Trainy
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About
Trainy is an enterprise-grade AI infrastructure platform that enables teams to run large-scale GPU workloads on-demand across various cloud providers. It simplifies the deployment of AI workloads with simple YAML files, handling networking, scaling, and issue resolution automatically. Trainy offers quick setup, allowing users to go from local to 64 H100s in under an hour. It supports any ML frameworks like PyTorch, HuggingFace, Jax, and Ray, and provides multi-node capabilities and automatic complex networking configuration. The platform is built for high reliability with comprehensive fault detection, automatic recovery, and direct cloud provider resolution, ensuring zero downtime and preventing costly GPU failures. Trainy's on-demand pricing model means users only pay when their code is running, maximizing ROI on AI development by eliminating idle GPU costs. It also offers a reserved plan for dedicated GPU allocation and advanced monitoring. Key features include preemptive queuing, multi-framework support, continuous health monitoring, and robust resource management, all designed to make ML infrastructure just work.
Platform
Task
Features
• resource management & utilization tracking
• health monitoring & fault detection
• preemptive queue
• automated networking configuration
• multi-node training
• any ml frameworks (pytorch, huggingface, jax, ray)
• multi-cloud compatibility
• quick setup (yaml based deployment)
FAQs
How do I submit jobs with Trainy?
Jobs are submitted via a simple YAML file. Enter your torchrun or equivalent launch command, and Trainy handles the rest across clouds. See docs for details.
Is Trainy a Cloud Provider?
No. We help customers pick suitable cloud provider offerings and validate hardware performance. Our solution can deploy on existing reserved GPU clusters, or help startups set up multi-node training fast.
Should my AI team access GPUs via On-Demand or Reserved?
Most Trainy customers use a hybrid. Reserved instances suit inference servers and dev boxes. On-demand is better for large-scale, bursty training workloads to reduce GPU spend.
Kubernetes seems too complicated. Why do I need software to manage my GPUs?
K8s boosts ROI on compute. Top AI teams use similar systems. Automated scheduling & cleanup ensure GPU availability. Decision makers gain visibility & control for informed purchasing.
What are the benefits of Trainy over a tool like Slurm?
Trainy offers all Slurm's resource sharing and scheduling benefits, plus workload isolation via containerization, integrated observability, and improved robustness with comprehensive health monitoring.
How does Trainy cut GPU costs?
By cutting idle time with a fault-tolerant scheduler that keeps GPUs busy 24/7 and ensures job restarts on healthy nodes. Advanced performance metrics also help optimize workload efficiency.
How do I connect data sources to my GPU cluster with Trainy’s platform?
Most Trainy customers stream data from object stores like Cloudflare R2. Distributed file system integrations are being explored for the future, but are not currently available.
Can I use Trainy to manage multi-cloud environments?
Yes, we provide access to multiple K8s clusters for different clouds. However, jobs are submitted to one cluster at a time, not simultaneously across multiple.
What is the best time to start working with Trainy?
The earlier, the better. On-demand clusters are cost-effective for exploring gen AI. We help navigate cloud provider offerings and ensure max performance when choosing a provider.
Pricing Plans
On-Demand
USD3.60 / per GPU per hour• High-Performance H100 GPU Clusters
• Zero code changes for deployment
• Multi-node training support
• High-bandwidth networking
• Cross-cloud compatibility
• Priority queuing system
• Usage-based billing
• Dashboard & Queue Management
• Team access controls
• Automated Job Failure Recovery
Reserved
USD50000.00 / per year• Dedicated GPU allocation
• Advanced monitoring & utilization insights
• Enterprise SLA
• Annual contract billing
• Support for Blackwell & all NVIDIA Data Center GPUs
• Multi-node training support
• High-bandwidth networking
• Cross-cloud compatibility
• GPU health monitoring
• Automated Job Failure Recovery
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
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