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Hybrid Cloud - Research #51

Open jaylong255 opened 2 months ago

jaylong255 commented 2 months ago

Description

I'm researching hybrid cloud deployments for a client that has some reserved GPU capacity coming online in a data center in Texas here in the coming weeks. I'm pretty sure we're going to want to use a hybrid approach in order to leverage familiar APIs while running our more intensive dedicated workloads on the reserve capacity. The problem is all my experience is in straight cloud infrastructure. So, I'm trying to determine what I don't know I don't know and answer a few of the things I know I don't know.

First Prompt

I have years of experience deploying cloud architecture in aws and a foundational amount of experience with gcp. i have a client that is preparing to deploy a fleet of gpus out of a data center in texas. i have no experience in onprem or hybrid cloud deployments. all i know is from foundational certifications in aws. how should i approach advising, consulting and planning for this project. i'm leaning towards using gpc or aws for their apis and pushing for a hybrid approach. this way, the experience of maintaining the infrastructure is familiar to more developers but we will still be able to use the reserved capacity in the data center for our most intensive workloads. we could also have spot capacity in the cloud if we need to scale beyond what we have provisioned in reserve on short notice. what kinds of common issues am i likely overlooking due to inexperience and lack of training on hybrid cloud deployments

jaylong255 commented 2 months ago

Hybrid Cloud

When approaching a hybrid cloud deployment involving a GPU fleet, especially with your background in AWS and foundational knowledge in GCP, here are some strategic considerations and common issues you might face:

Strategic Approach:

  1. Assessment and Planning:

    • Workload Analysis: Determine which workloads are suitable for on-premises vs. cloud. GPU-intensive tasks might benefit from the on-premises setup due to data gravity and latency, while less critical or burstable workloads could go to the cloud.
    • API and Service Integration: Since you're familiar with AWS, consider AWS Outposts for a consistent hybrid experience or look into Google's Anthos for GCP-related services. Both platforms offer tools and APIs that can integrate with on-premises environments.
  2. Architecture Design:

    • Hybrid Connectivity: Ensure robust, secure, and high-speed connectivity between your data center in Texas and the cloud providers. AWS Direct Connect or Google Cloud Interconnect can be options.
    • Data Management: Plan for data synchronization, backup, and disaster recovery. Consider how data will move between on-premises and the cloud, keeping in mind the GPU data processing might produce large datasets.
  3. Security and Compliance:

    • Ensure compliance with any industry-specific regulations, especially if data processed involves sensitive information. Encryption for data at rest and in transit is crucial.
  4. Cost Management:

    • Analyze the cost implications of running GPUs in the cloud vs. on-premises. Cloud can offer scalability but at potentially higher long-term costs for constant high-load scenarios.
  5. Scalability and Elasticity:

    • Use cloud for scalability, especially for workloads that might need to expand rapidly. However, remember that transferring large datasets or models to and from the cloud could become a bottleneck.

Common Issues in Hybrid Cloud Deployments:

Given your background, leaning towards a hybrid approach with AWS or GCP makes sense, but remember to:

By addressing these areas, you'll be better prepared to handle the complexities of hybrid cloud deployments involving GPU fleets.

jaylong255 commented 2 months ago

Google Cloud Anthos

https://cloud.google.com/anthos/?hl=en

jaylong255 commented 2 months ago

JupyterHub

JupyterHub is a multi-user server for Jupyter notebooks. Here's a breakdown of what it does and why it's useful:

Core Concept:

Key Features:

  1. Authentication: JupyterHub supports various authentication methods, including OAuth, GitHub, LDAP, or even simple PAM (Pluggable Authentication Modules). This flexibility makes it adaptable to different organizational security policies.

  2. Spawners: Users' notebook servers are started ("spawned") on demand. This can be done on a local machine, in containers (like Docker), or even on cloud services. This means resources are allocated dynamically, which can be more efficient for large groups.

  3. Customization: Admins can configure the environment for users, pre-installing libraries or setting up specific computational resources, which is particularly useful for teaching environments where all students need the same setup.

  4. Hub API: For more advanced use cases, JupyterHub provides an API for programmatically managing users, servers, and other resources.

  5. Integration: It integrates well with other tools and platforms, like nbgrader for automated grading of notebooks in educational settings.

Use Cases:

Deployment:

Challenges:

JupyterHub essentially democratizes access to computational resources and interactive coding environments, making it an invaluable tool for education, research, and collaborative data science work.

jaylong255 commented 2 months ago

Jupyter Notebooks

Jupyter Notebooks are an open-source web application that allows you to create and share documents that contain:

Key Features:

Use Cases:

Advantages:

Jupyter Notebooks have become a staple in data science and education due to their versatility, ease of use, and the ability to combine code execution with documentation in a single document.