haipera 0.1.11

Hassle-free configs, logging, and cluster management for research projects.

Haipera: Python scripts/notebooks to reproducible cloud deployments

日本語のREADMEはこちら！

Automatically track hyperparameters for your ML models without the boilerplate, and run 100s of experiments all at once, with 1 command.

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What is Haipera?

Haipera is an open-source framework to take scripts and notebooks and make them production ready.

• 🦥 Config files without any code. Automatically probes the source code to generate reproducible config files.
• 🐳 Deploy on virtualenv for reproducible experiments. Takes care of all the virtual environments (with auto-updates) of your code for maximum reproducibility of experiments.
• 🤖 Grid search from CLI. Use the command line to directly iterate through hyperparameters.
• 🪵 Automatic experiment logging. Automatically generates per-experiment output folders with reproducible configs.
• ☁️ Scale to the Cloud (coming soon!). Run everything locally, or send your model to Haipera Cloud or your own Cloud for parallel experimentation.

Other general features:

• supports running .ipynb notebook files as scripts
• supports running a notebook server (with configs)
• cached virtual environments
• debug as usual with pdb
• supports Windows, Linux, OSX
• saves console logs along with configs
• artifacts (images, models, etc) are also saved to separate experiment folders

What's next for haipera?

• bring-your-own-cloud GPU training infrastructure
• automatic logging
• automatic GPU profiling instrumentation
• dashboard for GPU profile analytics w/ LLMs
• experiment management web dashboard

Let us know at [e-mail] if you have opinions - or if you have dying problems or needs that you want us to hear! We're all ears.

Getting Started

Install haipera:

pip install haipera

If you want to use the notebook hosting, you can do

pip install "haipera[notebook]"

On Linux, you'll have to install a venv package, like:

apt install python3.10-venv

Make sure you have a requirements.txt file where script.py or any Python script you want to run is (or alternatively, somewhere in the Git repo for the script).

Example of using haipera

In a typical project, you may set up a script like:

import numpy

num_apples = 100
apple_price = 3.0
print("# apples: ", num_apples)
print("price of an apple: ", apple_price)
price = num_apples * apple_price
print("total: ", price)

And in the same folder, you may have a requirements.txt that lists the dependencies:

numpy

Say you want to start experimenting with code like this. You'll probably adjust num_apples and apple_price manually at first, but eventually you'll lose track of what changes caused the differences in the results.

To properly keep track of things, you may write code to load these variables from command line interfaces, set up a notebook, write dense JSON or YAML files, log the outputs in a logging service, save the outputs / configs in a separate experiment folder, etc. There's a lot of grunt work involved in making experimentation reproducible.

Haipera is designed to solve this. With haipera you can edit variables on the fly, which you can view with:

haipera run script.py --help

When you run haipera, you can pass in arguments without ever setting up argparse:

haipera run script.py --num-apples 30

This will also invoke a build of a virtual environment to run the code in, and generate a script.toml configuration file.

You can run these generated config files directly:

haipera run script.toml

You can also set up grid searches over parameters by:

haipera run script.py --num-apples 30,60 --apple-price 1.0,2.0

Running haipera will also generate a reports folder where you run haipera from, with isolated experiment outputs in that folder.

You can then re-run existing configs reproducibly with:

haipera run reports/experiment/script.toml

Using haipera with Jupyter Notebooks

You can even run haipera with Jupyter notebooks! Using haipera run on a notebook file will run the notebook as a script. This is convenient when you want to develop your script inside a notebook environment, but then scale out your runs across a bunch of parameters.

haipera run script.ipynb --num-apples 30,40,50

If you instead want to spin up a notebook with your chosen config, and have it run in an isolated environment (inside the generated reports folder), you can simply run the notebook with haipera notebook:

haipera notebook script.ipynb --num-apples 30

This will start a notebook server as usual with the provided configs, inside a dedicated folder inside reports.

This turns out to be a convenient way to do versioning for notebooks- if you have a notebook that you want to use for different data or different examples, instead of cloning 8 versions of the same notebook, you can just have a single notebook and 8 different config files for those notebooks!

You can also run a Python script as a notebook, although usually there are probably not great reasons to do this.

Demo on Google Colab

You can also try our Google Colab version which allows you to run Haipera in the cloud. Check out our Colab demo using the following notebook:

More examples

See https://github.com/haipera/haipera-samples for more complex examples that you can try running haipera on.

Have issues?

Haipera is still in its early stages, so it'll likely to have bugs. We're actively developing haipera, so if you file a GitHub issue or comment in the Discord server or drop us a line at [e-mail] we will try to resolve them ASAP!