Simplify Jupyter Notebook Deployments with Run AI

Table of Contents

1. Introduction
2. GPU Allocation Challenges
3. Run AI's Solution
4. Deploying a New Workspace
5. Choosing a Project
6. Selecting Templates
7. Choosing an Environment
8. Specifying Compute Resources
9. Connecting Data Sources
10. Configuring Networking
11. Accessing Jupyter Notebook
12. GPU Allocation in Action
13. Cloning GitHub Repositories
14. Conclusion

Simplifying Jupiter Notebook Deployments with Run AI

Jupyter notebooks are a popular tool for data scientists and machine learning engineers to develop and test their models. However, deploying these notebooks can be a challenge, especially when it comes to GPU allocation. In this article, we'll explore how Run AI simplifies Jupiter notebook deployments while simultaneously reducing the GPU allocation required to run them.

GPU Allocation Challenges

One of the major challenges with Jupiter notebooks is that when You're deploying them, quite often people are assigning entire GPUs to them. This is because of the limitations within Kubernetes, for example. However, it means that if you want to run 10 Jupiter notebooks, you need 10 GPUs. GPUs these days are very expensive and quite hard to get hold of, and this isn't an ideal Scenario because in a lot of scenarios where you're in that build phase and you're developing your models, you don't need an entire GPU in that period.

Run AI's Solution

Run AI has the capability to deliver fractional GPUs, allowing you to slice up a GPU into much smaller portions. This allows you to get much greater density on your GPUs and therefore free up GPU use for more GPU-intensive workloads like training models, etc.

Deploying a New Workspace

When you Create a new workspace in Run AI, you choose the project that you wish to deploy into. If there are templates associated with your project, you can select one of these. A template is made up of the various components that you can go through in a workspace, but in this instance, we're going to Show you those individual components.

Choosing a Project

The first thing you do when you create a new workspace is choose the project that you wish to deploy into. This is the project you're working on.

Selecting Templates

If there are templates associated with your project, you can select one of these. A template is made up of the various components that you can go through in a workspace, but in this instance, we're going to show you those individual components.

Choosing an Environment

Next, you choose which environment you wish to run. We're going to choose a Jupiter notebook, and you can see the image that we're going to be utilizing here. An environment specifies the image and any other settings around that image that you might have, and these are pre-created or you can create them at this phase.

Specifying Compute Resources

Once you've selected the environment, you can then specify how much compute resource you need. This is where Run AI's fractional GPU allocation comes into play. You can slice up a GPU into much smaller portions, allowing you to get much greater density on your GPUs and therefore free up GPU use for more GPU-intensive workloads like training models, etc.

Connecting Data Sources

Another big challenge for data scientists is how to connect your data sources. Run AI takes care of all of that for you. These can be pre-configured, and then you just select what you want. This could be an NFS share, an S3 bucket, a PVC on Kubernetes, or in this instance, we'll just use a GitHub repo.

Configuring Networking

A big thing that Run AI also does is configure the networking. The container is brought up, the image is running, but actually, how do you access it? Run AI takes care of all of that for you. You can literally select your workspace, choose connect, select the tool that's running within the workspace, in this instance Jupiter, and you could have multiple tools running in a workspace. Then we obviously allow you direct connection straight through to your Jupiter notebook.

Accessing Jupyter Notebook

As you can see, you can now access your Jupiter notebook. This is a big AdVantage of Run AI, as it simplifies the deployment process and allows you to focus on your work.

GPU Allocation in Action

Let's take a look at GPU allocation in action. By running Nvidia SMI, we can see that we've only assigned 0.1 of a GPU to this workspace. This is how we slice up the GPU in these instances. The other thing to note is that we've also cloned the GitHub repo that we attached the data source to. All of the Contents of that are already here waiting for you to start running.

Cloning GitHub Repositories

Cloning GitHub repositories is a breeze with Run AI. You can select a GitHub repo as your data source, and Run AI will Take Care of cloning it for you. This is a big advantage for data scientists who want to focus on their work and not worry about the deployment process.

Conclusion

In conclusion, Run AI simplifies Jupiter notebook deployments while simultaneously reducing the GPU allocation required to run them. With Run AI, you can slice up a GPU into much smaller portions, allowing you to get much greater density on your GPUs and therefore free up GPU use for more GPU-intensive workloads like training models, etc. Run AI also takes care of configuring the networking and connecting your data sources, making the deployment process much simpler. If you want to learn more about Run AI, visit docs.run.ai or drop us an email.

Highlights

• Run AI simplifies Jupiter notebook deployments while simultaneously reducing the GPU allocation required to run them.
• Run AI has the capability to deliver fractional GPUs, allowing you to slice up a GPU into much smaller portions.
• Run AI takes care of configuring the networking and connecting your data sources, making the deployment process much simpler.
• Cloning GitHub repositories is a breeze with Run AI.

FAQ

Q: What is Run AI? A: Run AI is a platform that simplifies Jupiter notebook deployments while simultaneously reducing the GPU allocation required to run them.

Q: How does Run AI reduce GPU allocation? A: Run AI has the capability to deliver fractional GPUs, allowing you to slice up a GPU into much smaller portions.

Q: What data sources can I connect to with Run AI? A: You can connect to an NFS share, an S3 bucket, a PVC on Kubernetes, or a GitHub repo.

Q: Does Run AI configure the networking for me? A: Yes, Run AI takes care of configuring the networking for you.

Q: Is cloning GitHub repositories easy with Run AI? A: Yes, cloning GitHub repositories is a breeze with Run AI.