For my university, I program a neuronal net that shall be able to distinguish between a few programming languages. The boring part would be DLing a code database for training. And I want to avoid to do this by hand: I guess I need at least 100 code snippets for each language to properly train my net.
So, do you know repositories where I can FTP down, say, FORTRAN or Lisp or Prolog or Java by the ton? (This should suffice for the start.) Luckily, what the snippets do is completely irrelevant. (And: The shorter, the better.)
You can use github rest based api, there are commands which lists all public repos and commands which returns language tag for repo, you can clone a repo as well.
Related
I have created an R code script that:
Reads some data from a database
Makes some transformations and..
exports into a csv the modified table.
This code needs to run in a client's machine, but we need to "hide" the actual code from the user.
Is there any useful suggestions on how we can achieve that?
Up front
... it will be nearly impossible to deploy an R <something> to another computer in a way that prevents curious users from accessing the source code.
From a mailing list conversation in 2011, in response to "I would not like anyone to be able to read the code.",
R is an open source project, so providing ways for you to do this is not
one of our goals.
Duncan Murdoch https://stat.ethz.ch/pipermail/r-help/2011-July/282755.html
(Prof Murdoch was on the R Core Team and R Foundation for many years.)
Background
Several (many?) programming languages provide the ability to compile a script or program into an executable, the .exe you reference. For example, python has tools like py2exe and PyInstaller. The tools range from merely compactifying the script into a zip-ball, perhaps obfuscating the script; ... to actually creating a exe with the script either tightly embedded or such. (This part could use some more citations/research.)
This is usually good enough for many people, by keeping the honest out. I say it that way because all you need to do is google phrases like decompile py2exe and you'll find tools, howtos, tutorials, etc, whose intent might be honestly trying to help somebody recover lost code. Regardless of the intentions, they will only slow curious users.
Unfortunately, there are no tools that do this easily for R.
There are tools with the intent of making it easy for non-R-users to use R-based tools. For instance, RInno and DesktopDeployR are two tools with the intent of creating Windows (no mac/linux) installers that support R or R/shiny tools. But the intent of tools like this is to facilitate the IT tasks involved with getting a user/client to install and maintain R on their computer, not with protecting the code that it runs.
Constrain R.exe?
There have been questions (elsewhere?) that ask if they can modify the R interpreter itself so that it does not do everything it is intended to do. For instance, one could redefine base::print in such a way that functions' contents cannot be dumped, and debug doesn't show the code it's about to execute, and perhaps several other protective steps.
There are a few problems with this approach:
There is always another way to get at a function's contents. Even if you stop print.default and the debugger from doing this, there are others ways to get to the functions (body(.), for one). How many of these rabbit holes do you feel you will accurately traverse, get them all ... with no adverse effect on normal R code?
Even if you feel you can get to them all, are you encrypting the source .R files that contain your proprietary content? Okay, encrypting is good, except you need to decrypt the contents somehow. Many tools that have encrypted contents do so to thwart reverse-engineering, so they also embed (obfuscatedly, of course) the decryption key in the application itself. Just give it time, somebody will find and extract it.
You might think that you can download the key on start-up (not stored within the app), so that the code is decrypted in real-time. Sorry, network sniffers will get the key. Even if you retrieve it over https://, tools such as https://mitmproxy.org/ will render this step much less effective.
Let's say you have recompiled R to mask print and such, have a way to distribute source code encrypted, and are able to decrypt it in a way that does not easily reveal the key (for full decryption of the source code files). While it takes a dedicated user to wade through everything above to get to the source code, none of the above steps are required: they may legally compel you to release your changes to the R interpreter itself (that you put in place to prevent printing function contents). This doesn't reveal your source code, but it will reveal many of your methods, which might be sufficient. (Or just the risk of legal costs.)
R is GPL, and that means that anything that links to it is also "tainted" with the GPL. This means that anything compiled with Rcpp, for instance, will also be constrained/liberated (your choice) by the GPL. This includes thoughts of using RInside: it is also GPL (>= 2).
To do it without touching the GPL, you'd need to write your interpreter (relatively from scratch, likely) without code from the R project.
Alternatives
Ultimately, if you want to release R-based utilities/apps/functionality to clients, the only sure-fire way to allow them to use your code without seeing it is to ... control the computers on which R will run (and source code will reside). I'll add more links supporting this claim as I find them, but a small start:
https://stat.ethz.ch/pipermail/r-help/2011-July/282717.html
https://www.researchgate.net/post/How_to_make_invisible_the_R_code
Options include anything that keeps the R code and R interpreter completely under your control. Simple examples:
Shiny apps, self-hosted (or on shinyapps.io if you trust their security); servers include Shiny Server (both free and commercial versions), RStudio Connect (commercial only), and ShinyProxy. (The list is not known to be exclusive.)
Rplumber is an API server, not a shiny server. The intent is for single HTTP(s) endpoint calls, possibly authenticated, supporting whatever HTTP supports (post, get, etc). This can be served in various ways, see its hosting page for options.
Rserve. I know less about this, but from what I've experienced with it, I've not had as much luck integrating with enterprise systems (where, e.g., authentication and fine-control over authorization is important). This does allow near-raw access to R, so it might not be what you want (especially when the intent is to give to clients who may not be strong R users themselves).
OpenCPU should be discussed, but not as a viable candidate for "protect your code". It is very similar to rplumber in that it provides HTTP endpoints, but it supports endpoints for every exported function in every package installed in its R library. This includes the base package, so it is not at all difficult to get the source code of any function that you could get on the R console. I believe this is a design feature, even if it is perfectly at odds with your intent to protect your code.
Anything that can call R or Rscript. This might be PHP or mod_python or similar. Any web-page serving language that can exec("/usr/bin/Rscript",...) can take its output and turn it around to the calling agent. (It might also be possible, for example, for a PHP front-end to call an opencpu endpoint that only permits connections from the PHP-serving host.)
I just joined a company that needs to build an ETL pipeline inside an AWS account owned by a client.
There's one part of the ETL pipeline that runs a code written in R. The problem is, this R code is a very important part of our business, and our intelectual property. Our clients can't see this code.
Is there any way to run this in their AWS environment without them having access to our code? R is not compilable, so we can't just deploy an executable file there. And we HAVE to run this in their environment. I suggested creating an API to run this in our AWS environment, but this is not an option.
In my experience, these are the options I've realized in situations like this, in increasing order of difficulty:
Take the computation off-premises. This sounds like not an option for you.
Generate an API (e.g., shiny, opencpu, plumber) that is callable from their premises. This might require some finessing on their end, as I'm inferring (since they want it all done within their environment) that they might prefer a locked-down computation (perhaps disabling network access).
Rewrite the sensitive portions in Rcpp. While this does have the possible benefit of speed improvements, it makes it slightly harder for them to "discover" the underlying intellectual property. Realize that R and Rcpp are both GPL, which means that anything linked to by R must also be GPL, meaning source-code available. (It is feasible that since you are not making it public that you can argue your case here, but I am not a lawyer and would not want to be the first consultant found on the wrong side of GPL law here. Again, IANAL.)
Rewrite the sensitive portions in a non-R executable (note that I don't say "as a non-R library and link to it via R calls", since the linking action taints the library with R's GPL). This executable can be called by your otherwise releasable R package (via system or processx::run).
(For the record, one might infer C or C++ here, but other higher-level languages do allow compilable executables and are not GPL. Python has some such modes. Be sure to obfuscate your variables :-)
I think your "safest" options are #2 and #4.
I know it is not best practice to use the R package called drake within a notebook tool, but I'm doing it anyway as a workaround for the limitations to the collaboration infrastructure we have on my team at work. Since my code is broken up into chunks that are distributed throughout sections of the notebook, it would be useful to have multiple analysis plans, which I would execute in the appropriate section, and other plans may be written and executed in subsequent sections of the notebook. Is it possible to write multiple plans in drake?
Sorry I am late to this thread. I am the maintainer of the drake R package, and I usually expect to receive questions on the issue tracker. A drake-r-package StackOverflow tag would really help me keep up, but I do not have that privilege.
Anyway, interesting use case. I do see some workarounds:
Separate caches for separate plans. drake uses storr to cache its targets, and you could create different caches for different sections of your report. Essentially, your report would manage a bunch of separate drake projects. See this chapter in the manual for more on the caching system. Use the cache argument to make() to supply a manual or non-default storr cache.
Separate plans and a single cache. Here, you would need to ensure that each plan has a completely unique set of targets. If there is overlap, then some targets will always rebuild whenever your run the report.
A single cumulative plan. Essentially, when it comes time to build additional targets as you move through the report, you can add new rows to an existing plan. In fact, this is the recommended approach for large complex projects (related example here). For even more control, use the targets argument to make() to only build a select few targets and their out-of-date dependencies.
I’m not sure i understand the question? We often use drake in jupyter notebook, and do try to support that use case (via the python bindings).
By “plans”, do you mean mathematical programs? Or inverse kinematics calls? Both should be ok in a notebook framework. Or are you actually calling them in parallel?
Not sure how R fits into it?
I have a bunch of R scripts which I am running on a Windows machine and want to ensure that the code remains unread by those not intended to see it. On a Linux box, I could wrap the R code in a bash script #! and make an encrypted (and perhaps even a limited-life) executable shell script. What are my options to do something on similar lines under Windows?
My answer is a bit late, but I believe this is a good question. Unfortunately, I don't believe that there is a solution, or at least an easy one, at the present time.
The difficulty is common because, for most interpreted languages, including R, it is often possible to turn on logging and inspection of all commands being run. This can negate many tricks to obfuscate the code.
For those who prefer to think of code being open == good, one should know that a common reason to obfuscate the code is if one is consulting with a client that hires multiple vendors. It is not uncommon for a client to take scripts from vendor A and ask vendor B why it doesn't work with their system. (This may be done by a low-level IT flunkie, rather than someone responsible for the NDA contracts.) If A & B are competitors, A's code has just been handed to B. When scripts == serious programs, then serious code has been given away.
The ways I've seen this addressed are:
Make a call to a compiled language, and use standard protections available there.
Host the executable on a different server, and use calls to the server to execute the calculations. (In R, there are multiple server-side options.)
Use compiled (preprocessed / bytecode) code within the language.
Option 2 is actually easier and better when the code may be widely distributed, not just for IP reasons. A major advantage is that it lets you upgrade the code without having to go through the pain of a site-wide release process. If new libraries are needed, no problem - update the server.
Option 3 is done in Matlab with .p files, and can be done with py2exe for Python on Windows. In R, the new bytecode compilation may be analogous, but I am not familiar enough with it to address any differences between .Rc files in the R context and .p files in the Matlab context. For more info on the compiler, see: http://www.inside-r.org/r-doc/compiler/compile
Hosting computations on the server is great for working with unsophisticated users, because it is easier to iterate quickly in response to bugs or feature requests. The IP protection is simply a benefit.
This is not a specifically R-oriented strategy. (And it's a bit unclear what your constraints or goals really are anyway.) If you want a cross-platform encryption method, you should look into the open-source program TrueCrypt. It supports creating encrypted files that can be mounted as volumes on any machine that supports the volume formatting method. I have tested this across the Mac PC divide , since the Mac can read FAT files, but have no experience with how it might work across the Linux-PC chasm.
(Their TODO list for Windows includes;"Command line options for volume creation (already implemented in Linux and Mac OS X versions)". So I don't see any clear way to use this from within R without you running the program from the OS.)
I don't think this is possible because the R interpreter has to be able to decrypt and read the code in order to execute it which means that whoever is using that interpreter will also be able to decrypt and read the code.
I am by no means an expert, so I reserve the right to be 100% wrong about that statement.
I believe the best solution is to ensure value comes from the expertise and services provided by your company and it's employers---not from keeping secrets.
Failing that, you could try separating the code into a client/server model. That way the client just sends data and receives results---they never have access to the code that runs on the server.
However, the scientist in me just said "that solution sucks and I would never trust results provided under such conditions".
I am looking for a solution that allows me to keep a track of a multitude of R scripts that I create for various projects and purposes. Some scripts are easily tracked to specific projects, whereas others are "convenience" functions created to serve a set of tasks.
Is there a way I can create a central DB and query it to find which scripts match most appropriately?
I could create a system using a DBMS manually, but are users aware of anything in general or specific to R, that comes in the form of a software tool (maybe FOSS) ?
EDIT: Thank you for the responses. My current system is just a set of scripts with comments that allow me to identify their intended task. Though I use StatET with SVN, I would like a search utility along the lines of the "sos" package.
The question
I am looking for a solution that allows me to keep a track of a multitude of R scripts
that I create for various projects and purposes. Some scripts are easily tracked to specific
projects, whereas others are "convenience" functions created to serve a set of tasks.
fails to address the obvious follow-up of why the existing mechanism is not suitable:
Create a local package for each project
Create one or more local packages for local utility functions
Use R's already existing mechanisms for searching, indexing, testing, cross-referencing
And use any revision control system of your liking, local or on the web, to host the code for 1. to 3. above.
Reinventing an RDBMS schema for 1. to 3. is just wrong in my book. But if you must, go ahead and replicate what you can already (mostly) get for free in tested and widely used code.
R comes with several mechanisms for searching for help, most of which naturally use CRAN. Some examples: the sos package, cranberries, crantastic, and rseek. In many cases, these could be adapted to use a local repository (you can find out how to create a local repository in the R manual, which is very easy to do). Otherwise, if you package your scripts and submit them to CRAN, you will naturally have these available to you. I would also highly recommend this presentation on the subject: Creating R Packages, Using CRAN, R-Forge, And Local R Archive Networks And Subversion (SVN) Repositories from Spencer Graves and Sundar Dorai-Raj.
These would require you to put your code in packages, and create documentation, all of which is worth doing anyway. The package documentation turns out to be very useful for both documenting what things do, and helping your find them in the future. You can use roxygen to create this documentation in-line with your code. Also read this related question: Organizing R Source Code.
Alternatively, the help.search() function can be very useful for searching local packages, regardless of whether you have a repository set up.
You'd probably be best working with a version control system. Many can be indexed and be made search-able. At my work, a stack of R, Eclipse, StatET, Subversion and Subclipse works very well for us.