All-in-one web-based development environment for machine learning

Introduction

The ML Workspace is an all-in-one web-based IDE tailored specifically for machine learning and data science. It’s designed to help developers build ML solutions efficiently and can be set up in just minutes on your local machine. With preloaded libraries such as TensorFlow, PyTorch, Keras, and tools like Jupyter and VS Code, it’s optimized for productive development.

Highlights

• Accessible web-based IDEs: Jupyter, JupyterLab, and Visual Studio Code.
• Pre-installed libraries for seamless development.
• Full Linux desktop GUI accessible via web browser.
• Optimized Git integration for version control.
• Integrated hardware training monitoring with Tensorboard and Netdata.
• Access from anywhere via Web, SSH, or VNC.
• Easy deployment on Mac, Linux, and Windows using Docker.

How to Get Started

• Ensure that Docker is installed on your machine.
• Run the following command to deploy a single instance:

docker run -p 8080:8080 mltooling/ml-workspace:0.13.2

• Access the workspace at http://localhost:8080.

Understanding the Command

Let’s use an analogy to explain the Docker command for deploying a workspace:

Imagine you are opening a restaurant. You need to ensure it’s in a good location, has the right permits, and is set up with all the kitchen appliances. The command you executed is like saying:

• -p 8080:8080: You’re setting up your restaurant’s front entrance (port 8080) to welcome customers while ensuring everything operates smoothly inside (also port 8080).
• mltooling/ml-workspace:0.13.2: This specifies which type of restaurant you are opening, allowing you to choose all the necessary ingredients and features you need for success.

Configuration Options

The workspace provides various configuration options through environment variables. You can customize aspects like:

• WORKSPACE_BASE_URL: The base URL for accessing Jupyter and other tools.
• WORKSPACE_SSL_ENABLED: Enable SSL for secure access.
• WORKSPACE_AUTH_USER: Set basic authentication for added security.

Troubleshooting

Running your ML environment may sometimes hit a snag. Here are common troubleshooting tips:

• If you face shared memory issues (common with tools like PyTorch), consider increasing the shared memory size with --shm-size=[size] in your run command, e.g., --shm-size=2G.
• If your Nginx terminates unexpectedly, ensure your hardware supports the necessary features such as SSE4.2.
• For additional assistance, feel free to reach out via fxis.ai!

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.