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https://github.com/azrael417/mlperf-deepcam

This is the public repo for the MLPerf DeepCAM climate data segmentation proposal.
https://github.com/azrael417/mlperf-deepcam

Last synced: 25 days ago
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This is the public repo for the MLPerf DeepCAM climate data segmentation proposal.

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README 

        
# Deep Learning Climate Segmentation Benchmark



PyTorch implementation for the climate segmentation benchmark, based on the

Exascale Deep Learning for Climate Analytics codebase here:

https://github.com/azrael417/ClimDeepLearn, and the paper:

https://arxiv.org/abs/1810.01993



## Dataset



The dataset for this benchmark comes from CAM5 [1] simulations and is hosted at

NERSC. The samples are stored in HDF5 files with input images of shape

(768, 1152, 16) and pixel-level labels of shape (768, 1152). The labels have

three target classes (background, atmospheric river, tropical cycline) and were

produced with TECA [2].



The current recommended way to get the data is to use GLOBUS and the following

globus endpoint:



https://app.globus.org/file-manager?origin_id=0b226e2c-4de0-11ea-971a-021304b0cca7&origin_path=%2F



The dataset folder contains a README with some technical description of the

dataset and an All-Hist folder containing all of the data files.



### Preprocessing

The dataset is already pre-split in train/val/test and a summary statistics file is shipped as well. Note that the test split is never used by the benchmark (i.e. not required for submission) but can be used to perform additional crosschecks in terms of generalization and training stability.



The split was generated by a selection of scripts which achieves this. The splitting scripts are under `src/utils`. If you want to manually split and summarize the dataset, feel free to use this scripts, but note that for a valid benchmark submission the dataset split and preprocessing has to be the same as in the reference implementation.



For *splitting* the dataset, please change the lines 5 and 6 (`inputdir` and `outputdir`) in `split_data.py` accordingly. The first variable should specify the absolute path to the full dataset, the second variable specifies the parent directory of where the train/validation/test splits end up. Instead of copying the files, symbolic links will be created. Therefore, if you plan to run the code from a container or system with different mount points than those used for the splitting, the links might be invalid and files not found. In this case, perform the splitting in the same environment used for the runs later.



For *summarizing* the dataset (i.e. computing summary statistics for input normalization), use script `summarize_data.py` in the same directory. Please modify line 85, `data_path_prefix` accordingly. It should point to the parent directory which hosts all the split, i.e. is equal to the `output_dir` from the above mentioned splitting script. Note that the summary script uses `mpi4py` for distributed computing, as the whole summarization on a single CPU can take a few hours. Once the `stats.h5` file is created, place it inside the training, test and validation directories.



## Before you run



Make sure you have a working python environment with `pytorch`, `h5py`, `basemap` and `wandb` setup. 

If you want to use learning rate warmup, you must also install the warmup-scheduler package

available at https://github.com/ildoonet/pytorch-gradual-warmup-lr.



The training uses Weights & Biases (WandB/W&B, https://app.wandb.ai) as logging facility. 

In order to use it, please sign up, log in and create a new project. 

Create a file named `.wandbirc` containing the user login and the API key as follows:



```bash

 

```



Place this file in a directory accessible by the workers.



## How to run the benchmark



Submission scripts are in `src/deepCam/run_scripts`.



### Running at NERSC



To submit to the Cori KNL system, set up a conda env called

`mlperf_deepcam` which contains all the prereqs, such as `h5py`, `wandb` and `basemap`.

Please edit the entries



```bash

export PROJ_LIB=/global/homes/t/tkurth/.conda/envs/mlperf_deepcam/share/basemap

export PYTHONPATH=/global/homes/t/tkurth/.conda/envs/mlperf_deepcam/lib/python3.7/site-packages:${PYTHONPATH}

```



in `src/deepCam/run_scripts/run_training_cori.sh` to point to the correct paths and add 



```bash

--wandb_certdir 

```

to the arguments passed to the python training script. Here, ``

should point to the directory which contains the `.wandbirc` file created before.

Then run



```bash

# This example runs on 64 nodes.

cd src/deepCam/run_scripts

sbatch -N 64 run_training_cori.sh

```



### Using Docker



The implementation comes with a Dockerfile optimized for NVIDIA DGX-2 workstations but usable on 

other NVIDIA multi-gpu systems. Use the Dockerfile 

`docker/Dockerfile.train` to build the container and the script `src/deepCam/run_scripts/run_training_dgx2.sh`

for training. Please create a file `no-git/wandb_cert.key` 

formatted as mentioned above before building the image so that the builder can properly place your WandB credentials inside the container.



## References



1. Wehner, M. F., Reed, K. A., Li, F., Bacmeister, J., Chen, C.-T., Paciorek, C., Gleckler, P. J., Sperber, K. R., Collins, W. D., Gettelman, A., et al.: The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5. 1, Journal of Advances in Modeling Earth Systems, 6, 980-997, 2014.

2. Prabhat, Byna, S., Vishwanath, V., Dart, E., Wehner, M., Collins, W. D., et al.: TECA: Petascale pattern recognition for climate science, in: International Conference on Computer Analysis of Images and Patterns, pp. 426-436, Springer, 2015b.