To run a deep learning experiment on a Linux server, first verify that the host, NVIDIA driver, GPU, and PyTorch environment work together. Then package the software in a versioned container where practical, keep data and results on persistent storage, validate with a short run, and launch through either a managed process on a standalone server or the site’s Slurm scheduler. Record enough configuration and checkpoint state to inspect and resume the experiment; add GPUs or nodes only after measuring the current run.
1. Check the server, GPU, and software environment
Confirm that the machine has the accelerator your workload expects and that your account or job allocation can access it. For NVIDIA hardware, check the host driver and GPU visibility, then verify that the container or Python environment is compatible with the host. NVIDIA’s PyTorch container instructions show checking framework access with torch.cuda.is_available().
Run the check inside the environment that will execute the experiment:
python -c "import torch; print(torch.cuda.is_available())"A result of True means PyTorch can access CUDA in that environment. It does not establish that the full model and batch will fit in GPU memory, that data loading is working, or that the run will perform well. Resolve device visibility or compatibility issues before submitting a long job.
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These NVIDIA-specific examples apply to compatible NVIDIA GPUs and software. Other accelerator vendors and server configurations require their own drivers, framework builds, and verification steps.
2. Make the software environment repeatable
When practical, run from a versioned container image so dependencies and environment configuration are less likely to drift between runs. A container does not replace the host: it shares the host kernel, and NVIDIA notes that the host driver must be compatible. See NVIDIA’s container user guide and PyTorch container instructions.
For an NVIDIA GPU, the documented Docker pattern includes GPU access and bind mounts. Adapt the image tag and paths to the installed runtime and the current image availability:
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nvcr.io/nvidia/pytorch:<version>-py3<version> is a placeholder, not a literal tag to copy. Choose an available, compatible tag and record it with the experiment. Mount datasets and output/checkpoint directories from persistent host storage; files written only to a disposable container can disappear when it exits. Mount source code as appropriate, or include a recorded source revision in the image.
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Run a short smoke test in the same environment and, on a cluster, under the same kind of allocation you plan to use. It should import the framework, confirm the intended device, load a small data sample, execute a few training or evaluation steps, and write an output or checkpoint. This is a practical validation workflow, not a guarantee of full-run behavior.
- Check that the job sees the expected GPU or devices.
- Confirm that data paths are readable and outputs can be written to persistent storage.
- Inspect logs, GPU memory use, and step time for errors or obvious bottlenecks.
- Verify that a saved checkpoint or result can be found after the process exits.
4. Launch the job in the right way
Standalone Linux server
For a single server, launch the command through a process or session manager appropriate to the host, and capture standard output and errors to persistent log files. Use storage that survives shell disconnection or container exit, and retain the exact command used. Check the server’s local policies before occupying a shared machine or reserving its GPU.
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Slurm cluster
On a Slurm-managed cluster, request resources through Slurm rather than starting GPU work directly on a login node. Request the number of GPUs and nodes, CPU resources, time limit, and partition required by the site’s policy. NVIDIA’s DGX Cloud Slurm guide documents srun for interactive work, sbatch for queued jobs, and squeue for checking queue status.
A batch script normally puts resource directives near the top and runs the training command inside the allocated job. Use the GPU and rank information provided by Slurm rather than assuming fixed node names or device ranks. Keep Slurm output logs, checkpoints, and metrics in persistent locations. Partition names, container integration, mount points, environment variables, and exact resource directives vary by cluster, so adapt the guide’s examples to local documentation.
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Save a run manifest alongside its logs and outputs. Include the source revision, command line, configuration, dataset identity or version, package and container versions, host and GPU details, random seed, metrics, and checkpoint location. This makes comparisons and debugging more useful than a run name alone.
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A seed helps control randomness, but it is not a promise of identical results. NVIDIA’s PyTorch reproducibility guidance discusses seeding Python, NumPy, and PyTorch, handling data-loader randomness, and selecting deterministic operations where supported. Some operations remain nondeterministic, and behavior can differ across hardware, software versions, operations, and distributed configurations.
For a meaningful resume, checkpoint more than model weights: preserve optimizer state, training progress, scaler state when applicable, and random-generator state as well. Record which checkpoint corresponds to which configuration and data version.
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Begin with one GPU when possible. Measure step time, input throughput, GPU utilization, and memory use; profile the data path as well as the model. These observations help distinguish a compute bottleneck from slow data loading or an unsuitable batch size.
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For multiple GPUs or nodes, use the framework’s distributed tools and the allocation’s rank information. PyTorch’s multi-node tutorial describes using torchrun and explains that inter-node communication latency can make four GPUs on one node faster than four nodes with one GPU each. That comparison illustrates a trade-off, not a universal performance result.
Before expanding an allocation, compare the measured throughput gain with communication overhead, GPU memory needs, queue wait, storage and data movement, cost, and operational complexity. More nodes do not automatically mean a shorter experiment.
Choosing a single server or a Slurm cluster
| Consideration | Single Linux server | Slurm cluster |
|---|---|---|
| How work starts | Run through an appropriate local process or session manager. | Request an allocation with srun or submit a batch job with sbatch; check it with squeue. |
| Resource access | Depends on the server’s hardware and local access policy. | Depends on requested resources, site policy, partition, and queue availability. |
| Scaling | Limited to resources available on that host. | Can allocate multiple nodes, but inter-node communication can reduce the benefit. |
| Operational needs | Manage long-running processes and persistent logs and outputs. | Follow site-specific scheduler, container, storage, and environment conventions. |
Use a single server when its available hardware and memory suit the experiment and you can manage the process and storage. Use the cluster when you need resources or scheduling the site provides, accounting for queue policy and the extra setup of distributed execution.
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