Deploying a Python (Dash) web app on Azure can be a straightforward process, but sometimes developers encounter issues that slow down deployment, such as a pipeline running too long with the message: "Building wheel for pandas still running." This can be a common scenario, especially when working with larger packages like Pandas. In this article, we will explore effective strategies to optimize your deployment process.
Understanding the Problem
The problem arises when the Azure pipeline takes an extended amount of time to build the wheel file for the Pandas library during the deployment of a Dash web application. Building wheels for certain libraries, especially those with heavy C extensions like Pandas, can lead to timeouts and delayed deployments. The original error can look something like this:
Building wheel for pandas (setup.py) ... still running
Optimizing Your Azure Pipeline
To resolve the long-running build time and optimize the deployment of your Dash web app, consider the following strategies:
1. Use Pre-Built Wheel Files
One of the simplest optimizations is to use pre-built wheel files instead of building from source. Pre-built wheel files can significantly reduce the installation time for packages like Pandas. You can do this by ensuring you have the correct version of Pandas in your requirements.txt file, which can specify wheel files directly or leverage a Python package index that has them available.
2. Optimize the Requirements
Evaluate your requirements.txt to ensure that you are only including the packages that are absolutely necessary for your application. Reducing unnecessary dependencies can greatly improve build times. Here is a sample of an optimized requirements.txt:
dash==2.0.0
pandas==1.3.3
numpy==1.21.2
3. Utilize Azure Pipelines Caching
Azure DevOps offers caching features that can significantly speed up your builds. By caching your dependencies, you prevent the pipeline from reinstalling packages each time it runs. You can configure caching in your azure-pipelines.yml file:
- task: Cache@2
inputs:
key: 'pip | "$(Agent.OS)" | requirements.txt'
path: ~/.cache/pip
This will cache the installed pip packages, reducing future build times.
4. Build the Docker Image Locally
If your application is containerized, consider building the Docker image locally rather than in the Azure pipeline. Once the image is built, you can push it to Azure Container Registry, which can significantly speed up your deployments as Azure won't have to build the image from scratch each time.
5. Upgrade to a More Powerful Build Agent
If your application continues to experience long build times, you may want to consider upgrading to a more powerful build agent. Azure DevOps offers different tiered agents with varying capabilities. Sometimes, using a more robust agent can reduce build times significantly.
6. Split Your Pipeline into Multiple Stages
Consider splitting your deployment pipeline into multiple stages. By structuring your pipeline to only build and test what has changed, you can save time. This requires a more advanced setup but can significantly improve deployment speeds.
Conclusion
Deploying a Python (Dash) web app on Azure doesn’t have to be a time-consuming process. By optimizing your pipeline using the strategies mentioned above, you can effectively tackle issues like long build times associated with installing libraries like Pandas.
Useful Resources
By implementing these optimizations, you can create a more efficient deployment process and enjoy a smoother experience when deploying your Python (Dash) applications on Azure. Remember to continually monitor and iterate on your deployment strategy for ongoing performance improvements.
