Build and Run Your AI Code with Confidence
How to Set Up Your Developer Environment for Practical Deep Learning Exercises: Boost Efficiency and Ensure Quality in Your Coding Workflow
Embarking on my journey into artificial intelligence, I discovered the Practical Deep Learning for Coders course by fast.ai. This course stands out not only for its rich content but also for Jeremy Howard’s outstanding teaching methodology. The name "Practical Deep Learning for Coders" is fitting, as it guides learners through launching their first model into production while offering a comprehensive understanding of the end-to-end process.
This approach reminded me of Booking.com’s philosophy of “failing fast.” When I joined Booking.com, new hires were encouraged to launch the main website on their first day. This hands-on experience instills confidence and reveals the practical aspects of onboarding before diving into writing code. Such an approach is possible only with a robust platform—one that offers excellent observability, fosters a culture of experimentation, and ensures seamless rollouts and rollbacks.
The course provides various options for setting up a development environment, but many are cloud-focused and lack the stability of a local development setup. I want to highlight the importance and benefits of a reliable local environment.
The Importance of a Local Setup
For me, having a local development setup is crucial. It enables a deep understanding of the system’s core details, including libraries, versions, and dependencies, which helps identify and resolve issues efficiently. When setting up a functional working environment, factors like your laptop’s specifications, software versions (e.g., Jupyter), and compatibility with other tools play a significant role. As the team size grows, managing these variables becomes increasingly complex.
If your goal is a simple, one-off script, a basic installation might suffice. However, if you aim to build scalable systems, investing in a robust initial setup pays dividends in the long run.
My Approach to Building a Local Setup
Recognizing the challenges of setting up a reliable local environment, I opted for a structured approach. Using Docker, I created a controlled environment for running Jupyter Notebook with the Fastbook setup. With a few tweaks, the process became seamless. This setup now allows me to:
Spawn new instances effortlessly.
Experiment with new libraries, models and structures without risking my local configuration.
Focus entirely on learning and experimentation.
Steps to Install Docker:
Install and run Docker, ensuring your Git repository is mounted on the machine.
Install the necessary fast.ai libraries within the Docker container.
Launch Jupyter Notebook on your local computer.
And that’s it—you’re ready to dive into deep learning!
Benefits of a Local Setup
1. Build and Run It
A local setup provides the confidence to run models in any production environment.
It helps you understand operational needs such as low latency, high uptime, and hardware scalability.
2. Cloud/Platform Independence
A robust local environment allows you to transition seamlessly between cloud providers or platforms.
3. Streamlined Integration
Testing locally ensures smooth integration of your models into existing applications.
Reflecting on My Progress
Although I’m still progressing through the course, I’ve already deployed my first model into production. While modest, this achievement has given me a practical understanding of deep learning and its applications.
If you’re interested in following my journey, check out my GitHub Repository.
Final Thoughts
Jeremy Howard’s encouragement to document and share the learning process has been incredibly motivating. Writing about even straightforward topics, such as the benefits of a local setup, has helped me organize my thoughts and reflect on my progress. I hope this inspires others to prioritize local development as an essential step in their AI journey.