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Why would I care about security when using pickle?

Because pickle is so easy to attack. It's a format that can be deserialized to pretty much any Python code, and Python can do pretty much anything, so if you unpickle a compromised payload, it's free game.

Because some people make processed data/pretrained models available online as pickle files.

It'd be nice to be able to open them without having to worry about bad actors nuking my home directory.

I have found it to be about dependance more than security. Dépendance created by pickle are rather strict. I have found it to render significant part of my work unusable when upgrading python version.

Pickle objects can be (almost) anything. Including arbitrary code. Now, imagine a bad actor claiming to be publishing a SOTA Random Forests model. However, embedded in their .pkl file is a statement like import shutils; shutils.rmtree(‘./’);.

Pickle will happily execute this code. There is nothing checking whether or not the pickle file is safe or not.

P.S. of course the syntax is not that simple, but I hope you get it (and I’m on mobile, yada yada…)

It can execute arbitrary code as others said. Other ML frameworks (TF/Keras, PyTorch) are also researching alternative solutions to this at the moment. you should never deserialize a pickle on your local unless it's made by you. pickle is made for python in general, not specifically for machine learning. this format is used to serialize sklearn models/pipelines avoiding pickle.

It's so strange the python ML community has still not found a suitable model format, despite years and years of effort. What even happened to efforts like PMML?

Meanwhile I'm quite happy with the R infrastructure with storing tidymodels pipelines.

I think it's because this was raised recently and people really didn't know! since it was raised by Yannic Kilcher couple of months ago, François Chollet has announced a format specific to Keras models, Pytorch folks are cooking something for this too. hopefully this will be solved 🙂

this is a good explanation 🤌🏼💜

Thanks Merve! (Btw, love your HF notebooks. 😀)

You also can use joblib.dump, since it already comes with sklearn

it uses pickle's way of serialization under the hood. the difference between pickle and joblib is that one is performing better with numpy objects AFAIK. you shouldn't deserialize any joblib file on your local.

see one of the sklearn core developers' neat response on difference between them: https://stackoverflow.com/a/12617603

Didn't know, thanks :) It even has a warning about it: https://github.com/joblib/joblib/blob/6836640abba55611d6e57f20338ea54b3e27f296/joblib/numpy_pickle.py#L609

Tidymodels is a specific example of an R extension package with it's own file format. That would be like saying you are quite happy with the Python infrastructure for saving PyTorch models. It's still specific to that thing.

There are plenty of good ways of storing model weights, those based on hdf5 archives being a great choice since they are optimized for block tensor operations, on disk chunking, support lazy slicing, and support nested groups of tensors. Keras uses hdf5 for it's save_weights and load_weights functions.

If your models are getting huge you need a different strategy anyway. And this is where S3 object store backed systems like TensorStore become more ideal.

h5 and SavedModel of TF are the safest options, yet you can still inject code through Lambda layers or subclassed models (that's why Keras developed a new format too!) AFAIK. What SavedModel does is that it reconstructs the architecture and loads weights into it, and this architecture part is essentially code (loading the weights is never the problem for any framework anyway, it's the code part!). so again, you shouldn't deserialize it. (safest code is no code) if you can see the architecture and confirm that it doesn't have any custom layers, you should be fine. (this is also essentially what we do with skops (we audit the model) (or reconstruct it yourself and load weights into it but it's a little tricky, you might have custom objects or e.g. preprocessing layers for keras)

>The architecture of subclassed models and layers are defined in the methods __init__ and call. They are considered Python bytecode, which cannot be serialized into a JSON-compatible config -- you could try serializing the bytecode (e.g. via pickle), but it's completely unsafe and means your model cannot be loaded on a different system. (in model subclassing guide)
>
>WARNING: tf.keras.layers.Lambda layers have (de)serialization limitations! (in lambda layers guide)

Hugging Face also introduced a new format called safetensors if you're interested: https://github.com/huggingface/safetensors in README there's a detailed explanation & comparison.

no worries, I hope you'll find it useful! 🙂

What about ONNX? Most if not all feedforward models can be represented as ONNX.

Yes! I knew there was another standard but I couldn't for the life of me remember the name.

Perhaps it's also just a matter of the Python crowd doing a bit more complicated stuff than the R crowd. For me the models tend to be quite straightforward RF or related models (like I said; tidymodels), but the demand is much more on the correct pipeline with pre- and postprocessing. Things become a bit less easy to store once you go into deep neural networks I'd imagine.

I'm not sure but I was told a lot of times that ONNX support for sklearn was sub-par. I haven't researched that one yet. I can ask to maintainers if you're interested.

I don't use python so no need! I just remember being quite confused when I was learning sklearn and realised saving models or pipelines was weirdly complicated compared to R.

More generally speaking I suppose the RDS data format is pretty great to work with within R.

Just wanted to drop by and say this is really cool and important work. I follow you on twitter so I've been following updates for a bit longer now lol and i also applied to the skops internship as soon as I saw it when HF opened internships

Thanks for the great work :))

I deployed sklearn models via onnx in two companies, and it works perfect.

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