rr
nbdev
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rr | nbdev | |
---|---|---|
102 | 45 | |
8,621 | 4,740 | |
1.1% | 0.7% | |
9.6 | 6.5 | |
7 days ago | 27 days ago | |
C++ | Jupyter Notebook | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rr
- rr: Lightweight Recording and Deterministic Debugging
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Hermit is a hermetic and reproducible sandbox for running programs
I think this tool must share a lot techniques and use cases with rr. I wonder how it compares in various aspects.
https://rr-project.org/
rr "sells" as a "reversible debugger", but it obviously needs the determinism for its record and replay to work, and AFAIK it employs similar techniques regarding system call interception and serializing on a single CPU. The reversible debugger aspect is built on periodic snapshotting on top of it and replaying from those snapshots, AFAIK. They package it in a gdb compatible interface.
Hermit also lists record/replay as a motivation, although it doesn't list reversible debugging in general.
- Rr: Lightweight Recording and Deterministic Debugging
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Deep Bug
Interesting. Perhaps you can inspect the disassembly of the function in question when using Graal and HotSpot. It is likely related to that.
Another debugging technique we use for heisenbugs is to see if `rr` [1] can reproduce it. If it can then that's great as it allows you to go back in time to debug what may have caused the bug. But `rr` is often not great for concurrency bugs since it emulates a single-core machine. Though debugging a VM is generally a nightmare. What we desperately need is a debugger that can debug both the VM and the language running on top of it. Usually it's one or the other.
> In general I’d argue you haven’t fixed a bug unless you understand why it happened and why your fix worked, which makes this frustrating, since every indication is that the bug exists within proprietary code that is out of my reach.
Were you using Oracle GraalVM? GraalVM community edition is open source, so maybe it's worth checking if it is reproducible in that.
[1]: https://github.com/rr-debugger/rr
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So you think you want to write a deterministic hypervisor?
https://rr-project.org/ had the same problem. They use the retired conditional branch counter instead of instruction counter, and then instruction steeping until at the correct address.
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Is Something Bugging You?
That'll work great for your Distributed QSort Incorporated startup, where the only product is a sorting algorithm.
Formal software verification is very useful. But what can be usefully formalized is rather limited, and what can be formalized correctly in practice is even more limited. That means you need to restrict your scope to something sane and useful. As a result, in the real world running thousands of tests is practically useful. (Well, it depends on what those tests are; it's easy to write 1000s of tests that either test the same thing, or only test the things that will pass and not the things that would fail.) They are especially useful if running in a mode where the unexpected happens often, as it sounds like this system can do. (It's reminiscent of rr's chaos mode -- https://rr-project.org/ linking to https://robert.ocallahan.org/2016/02/introducing-rr-chaos-mo... )
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When "letting it crash" is not enough
The approach of check-pointing computation such that it is resumable and restartable sounds similar to a time-traveling debugger, like rr or WinDbg:
https://rr-project.org/
https://learn.microsoft.com/windows-hardware/drivers/debugge...
- When I got started I debugged using printf() today I debug with print()
- Rr: Record and Replay Debugger – Reverse Debugger
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OpenBSD KDE Plasma Desktop
https://github.com/rr-debugger/rr?tab=readme-ov-file#system-...
nbdev
- The Jupyter+Git problem is now solved
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What is literate programming used for?
One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev
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GitHub Accelerator: our first cohort and what's next
- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.
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Startups are in first batch of GitHub OS Accelerator
9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/
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Start learning python for a Statistician with SAS experience and little R experience
See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.
- FastKafka - free open source python lib for building Kafka-based services
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isn't this just too much for a take home assignment?
You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff
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Any experience dealing with a non-technical manager?
nbdev: jupyter notebooks -> python package
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Resources to bridge the gap between jupyter notebooks and regular python development
Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)
- Rant: Jupyter notebooks are trash.
What are some alternatives?
CodeLLDB - A native debugger extension for VSCode based on LLDB
papermill - 📚 Parameterize, execute, and analyze notebooks
rrweb - record and replay the web
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
gef - GEF (GDB Enhanced Features) - a modern experience for GDB with advanced debugging capabilities for exploit devs & reverse engineers on Linux
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]
Module Linker - browse modules by clicking directly on "import" statements on GitHub
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
clog-cli - Generate beautiful changelogs from your Git commit history
Jupyter-PowerShell - Jupyter Kernel for PowerShell
rustfmt - Format Rust code
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.