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Cloud Infrastructure: Tailored Cloud Solutions for Your Gen AI Initiatives

In the evolving world of AI, the right infrastructure makes all the difference. Iguana Solutions offers AI KUBE by IG1 a Kubernetes solution optimized to handle intensive computations, vast data and complex algorithms required by AI workloads. Imagine your AI models training at lightning speed, scaling effortlessly, and running securely with minimal downtime. Our clusters, equipped with the latest GPUs and TPUs, advanced resource management, and a comprehensive AI-specific software stack, make this a reality. Whether you’re developing cutting-edge AI applications or deploying models at scale, our infrastructure delivers the performance, efficiency, and security you need. Join Iguana Solutions and empower your AI initiatives with infrastructure as intelligent and dynamic as your solutions.

AI KUBE by IG1 Core Benefits

Superior Performance


Our Gen AI clusters are equipped with powerful GPUs and TPUs, enabling accelerated model training and deployment. The native integration of NVIDIA GPUs with Kubernetes enhances the hardware’s capabilities, significantly reducing processing times and improving overall performance. This results in efficient and effective AI workload execution on KUBE by IG1.

Enhanced Resource Management

Our clusters use advanced Kubernetes schedulers and autoscaling mechanisms to dynamically allocate resources, ensuring optimal utilization and efficiency. Features like node affinity, taints/tolerations, and pod priority ensure that critical AI workloads get the resources they need. Resource isolation using namespaces, cgroups, and QoS classes guarantees fair distribution and predictable performance, minimizing resource contention and maximizing efficiency.

Specialized Software Stack


Our AI clusters are pre-equipped with optimized AI frameworks such as TensorFlow, PyTorch, vLLM, and TGI for GPU/TPU utilization. The integrated development tools like Jupyter Notebooks and Kubeflow facilitate the entire AI lifecycle, from data preparation to model deployment. Advanced data processing, storage solutions, and model management tools ensure the efficiency, reproducibility, and scalability of your AI projects, providing a comprehensive environment for AI innovation. To seamlessly incorporate AI into your applications using OpenAI, we offer a service with an OpenAI compatible API, allowing any OpenAI-developed application to work immediately with this managed stack.

Superior Performance

High-Performance Hardware

  • GPUs and TPUs: Our clusters are equipped with cutting-edge GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) from industry leaders such as NVIDIA. These accelerators are specifically designed to handle the intensive computations required for AI model training and inference.
  • High-Bandwidth Memory: The use of high-bandwidth memory (HBM) in GPUs significantly improves data throughput, ensuring faster data processing and reduced latency.

Native Integration

  • GPUs to Pods: Expose the number of GPUs on each nodes of your cluster to your Kubernetes pods to run GPU enabled containers in your Kubernetes cluster.
  • Best in class network: Based on the successful model of KUBE by IG1. We utilize metalLB directly connected to the core network in BGP configuration, ensuring that all AI operations are executed with the utmost speed and efficiency.

Enhanced Resource Management

Dynamic Resource Allocation


  • Enhanced Kubernetes Scheduler: Our clusters utilize an advanced Kubernetes scheduler optimized for AI workloads. This scheduler prioritizes GPU and TPU resources, ensuring that AI tasks receive the necessary computational power. It intelligently allocates resources based on workload characteristics, minimizing idle times and maximizing utilization.
  • Node Affinity and Taints/Tolerations: We implement node affinity rules and taints/tolerations to ensure that specific workloads are scheduled on appropriate nodes. This helps in isolating AI workloads to nodes with specialized hardware, avoiding resource contention with non-AI workloads.
  • Pod Priority and Preemption: Our clusters leverage Kubernetes' pod priority and preemption features to ensure that critical AI workloads have priority access to resources. Lower-priority pods can be preempted to free up resources for higher-priority tasks, ensuring timely execution of important AI jobs.
  • Custom Resource Definitions (CRDs): We use CRDs to extend Kubernetes capabilities, enabling the management of custom resources specific to AI workloads, such as specialized GPU configurations or AI model artifacts.

Autoscaling

  • Horizontal Pod Autoscaler (HPA): The HPA dynamically adjusts the number of pod replicas based on observed CPU, memory, or custom metrics. This ensures that your AI applications can scale out during peak loads and scale in during off-peak times, optimizing resource usage and costs.
  • Vertical Pod Autoscaler (VPA): The VPA automatically adjusts the CPU and memory requests and limits for your pods based on historical usage. This ensures that each pod has the right amount of resources allocated, improving efficiency and reducing resource wastage.
  • Cluster Autoscaler: The Cluster Autoscaler automatically adjusts the size of your Kubernetes cluster based on the resource requirements of your AI workloads. It adds nodes when there are pending pods that cannot be scheduled due to resource constraints and removes nodes when they are underutilized.


Resource Isolation


  • Namespaces and Resource Quotas: Kubernetes namespaces are used to logically isolate different AI projects or teams within the same cluster. Resource quotas are applied to ensure fair resource distribution and prevent any single project from monopolizing cluster resources.
  • Control Groups (cgroups): Cgroups are employed to allocate, prioritize, and limit the CPU and memory resources for AI workloads. This ensures that each workload operates within its allocated resource boundaries, preventing resource contention and ensuring predictable performance.
  • QoS Classes: Kubernetes Quality of Service (QoS) classes (Guaranteed, Burstable, BestEffort) are used to manage resource allocation and priority for AI pods. Critical AI workloads can be assigned Guaranteed QoS to ensure they always have access to the required resources.

Specialized Software Stack

Pre-Installed AI Frameworks


  • TensorFlow: Pre-installed TensorFlow with GPU/TPU support, including TensorFlow Serving for efficient model deployment. TensorFlow's integration with Kubernetes enables distributed training and hyperparameter tuning, optimizing model performance.
  • PyTorch: Pre-installed PyTorch with GPU/TPU support, including TorchServe for scalable and efficient model serving. PyTorch's flexibility and dynamic computation graph capabilities make it ideal for research and production.
  • ONNX Runtime: The ONNX Runtime allows for the execution of models trained in different frameworks, promoting interoperability and flexibility in model deployment. It is optimized for performance on various hardware accelerators.

AI Development Tools

Planning to create your own models ? Your data science and Machine Learning professionals will greatly appreciate the deployment and management of popular tools and services that are compatible with Kubernetes.

  • Jupyter Notebooks: Integrated Jupyter Notebooks provide an interactive environment for data exploration, model development, and experimentation. Notebooks can be directly connected to Kubernetes pods with GPU/TPU access, streamlining the development workflow.
  • Kubeflow: Kubeflow is a comprehensive machine learning platform on Kubernetes, supporting the entire ML lifecycle from data preparation to model training, tuning, deployment, and monitoring. It includes components like Kubeflow Pipelines for workflow orchestration and Katib for hyperparameter tuning.
  • MLflow: Integration with MLflow for tracking experiments, managing models, and deploying AI models. MLflow provides a unified interface for managing the end-to-end ML lifecycle, ensuring reproducibility and collaboration.

Model Training and Deployment


  • TFJob and PyTorchJob: Custom Kubernetes controllers (TFJob and PyTorchJob) are used to manage distributed training jobs for TensorFlow and PyTorch, respectively. These controllers handle the orchestration of multi-worker training setups, ensuring efficient resource utilization.
  • 
 Seldon Core: Seldon Core is used for deploying, scaling, and managing thousands of machine learning models on Kubernetes. It supports various model formats and provides advanced features like A/B testing, canary deployments, and model explainability.
  • KFServing: KFServing provides a Kubernetes-native way to deploy and serve machine learning models. It supports multiple frameworks and ensures high scalability, performance, and security for model inference.

DataOps and MLOps Tools


  • Airflow: Apache Airflow is used for orchestrating complex AI workflows, including data ingestion, preprocessing, training, and deployment. Airflow's DAG-based approach ensures reliable and repeatable workflow execution.
  • DVC (Data Version Control): Integration with DVC for versioning datasets and models, enabling reproducibility and collaboration in AI projects. DVC seamlessly integrates with Git, providing a version control system for data and models.
  • Kubeflow Pipelines: Kubeflow Pipelines provide a platform for building, deploying, and managing end-to-end ML workflows. It supports reusable components and easy sharing of workflows within teams.

Visualization and Monitoring


  • TensorBoard: Integrated TensorBoard for visualizing training metrics, model graphs, and performance profiles. TensorBoard helps in tracking and debugging model training processes.
  • Grafana: Grafana dashboards provide real-time insights into cluster performance, resource usage, and AI workload metrics. Customizable dashboards allow for monitoring specific metrics relevant to AI workloads.


AI Model Management


  • Model Registry: A centralized model registry for tracking versions of AI models, along with metadata such as training parameters, evaluation metrics, and deployment history. This ensures traceability and easy management of models throughout their lifecycle.
  • 
A/B Testing and Canary Deployments: Support for A/B testing and canary deployments to experiment with different model versions in production, ensuring that only the best-performing models are fully deployed.

Inside Look:
Kubernetes Event at Iguana Solutions'
Paris Office

REX from Cyril Janssens, CTO of EasyBourse, who relied on Iguana Solutions' expertise to optimize his infrastructure and presentation of our work on

managed Kubernetes solutions : KUBE by IG1.

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“ With our previous partner, our ability to grow had come to a halt.. Opting for Iguana Solutions allowed us to multiply our overall performance by at least 4. “

Cyril Janssens

CTO, easybourse

Testimonials

KUBE by IG1

AI KUBE by IG1: Key Features

Automated, reliable, and flexible managed Kubernetes 
for optimized cloud efficiency and support.

CI/CD

Default Delivered Forge and CI/CD

Pre-set configuration that streamlines software development by automating integration and deployment processes, enhancing efficiency and consistency in project workflows.

On-prem self-service LoadBalancer

Offers streamlined, user-driven control over traffic distribution, enhancing application performance and reliability within an on-premises infrastructure.

On-prem self-service PersistentVolumes

Allow for flexible, autonomous management of storage resources, tailored to specific application needs while maintaining data persistence and reliability..

Professional Services

Support 24 / 7 via slack

Offers the advantage of immediate, round-the-clock assistance, ensuring quick resolution of issues and uninterrupted operations for businesses.

Automation

Automated
Deployments and Updates

Enhance operational efficiency by ensuring consistent, error-free application rollouts and maintaining the latest security and performance updates without manual intervention.

Kube by IG1 feature Showdown

Kube by IG1 vs. Public Cloud Kubernetes: The Ultimate Feature Showdown

Kube by IG1 In-Depth Feature Comparison of On-Premise vs. Public Cloud Solutions

AI KUBE by IG1


Kubernetes On Premise

Get a quote

Public Cloud Kubernetes

Kubernetes On Public Cloud

Kubernetes Features

Unlimited Load Balancer

kubect1 Interface for developers

Persistent Volumes Multi write

Storage Encryption

RBAC with OIDC

Physical Server Support

Load Balancing with ingress
“Services of type LoadBalancer offered by MetalLB”

IP Co-Sharing to Use a Single Public IP

Only on Google GKE

Persistent Volume Multi-Write on SSD

Cluster Topology Backup

Only on Google GKE

Cluster Migration

Managed CI/CD

Log Export

99.95% SLA and 24/7 Support

Professional Services

Your Transformation Partner: Navigate the Gen AI Revolution with Us

Navigate the Gen AI revolution with Iguana Solutions as your key partner. 
We provide tailored Gen AI solutions, focusing on fine-tuning models, Retrieval-Augmented Generation (RAG), and more. Specializing in on-premise environments, we offer secure, customizable deployments. Partnering with us transforms your business model and enhances team / customer experiences. Empower your organization to achieve sustainable success and unlock your full potential in the Gen AI era.

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Start Your DevOps Transformation Today

Embark on your DevOps journey with Iguana Solutions and experience a transformation that aligns with the highest standards of efficiency and innovation. Our expert team is ready to guide you through every step, from initial consultation to full implementation. Whether you’re looking to refine your current processes or build a new DevOps environment from scratch, we have the expertise and tools to make it happen. Contact us today to schedule your free initial consultation or to learn more about how our tailored DevOps solutions can benefit your organization. Let us help you unlock new levels of performance and agility. Don’t wait—take the first step towards a more dynamic and responsive IT infrastructure now.

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