diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/START_study.py b/applications/harnesses/ch_DDP_loderunner_cylex/START_study.py
new file mode 100644
index 00000000..846b445d
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/START_study.py
@@ -0,0 +1 @@
+../START_study.py
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/cp_files.txt b/applications/harnesses/ch_DDP_loderunner_cylex/cp_files.txt
new file mode 100644
index 00000000..ad5fbb3d
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/cp_files.txt
@@ -0,0 +1 @@
+train_LodeRunner_ddp_cylex.py
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/ddp_test.csv b/applications/harnesses/ch_DDP_loderunner_cylex/ddp_test.csv
new file mode 100644
index 00000000..0fe2c760
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/ddp_test.csv
@@ -0,0 +1,4 @@
+studyIDX,YOKE_TORCH_ENV,KNODES,NGPUS,EMBED_DIM,B0,B1,B2,B3,NUM_WORKERS,BATCH_SIZE,NTRN_BATCH,NVAL_BATCH,ANCHOR_LR,NUM_CYCLES,MIN_FRACTION,TERMINAL_STEPS,WARMUP_STEPS,train_script
+# This is a longer epoch production run after tuning.
+# Single epoch with validation should be ~30 mins
+1,torch_se_gpu_120226,1,4,128,1,1,9,1,2,1,1000,500,5.0e-4,0.5,0.5,1000,500,train_LodeRunner_ddp_cylex.py
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/eval/avg_eval_csv.py b/applications/harnesses/ch_DDP_loderunner_cylex/eval/avg_eval_csv.py
new file mode 100644
index 00000000..cdd09a90
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/eval/avg_eval_csv.py
@@ -0,0 +1,40 @@
+"""Compute the statistics for MSE over evaluation CSV."""
+
+import argparse
+import pandas as pd
+
+
+def main():
+    """Compute and print."""
+    # Parser CLI CSV-filename
+    parser = argparse.ArgumentParser(
+        description="Compute summary statistics of evaluation CSV."
+        )
+    parser.add_argument(
+        '--csv',
+        type=str,
+        default='./testing_evaluation.csv',
+        help="Path to evaluation CSV."
+        )
+    
+    args = parser.parse_args()
+    
+    # Read the CSV file
+    df = pd.read_csv(args.csv, header=None)
+
+    # Extract the third column
+    col = df[2]
+    
+    # Compute statistics of the third column
+    mean_MSE = col.mean()
+    p2_5, p50, p97_5 = col.quantile([0.025, 0.5, 0.975])
+
+    # Print results
+    print(f"Average MSE: {mean_MSE}")
+    print(f"2.5-percentile: {p2_5}")
+    print(f"50-percentile: {p50}")
+    print(f"97.5-percentile: {p97_5}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_LodeRunner_cylex.py b/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_LodeRunner_cylex.py
new file mode 100644
index 00000000..5b0587c0
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_LodeRunner_cylex.py
@@ -0,0 +1,235 @@
+"""Evaluate trained model on test set.
+
+Model is evaluated on `cycle_epoch` number of epochs with `test_batches` number of
+batches each of a set `batch_size`.
+
+"""
+
+import argparse
+import os
+import time
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+
+from yoke.models.vit.swin.bomberman import LodeRunner
+from yoke.datasets.load_npz_dataset import TemporalDataSet
+from yoke.utils.checkpointing import load_model_and_optimizer
+from yoke.utils.training.epoch.loderunner import eval_loderunner_epoch
+from yoke.helpers import cli
+
+
+descr_str = (
+    "Single-GPU evaluation for a saved Yoke LodeRunner checkpoint on real dataset "
+    "batches. Mirrors train_LodeRunner_ddp.py structure but without DDP."
+)
+parser = argparse.ArgumentParser(
+    prog="LodeRunner Evaluation", description=descr_str, fromfile_prefix_chars="@"
+)
+
+# Reuse the same CLI arg groups as training so you can pass the same @argfiles.
+parser = cli.add_default_args(parser=parser)
+parser = cli.add_filepath_args(parser=parser)
+parser = cli.add_computing_args(parser=parser)
+parser = cli.add_training_args(parser=parser)
+
+# Keep the same default filelists as train_LodeRunner_ddp.py
+parser.set_defaults(
+    train_filelist="cx241203_prefixes_train_80pct_noBe_noVoid_truncated.txt",
+    validation_filelist="cx241203_prefixes_val_10pct_noBe_noVoid_truncated.txt",
+    test_filelist="cx241203_prefixes_test_10pct_noBe_noVoid_truncated.txt",
+)
+
+def main(args: argparse.Namespace) -> None:
+    """Main evaluation function."""
+    #############################################
+    # Process Inputs
+    #############################################
+    # Device (single GPU)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    # Paths
+    filelist_dir = args.FILELIST_DIR
+    filelist_path = os.path.join(filelist_dir, args.test_filelist)
+
+    # Dataloader params
+    batch_size = args.batch_size
+    num_workers = args.num_workers
+    test_batches = args.test_batches
+    batch_size = args.batch_size
+    cycle_epochs = args.cycle_epochs
+    test_rcrd_filename = args.test_rcrd_filename
+
+    model_args = {
+        "default_vars": [
+            "Rcoord",  # 4 kinematic variable fields
+            "Zcoord",
+            "Uvelocity",
+            "Wvelocity",
+            "density_Air",  # 39 thermodynamic variable fields
+            "energy_Air",
+            "pressure_Air",
+            "density_Al",
+            "energy_Al",
+            "pressure_Al",
+            "density_Be",
+            "energy_Be",
+            "pressure_Be",
+            "density_booster",
+            "energy_booster",
+            "pressure_booster",
+            "density_Cu",
+            "energy_Cu",
+            "pressure_Cu",
+            "density_U.DU",
+            "energy_U.DU",
+            "pressure_U.DU",
+            "density_maincharge",
+            "energy_maincharge",
+            "pressure_maincharge",
+            "density_N",
+            "energy_N",
+            "pressure_N",
+            "density_Sn",
+            "energy_Sn",
+            "pressure_Sn",
+            "density_Steel.alloySS304L",
+            "energy_Steel.alloySS304L",
+            "pressure_Steel.alloySS304L",
+            "density_Polymer.Sylgard",
+            "energy_Polymer.Sylgard",
+            "pressure_Polymer.Sylgard",
+            "density_Ta",
+            "energy_Ta",
+            "pressure_Ta",
+            "density_Void",
+            "energy_Void",
+            "pressure_Void",
+            "density_Water",
+            "energy_Water",
+            "pressure_Water",
+        ],
+        "image_size": (1120, 400),
+        "patch_size": (10, 5),
+        "embed_dim": 128,
+        "emb_factor": 2,
+        "num_heads": 8,
+        "block_structure": (1, 1, 9, 1),
+        "window_sizes": [(8, 8), (8, 8), (4, 4), (2, 2)],
+        "patch_merge_scales": [(2, 2), (2, 2), (2, 2)],
+    }
+
+    model = LodeRunner(**model_args)
+
+    #############################################
+    # Load Model Checkpoint
+    #############################################
+    available_models = {"LodeRunner": LodeRunner}
+
+    # NOTE: optimizer args are required by load_model_and_optimizer, even for eval.
+    model, _optimizer, starting_epoch = load_model_and_optimizer(
+        args.pretrained_model,
+        optimizer_class=torch.optim.AdamW,
+        optimizer_kwargs={
+            "lr": 1e-6,
+            "betas": (0.9, 0.999),
+            "eps": 1e-08,
+            "weight_decay": 0.01,
+        },
+        available_models=available_models,
+        device=device,
+    )
+    starting_epoch = 0
+    model.to(device)
+    model.eval()
+
+    # load_and_eval_YokePth.py prints these; keep similar behavior here
+    print(f"Loaded checkpoint: {args.pretrained_model}", flush=True)
+    print(f"Checkpoint starting_epoch: {starting_epoch}", flush=True)
+    if hasattr(model, "default_vars"):
+        print("Default LodeRunner fields:", model.default_vars, flush=True)
+    if hasattr(model, "image_size"):
+        print("LodeRunner image size:", model.image_size, flush=True)
+
+    #############################################
+    # Dataset / Dataloader (non-distributed)
+    #############################################
+    testing_dataset = TemporalDataSet(
+        args.NPZ_DIR,
+        args.CSV_FILEPATH,
+        file_prefix_list=filelist_path,
+        max_timeIDX_offset=2,
+        max_file_checks=10,
+        half_image=True,
+    )
+
+    from torch.utils.data.dataloader import default_collate
+
+    def collate_skip_none(batch):
+        batch = [b for b in batch if b is not None]
+        if len(batch) == 0:
+            return None
+        return default_collate(batch)
+
+    test_dataloader = DataLoader(
+        dataset=testing_dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=torch.cuda.is_available(),
+        #drop_last=False,
+        collate_fn=collate_skip_none,
+        #prefetch_factor=2,
+    )
+
+    #############################################
+    # Loss + Evaluation Loop
+    #############################################
+    # Match train_LodeRunner_ddp.py: use per-element MSE so we can reduce ourselves
+    loss_fn = nn.MSELoss(reduction="none")
+
+
+    #############################################
+    # Testing Loop
+    #############################################
+    # Train Model
+    print("Testing Model . . .")
+    starting_epoch += 1
+    ending_epoch = starting_epoch + cycle_epochs
+
+    for epochIDX in range(starting_epoch, ending_epoch):
+        # Time each epoch and print to stdout
+        startTime = time.time()
+
+        # Testing epoch
+        # for cylex channel_map changes per sample. So pass None here
+        # & calculate channel_map later in the datastep function.
+        eval_loderunner_epoch(
+            testing_data=test_dataloader,
+            num_test_batches=test_batches,
+            model=model,
+            dataset='cylex',
+            channel_map=None,
+            loss_fn=loss_fn,
+            epochIDX=epochIDX,
+            test_rcrd_filename=test_rcrd_filename,
+            device=device,
+        )
+
+        # Time each epoch and print to stdout
+        endTime = time.time()
+
+        epoch_time = (endTime - startTime) / 60
+
+        # Print Summary Results
+        print(f"Completed epoch {epochIDX}...", flush=True)
+        print(f"Epoch time (minutes): {epoch_time:.2f}", flush=True)
+
+
+if __name__ == "__main__":
+    """Parse arguments and run main evaluation function."""
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_START.input b/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_START.input
new file mode 100644
index 00000000..7b4a3ccb
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_START.input
@@ -0,0 +1,22 @@
+--pretrain_checkpoint
+/net/sescratch1/exempt/artimis/soumide/projects/yoke_runs/cylex_full_run_lr5e-4_w_val/runs/study_001/study001_modelState_epoch0100.pth
+--FILELIST_DIR
+/usr/projects/artimis/mpmm/hickmank/github_yoke/applications/filelists/
+--NPZ_DIR
+/net/sescratch1/exempt/artimis/data/cx241203/
+--CSV_FILEPATH
+/net/sescratch1/exempt/artimis/mpmm/design_cx241203_MASTER.csv
+--test_filelist
+cx241203_prefixes_test_10pct_noBe_noVoid.txt
+--test_rcrd_filename
+./testing_evaluation.csv
+--batch_size
+1
+--num_workers
+2
+--total_epochs
+10
+--cycle_epochs
+10
+--test_batches
+1000
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_START.slurm b/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_START.slurm
new file mode 100644
index 00000000..4add9f9d
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/eval/eval_START.slurm
@@ -0,0 +1,79 @@
+#!/bin/bash
+
+# This is a setup for GPU training on Selene. Find out how much
+# memory per node, number of CPUs/node.
+
+# NOTE: Number of CPUs per GPU must be an even number since there are
+# 2 threads per core. If an odd number is requested the next higher
+# even number gets used. 
+
+# The following are one set of SBATCH options for the Selene GPU
+# partition. There are optional other constraints.
+
+#SBATCH --job-name=cylfull_eval
+#SBATCH --account=y26_artimis-fmod_g
+#SBATCH --partition=standard
+#SBATCH --time=8:00:00
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=4
+#SBATCH --gpus-per-node=4
+#SBATCH --cpus-per-task=8
+#SBATCH --mem-per-gpu=120G
+#SBATCH --output=eval_test.out
+#SBATCH --error=eval_test.err
+#SBATCH -vvv
+
+# Set the master node's address and port
+MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+MASTER_PORT=$(shuf -i 1024-65535 -n 1)  # Choose a random port
+export MASTER_ADDR
+export MASTER_PORT
+
+# Enable shell debugging: Debugging selene slurm
+set -xv
+
+# Check available GPUs
+sinfo  -o "%P %.24G %N"
+#srun -vv --cpu-bind=verbose /usr/bin/echo $CUDA_AVAILABLE_DEVICES
+nvidia-smi
+
+# Specify NCCL communication
+export NCCL_SOCKET_IFNAME=ib0  # Check possible interfaces with `ip link show`
+
+# for multi-node training
+export NCCL_IB_HCA=mlx5_0
+export NCCL_IB_GID_INDEX=3  # sometimes 0 or 3 depending on subnet manager config
+
+# Debugging distributed data parallel
+# export NCCL_DEBUG=INFO
+# export NCCL_DEBUG_SUBSYS=INIT
+
+# Debugging selene slurm
+export SLURM_CPU_BIND=verbose
+
+# Load correct conda environment
+module load anaconda/3.12
+source activate
+conda activate torch_se_gpu_120226
+
+# Set number of threads per GPU
+export OMP_NUM_THREADS=10
+
+# Get start time
+export date00=`date`
+
+# Start the Code
+# Explicitly set TCP environment for the following...
+#srun -vv --cpu-bind=verbose python eval_LodeRunner.py @eval_START.input
+python eval_LodeRunner.py @eval_START.input
+
+# Start the Code
+#python eval_LodeRunner.py @eval_START.input
+
+# Get end time and print to stdout
+export date01=`date`
+
+echo "===================TIME STARTED==================="
+echo $date00
+echo "===================TIME FINISHED==================="
+echo $date01
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/train_LodeRunner_ddp_cylex.py b/applications/harnesses/ch_DDP_loderunner_cylex/train_LodeRunner_ddp_cylex.py
new file mode 100644
index 00000000..3a0b2ee3
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/train_LodeRunner_ddp_cylex.py
@@ -0,0 +1,403 @@
+import os
+import time
+import argparse
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from yoke.models.vit.swin.bomberman import LodeRunner
+from yoke.datasets.load_npz_dataset import TemporalDataSet
+
+from yoke.utils.training.epoch.loderunner import train_DDP_loderunner_epoch
+from yoke.utils.restart import continuation_setup
+from yoke.utils.dataload import make_distributed_dataloader
+from yoke.utils.checkpointing import load_model_and_optimizer
+from yoke.utils.checkpointing import save_model_and_optimizer
+
+from yoke.lr_schedulers import CosineWithWarmupScheduler
+from yoke.helpers import cli
+
+
+#############################################
+# Inputs
+#############################################
+descr_str = (
+    "Uses DDP to train LodeRunner architecture on single-timstep input and output "
+    "of the lsc240420 per-material density fields."
+)
+parser = argparse.ArgumentParser(
+    prog="DDP LodeRunner Training", description=descr_str, fromfile_prefix_chars="@"
+)
+parser = cli.add_default_args(parser=parser)
+parser = cli.add_filepath_args(parser=parser)
+parser = cli.add_computing_args(parser=parser)
+parser = cli.add_model_args(parser=parser)
+parser = cli.add_training_args(parser=parser)
+parser = cli.add_cosine_lr_scheduler_args(parser=parser)
+
+# Change some default filepaths.
+parser.set_defaults(
+    train_filelist="cx241203_prefixes_train_80pct.txt",
+    validation_filelist="cx241203_prefixes_val_10pct.txt",
+    test_filelist="cx241203_prefixes_test_10pct.txt",
+)
+
+
+def setup_distributed():
+    # ----- 1) Basic setup & environment variables -----
+    # Rely on Slurm variables: SLURM_PROCID, SLURM_NTASKS, SLURM_LOCALID, etc.
+    rank = int(os.environ["SLURM_PROCID"])  # global rank
+    world_size = int(os.environ["SLURM_NTASKS"])  # total number of processes
+    local_rank = int(os.environ["SLURM_LOCALID"])  # local rank (GPU index on this node)
+
+    master_addr = os.environ["MASTER_ADDR"]
+    master_port = os.environ["MASTER_PORT"]
+
+    print("============================", flush=True)
+    print(f"[Rank {rank}] DDP setup, master_addr: {master_addr}", flush=True)
+    print(f"[Rank {rank}] DDP setup, master_port: {master_port}", flush=True)
+    print(f"[Rank {rank}] DDP setup, rank: {rank}", flush=True)
+    print(f"[Rank {rank}] DDP setup, local_rank: {local_rank}", flush=True)
+    print(f"[Rank {rank}] DDP setup, world_size: {world_size}", flush=True)
+    print("============================", flush=True)
+
+    # ----- 2) Set the current GPU device for this process -----
+    torch.cuda.set_device(local_rank)
+    device = torch.device(f"cuda:{local_rank}")
+
+    # ----- 3) Initialize the process group -----
+    dist.init_process_group(
+        backend="nccl",
+        init_method=f"tcp://{master_addr}:{master_port}",
+        world_size=world_size,
+        rank=rank,
+    )
+
+    return rank, world_size, local_rank, device
+
+
+def cleanup_distributed():
+    # ----- 8) Clean up (optional) -----
+    dist.destroy_process_group()
+
+
+def main(args, rank, world_size, local_rank, device):
+    #############################################
+    # Process Inputs
+    #############################################
+    # Study ID
+    studyIDX = args.studyIDX
+
+    # Resources
+    Ngpus = args.Ngpus
+    Knodes = args.Knodes
+
+    # Data Paths
+    train_filelist = args.FILELIST_DIR + args.train_filelist
+    validation_filelist = args.FILELIST_DIR + args.validation_filelist
+    test_filelist = args.FILELIST_DIR + args.test_filelist
+
+    # Model Parameters
+    embed_dim = args.embed_dim
+    block_structure = tuple(args.block_structure)
+
+    # Training Parameters
+    anchor_lr = args.anchor_lr
+    num_cycles = args.num_cycles
+    min_fraction = args.min_fraction
+    terminal_steps = args.terminal_steps
+    warmup_steps = args.warmup_steps
+
+    # Number of workers controls how batches of data are prefetched and,
+    # possibly, pre-loaded onto GPUs. If the number of workers is large they
+    # will swamp memory and jobs will fail.
+    num_workers = args.num_workers
+
+    # Epoch Parameters
+    batch_size = args.batch_size
+    total_epochs = args.total_epochs
+    cycle_epochs = args.cycle_epochs
+    train_batches = args.train_batches
+    val_batches = args.val_batches
+    train_per_val = args.TRAIN_PER_VAL
+    trn_rcrd_filename = args.trn_rcrd_filename
+    val_rcrd_filename = args.val_rcrd_filename
+    CONTINUATION = args.continuation
+    START = not CONTINUATION
+    checkpoint = args.checkpoint
+
+    # Dictionary of available models.
+    available_models = {
+        "LodeRunner": LodeRunner
+    }
+    
+    #############################################
+    # Model Arguments for Dynamic Reconstruction
+    #############################################
+    model_args = {
+        "default_vars": [
+            "Rcoord",  # 4 kinematic variable fields
+            "Zcoord",
+            "Uvelocity",
+            "Wvelocity",
+            "density_Air",  # 39 thermodynamic variable fields
+            "energy_Air",
+            "pressure_Air",
+            "density_Al",
+            "energy_Al",
+            "pressure_Al",
+            "density_Be",
+            "energy_Be",
+            "pressure_Be",
+            "density_booster",
+            "energy_booster",
+            "pressure_booster",
+            "density_Cu",
+            "energy_Cu",
+            "pressure_Cu",
+            "density_U.DU",
+            "energy_U.DU",
+            "pressure_U.DU",
+            "density_maincharge",
+            "energy_maincharge",
+            "pressure_maincharge",
+            "density_N",
+            "energy_N",
+            "pressure_N",
+            "density_Sn",
+            "energy_Sn",
+            "pressure_Sn",
+            "density_Steel.alloySS304L",
+            "energy_Steel.alloySS304L",
+            "pressure_Steel.alloySS304L",
+            "density_Polymer.Sylgard",
+            "energy_Polymer.Sylgard",
+            "pressure_Polymer.Sylgard",
+            "density_Ta",
+            "energy_Ta",
+            "pressure_Ta",
+            "density_Void",
+            "energy_Void",
+            "pressure_Void",
+            "density_Water",
+            "energy_Water",
+            "pressure_Water",
+        ],
+        "image_size": (1120, 400),
+        "patch_size": (10, 5),
+        "embed_dim": embed_dim,
+        "emb_factor": 2,
+        "num_heads": 8,
+        "block_structure": block_structure,
+        "window_sizes": [(8, 8), (8, 8), (4, 4), (2, 2)],
+        "patch_merge_scales": [(2, 2), (2, 2), (2, 2)],
+    }
+
+    #############################################
+    # Load Model for Continuation (Rank 0 only)
+    #############################################
+    # Wait to move model to GPU until after the checkpoint load. Then
+    # explicitly move model and optimizer state to GPU.
+    if CONTINUATION:
+        model, optimizer, starting_epoch = load_model_and_optimizer(
+            checkpoint,
+            optimizer_class=torch.optim.AdamW,
+            optimizer_kwargs={
+                "lr": 1e-6,
+                "betas": (0.9, 0.999),
+                "eps": 1e-08,
+                "weight_decay": 0.01,
+            },
+            available_models=available_models,
+            device=device,
+        )
+        print("Model state loaded for continuation.")
+    else:
+        # Initialize model and optimizer state.
+        # If not continuing, set starting_epoch to 0.
+        starting_epoch = 0
+        model = LodeRunner(**model_args)
+        # Move model to GPU before instantiating optimizer and DDP.
+        model.to(device)
+
+        # Instantiate optimizer and move state to GPU.
+        optimizer = torch.optim.AdamW(
+            model.parameters(),
+            lr=1e-6,
+            betas=(0.9, 0.999),
+            eps=1e-08,
+            weight_decay=0.01
+        )
+
+        for state in optimizer.state.values():
+            for key, value in state.items():
+                if isinstance(value, torch.Tensor):
+                    state[key] = value.to(device)
+
+    #############################################
+    # Initialize Loss
+    #############################################
+    # Use `reduction='none'` so loss on each sample in batch can be recorded.
+    loss_fn = nn.MSELoss(reduction="none")
+
+    #############################################
+    # Move Model to DistributedDataParallel
+    #############################################
+    model = DDP(model, device_ids=[local_rank], output_device=local_rank)
+
+    #############################################
+    # Learning Rate Scheduler
+    #############################################
+    if starting_epoch == 0:
+        last_epoch = -1
+    else:
+        last_epoch = train_batches * (starting_epoch - 1)
+
+    # Scale the anchor LR by global batchsize
+    #
+    # # For multi-node
+    lr_scale = np.sqrt(float(Ngpus) * float(Knodes) * float(batch_size))
+    original_batchsize = 40.0  # 1 node, 4 gpus, 10 samples/gpu
+    ddp_anchor_lr = anchor_lr * lr_scale / original_batchsize
+    #
+    # For single node
+    # ddp_anchor_lr = anchor_lr
+    
+    LRsched = CosineWithWarmupScheduler(
+        optimizer,
+        anchor_lr=ddp_anchor_lr,
+        terminal_steps=terminal_steps,
+        warmup_steps=warmup_steps,
+        num_cycles=num_cycles,
+        min_fraction=min_fraction,
+        last_epoch=last_epoch,
+    )
+
+    #############################################
+    # Data Initialization (Distributed Dataloader)
+    #############################################
+    train_dataset = TemporalDataSet(
+        args.NPZ_DIR,
+        args.CSV_FILEPATH,
+        file_prefix_list=train_filelist,
+        max_timeIDX_offset=2,
+        max_file_checks=10,
+        half_image=True,
+    )
+    val_dataset = TemporalDataSet(
+        args.NPZ_DIR,
+        args.CSV_FILEPATH,
+        file_prefix_list=validation_filelist,
+        max_timeIDX_offset=2,
+        max_file_checks=10,
+        half_image=True,
+    )
+
+    # NOTE: For DDP the batch_size is the per-GPU batch_size!!!
+    train_dataloader = make_distributed_dataloader(
+        train_dataset,
+        batch_size,
+        shuffle=True,
+        num_workers=num_workers,
+        rank=rank,
+        world_size=world_size,
+    )
+    val_dataloader = make_distributed_dataloader(
+        val_dataset,
+        batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        rank=rank,
+        world_size=world_size,
+    )
+
+    #############################################
+    # Training Loop (Modified for DDP)
+    #############################################
+    # Train Model
+    print("Training Model . . .")
+    starting_epoch += 1
+    ending_epoch = min(starting_epoch + cycle_epochs, total_epochs + 1)
+
+    TIME_EPOCH = True
+    for epochIDX in range(starting_epoch, ending_epoch):
+        train_sampler = train_dataloader.sampler
+        train_sampler.set_epoch(epochIDX)
+
+        # For timing epochs
+        if TIME_EPOCH:
+            # Synchronize before starting the timer
+            dist.barrier()  # Ensure that all nodes sync
+            torch.cuda.synchronize(device)  # Ensure GPUs on each node sync
+            # Time each epoch and print to stdout
+            startTime = time.time()
+
+        # Train and Validate
+        train_DDP_loderunner_epoch(
+            training_data=train_dataloader,
+            validation_data=val_dataloader,
+            dataset='cylex',
+            num_train_batches=train_batches,
+            num_val_batches=val_batches,
+            model=model,
+            optimizer=optimizer,
+            loss_fn=loss_fn,
+            LRsched=LRsched,
+            epochIDX=epochIDX,
+            train_per_val=train_per_val,
+            train_rcrd_filename=trn_rcrd_filename,
+            val_rcrd_filename=val_rcrd_filename,
+            device=device,
+            rank=rank,
+            world_size=world_size,
+        )
+
+        if TIME_EPOCH:
+            # Synchronize before stopping the timer
+            torch.cuda.synchronize(device)  # Ensure GPUs on each node sync
+            dist.barrier()  # Ensure that all nodes sync
+            # Time each epoch and print to stdout
+            endTime = time.time()
+
+        epoch_time = (endTime - startTime) / 60
+
+        # Print Summary Results
+        if rank == 0:
+            print(f"Completed epoch {epochIDX}...", flush=True)
+            print(f"Epoch time (minutes): {epoch_time:.2f}", flush=True)
+
+    # Save model and optimizer state in hdf5
+    chkpt_name_str = "study{0:03d}_modelState_epoch{1:04d}.pth"
+    new_chkpt_path = os.path.join("./", chkpt_name_str.format(studyIDX, epochIDX))
+
+    save_model_and_optimizer(
+        model, 
+        optimizer, 
+        epochIDX,
+        new_chkpt_path, 
+        model_class=LodeRunner,
+        model_args=model_args
+    )
+
+    if rank == 0:
+        #############################################
+        # Continue if Necessary
+        #############################################
+        FINISHED_TRAINING = epochIDX + 1 > total_epochs
+        if not FINISHED_TRAINING:
+            new_slurm_file = continuation_setup(
+                new_chkpt_path, studyIDX, last_epoch=epochIDX
+            )
+            os.system(f"sbatch {new_slurm_file}")
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    rank, world_size, local_rank, device = setup_distributed()
+
+    main(args, rank, world_size, local_rank, device)
+
+    cleanup_distributed()
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/training_START.input b/applications/harnesses/ch_DDP_loderunner_cylex/training_START.input
new file mode 100644
index 00000000..c796aac4
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/training_START.input
@@ -0,0 +1,53 @@
+--studyIDX
+<studyIDX>
+--FILELIST_DIR
+/usr/projects/artimis/mpmm/hickmank/github_yoke/applications/filelists/
+--NPZ_DIR
+/lustre/scratch5/exempt/artimis/data/cx241203_copy/cx241203/
+--CSV_FILEPATH
+/lustre/scratch5/exempt/artimis/mpmm/design_cx241203_MASTER.csv
+--train_filelist
+cx241203_prefixes_train_80pct_noBe_noVoid_truncated.txt
+--validation_filelist
+cx241203_prefixes_val_10pct_noBe_noVoid_truncated.txt
+--test_filelist
+cx241203_prefixes_test_10pct_noBe_noVoid_truncated.txt
+--block_structure
+<B0>
+<B1>
+<B2>
+<B3>
+--embed_dim
+<EMBED_DIM>
+--anchor_lr
+<ANCHOR_LR>
+--num_cycles
+<NUM_CYCLES>
+--min_fraction
+<MIN_FRACTION>
+--terminal_steps
+<TERMINAL_STEPS>
+--warmup_steps
+<WARMUP_STEPS>
+--trn_rcrd_filename
+./training_study<studyIDX>_epoch<epochIDX>.csv
+--val_rcrd_filename
+./validation_study<studyIDX>_epoch<epochIDX>.csv
+--batch_size
+<BATCH_SIZE>
+--num_workers
+<NUM_WORKERS>
+--Ngpus
+<NGPUS>
+--Knodes
+<KNODES>
+--total_epochs
+100
+--cycle_epochs
+1
+--train_batches
+<NTRN_BATCH>
+--val_batches
+<NVAL_BATCH>
+--TRAIN_PER_VAL
+5
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/training_START.slurm b/applications/harnesses/ch_DDP_loderunner_cylex/training_START.slurm
new file mode 100644
index 00000000..ed5eede8
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/training_START.slurm
@@ -0,0 +1,75 @@
+#!/bin/bash
+
+# This is a setup for GPU training on Venado. Find out how much
+# memory per node, number of CPUs/node.
+
+# NOTE: Number of CPUs per GPU must be an even number since there are
+# 2 threads per core. If an odd number is requested the next higher
+# even number gets used. 
+
+# The following are one set of SBATCH options for the Venado GPU
+# partition. There are optional other constraints.
+
+#SBATCH --job-name=ddp_s<studyIDX>_e0001
+#SBATCH --account=y26_artimis-fmod_g
+#SBATCH --partition=standard
+#SBATCH --time=1:30:00
+#SBATCH --nodes=<KNODES>
+#SBATCH --ntasks-per-node=<NGPUS>
+#SBATCH --gpus-per-node=<NGPUS>
+#SBATCH --cpus-per-task=8
+#SBATCH --mem-per-gpu=120G
+#SBATCH --output=study<studyIDX>_epoch0001.out
+#SBATCH --error=study<studyIDX>_epoch0001.err
+#SBATCH -vvv
+
+# Set the master node's address and port
+MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+MASTER_PORT=$(shuf -i 1024-65535 -n 1)  # Choose a random port
+export MASTER_ADDR
+export MASTER_PORT
+
+# Enable shell debugging: Debugging selene slurm
+set -xv
+
+# Check available GPUs
+sinfo  -o "%P %.24G %N"
+srun -vv --cpu-bind=verbose /usr/bin/echo $CUDA_AVAILABLE_DEVICES
+nvidia-smi
+
+# Specify NCCL communication
+export NCCL_SOCKET_IFNAME=ib0  # Check possible interfaces with `ip link show`
+
+# for multi-node training
+export NCCL_IB_HCA=mlx5_0
+export NCCL_IB_GID_INDEX=3  # sometimes 0 or 3 depending on subnet manager config
+
+# Debugging distributed data parallel
+# export NCCL_DEBUG=INFO
+# export NCCL_DEBUG_SUBSYS=INIT
+
+# Debugging selene slurm
+export SLURM_CPU_BIND=verbose
+
+# Load correct conda environment
+module load anaconda/3.12
+source activate
+conda activate <YOKE_TORCH_ENV>
+
+# Set number of threads per GPU
+export OMP_NUM_THREADS=10
+
+# Get start time
+export date00=`date`
+
+# Start the Code
+# Explicitly set TCP environment for the following...
+srun -vv --cpu-bind=verbose python -u <train_script> @study<studyIDX>_START.input
+
+# Get end time and print to stdout
+export date01=`date`
+
+echo "===================TIME STARTED==================="
+echo $date00
+echo "===================TIME FINISHED==================="
+echo $date01
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/training_input.tmpl b/applications/harnesses/ch_DDP_loderunner_cylex/training_input.tmpl
new file mode 100644
index 00000000..bb857811
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/training_input.tmpl
@@ -0,0 +1,56 @@
+--studyIDX
+<studyIDX>
+--FILELIST_DIR
+/usr/projects/artimis/mpmm/hickmank/github_yoke/applications/filelists/
+--NPZ_DIR
+/lustre/scratch5/exempt/artimis/data/cx241203_copy/cx241203/
+--CSV_FILEPATH
+/lustre/scratch5/exempt/artimis/mpmm/design_cx241203_MASTER.csv
+--train_filelist
+cx241203_prefixes_train_80pct_noBe_noVoid_truncated.txt
+--validation_filelist
+cx241203_prefixes_val_10pct_noBe_noVoid_truncated.txt
+--test_filelist
+cx241203_prefixes_test_10pct_noBe_noVoid_truncated.txt
+--block_structure
+<B0>
+<B1>
+<B2>
+<B3>
+--embed_dim
+<EMBED_DIM>
+--anchor_lr
+<ANCHOR_LR>
+--num_cycles
+<NUM_CYCLES>
+--min_fraction
+<MIN_FRACTION>
+--terminal_steps
+<TERMINAL_STEPS>
+--warmup_steps
+<WARMUP_STEPS>
+--trn_rcrd_filename
+./training_study<studyIDX>_epoch<epochIDX>.csv
+--val_rcrd_filename
+./validation_study<studyIDX>_epoch<epochIDX>.csv
+--batch_size
+<BATCH_SIZE>
+--num_workers
+<NUM_WORKERS>
+--Ngpus
+<NGPUS>
+--Knodes
+<KNODES>
+--total_epochs
+100
+--cycle_epochs
+1
+--train_batches
+<NTRN_BATCH>
+--val_batches
+<NVAL_BATCH>
+--TRAIN_PER_VAL
+5
+--continuation
+--checkpoint
+<CHECKPOINT>
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/training_slurm.tmpl b/applications/harnesses/ch_DDP_loderunner_cylex/training_slurm.tmpl
new file mode 100644
index 00000000..4708024c
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/training_slurm.tmpl
@@ -0,0 +1,74 @@
+#!/bin/bash
+
+# This is a setup for GPU training on Venado. Find out how much
+# memory per node, number of CPUs/node.
+
+# NOTE: Number of CPUs per GPU must be an even number since there are
+# 2 threads per core. If an odd number is requested the next higher
+# even number gets used. 
+
+# The following are one set of SBATCH options for the Venado GPU
+# partition. There are optional other constraints.
+
+#SBATCH --job-name=ddp_s<studyIDX>_e<epochIDX>
+#SBATCH --account=y26_artimis-fmod_g
+#SBATCH --time=1:30:00
+#SBATCH --nodes=<KNODES>
+#SBATCH --ntasks-per-node=<NGPUS>
+#SBATCH --gpus-per-node=<NGPUS>
+#SBATCH --cpus-per-task=8
+#SBATCH --mem-per-gpu=120G
+#SBATCH --output=study<studyIDX>_epoch<epochIDX>.out
+#SBATCH --error=study<studyIDX>_epoch<epochIDX>.err
+#SBATCH -vvv
+
+# Set the master node's address and port
+MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
+MASTER_PORT=$(shuf -i 1024-65535 -n 1)  # Choose a random port
+export MASTER_ADDR
+export MASTER_PORT
+
+# Enable shell debugging: Debugging selene slurm
+set -xv
+
+# Check available GPUs
+sinfo  -o "%P %.24G %N"
+srun -vv --cpu-bind=verbose /usr/bin/echo $CUDA_AVAILABLE_DEVICES
+nvidia-smi
+
+# Specify NCCL communication
+export NCCL_SOCKET_IFNAME=ib0  # Check possible interfaces with `ip link show`
+
+# for multi-node training
+export NCCL_IB_HCA=mlx5_0
+export NCCL_IB_GID_INDEX=3  # sometimes 0 or 3 depending on subnet manager config
+
+# Debugging distributed data parallel
+# export NCCL_DEBUG=INFO
+# export NCCL_DEBUG_SUBSYS=INIT
+
+# Debugging selene slurm
+export SLURM_CPU_BIND=verbose
+
+# Load correct conda environment
+module load anaconda/3.12
+source activate
+conda activate <YOKE_TORCH_ENV>
+
+# Set number of threads per GPU
+export OMP_NUM_THREADS=10
+
+# Get start time
+export date00=`date`
+
+# Start the Code
+# Explicitly set TCP environment for the following...
+srun -vv --cpu-bind=verbose python -u <train_script> @<INPUTFILE>
+
+# Get end time and print to stdout
+export date01=`date`
+
+echo "===================TIME STARTED==================="
+echo $date00
+echo "===================TIME FINISHED==================="
+echo $date01
diff --git a/applications/harnesses/ch_DDP_loderunner_cylex/training_study001_epoch0097.csv b/applications/harnesses/ch_DDP_loderunner_cylex/training_study001_epoch0097.csv
new file mode 100644
index 00000000..010559e9
--- /dev/null
+++ b/applications/harnesses/ch_DDP_loderunner_cylex/training_study001_epoch0097.csv
@@ -0,0 +1,4000 @@
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diff --git a/src/yoke/helpers/cli.py b/src/yoke/helpers/cli.py
index 30072f8b..8a999f3d 100644
--- a/src/yoke/helpers/cli.py
+++ b/src/yoke/helpers/cli.py
@@ -112,6 +112,20 @@ def add_filepath_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParse
         default=os.path.join(YOKE_PATH, "data_examples/lsc240420/"),
         help="Directory in which LSC *.npz files live.",
     )
+    parser.add_argument(
+        "--NPZ_DIR",
+        action="store",
+        type=str,
+        default=os.path.join(YOKE_PATH, "data_examples/cx241203_fp16_half/"),
+        help="Directory in which CX *.npz files live.",
+    )
+    parser.add_argument(
+        "--CSV_FILEPATH",
+        action="store",
+        type=str,
+        default=os.path.join(YOKE_PATH, "data_examples/design_cx241203_MASTER.csv"),
+        help="Filepath to CX csv file.",
+    )
     parser.add_argument(
         "--NC_NPZ_DIR",
         action="store",
@@ -289,6 +303,13 @@ def add_training_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParse
         default="./default_validation.csv",
         help="Filename for text file of validation loss and metrics on each batch",
     )
+    parser.add_argument(
+        "--test_rcrd_filename",
+        action="store",
+        type=str,
+        default="./default_testing.csv",
+        help="Filename for text file of test evaluation loss and metrics on each batch",
+    )
     parser.add_argument(
         "--continuation",
         action="store_true",
diff --git a/src/yoke/utils/training/datastep/loderunner.py b/src/yoke/utils/training/datastep/loderunner.py
index 600996ab..89bb444a 100644
--- a/src/yoke/utils/training/datastep/loderunner.py
+++ b/src/yoke/utils/training/datastep/loderunner.py
@@ -224,6 +224,71 @@ def train_DDP_loderunner_datastep(
     return end_img, pred_img, all_losses
 
 
+def train_DDP_loderunner_datastep_cylex(
+    data: tuple,
+    model,
+    optimizer,
+    loss_fn,
+    device: torch.device,
+    rank: int,
+    world_size: int,
+):
+
+    """A DDP-compatible training step for multi-input, multi-output data.
+
+        Args:
+        data (tuple): tuple of model input, corresponding ground truth, and lead time
+        model (loaded pytorch model): model to train
+        optimizer (torch.optim): optimizer for training set
+        loss_fn (torch.nn Loss Function): loss function for training set
+        device (torch.device): device index to select
+        rank (int): Rank of device
+        world_size (int): Number of total DDP processes
+    """
+    # Extract data
+    start_img, channel_map, end_img, channel_map, Dt = data
+    print("In train_DDP_loderunner_datastep: channel_map =", channel_map)
+
+    start_img = start_img.to(device, non_blocking=True)
+    Dt = Dt.to(device, non_blocking=True)
+    end_img = end_img.to(device, non_blocking=True)
+
+    # Set model to train mode
+    model.train()
+
+    # Map input and output variable indices according to channel map
+    in_vars = torch.tensor(channel_map).flatten().to(device, non_blocking=True)
+    out_vars = torch.tensor(channel_map).flatten().to(device, non_blocking=True)
+
+    # Forward pass
+    pred_img = model(start_img, in_vars, out_vars, Dt)
+
+    # Compute loss
+    loss = loss_fn(pred_img, end_img)
+    per_sample_loss = loss.mean(dim=[1, 2, 3])  # Per-sample loss
+
+    # Backward pass and optimization
+    optimizer.zero_grad(set_to_none=True)
+    loss.mean().backward()
+    optimizer.step()
+
+    # Gather per-sample losses from all processes
+    gathered_losses = [torch.zeros_like(per_sample_loss) for _ in range(world_size)]
+    dist.all_gather(gathered_losses, per_sample_loss)
+
+    # Rank 0 concatenates and saves or returns all losses
+    if rank == 0:
+        all_losses = torch.cat(gathered_losses, dim=0)  # Shape: (total_batch_size,)
+
+    else:
+        all_losses = None
+
+    # Free memory
+    del in_vars, out_vars
+    torch.cuda.empty_cache()
+
+    return end_img, pred_img, all_losses
+
 ####################################
 # Evaluating on a Datastep
 ####################################
@@ -294,6 +359,58 @@ def eval_loderunner_datastep(
     return end_img, pred_img, per_sample_loss
 
 
+def eval_loderunner_datastep_cylex(
+    data: tuple,
+    model: torch.nn.Module,
+    loss_fn: torch.nn.Module,
+    device: torch.device,
+    channel_map: None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """An evaluation step for which the data is of multi-input, multi-output type.
+
+    This is currently a proto-type function to get the LodeRunner architecture
+    training on a non-variable set of channels.
+
+    Args:
+        data (tuple): tuple of model input, corresponding ground truth, and lead time
+        model (torch.nn.Module): model to evaluate
+        loss_fn (torch.nn.Module): loss function for evaluation
+        device (torch.device): device index to select
+        channel_map (list[int]): list of channel indices to use
+
+    Returns:
+        end_img (torch.Tensor): Ground truth end image
+        pred_img (torch.Tensor): Predicted end image
+        per_sample_loss (torch.Tensor): Per-sample loss for the batch
+
+    """
+    # Set model to train
+    model.eval()
+
+    # Extract data
+    start_img, channel_map, end_img, channel_map, Dt = data
+    start_img = start_img.to(device, non_blocking=True)
+    Dt = Dt.to(device, non_blocking=True)
+    end_img = end_img.to(device, non_blocking=True)
+
+    in_vars = torch.tensor(channel_map).flatten().to(device, non_blocking=True)
+    out_vars = torch.tensor(channel_map).flatten().to(device, non_blocking=True)
+
+    # Perform a forward pass
+    # NOTE: If training on GPU model should have already been moved to GPU
+    # prior to initalizing optimizer.
+    pred_img = model(start_img, in_vars, out_vars, Dt)
+
+    # Expecting to use a *reduction="none"* loss function so we can track loss
+    # between individual samples. However, this will make the loss be computed
+    # element-wise so we need to still average over the (channel, height,
+    # width) dimensions to get the per-sample loss.
+    loss = loss_fn(pred_img, end_img)
+    per_sample_loss = loss.mean(dim=[1, 2, 3])  # Shape: (batch_size,)
+
+    return end_img, pred_img, per_sample_loss
+
+
 def eval_scheduled_loderunner_datastep(
     data: tuple,
     model: torch.nn.Module,
@@ -419,3 +536,61 @@ def eval_DDP_loderunner_datastep(
         all_losses = None
 
     return end_img, pred_img, all_losses
+
+
+def eval_DDP_loderunner_datastep_cylex(
+    data: tuple,
+    model,
+    loss_fn,
+    device: torch.device,
+    rank: int,
+    world_size: int,
+):
+    """A DDP-compatible evaluation step.
+
+    Args:
+        data (tuple): tuple of model input, corresponding ground truth, and lead time
+        model (loaded pytorch model): model to train
+        loss_fn (torch.nn Loss Function): loss function for training set
+        device (torch.device): device index to select
+        rank (int): Rank of device
+        world_size (int): Total number of DDP processes
+
+    """
+    print("starting in eval_DDP_loderunner_datastep")
+    # Set model to evaluation mode
+    model.eval()
+
+    # Extract data
+    start_img, channel_map, end_img, channel_map, Dt = data
+    start_img = start_img.to(device, non_blocking=True)
+    Dt = Dt.to(device, non_blocking=True)
+    end_img = end_img.to(device, non_blocking=True)
+
+    # Map input and output variable indices according to channel map
+    in_vars = torch.tensor(channel_map).flatten().to(device, non_blocking=True)
+    out_vars = torch.tensor(channel_map).flatten().to(device, non_blocking=True)
+
+    # Forward pass
+    with torch.no_grad():
+        pred_img = model(start_img, in_vars, out_vars, Dt)
+
+    # Compute loss
+    loss = loss_fn(pred_img, end_img)
+    per_sample_loss = loss.mean(dim=[1, 2, 3])  # Per-sample loss
+
+    # Gather per-sample losses from all processes
+    gathered_losses = [torch.zeros_like(per_sample_loss) for _ in range(world_size)]
+    dist.all_gather(gathered_losses, per_sample_loss)
+
+    # Rank 0 concatenates and saves or returns all losses
+    if rank == 0:
+        all_losses = torch.cat(gathered_losses, dim=0)  # Shape: (total_batch_size,)
+    else:
+        all_losses = None
+
+    # Free memory
+    del in_vars, out_vars
+    torch.cuda.empty_cache()
+
+    return end_img, pred_img, all_losses
diff --git a/src/yoke/utils/training/epoch/loderunner.py b/src/yoke/utils/training/epoch/loderunner.py
index 26629e4e..4b51d41c 100644
--- a/src/yoke/utils/training/epoch/loderunner.py
+++ b/src/yoke/utils/training/epoch/loderunner.py
@@ -8,12 +8,27 @@
 from yoke.utils.training.datastep.loderunner import (
     train_loderunner_datastep,
     eval_loderunner_datastep,
+    eval_loderunner_datastep_cylex,
     train_scheduled_loderunner_datastep,
     eval_scheduled_loderunner_datastep,
     train_DDP_loderunner_datastep,
     eval_DDP_loderunner_datastep,
+    train_DDP_loderunner_datastep_cylex,
+    eval_DDP_loderunner_datastep_cylex,
 )
 
+DATASTEP_FN = {
+    "pli": {
+        "train_ddp": train_DDP_loderunner_datastep,
+        "eval_ddp": eval_DDP_loderunner_datastep,
+        "eval": eval_loderunner_datastep
+    },
+    "cylex": {
+        "train_ddp": train_DDP_loderunner_datastep_cylex,
+        "eval_ddp": eval_DDP_loderunner_datastep_cylex,
+        "eval": eval_loderunner_datastep_cylex
+    },
+}
 
 def train_simple_loderunner_epoch(
     channel_map: list,
@@ -346,6 +361,7 @@ def train_DDP_loderunner_epoch(
     device: torch.device,
     rank: int,
     world_size: int,
+    dataset: str = "pli",
 ) -> None:
     """Distributed data-parallel LodeRunner Epoch.
 
@@ -370,6 +386,7 @@ def train_DDP_loderunner_epoch(
         device (torch.device): device index to select
         rank (int): rank of process
         world_size (int): number of total processes
+        dataset (str): Name of dataset to train on. Options are "pli" and "cylex".
 
     """
     # Initialize things to save
@@ -386,9 +403,16 @@ def train_DDP_loderunner_epoch(
             # Stop when number of training batches is reached
             if trainbatch_ID >= num_train_batches:
                 break
+            
+            # Get correct datastep function
+            dataset_fns = DATASTEP_FN.get(dataset)
+            if dataset_fns is None:
+                raise ValueError(f"Unsupported dataset: {dataset}")
+
+            train_fn = dataset_fns["train_ddp"]
 
             # Perform a single training step
-            truth, pred, train_losses = train_DDP_loderunner_datastep(
+            truth, pred, train_losses = train_fn(
                 traindata, model, optimizer, loss_fn, device, rank, world_size
             )
 
@@ -419,8 +443,9 @@ def train_DDP_loderunner_epoch(
                     # Stop when number of training batches is reached
                     if valbatch_ID >= num_val_batches:
                         break
-
-                    end_img, pred_img, val_losses = eval_DDP_loderunner_datastep(
+ 
+                    eval_fn  = dataset_fns["eval_ddp"]
+                    end_img, pred_img, val_losses = eval_fn(
                         valdata,
                         model,
                         loss_fn,
@@ -439,3 +464,71 @@ def train_DDP_loderunner_epoch(
                             ]
                         )
                         np.savetxt(val_rcrd_file, batch_records, fmt="%d, %d, %.8f")
+
+
+def eval_loderunner_epoch(
+    testing_data: torch.utils.data.DataLoader,
+    num_test_batches: int,
+    model: torch.nn.Module,
+    channel_map: list[int],
+    loss_fn: torch.nn.Module,
+    epochIDX: int,
+    test_rcrd_filename: str,
+    device: torch.device,
+    dataset: str = "pli",
+) -> None:
+    """LodeRunner Evaluation-Only Epoch.
+
+    Function to complete a testing epoch on the LodeRunner architecture with
+    fixed channels in the input and output. Testing information is saved to successive
+    CSV files.
+
+    Args:
+        testing_data (torch.utils.data.DataLoader): testing dataloader
+        num_test_batches (int): Number of batches in training epoch
+        model (torch.nn.Module): model to train
+        channel_map (list[int]): list of channel indices to use
+        loss_fn (torch.nn.Module): loss function for training set
+        epochIDX (int): Index of current training epoch
+        test_rcrd_filename (str): Name of CSV file to save testing sample stats to
+        device (torch.device): device index to select
+
+    """
+    # Initialize things to save
+    testbatch_ID = 0
+
+    # Testing loop
+    model.eval()
+    test_rcrd_filename = test_rcrd_filename.replace("<epochIDX>", f"{epochIDX:04d}")
+
+    # Get correct datastep function
+    dataset_fns = DATASTEP_FN.get(dataset)
+    if dataset_fns is None:
+        raise ValueError(f"Unsupported dataset: {dataset}")
+
+    eval_fn  = dataset_fns["eval"]
+
+    with open(test_rcrd_filename, "a") as test_rcrd_file:
+        for testbatch_ID, testdata in enumerate(testing_data):
+            # Stop when number of training batches is reached
+            if testbatch_ID >= num_test_batches:
+                break
+
+            # Perform a single test step
+            end_img, pred_img, test_losses = eval_fn(
+                testdata,
+                model,
+                loss_fn,
+                device,
+                channel_map,
+                )
+
+            # Save testing record
+            batch_records = np.column_stack(
+                [
+                    np.full(len(test_losses), epochIDX),
+                    np.full(len(test_losses), testbatch_ID),
+                    test_losses.cpu().detach().numpy().flatten(),
+                ]
+            )
+            np.savetxt(test_rcrd_file, batch_records, fmt="%d, %d, %.8f")