Oh man, can I sign onto this rant. While Python has spent a decade+ trying-and-failing to standardize on one of a dozen tools to properly manage dependencies, at least it is trying. R is still global-namespace, no-pinning by default. Sorta-kinda you can squint where renv is going, but still needs a lot of development.
I had some hopes of Julia stealing the R mindshare and righting some of the more egregious wrongs in that ecosystem, but (as an outsider) it feels like Julia has lost a lot of steam.
The same framework can provide an identical-to-production R or RStudio environment for development.
I have a harder time with Julia since its tooling is more opinionated. I don't know a better solution than wrapping it in a Docker container.
Disappointing about Julia. Only played with it a small bit, but I had assumed it had identified dependency management as a huge problem that needed to be addressed.
julia --project=. -e 'import Pkg; Pkg.instantiate()'
python -m pip install -r requirements.txt
That said, there is a command-line utility jlpkg [1] that makes package management available from the shell. It's not very widely used, but maybe it suits your needs.
Although, the way you phrased
> it still required you to issue commands into the repl to setup your packages
makes me think maybe you didn't know about the Pkg mode in the REPL, and assumed you had to do everything with commands like `Pkg.add("DataFrames"); Pkg.update()` and such? If so, there's a package management mode to the REPL you can access with the ] key, which is kind of its own subshell within the REPL where you can do `add DataFrames`, `update`, and such instead.
Please, be my guest hahaha.
I think my issue with so many of these tools is that they’re fundamentally meant to be used interactively. R (renv/etc) doesn’t have a package manager separate from the runtime, so you can’t just “yarn/pip/cargo/etc install” and have your dependencies setup prior to your script running, you have to invoke manual R commands and hope everything goes off ok, it doesn’t spontaneously pick a different directory to install your stuff in, or have a tantrum about permissions because it wants to install in some root dir.
I do not believe the situation is unsolvable, but there is significant work to be done. Renv provides value today, and I will encourage everyone to use it. However, it has significant blind spots which continue to make R deployments challenging.
[0] https://github.com/rstudio/renv/issues/1055 [1] https://github.com/rstudio/renv/issues/471 [2] https://github.com/rstudio/renv/issues/233
From point of view of the integration process the pickle file is just a black box input data flows in, response flows out of it. If something changes with the model (trained it on a new dataset etc.) then it's just a matter of drop in a new pickle file and it all keeps working.
I don't know if you can do the equivalent with packaging R code like this. All of the tooling is setup around Python and I think this is pretty industry standard we use Azure and I'm pretty sure this is all out of the box stuff.
Pickling is very convenient for its purpose. It's also... very bad in production. The problem is that the serialization format is derived from the implementation and not very stable, so if the implementation changes - sometimes in subtle ways like upgrading your Python version - the serialized data isn't readable anymore. You're almost always better off either using an explicit data serialization format (JSON with schemas, protocol buffers, etc.) and writing your own logic for saving and restoring to/from it.
> the pickle file is just a black box input data flows in, response flows out of it
There's 100% more to it than that - there's got to be something that's evaluating the data in the pickle file and actually using it to compute the response. That substrate layer probably doesn't change often, but it should have its own versioning and deployment story.
Could be wrong, but I do not believe this is true anymore. There are pickle versions, but I believe the format has been standardized such that you should be able to freely move between Python releases.
I suspect that is the Microsoft Azure ML part I'm not sure. As I said I don't write the models, I just come along afterwards and review the results. I don't know anything about Python.
The equivalent would be a .dat file :-)
Kotlin is coming up on the outside. Late to the party, but the data science ecosystem is progressing nicely.
It seems that Roman Elizarov (Kotlin Project Lead) has identified the opportunity for a better language ecosystem to enter the data science space
https://discuss.kotlinlang.org/t/ai-and-deep-learning-define...
Same applies to many other research institutions.
Good luck catching up with a 20 years old ecosystem, with a language that only gets used mainly thanks to Google's sponsorhip for Android apps.
Do I care if it is at parity with Python? Hell no I don't, as long as it allows me to accomplish the tasks I need -- and it does, faster, easier, with fewer errors -- then I'm very happy.
Tried putting R into production recently? It’s a frustrating and brittle experience. Don’t get me wrong, R is fantastic at what it does - analysis, research, statistics, and arguably the API’s on the R data frame packages are a lot saner than Pandas.
C++ is out for different reasons I suspect. As this touches on, “modellers” (and data scientists/engineers) ought to be decent developers, but a lot of them are not, and in my experience actively refuse to learn any of these skills, the comfort zone is “jupyter notebooks” and that’s it. Getting them to write C++ (disregarding language debates), a language that is unequivocally more difficult and fraught with complexity than Python is basically a non-starter.
Do I wish it was different? Yep. Do I wish there was some more variety in the “language ecosystem” so it’s more than just the “lowest common denominator Python” dominance? Absolutely.