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GPUlayer and the alternatives
Training frameworks shape how a model trains on the machines they are started on, and cloud orchestrators rent capacity. GPUlayer places any GPU workload on the machines you can reach, starts it there and brings the results back. Several of these tools work well together.
Where each tool sits
Most of these tools work at different layers, so they combine more often than they compete.
Uses Modal's own GPUs
Capabilities
| Capability | GPUlayer | PyTorch Lightning | Accelerate | Ray | SkyPilot | dstack | Modal | Slurm / K8s |
|---|---|---|---|---|---|---|---|---|
Run any command or binary | ||||||||
Run Python functions remotely | ||||||||
PyTorch training helpers (devices, DDP, precision) GPUlayer's Trainer is small; Kubernetes via training operators | ||||||||
Multi-node jobs GPUlayer: torchrun groups, tested on one host only | ||||||||
Chooses a machine that fits the job | ||||||||
Uses machines you already have | ||||||||
Provisions cloud capacity GPUlayer: provider interface exists, only a simulated adapter | ||||||||
Long-running services Kubernetes yes, Slurm no | ||||||||
Start without operating a cluster Ray: a cluster for multi-node; dstack: a small server | ||||||||
Runs on your own hardware (self-hosted) |
"Accelerate" is Hugging Face Accelerate. Marks summarize each project's publicdocumentation at the time of writing and simplify features with many options; check each project for current details.
At a glance
| Tool | What it does | Runs on | Chooses machines? | Workloads | Hosting |
|---|---|---|---|---|---|
| GPUlayer | Places workloads on machines you can reach, starts them, returns logs and results | This machine, servers that join, cloud VMs that join | Picks a compatible machine per job (GPU, memory, frameworks); cloud provisioning is simulated today | Any command, Python functions with any library, torchrun groups, PyTorch training | Self-hosted; embedded mode needs no server |
| PyTorch Lightning | Organizes PyTorch training code (LightningModule, Trainer) and handles devices, precision and parallel strategies | The machines it is launched on | No; you start it on each node (for example with Slurm or torchrun) | PyTorch training, fine-tuning, evaluation | Open-source library (a hosted platform is sold separately) |
| Hugging Face Accelerate | Adapts a PyTorch training loop to multi-GPU, multi-node and mixed precision with little code change | The machines it is launched on | No; accelerate launch runs on machines you provide | PyTorch training and inference | Open-source library |
| Ray | General distributed computing framework (tasks, actors) with libraries for training, tuning, serving and data | A Ray cluster (head node plus workers) on VMs or Kubernetes | Schedules tasks inside the cluster; an autoscaler can add cloud nodes | Python programs written against Ray's API; functions and objects are serialized | Open source, self-hosted; managed offerings exist |
| SkyPilot | Runs jobs and clusters across many clouds and Kubernetes from a YAML or Python spec | Cloud VMs and Kubernetes it provisions | Yes; provisions VMs, chooses regions and instance types, can recover spot jobs | Any command in a task spec | Open source, runs from your machine or a shared API server |
| dstack | Orchestrates AI dev environments, tasks and services | Cloud providers, Kubernetes, and on-prem servers added over SSH | Yes; provisions cloud capacity or uses fleets you register | Containers running any command; long-running services | Open source, self-hosted server; a hosted version exists |
| Modal | Serverless platform: decorate Python functions and run them on Modal's GPUs | Modal's infrastructure | Modal allocates them; you do not bring your own GPUs | Python functions, batch jobs, web endpoints, containers defined in code | Hosted service, pay per use |
| Slurm / Kubernetes | Cluster schedulers for shared HPC or container infrastructure | A cluster you operate | Yes, within the cluster; queues, quotas, priorities | Batch jobs (Slurm); containers and services (Kubernetes, plus operators for training) | Open source; needs cluster administration |
Descriptions of other projects are summaries of their public documentation at the time of writing; check each project for current details.
Training speed: GPUlayer and PyTorch Lightning
The same model, data and optimizer, trained with GPUlayer's Trainer and with Lightning's Trainer on one Apple Silicon Mac. Lower is better.
| Case | GPUlayer, ms per step | Lightning, ms per step | Step time ratio | GPUlayer start-up | Lightning start-up |
|---|---|---|---|---|---|
| CPU, 1 process, width 256 | 0.51 | 0.83 | 1.63x faster | 1.06 s | 1.14 s |
| CPU, 1 process, width 2048 | 3.59 | 3.99 | 1.11x faster | 1.07 s | 1.17 s |
| Apple GPU (MPS), width 256 | 0.57 | 1.26 | 2.21x faster | 1.16 s | 1.25 s |
| Apple GPU (MPS), width 2048 | 0.83 | 3.33 | 4.01x faster | 1.23 s | 1.14 s |
| DDP, 2 CPU processes, width 256 | 1.82 | 3.52 | 1.93x faster | 3.79 s | 2.46 s |
| DDP, 2 CPU processes, width 2048 | 40.17 | 41.03 | about the same | 3.72 s | 2.70 s |
Method: Apple Silicon Mac with 10 CPU cores, Python 3.12, torch 2.15.0, Lightning 2.6.6. A three-layer MLP (width 256 or 2048), batch 64, SGD, one logged loss per step. Lightning runs without logger, progress bar, model summary or checkpoints; GPUlayer runs without checkpoints. Each case runs 100 and 600 steps, three times each in fresh processes; ms per step is the median difference divided by 500 steps. Start-up is the import time plus the fixed time inside fit.
Differences under about 10% are within run-to-run noise on this machine, so the larger DDP case is a tie. GPUlayer runs DDP as a local GPUlayer job (packaged, scheduled and started with torchrun), which is why its DDP start-up is about a second longer. These are CPU and Apple GPU results on one machine; NVIDIA and multi-machine results have not been measured yet.
GPUlayer and PyTorch Lightning and Hugging Face Accelerate
Both decide how a PyTorch model trains inside the machines they are started on: device placement, precision, DDP, FSDP or DeepSpeed, callbacks, logging. They do not choose those machines, copy code to them, or start the processes on each node. GPUlayer works one level below: it decides where a workload runs and starts it there. Its own PyTorch Trainer is deliberately small: it has callbacks, early stopping and gradient accumulation, but no FSDP, DeepSpeed or logger integrations yet.
- When to choose it
- Choose them for training features. Lightning has a far richer Trainer than GPUlayer's, and Accelerate makes an existing loop portable.
- Using both
- Keep your training code and add
gpulayer.pytorch.launch(...)at the top of the script. GPUlayer then places it on workers and starts torchrun with the standard environment that both libraries read. This combination follows from the torchrun environment but has not been tested in this repository yet.
GPUlayer and Ray
Ray is a programming model as much as a scheduler: you write tasks and actors against its API, run a Ray cluster, and use its libraries for training, tuning and serving. Functions and arguments are serialized and shipped between processes. GPUlayer ships source code and calls functions by name with JSON arguments, has no distributed object store, and runs plain commands written in any language.
- When to choose it
- Choose Ray for fine-grained distributed Python (many small tasks, actors, shared objects), Ray Tune sweeps, or Ray Serve.
- Using both
- A Ray program can be a GPUlayer job on one machine; a multi-node Ray cluster is better managed by Ray itself or KubeRay.
GPUlayer and SkyPilot and dstack
Both provision and manage cloud capacity: they choose instance types and regions, start VMs or pods, and run your commands there. dstack also runs services and dev environments and can use on-prem servers. GPUlayer's cloud provisioning exists only as a simulated adapter today. Its strengths are a lightweight join for any machine you already have, capability-aware placement with explanations, and the Python and PyTorch APIs.
- When to choose it
- Choose SkyPilot or dstack when you mainly need cloud capacity across providers, spot recovery, or long-running services today.
- Using both
- VMs they start can run
gpulayer joinand become GPUlayer workers.
GPUlayer and Modal
Modal's developer experience (decorate a function, call .remote() or .map()) is close to GPUlayer's @gl.remote, but it runs on Modal's own infrastructure. GPUlayer runs on hardware you control: your laptop, office servers and VMs you rent, including Apple Silicon Macs.
- When to choose it
- Choose Modal if you want hosted, autoscaled GPUs with no machines to manage and are fine paying per use.
- Using both
- They solve the same problem for different hardware, so teams usually pick one.
GPUlayer and Slurm and Kubernetes
Cluster schedulers with queues, quotas, priorities and multi-tenant isolation, run by an operations team. GPUlayer is much lighter, with one command to start a server and one to join a machine, and it is meant for groups whose members trust each other. Shared multi-tenant clusters are a job for Slurm or Kubernetes.
- When to choose it
- Choose them for shared, policy-heavy clusters with many users.
- Using both
- Integrations (submitting to Slurm or Kubernetes as a GPUlayer worker pool) are not built yet.
GPUlayer fits when
- Your GPUs are spread over a laptop, a few servers and some rented VMs, and you want one queue for all of them.
- Your team mixes NVIDIA boxes and Apple Silicon Macs and wants jobs to land on hardware that can run them.
- Your workloads are not only training: rendering, inference batches, simulations, data jobs, in any language.
- You want placement to explain itself: per-worker reasons when a job cannot run.
- You want to start small:
gpulayer upon one machine,gpulayer joinon the others, no cluster to operate.
Where GPUlayer is behind today
- No real cloud provisioning or autoscaling yet (simulated adapter only); SkyPilot, dstack and Ray already do this.
- No long-running services yet; dstack, Ray Serve, Modal and Kubernetes handle them.
- A small PyTorch Trainer: no FSDP, DeepSpeed or logger integrations, unlike PyTorch Lightning.
- Distributed jobs start about a second slower than PyTorch Lightning's DDP on one machine, because they go through job packaging and scheduling.
- Distributed jobs use torchrun only; no JAX multi-host or MPI launcher yet.
- First release (v0.1.0): NVIDIA GPUs and multi-machine runs are implemented but not validated yet. Not a sandbox for untrusted users.