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+# 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:
+```bash
+git clone https://gitlab.version.fz-juelich.de/toar/ambs.git
+```
+
+### Set-up env on JUWELS and ZAM347
+
+- Set up env and install packages
+
+```bash
+cd video_prediction_savp
+source env_setup/create_env.sh <dir_name> <env_name>
+```
+
+### Run on ZAM347
+
+
+### Recomendation for output folder structure and name convention
+The details can be found [name_convention](docs/structure_name_convention.md)
+
+```
+├── ExtractedData
+│ ├── [Year]
+│ │ ├── [Month]
+│ │ │ ├── **/*.netCDF
+├── PreprocessedData
+│ ├── [Data_name_convention]
+│ │ ├── hickle
+│ │ │ ├── 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]<https://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf>,[code]<https://github.com/loliverhennigh/Convolutional-LSTM-in-Tensorflow>
+- Varational Autoencoder:[paper]
+- Stochastic Adversarial Video Prediction (SAVP)
+- Motion and Content Network (MCnet)
+
+
+
+### 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
diff --git a/video_prediction_savp/README.md b/video_prediction_savp/README.md
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-# Video Prediction by GAN
-
-This project aims to adopt the GAN-based architectures, which original proposed by [[Project Page]](https://alexlee-gk.github.io/video_prediction/) [[Paper]](https://arxiv.org/abs/1804.01523), to predict temperature based on ERA5 data
-
-## Getting Started ###
-### Prerequisites
-- Linux or macOS
-- Python 3
-- CPU or NVIDIA GPU + CUDA CuDNN
-
-### Installation
-This project need to work with [Workflow_parallel_frame_prediction project](https://gitlab.version.fz-juelich.de/gong1/workflow_parallel_frame_prediction)
-- Clone this repo:
-```bash
-git clone master https://gitlab.version.fz-juelich.de/gong1/video_prediction_savp.git
-git clone master https://gitlab.version.fz-juelich.de/gong1/workflow_parallel_frame_prediction.git
-```
-
-### Set-up env on JUWELS
-
-- Set up env and install packages
-
-```bash
-cd video_prediction_savp
-source env_setup/create_env.sh <dir_name> <env_name>
-```
-
-## Workflow by steps
-
-### Data Extraction
-
-```python
-python3 ../workflow_video_prediction/DataExtraction/mpi_stager_v2.py --source_dir <input_dir1> --destination_dir <output_dir1>
-```
-
-e.g.
-```python
-python3 ../workflow_video_prediction/DataExtraction/mpi_stager_v2.py --source_dir /p/fastdata/slmet/slmet111/met_data/ecmwf/era5/nc/2017/ --destination_dir /p/scratch/deepacf/bing/extractedData
-```
-
-### Data Preprocessing
-```python
-python3 ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir <output_dir1> --destination_dir <output_dir2>
-
-python3 video_prediction/datasets/era5_dataset_v2.py --source_dir <output_dir2> --destination_dir <.data/exp_name>
-```
-
-Example
-```python
-python3 ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir /p/scratch/deepacf/bing/extractedData --destination_dir /p/scratch/deepacf/bing/preprocessedData
-
-python3 video_prediction/datasets/era5_dataset_v2.py /p/scratch/deepacf/bing/preprocessedData ./data/era5_64_64_3_3t_norm
- ```
-
-### Trarining
-
-```python
-python3 scripts/train_v2.py --input_dir <./data/exp_name> --dataset era5 --model <savp> --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir <./logs/{exp_name}/{mode}/>
-```
-
-Example
-```python
-python3 scripts/train_v2.py --input_dir ./data/era5_size_64_64_3_3t_norm --dataset era5 --model savp --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir logs/era5_64_64_3_3t_norm/end_to_end
-```
-### Postprocessing
-
-Generating prediction frames, model evaluation, and visulization
-You can trained your own model from the training step , or you can copy the Bing's trained model
-
-```python
-python3 scripts/generate_transfer_learning_finetune.py --input_dir <./data/exp_name> --dataset_hparams sequence_length=20 --checkpoint <./logs/{exp_name}/{mode}/{model}> --mode test --results_dir <./results/{exp_name}/{mode}> --batch_size <batch_size> --dataset era5
-```
-
-- example: use end_to_end training model from bing for exp_name:era5_size_64_64_3_3t_norm
-```python
-python3 scripts/generate_transfer_learning_finetune.py --input_dir data/era5_size_64_64_3_3t_norm --dataset_hparams sequence_length=20 --checkpoint /p/project/deepacf/deeprain/bing/video_prediction_savp/logs/era5_size_64_64_3_3t_norm/end_to_end/ours_savp --mode test --results_dir results_test_samples/era5_size_64_64_3_3t_norm/end_to_end --batch_size 4 --dataset era5
-```
-
-
-# End-to-End run the entire workflow
-
-```bash
-./bash/workflow_era5.sh <model> <train_mode> <exp_name>
-```
-
-example:
-```bash
-./bash/workflow_era5.sh savp end_to_end era5_size_64_64_3_3t_norm
-```
-
-
-
-### Recomendation for output folder structure and name convention
-The details can be found [name_convention](docs/structure_name_convention.md)
-
-```
-├── ExtractedData
-│ ├── [Year]
-│ │ ├── [Month]
-│ │ │ ├── **/*.netCDF
-├── PreprocessedData
-│ ├── [Data_name_convention]
-│ │ ├── hickle
-│ │ │ ├── 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]
-
-```
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