Launch many tasks on same GPU
If you want to use a powerful GPU efficiently, you can run many tasks on same GPU
using a combination of sbatch arguments. In your sbatch script:
Specify only 1 GPU to use, e.g. with
--gres=gpu:rtx8000:1Specify number of tasks to run on the selected GPU with
--ntasks-per-gpu=NLaunch your job using
srun main.pyinstead of justmain.py.
srun will then launch main.py script N times.
Each task will receive specific environment variables, such as SLURM_PROCID,
which you can then use to parameterize the script execution.
Prerequisites Make sure to read the following sections of the documentation before using this example:
The full source code for this example is available on the mila-docs GitHub repository.
job.sh
# distributed/single_gpu/job.sh -> good_practices/many_tasks_per_gpu/job.sh
old mode 100644
new mode 100755
#!/bin/bash
-#SBATCH --gpus-per-task=rtx8000:1
+#SBATCH --gres=gpu:rtx8000:1
#SBATCH --cpus-per-task=4
-#SBATCH --ntasks-per-node=1
+#SBATCH --ntasks-per-gpu=2
#SBATCH --mem=16G
#SBATCH --time=00:15:00
# Echo time and hostname into log
echo "Date: $(date)"
echo "Hostname: $(hostname)"
# Ensure only anaconda/3 module loaded.
module --quiet purge
# This example uses Conda to manage package dependencies.
# See https://docs.mila.quebec/Userguide.html#conda for more information.
module load anaconda/3
module load cuda/11.7
# Creating the environment for the first time:
# conda create -y -n pytorch python=3.9 pytorch torchvision torchaudio \
# pytorch-cuda=11.7 -c pytorch -c nvidia
# Other conda packages:
# conda install -y -n pytorch -c conda-forge rich tqdm
# Activate pre-existing environment.
conda activate pytorch
# Stage dataset into $SLURM_TMPDIR
mkdir -p $SLURM_TMPDIR/data
cp /network/datasets/cifar10/cifar-10-python.tar.gz $SLURM_TMPDIR/data/
# General-purpose alternatives combining copy and unpack:
# unzip /network/datasets/some/file.zip -d $SLURM_TMPDIR/data/
# tar -xf /network/datasets/some/file.tar -C $SLURM_TMPDIR/data/
# Fixes issues with MIG-ed GPUs with versions of PyTorch < 2.0
unset CUDA_VISIBLE_DEVICES
-# Execute Python script
-python main.py
+# Execute Python script using srun.
+# Because of sbatch's `--ntasks-per-gpu=2` above, Python script will be launch twice.
+# Each run will receive specific environment variables, such as SLURM_PROCID.
+srun python main.py
main.py
# distributed/single_gpu/main.py -> good_practices/many_tasks_per_gpu/main.py
"""Single-GPU training example."""
import argparse
import logging
import os
+import random
from pathlib import Path
+import numpy
import rich.logging
import torch
from torch import Tensor, nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from torchvision.datasets import CIFAR10
from torchvision.models import resnet18
from tqdm import tqdm
def main():
# Use an argument parser so we can pass hyperparameters from the command line.
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--epochs", type=int, default=10)
parser.add_argument("--learning-rate", type=float, default=5e-4)
parser.add_argument("--weight-decay", type=float, default=1e-4)
parser.add_argument("--batch-size", type=int, default=128)
+ # Get SLURM_PROCID and use it as a random seed for the script.
+ # This makes it so each task within a job uses a different initialization with the same
+ # hyper-parameters.
+ parser.add_argument(
+ "--random-seed",
+ type=int,
+ default=int(os.environ.get("SLURM_PROCID", 0)),
+ help="Random seed used for network initialization and the training loop.",
+ )
args = parser.parse_args()
epochs: int = args.epochs
learning_rate: float = args.learning_rate
weight_decay: float = args.weight_decay
batch_size: int = args.batch_size
+ random_seed: int = args.random_seed
+
+ # Seed the random number generators as early as possible.
+ random.seed(random_seed)
+ numpy.random.seed(random_seed)
+ torch.random.manual_seed(random_seed)
+ torch.cuda.manual_seed_all(random_seed)
# Check that the GPU is available
assert torch.cuda.is_available() and torch.cuda.device_count() > 0
device = torch.device("cuda", 0)
# Setup logging (optional, but much better than using print statements)
logging.basicConfig(
level=logging.INFO,
handlers=[rich.logging.RichHandler(markup=True)], # Very pretty, uses the `rich` package.
)
logger = logging.getLogger(__name__)
# Create a model and move it to the GPU.
model = resnet18(num_classes=10)
model.to(device=device)
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# Setup CIFAR10
num_workers = get_num_workers()
dataset_path = Path(os.environ.get("SLURM_TMPDIR", ".")) / "data"
train_dataset, valid_dataset, test_dataset = make_datasets(str(dataset_path))
train_dataloader = DataLoader(
train_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True,
)
valid_dataloader = DataLoader(
valid_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False,
)
test_dataloader = DataLoader( # NOTE: Not used in this example.
test_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False,
)
# Checkout the "checkpointing and preemption" example for more info!
logger.debug("Starting training from scratch.")
for epoch in range(epochs):
logger.debug(f"Starting epoch {epoch}/{epochs}")
# Set the model in training mode (important for e.g. BatchNorm and Dropout layers)
model.train()
# NOTE: using a progress bar from tqdm because it's nicer than using `print`.
progress_bar = tqdm(
total=len(train_dataloader),
desc=f"Train epoch {epoch}",
)
# Training loop
for batch in train_dataloader:
# Move the batch to the GPU before we pass it to the model
batch = tuple(item.to(device) for item in batch)
x, y = batch
# Forward pass
logits: Tensor = model(x)
loss = F.cross_entropy(logits, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Calculate some metrics:
n_correct_predictions = logits.detach().argmax(-1).eq(y).sum()
n_samples = y.shape[0]
accuracy = n_correct_predictions / n_samples
logger.debug(f"Accuracy: {accuracy.item():.2%}")
logger.debug(f"Average Loss: {loss.item()}")
# Advance the progress bar one step and update the progress bar text.
progress_bar.update(1)
progress_bar.set_postfix(loss=loss.item(), accuracy=accuracy.item())
progress_bar.close()
val_loss, val_accuracy = validation_loop(model, valid_dataloader, device)
logger.info(f"Epoch {epoch}: Val loss: {val_loss:.3f} accuracy: {val_accuracy:.2%}")
print("Done!")
@torch.no_grad()
def validation_loop(model: nn.Module, dataloader: DataLoader, device: torch.device):
model.eval()
total_loss = 0.0
n_samples = 0
correct_predictions = 0
for batch in dataloader:
batch = tuple(item.to(device) for item in batch)
x, y = batch
logits: Tensor = model(x)
loss = F.cross_entropy(logits, y)
batch_n_samples = x.shape[0]
batch_correct_predictions = logits.argmax(-1).eq(y).sum()
total_loss += loss.item()
n_samples += batch_n_samples
correct_predictions += batch_correct_predictions
accuracy = correct_predictions / n_samples
return total_loss, accuracy
def make_datasets(
dataset_path: str,
val_split: float = 0.1,
val_split_seed: int = 42,
):
"""Returns the training, validation, and test splits for CIFAR10.
NOTE: We don't use image transforms here for simplicity.
Having different transformations for train and validation would complicate things a bit.
Later examples will show how to do the train/val/test split properly when using transforms.
"""
train_dataset = CIFAR10(
root=dataset_path, transform=transforms.ToTensor(), download=True, train=True
)
test_dataset = CIFAR10(
root=dataset_path, transform=transforms.ToTensor(), download=True, train=False
)
# Split the training dataset into a training and validation set.
n_samples = len(train_dataset)
n_valid = int(val_split * n_samples)
n_train = n_samples - n_valid
train_dataset, valid_dataset = random_split(
train_dataset, (n_train, n_valid), torch.Generator().manual_seed(val_split_seed)
)
return train_dataset, valid_dataset, test_dataset
def get_num_workers() -> int:
"""Gets the optimal number of DatLoader workers to use in the current job."""
if "SLURM_CPUS_PER_TASK" in os.environ:
return int(os.environ["SLURM_CPUS_PER_TASK"])
if hasattr(os, "sched_getaffinity"):
return len(os.sched_getaffinity(0))
return torch.multiprocessing.cpu_count()
if __name__ == "__main__":
main()
Running this example
This assumes you already created a conda environment named “pytorch” as in Pytorch example:
Exit the interactive job once the environment has been created and Oríon installed. You can then launch the example:
$ sbatch job.sh