Introducing One-Click AI Development Environments: Jupyter Notebook, PyTorch, TensorFlow
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In the fast-paced world of AI development, accessible and efficient tools are key. Denvr Cloud now empowers users with direct access to a suite of powerful applications, including Jupyter Notebook, PyTorch, TensorFlow, and more—all available with just a few clicks from our cloud console. This new feature brings flexibility, speed, and convenience, ideal for data scientists, AI developers, and machine learning researchers who need seamless access to computational tools and environments. Let’s dive into this launch and explore how you can get started!
Application Catalog on Denvr Cloud Console
Here’s a quick look at what Denvr Cloud now offers in its application catalog:
Custom Application: Bring your own container image and launch a custom application to meet specific needs.
SSH Access: Direct SSH connections for advanced management and configuration.
JAX: A cutting-edge framework for high-performance numerical computing and machine learning research, including Numpy-like APIs and XLA acceleration.
Jupyter Notebook: An open-source notebook environment enabling code sharing, data visualization, and interactive graphing.
PyTorch: A GPU-accelerated framework for deep learning with extensive library support, making it easy to use with NumPy and SciPy.
RAPIDS Notebooks: Execute end-to-end data science and analytics pipelines on GPUs, allowing deep data exploration and visualization.
TensorFlow: A robust open-source platform for machine learning that spans deployment across diverse platforms.
Triton Inference Server: An inference server for deploying trained models from multiple frameworks on any GPU or CPU infrastructure.
With this range, you can choose exactly the environment needed for each project. Let’s look at how to get started.
How to Launch Jupyter Notebook and Other Applications
Below is a walkthrough for launching a Jupyter Notebook on Denvr Cloud to demonstrate Python code for AI model interaction. This process will be similar for other applications in our catalog.
Step 1: Accessing Denvr Cloud Console
Log in to the Denvr Cloud Console. From the main dashboard, you’ll see a catalog of applications, each with clear labels and logos. Here, you can select the environment you want to launch.
Application catalog
Once you click the "Create Application", it will show you list of available ( pre baked ) application bundles ready to be launched. In this example we will be selecting Jupyter Notebook Application bundle for illustration purpose.
Step 2: Choosing Jupyter Notebook
In the catalog, select Jupyter Notebook from the options. This application provides a familiar Python environment for data visualization, interactive coding, and AI prototyping.
In this selection window you can choose which GPU ( A40, A100, H100 ) you would like to use.
Next you can choose how much compute capacity of the application instance. You can choose single GPU card or a full node ( 4 X GPU or 8X GPU )
Ensure you enter your Jupyter authentication token
If you would like to have SSH access to your container, then copy paste your SSH public key.
Step 3: Launching the Application
After selecting Jupyter Notebook, click the launch button. You’ll be taken directly to a Jupyter environment, preconfigured for Python-based AI programming.
Key things to note in this screen ( highlighted ). Ensure the application status is showing green online
Once the application status is online, use the "Access" URL to access the application.
Step 4: Using Jupyter Notebook in Denvr Cloud
Now your IDE is ready to be used.
Once in the Jupyter Notebook environment, you can start coding right away.
You can quickly validate active GPU from the Jupyter Notebook
Here’s a sample Python script to interact with an AI model endpoint via Ollama API. You can follow our previous blog post about how to launch Llama3.2 with Ollama on Denvr Cloud (https://www.denvrdata.com/post/optimizing-llm-deployment-with-llama-3-2-and-denvr-cloud) or you can run on your laptop too.
Full Python Notebook file: could be found https://github.com/denvrdata/examples/blob/main/simple-application-jupyternotebook/Llama3.2-example.ipynb
This script demonstrates how to interact with a model API endpoint directly from the Jupyter environment. You can use similar setups for PyTorch, TensorFlow, or other tools, depending on your project needs.
Why Choose Denvr Cloud?
Denvr Cloud is designed for professionals seeking high-performance, cloud-based AI and machine learning environments. With easy app launches, SSH access, and support for custom applications, Denvr Cloud is an excellent choice for data science, model training, and inference workflows.
Try It Out
With our new applications catalog, you have direct access to tools that make AI development easier than ever. Try launching your next project on Denvr Cloud and experience the convenience of ready-to-use environments tailored for machine learning, data science, and high-performance computing.
Get Started Today with Denvr Cloud and bring your AI ideas to life without the usual infrastructure hassles!
