AMBS

Atmopsheric Machine learning Benchmarking Systems (AMBS) aims to privde state-of-the-art benchmarking machine learning architectures for video prediction on HPC in the context of atmospheric domain, which is developed by Amirpasha, Michael, Bing, and Scarlet

Prerequisites

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN
• MPI
• Tensorflow 1.13.1

Installation

• Clone this repo:

git clone https://gitlab.version.fz-juelich.de/toar/ambs.git

Set-up env on Jülich's HPC systems and zam347

The following commands will setup a user-specific virtual environment either on Juwels, HDF-ML (HPC clusters) or on zam347 for you. The script create_env.sh automatically detects on which machine it is executed and loads/installs all required Python (binary) modules and packages. The virtual environment is set up under the subfolder video_prediction_savp/<env_name>. Besides, user-specific runscripts for each step of the workflow may be created, e.g. train_era5_exp1.sh where exp1 denotes the default experiment identifier. The name of this identifier can be controlled by the optional second argument <exp_id>.

cd video_prediction_savp/env_setup
source create_env.sh <env_name> [<exp_id>]

Run workflow the workflow

Depending on the machine you are workin on, change either to video_prediction_savp/HPC_scripts (on Juwels and HDF-ML) or to video_prediction_savp/Zam347_scripts. There, the respective runscripts for all steps of the workflow are located whose order is the following:

1. Data Extraction: Retrieve ERA5 reanalysis data for one year. For multiple year, execute the runscript sequentially.

./DataExtraction_<exp_id>.sh

2. Data Preprocessing: Crop all data (multiple years possible) to the region of interest and perform normalization

./DataPreprocess_<exp_id>.sh
./DataPreprocess2tf_<exp_id>.sh

3. Training: Training of one of the available models (see bewlow) with the preprocessed data.

./train_era5_<exp_id>.sh

4. Postprocess: Create some plots and calculate evaluation metrics for test dataset.

./generate_era5_<exp_id>.sh

Create additional runscripts

In case that you want to perform experiments with varying configuration (e.g. another set of hyperparameters, but still the same input dataset for training), it is convenient to create individual runscripts from the templates. This can be done with the help of generate_workflow_runscripts.sh.

The first argument <runscript_name> defines the (relative) path to the template runscript which should be converted to an executable one. Note that only the suffix of the template's name must be passed, e.g. ../HPC_scripts/train_era5 in order to create a runscript for the training substep. The second argument <venv_name> denotes the name of the virtual environment which has to be set up in advanceand which should be used by the runscript.

Additional optional arguments can be passed to control the experimental identifier and to set manually the realtive path to the directory where the preprocessed data is stored (used for the training and postprocessing substep). These optional arguments have to follow a naming convention in order to be identified by generate_workflow_runscripts.sh. The experimental identifer can be passed by adding -exp_id=<id>while the path to the preprocessed data requires passing of -exp_dir=<relative_path_to_dir>. Note, that the default value exp1 is used as experimental identifier if the -exp_id=<id> is omitted.

./generate_workflow_runscripts.sh <runscript_name> <venv_name> [-exp_id=<id>] [-exp_dir=<relative_dir_to_path>]

Specific example:

./generate_workflow_runscripts.sh train_era5 venv_juwels -exp_id=exp_test -exp_dir=era5-Y2010toY2222M01to12-160x128-2970N1500W-T2_MSL_gph500

Output folder structure and naming convention

The details can be found name_convention

├── ExtractedData
│   ├── [Year]
│   │   ├── [Month]
│   │   │   ├── **/*.netCDF
├── PreprocessedData
│   ├── [Data_name_convention]
│   │   ├── pickle
│   │   │   ├── train
│   │   │   ├── val
│   │   │   ├── test
│   │   ├── tfrecords
│   │   │   ├── train
│   │   │   ├── val
│   │   │   ├── test
├── Models
│   ├── [Data_name_convention]
│   │   ├── [model_name]
│   │   ├── [model_name]
├── Results
│   ├── [Data_name_convention]
│   │   ├── [training_mode]
│   │   │   ├── [source_data_name_convention]
│   │   │   │   ├── [model_name]

Benchmarking architectures:

• convLSTM: paper,code
• Variational Autoencoder:paper
• Stochastic Adversarial Video Prediction (SAVP): paper,code
• Motion and Content Network (MCnet): paper, code

Contact

• Amirpash Mozafarri: a.mozafarri@fz-juelich.de
• Michael Langguth: m.langguth@fz-juelich.de
• Bing Gong: b.gong@fz-juelich.de
• Scarlet Stadtler: s.stadtler@fz-juelich.de