# Run 
Each case in `Cases` folder is unique. They, however, share the same parsable arguments located in [`./Cases/src/parsings.py`](https://gitlab.jsc.fz-juelich.de/CoE-RAISE/FZJ/ai4hpc/ai4hpc/-/blob/main/Cases/src/parsings.py). These arguments are: 

```bash
--data-dir                   location of the dataset (str)
--restart-int                checkpoint interval % epoch (int)
--concM                      multiply dataset size with concM (test purposes) (int)

### model parsers
--batch-size                 batch size (int)
--epochs                     epochs (int)
--lr                         learning rate (float)
--wdecay                     weight decay in schedular (float)
--wdecay                     gamma in schedular (float) 
--shuff                      shuffle dataset / epoch (bool)
--schedule                   enable schedulers (bool)
# Horovod only
--gradient-predivide-factor  divide gradients during backprop (float)

### debug parsers
--testrun                    do a test-run (bool)
--skipplot                   disable plots in post-processing (bool)
--nseed                      fix parameter for deterministic run (int)
--log-int                    print out interval % batch (int)
--export-latent              export latent space (bool)

### parallel parsers
--backend                    parallelization backend (str: nccl,gloo,mpi)
--nworker                    dataloader CPU workers (int)
--prefetch                   dataloader prefetch dataset (int)
--no-cuda                    disable CUDA for CPU runs (bool)
# Horovod only
--use-fork                   use forkserver for dataloader (bool) 
# DeepSpeed only
--local_rank                 local rank of a worker in a node (int: from env)

### benchmarking parsers
--synt                       use a synthetic data instead of real (disables I/O) (bool)
--synt-dpw                   data per worker (if synt is selected) (int)
--benchrun                   do a benchmark with profiler (bool)

### optimization parsers
--cudnn                      enable cuDNN optimizations (bool)
--amp                        enable AMP (bool)
--reduce-prec                reduce dataset precision (bool)
--scale-lr                   scale LR with #workers (bool)
--accum-iter                 gradient accumulation interval % epoch (int)
# Horovod only
--fp16-allreduce             reduce parameter precision during allreduce (bool)
--use-adasum                 adaptive summation algorithm (bool)
--batch-per-allreduce        skip allreduce if gradients are accumulated (int)

### CDM additions
--cube                       enable patching of input with cube-cuts (true enables next 3 args) (bool)
--cubeC                      random (if 1) or monotonic (if 2) patch locations (int: 1,2)
--cubeD                      cube's edge dimension n (size is n**3) (int)
--cubeM                      # patches (int)
```

This argument list with more details on accepted values can simply be found out via
 ```bash
 $ ai4hpc.py --help
```

In default `startscript.sh`, none of these parameters are explicitly defined. Therefore, depending on the user's needs, do not forget to add them!

To run AI4HPC, submitting `startscript.sh` would be enough, but, is only meant for a guide. As usual, user should modify the number of nodes, partition, dataset folder etc. A great guide to these can be found [here](https://apps.fz-juelich.de/jsc/hps/jureca/batchsystem.html).