88 lines
5.4 KiB
Bash
Executable File
88 lines
5.4 KiB
Bash
Executable File
#!/usr/bin/env bash
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#SBATCH -J DeepRain
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#SBATCH -N 1
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#SBATCH -n 14
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#SBATCH --gres=gpu:1
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#SBATCH -o %j.%N.%a.deeplab-rainfall.out.log
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#SBATCH -e %j.%N.%a.deeplab-rainfall.err.log
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#SBATCH -p gpu
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#SBATCH --no-requeue
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#SBATCH --time=5-00:00:00
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#SBATCH --mem=30000
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# ---> in MiB
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# no-requeue: ref https://support.hull.ac.uk/tas/public/ssp/content/detail/incident?unid=652db7ac6e73485c9f7658db78b2b628
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module load utilities/multi
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module load readline/7.0
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module load gcc/10.2.0
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module load cuda/11.5.0
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export XLA_FLAGS="--xla_gpu_cuda_data_dir=/home/ViperAppsFiles/cuda/11.5.0"; # weird... this wasn't needed before?
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module load python/anaconda/4.6/miniconda/3.7
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show_help() {
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echo -e "Trains a TEST DeepLabv3+ model using rainfall radar and water depth data." >&2;
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echo -e "" >&2;
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echo -e "Usage:" >&2;
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echo -e " sbatch slurm-TEST-deeplabv3p-rainfall.job" >&2;
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echo -e "" >&2;
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echo -e "....where:" >&2;
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echo -e " IMAGE_SIZE=128 Optional. Sets the size of the 'images' that the DeepLabV3+ model will work with." >&2;
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echo -e " BATCH_SIZE=64 Optional. Sets the batch size to train the model with." >&2;
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echo -e " DIR_RAINFALLWATER The path to the directory the .tfrecord files containing the rainfall radar / water depth data." >&2;
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echo -e " PATH_HEIGHTMAP The path to the heightmap jsonl file to read in." >&2;
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echo -e " PATH_COLOURMAP The path to the colourmap for predictive purposes." >&2;
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echo -e " PARALLEL_READS Multiplier for the number of files to read in parallel. 1 = number of CPU cores available. Very useful on high-read-latency systems (e.g. HPC like Viper) to avoid starving the GPU of data. WILL MANGLE THE ORDERING OF DATA. Set to 0 to disable and read data sequentially. WILL ONLY NOT MANGLE DATA IF PREDICT_AS_ONE IS SET. Defaults to 1.5." >&2;
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echo -e " STEPS_PER_EPOCH The number of steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2;
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echo -e " NO_REMOVE_ISOLATED_PIXELS Set to any value to avoid the engine from removing isolated pixels - that is, water pixels with no other surrounding pixels, either side to side to diagonally." >&2;
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echo -e " EPOCHS The number of epochs to train for." >&2;
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echo -e " LOSS The loss function to use. Default: cross-entropy (possible values: cross-entropy, cross-entropy-dice)." >&2;
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echo -e " DICE_LOG_COSH When in cross-entropy-dice mode, in addition do loss = cel + log(cosh(dice_loss)) instead of just loss = cel + dice_loss." >&2;
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echo -e " WATER_THRESHOLD The threshold to cut water off at when training, in metres. Default: 0.1" >&2;
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echo -e " PATH_CHECKPOINT The path to a checkpoint to load. If specified, a model will be loaded instead of being trained." >&2;
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echo -e " LEARNING_RATE The learning rate to use. Default: 0.001." >&2;
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echo -e " UPSAMPLE How much to upsample by at the beginning of the model. A value of disables upscaling. Default: 2." >&2;
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echo -e " STEPS_PER_EXECUTION How many steps to perform before surfacing from the GPU to e.g. do callbacks. Default: 16." >&2;
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echo -e " RANDSEED The random seed to use when shuffling filepaths. Default: unset, which means use a random value." >&2;
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echo -e " JIT_COMPILE Set to any value to compile the model with XLA." >&2;
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echo -e " PREDICT_COUNT The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2;
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echo -e " PREDICT_AS_ONE [prediction only] Set to any value to avoid splitting the input dataset into training/validation and instead treat it as a single dataset. Default: False (treat it as training/validation)" >&2;
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echo -e " POSTFIX Postfix to append to the output dir (auto calculated)." >&2;
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echo -e " ARGS Optional. Any additional arguments to pass to the python program." >&2;
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echo -e "" >&2;
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echo -e "It is strongly advised that all filepaths do NOT contain spaces." >&2;
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echo -e "" >&2;
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echo -e "The code used to identify the run is taken automatically from the filename of the config file." >&2;
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exit;
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}
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DIR_RAINFALLWATER="${DIR_RAINFALLWATER:-$HOME/rainfallwater_records_tfrecord}";
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PATH_HEIGHTMAP="${PATH_HEIGHTMAP:-$HOME/data/terrain50-nimrodsized.json.gz}";
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PATH_COLOURMAP="${PATH_COLOURMAP:-$HOME/data/instance-level-human-parsing/instance-level_human_parsing/human_colormap.mat}";
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CODE="deeplabv3+_rainfall";
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if [[ -n "${POSTFIX}" ]]; then
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echo -e ">>> Applying postfix of ${POSTFIX}" >&2;
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CODE="${CODE}_${POSTFIX}";
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fi
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DIR_OUTPUT="${DIR_OUTPUT:-output/$(date -u --rfc-3339=date)_${CODE}}";
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mkdir -p "${DIR_OUTPUT}";
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echo -e ">>> NOW: $(date)";
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echo -e ">>> DIR_OUTPUT: ${DIR_OUTPUT}";
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echo -e ">>> Additional args: ${ARGS}";
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export PATH=$HOME/software/bin:$PATH;
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export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS LEARNING_RATE DICE_LOG_COSH WATER_THRESHOLD UPSAMPLE STEPS_PER_EXECUTION JIT_COMPILE RANDSEED PREDICT_AS_ONE;
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echo ">>> Installing requirements";
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conda run -n py38 pip install -q -r requirements.txt;
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echo ">>> Training model";
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#shellcheck disable=SC2016
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/usr/bin/env time -v conda run -n py38 bash -c 'src/deeplabv3_plus_test_rainfall.py >>"${DIR_OUTPUT}/experiment.${SLURM_JOB_ID}.out.log" 2>>"${DIR_OUTPUT}/experiment.${SLURM_JOB_ID}.err.log"; echo "[slurm_runner] EXIT_CODE: $?" >>"${DIR_OUTPUT}/experiment.${SLURM_JOB_ID}.out.log";';
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echo ">>> exited with code $?";
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