version lighting CLI example (#128)

* version lighting CLI example

* add some documentation

* ignore slurm output

* add more tips

.gitignore

@@ -7,6 +7,9 @@ __pycache__/
 # written by setuptools_scm
 */_version.py
 
+# slurm output files
+slurm-*
+
 # Distribution / packaging
 .Python
 build/

applications/contrastive_phenotyping/demo_cli_fit.yml

@@ -0,0 +1,115 @@
+# See help here on how to configure hyper-parameters with config files: https://lightning.ai/docs/pytorch/stable/cli/lightning_cli_advanced.html
+seed_everything: 42
+trainer:
+  accelerator: gpu
+  strategy: ddp
+  devices: 4
+  num_nodes: 1
+  precision: 32-true
+  logger:
+    class_path: lightning.pytorch.loggers.TensorBoardLogger
+    init_args:
+      save_dir: /hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations
+      version: chocolate # this is the name of the experiment. The logs will be saved in save_dir/lightning_logs/version
+      log_graph: True
+  # Nesting the logger config like this is equivalent to supplying the following argument to lightning.pytorch.Trainer
+  #  logger=TensorBoardLogger(
+  #           "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/contrastive_tune_augmentations",
+  #           log_graph=True,
+  #           version="vanilla",
+  #       )
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: step
+    - class_path: lightning.pytorch.callbacks.ModelCheckpoint
+      init_args:
+        monitor: loss/val
+        every_n_epochs: 1
+        save_top_k: 4
+        save_last: true
+  fast_dev_run: false
+  max_epochs: 100
+  log_every_n_steps: 10
+  enable_checkpointing: true
+  inference_mode: true
+  use_distributed_sampler: true
+model:
+  backbone: convnext_tiny
+  in_channels: 2
+  log_batches_per_epoch: 3
+  log_samples_per_batch: 3
+  lr: 0.0002
+data:
+  data_path: /hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr
+  tracks_path: /hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr
+  source_channel:
+    - Phase3D
+    - RFP
+  z_range: [25, 40]
+  batch_size: 32
+  num_workers: 12
+  initial_yx_patch_size: [384, 384]
+  final_yx_patch_size: [192, 192]
+  normalizations:
+    - class_path: viscy.transforms.NormalizeSampled
+      init_args:
+        keys: [Phase3D]
+        level: fov_statistics
+        subtrahend: mean
+        divisor: std
+    - class_path: viscy.transforms.ScaleIntensityRangePercentilesd
+      init_args:
+        keys: [RFP]
+        lower: 50
+        upper: 99
+        b_min: 0.0
+        b_max: 1.0
+  augmentations:
+    - class_path: viscy.transforms.RandAffined
+      init_args:
+        keys: [Phase3D, RFP]
+        prob: 0.8
+        scale_range: [0, 0.2, 0.2]
+        rotate_range: [3.14, 0.0, 0.0]
+        shear_range: [0.0, 0.01, 0.01]
+        padding_mode: zeros
+    - class_path: viscy.transforms.RandAdjustContrastd
+      init_args:
+        keys: [RFP]
+        prob: 0.5
+        gamma: [0.7, 1.3]
+    - class_path: viscy.transforms.RandAdjustContrastd
+      init_args:
+        keys: [Phase3D]
+        prob: 0.5
+        gamma: [0.8, 1.2]
+    - class_path: viscy.transforms.RandScaleIntensityd
+      init_args:
+        keys: [RFP]
+        prob: 0.7
+        factors: 0.5
+    - class_path: viscy.transforms.RandScaleIntensityd
+      init_args:
+        keys: [Phase3D]
+        prob: 0.5
+        factors: 0.5
+    - class_path: viscy.transforms.RandGaussianSmoothd
+      init_args:
+        keys: [Phase3D, RFP]
+        prob: 0.5
+        sigma_x: [0.25, 0.75]
+        sigma_y: [0.25, 0.75]
+        sigma_z: [0.0, 0.0]
+    - class_path: viscy.transforms.RandGaussianNoised
+      init_args:
+        keys: [RFP]
+        prob: 0.5
+        mean: 0.0
+        std: 0.5
+    - class_path: viscy.transforms.RandGaussianNoised
+      init_args:
+        keys: [Phase3D]
+        prob: 0.5
+        mean: 0.0
+        std: 0.2

applications/contrastive_phenotyping/demo_cli_fit_slurm.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+
+#SBATCH --job-name=contrastive_origin
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=4
+#SBATCH --gres=gpu:4
+#SBATCH --partition=gpu
+#SBATCH --cpus-per-task=14
+#SBATCH --mem-per-cpu=15G
+#SBATCH --time=0-20:00:00
+
+# debugging flags (optional)
+# https://lightning.ai/docs/pytorch/stable/clouds/cluster_advanced.html
+export NCCL_DEBUG=INFO
+export PYTHONFAULTHANDLER=1
+
+
+# Cleanup function to remove the temporary files
+function cleanup() {
+  rm -rf /tmp/$SLURM_JOB_ID/*.zarr
+  echo "Cleanup Completed."
+}
+
+trap cleanup EXIT
+# trap the EXIT signal sent to the process and invoke the cleanup.
+
+# Activate the conda environment - specfic to your installation!
+module load anaconda/2022.05
+# You'll need to replace this path with path to your own conda environment.
+conda activate /hpc/mydata/$USER/envs/viscy
+
+config=./demo_cli_fit.yml
+
+# Printing this to the stdout lets us connect the job id to config.
+scontrol show job $SLURM_JOB_ID
+cat $config
+
+# Run the training CLI
+srun python -m viscy.cli.contrastive_triplet fit -c $config
+
+# Tips:
+# 1. run this script with `sbatch demo_cli_fit_slurm.sh`
+# 2. check the status of the job with `squeue -u $USER`
+# 3. use turm to monitor the job with `turm -u first.last`. Use module load turm to load the turm module.