- About Us
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Secure, unlimited storage to ensures efficiency as well as high and continuous data access demand.
Virtual server integration for 3D Rendering, AI or ML
Enhance application capacity and availability.
Access to an all-inclusive stack for AI development, driven by NVIDIA’s powerful technology!
FPT Web Application Firewall provides powerful protection for web applications
Advanced virtual server with rapid scalability
Backup and restore data instantly, securely and maintain data integrity.
Advanced virtual server with rapid scalability
Access to an all-inclusive stack for AI development, driven by NVIDIA’s powerful technology!
Enhance application capacity and availability.
Backup and restore data instantly, securely and maintain data integrity.
Recovery, ensuring quick operation for the business after all incidents and disasters.
Diverse throughput and capacity to meet various business workloads.
Secure, unlimited storage to ensures efficiency as well as high and continuous data access demand.
FPT Web Application Firewall provides powerful protection for web applications
Intelligent and Comprehensive Virtual Web Application Firewall - Security Collaboration between FPT Cloud and Penta Security.
The Next generation firewall security service
Easily store, manage, deploy, and secure Container images
Safe, secure, stable, high-performance Kubernetes platform
Provided as a service to deploy, monitor, backup, restore, and scale MongoDB databases on cloud.
Provided as a service to deploy, monitor, backup, restore, and scale Redis databases on cloud.
Provided as a service to deploy, monitor, backup, restore, and scale PostgreSQL databases on cloud.
System Monitoring Solution anywhere, anytime, anyplatform
Helps reduce operational costs by up to 40% compared to traditional BI solutions, while improving efficiency through optimized resource usage and infrastructure scaling. - Pricing
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How to Create Pipeline?
How to Create Pipeline?
Step 1
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Select Base Model:
- Choose a foundational model from the Model Catalog (e.g., DeepSeek, Gemma, Llama, Qwen) or your Private Model repository. Models vary by size (from 0.5B to 72B parameters), type (LLM, VLM), and training stage (pre-trained or instruction-tuned).
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Select Trainer:
- Pick a training method such as SFT (Supervised Fine-tuning), DPO (Direct Preference Optimization) or Pre-training, depending on your task and data type.
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Select Volume:
- Select a storage type based on dataset size: Managed Volume for datasets under 20GiB or Dedicated Network Volume for larger datasets requiring manual provisioning.
Step 2
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Select Dataset Format:
- Choose the appropriate format (Alpaca, ShareGPT, Corpus, etc.) based on your selected trainer.
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Select Dataset:
- Upload training and evaluation datasets or connect to Data Hub. Ensure data quality, consistency, and correct formatting to optimize model performance.
Step 3
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Set up Hyperparameters:
- Configure training parameters like batch size, learning rate, epochs and optimizer settings. Advanced options are available for DPO, LoRA, and runtime optimization.
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Set up Infrastructure:
- Supported both single-node and multi-node, with up to 16 nodes
Step 4
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Finish:
- Name your pipeline and optionally add a description. You'll receive email notifications when the pipeline succeeds or fails.
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