How does dstack differ from Slurm?

Slurm is designed for HPC environments, while dstack is built for modern ML/AI workloads with cloud-native provisioning and a container-first architecture. dstack natively supports development and production-grade inference in addition to training and batch jobs.

How does dstack compare to Kubernetes?

Kubernetes is a general-purpose container orchestrator. dstack is purpose-built for ML, providing a streamlined interface for dev environments, tasks, services, and fleets with simple configurations. dstack provisions GPUs, manages clusters, and optimizes cost and utilization while keeping a simple UI and CLI.

Can I use dstack with Kubernetes?

Yes, you can connect existing Kubernetes clusters using the Kubernetes backend and run dev environments, tasks, and services on it.

When should I use dstack?

dstack accelerates ML development with a simple, ML-native interface. Spin up dev environments, run single-node or distributed tasks, and deploy services without infrastructure overhead. It radically reduces GPU costs via smart orchestration.

What backends are supported by dstack?

dstack supports Kubernetes, SSH fleets, and native integrations with leading GPU clouds such as AWS and GCP.

Does dstack support auto-scaling?

Yes, dstack supports auto-scaling for model inference services, allowing you to handle varying workloads efficiently.

Is dstack open-source?

Yes, dstack is an open-source platform.

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Use Cases

Distributed Training of Large Language Models

Efficiently distributing training workloads across multiple GPUs and nodes to reduce training time for LLMs.

VIEW EXECUTION STEPS

Define a task configuration specifying the distributed training setup and resource requirements.

Use the 'dstack apply' command to deploy the task to a GPU cluster.

dstack automatically provisions the required resources and distributes the training workload.

Monitor GPU utilization and training progress through the dstack UI or CLI.

Collect training checkpoints and final models upon completion.

High-Throughput Inference Serving

Deploying and scaling machine learning models for high-volume inference requests.

VIEW EXECUTION STEPS

Define a service configuration for the model inference endpoint.

Specify the auto-scaling parameters and resource limits.

Deploy the service using the 'dstack apply' command.

dstack automatically provisions and manages the required GPU resources.

The service integrates with open-source serving frameworks and provides an OpenAI-compatible API.

Interactive Development and Debugging with GPU Access

Providing ML engineers with interactive access to GPU resources for experimentation and debugging.

VIEW EXECUTION STEPS

Create a dev environment configuration specifying the desired IDE (VS Code, Cursor) and GPU resources.

Use the 'dstack apply' command to launch the dev environment.

Connect the desktop IDE directly to the cloud or on-prem GPU.

Debug and iterate on code with real-time GPU access.

Orchestrating AI Workloads Across Hybrid Environments

Managing and deploying AI workloads seamlessly across cloud, Kubernetes, and on-premise GPU infrastructure.

VIEW EXECUTION STEPS

Configure dstack with multiple backends (e.g., Kubernetes, SSH fleets, cloud providers).

Define a task or service configuration that can run on any of these backends.

Use 'dstack apply' to deploy the workload, and dstack intelligently selects the appropriate backend.

Monitor the workload's performance and resource utilization from the dstack UI.

Cost Optimization for GPU Resources

Reducing the overall cost of running GPU-intensive AI workloads.

VIEW EXECUTION STEPS

Utilize dstack's fleet controls to enable efficient resource reuse and right-sizing.

Configure dstack to use spot instances or reserved capacity where appropriate.

Monitor GPU utilization metrics to identify underutilized resources.

Adjust resource allocations based on utilization data to optimize costs.

Single Node Training and Experimentation

Quickly iterating on models without needing to handle complex infrastructure.

VIEW EXECUTION STEPS

Create a task configuration specifying the training script and a single GPU resource.

Use the 'dstack apply' command to run the training task.

Monitor the training process, including GPU usage and logs, through the dstack CLI or UI.

Evaluate and refine the model based on the results.

Streamlining ML Workflow From Development to Deployment

Simplifying the end-to-end machine learning lifecycle by reducing complexities involved in deploying to production.

VIEW EXECUTION STEPS

Start with the dev environment for experimentation.

Transition to single-node/distributed tasks for training.

Deploy the trained model as a scalable service with optimized inference configurations.

Monitor the service performance metrics to ensure availability and performance.

dstack

About dstack

Core Capabilities

Main Tasks

GPU Orchestration

Model Training

Model Inference

What this tool is best suited for

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Key Features

Native GPU Cloud Integration

Dev Environments

Scalable Model Inference

Unified Control Plane

SSH Fleets

Kubernetes Backend

Fine-Grained Fleet Controls

Use Cases

Distributed Training of Large Language Models

High-Throughput Inference Serving

Interactive Development and Debugging with GPU Access

Orchestrating AI Workloads Across Hybrid Environments

Cost Optimization for GPU Resources

Single Node Training and Experimentation

Streamlining ML Workflow From Development to Deployment

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