Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge. Learn more →
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ploomber reviews and mentions
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Runme – Interactive Runbooks Built with Markdown
For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel
And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber
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Rant: Jupyter notebooks are trash.
Develop notebook-based pipelines
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Who needs MLflow when you have SQLite?
Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.
We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.
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Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
We started with an open-source framework to help data practitioners make their work reproducible. However, after months of building and learning from our community, we realized that many needed help with the setup: getting Python installed, getting dependencies, running experiments locally, etc.
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Alternatives to nextflow?
It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2
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"Do I need to know {insert advanced math} to get a Data Science job?" [Rant]
btw, you can export Ploomber to Argo and Airflow!
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Running Jupyter notebooks in parallel
As a second option, we will use Ploomber with serial execution, which also has a Python API that allows us to execute different notebooks using the NotebookRunner function:
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How do you deal with parallelising parts of an ML pipeline especially on Python?
I also recommend checking ploomber out, this open source can help you build code as templates, parallelize it and parameterize it. There are also some reporting and debugging tools in there!
Multiprocessing works well but you probably need an abstraction on top to make it work reliably. For starters, it's best to use a pool of processes because creating new ones is expensive, you also need to ensure that errors in the sub-processes are correctly displayed in the main process, otherwise, it becomes frustrating. Also, sometimes sub-processings might get stuck so you have to monitor them. I implemented something that takes care of all of that for a project I'm working on, it'll give you an idea of what it looks like (of course, you can use the framework as well, which lets you parallelize functions and notebooks).
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A note from our sponsor - InfluxDB
www.influxdata.com | 29 Sep 2023
Stats
ploomber/ploomber is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of ploomber is Python.