -3.07. Using --debug flag will prevent the program from creating a log folder.

Notes on Saving Memory

What if you got a CUDA out of memory error? We suggest a few solutions:

  • If you have a multi-GPU machine, use the --multi_gpu flag, and tune the --gpu0_bsz flag (which controls the minibatch size passed to GPU device 0). For instance, on a 4-GPU machine, you can do python [...] --batch_size 47 --multi_gpu --gpu0_bsz 11, which assigns a batch size of 12 to GPU 1,2,3 and a batch size of 11 to GPU 0.
  • Use the --fp16 option, which applies NVIDIA APEX's mixed precision training.
  • Use the --batch_chunk option, which chunks a larger batch into a few smaller (equal) shares. The gradients from the smaller minibatches will accumulate, so the effective batch size is still the same as --batch_size.
  • Use fewer --n_layer, or smaller --batch_size :P
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