I wanted to share with you some recommended best practices that I battle tested over about four or so years working with Metaflow at Netflix. The goals are a short development loop; reusable, maintainable, and reliable code; and just an overall fun and rewarding developer experience. This list is probably not complete but I can add more later.

# Minimal directory structure

I’ll start with a suggested minimal directory structure. This example has a training flow and a prediction flow, with common code across the two plus a Jupyter notebook for debugging.

<your-repo>
├── flows
|  ├── train.py
|  ├── predict.py
|  ├── debug.ipynb
|  └── common.py
└── .gitignore


The training and prediction flow specs import common code and do… you know, machine learning. They are separated out to support ongoing predictions on a model that is potentially re-trained on a different schedule. So you might run train.py quarterly, and run predict.py weekly on fresh incoming data.

Check out this minimal flow example and

Note import common. That brings me to…

# Put common code into a separate script

In the minimal code structure and flow example I’ve got a script called common.py. That contains reusable functions, classes, and variables that both train.py and predict.py can use. Pulling your code into a separate script makes it more easily reusable and testable and shortens your Metaflow steps and overall flow spec.

# Git-ignore .metaflow

Don’t forget to add .metaflow to your .gitignore because those directories contain the local data artifacts.

# Debug in a Jupyter notebook

You can debug common code and access Metaflow data artifacts in Jupyter notebooks. Here’s a minimal example of all of that.

I’m using autoreload magic so that I can make changes to common.py and have those changes immediately reflected at the cell level without having to re-import common or otherwise run a bunch of other cells. Your working directory in the notebook should be <your-repo>/flows/ in this case.

(Note: that debug snippet shows artifacts from my previous Metaflow post.)

# Develop a separate Python package

When it makes sense to (and not earlier) I try to separate out the more broadly reusable code into a separate, pip-installable Python package. That’s in addition to using local common.py scripts. You can put the code in the same repo, or break it out into another one. Here’s a minimal example of putting it in the same repo. You’ll see a full working example of this at metaflow-helper.

<your-repo>
├── flows
|  ├── train.py
|  ├── predict.py
|  ├── debug.ipynb
|  └── common.py
├── .gitignore
├── your_package
|  ├── __init__.py
|  └── models.py
└── setup.py


Adding the setup.py makes your_package pip installable. During development I’ll fire up a Python venv with python -m venv venv && . venv/bin/activate and then install the package in editable mode with pip install -e ., all from the top level of the repo.

In each Metaflow step I’ll pip install from git if the package is not already locally available. Here are the functions that do that, which I put into common.py.

Now at the top of each step you would do something like:

common.install_dependencies(
{'your_package': 'git+ssh://git@github.com/<github-username>/<your-repo>.git'}
)


This will try to import your_package (the dictionary key) and if it fails, pip-install from Github. Doing this will seem like nonsense during development, but when you deploy to a production environment this will become necessary. Installing via pip lets you get athe code from Github or PyPI, and will let you pin in both cases. Here are some different ways to pin.

# Install the latest commit from the default branch
{'your_package': 'git+ssh://git@github.com/<github-username>/<your-repo>.git'}
# Pin by installing a tagged commit
{'your_package': 'git+ssh://git@github.com/<github-username>/<your-repo>.git@v0.0.1'}
# Pin by installing a commit hash
{'your_package': 'git+ssh://git@github.com/<github-username>/<your-repo>.git@00db203'}
# Install from PyPI
{'your_package': 'your_package'}
# Pin by install from a PyPI version
{'your_package': 'your_package==0.0.1'}
# etc etc


You can call install_dependencies in your debugging Jupyter notebook, too. If your_package is already available to it, nothing will happen. This means you can test your Metaflow artifacts, common flow code, and your external package code all in the same notebook.

And speaking of pinning…

# Pin your packages

If you plan on running your flows on a cron schedule or against triggers over long periods of time, do yourself a favor and pin your packages. This increases stability of repeated flow runs that use artifacts from other flows that ran earlier. For example, predict.py needs to load the model artifact persisted in train.py, potentially days, weeks, or months later, depending on your design.

It’s useful to think of your Metaflow jobs like you would any long-running application, for instance a web app. Pin for reproducibility and to minimize maintenance over the long term.

You can take this thinking one step further with Metaflow: think of each Metaflow step as an independent, long-running application and pin potentially different packages at the top of every step. One example where I’ve seen this come up is in using Tensorflow. Tensorflow requires a specific version range of numpy, but otherwise I want access to a more recent numpy release elsewhere. If I isolate my Tensorflow modeling code to a single step or set of steps, and do pre- and post-processing in separate steps, I can pin Tensorflow with a floating numpy version and in the other steps I’ll in general get a different numpy version. The install_dependencies function pattern I mentioned above in Develop a separate Python package will let me do this.

Now that I’ve said all this stuff about pip…

# Migrate to conda

The pip-install pattern is useful for shortening the development cycle, but the Metaflow maintainers recommend adopting conda to maximize reproducibility. I don’t have a good recommendation at this time on how to adopt the conda pattern while still keeping the development loop short. My guess is one of pip-installing inside conda-decorated steps or conda-installing from git, might work. I’ll give these a try as soon as I have a need to.

# Keep flows and flow steps short

If you pull common code into local Python scripts or into a separate package, you’ll be in a good position to make your flow spec and each of its steps as short as possible.

Keeping them short is useful for readability and maintainability. You’ll also invariably have to do other high-level stuff at the step level without the option of pulling that code into common functions, for example Metaflow step-level exception handling. Do flow-control-level operations in steps and otherwise call just a few functions per step if you can.

Keeping the scope of steps small is also useful for debugging different logical chunks of your pipeline without having to rerun upstream code and for resuming execution after a failed run. Often times in production you’ll get failures due to platform failures, and it’s useful to have completed as much upstream processing successsfully as possible. Then you can resume from the failed steps forward. Or you’ll get a runtime failure from an unhandled edge case. It’s helpful when upstream, smaller scope steps have completed successfully and the runtime failure is isolated to a small step. Small steps make the whole debugging and maintenance experience more enjoyable.

# Fail fast in your start step

Your start step is an opportunity to fail fast. This means things like:

• Try to ping your external services.
• Load the pointers for you Metaflow artifact dependencies.
• Validate configuration and variables.

If any of your canary procedures fail, let the flow error out and report back to you. It’s far better to fail in the start step if you can rather than failing toward the end of a potentially very long running flow.

# Implement a test mode

Implement a test mode that will run your flow as-is but on as small a data set as possible and with hyperparameter settings that make the ML training optimization as quick as possible. I like to do this by creating a flag parameter that I can use to subset the data and reduce parallelism to one concurrent task for any given step. If you’re training a model, reduce the number of maximum possible optimization iterations to something small like 10 epochs. Here’s one way to create a test-mode using a Metaflow Parameter.

Now I can run the flow normally with

python train.py


or in test mode with

python train.py --test_mode 1


I did a variant of this in my model selection example from my previous Metaflow post. Instead of using a boolean flag I point to different configuration files by string, some of which perform the same tasks of subsetting the data down and shortening the model training times dramatically.

# Run flows in test mode in a CI/CD pipeline

If you’ve got a nice and short test mode working you can run it as part of continuous integration/continuous delivery & deployment. You’ll see working examples of this in metaflow-helper. I’ve got separate jobs and badges set up for unit testing and for running the Metaflow examples in test mode.

# Use an IDE

I prefer PyCharm. It plays nice with Metaflow. Debugging seems to work fine, but it can be a bit tricky to debug parallel tasks in foreach steps. Using test-mode (see Implement a test mode) and eliminating parallel tasks helps. Make sure to reuse your virtual environment interpreter if you set one up already. I’ve also set PyCharm up for Metaflow development against a remote server – that works pretty well, though there are a lot of configuration options to set.

VS Code works as well. It’s faster but has reduced functionality. I especially miss refactoring and fully-featured inspection when I use VS Code. There’s a time and a place for both and as always it boils down to personal preference.

I haven’t tested other IDEs like Spyder. I’d like to hear if others have and what the good and the bad are about each one.

# What did I miss?

I didn’t talk about more advanced practices I like, such as Metaflow run tagging and setting up isolated test and development environments that operate without affecting the production environment. I’ll cover those future posts.

I’d like to hear from you on what I may have missed or how you do things differently!

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