In my project I’m trying to embed the same matplotlib figure in two places and seem to need to make a copy, as I’m using blitting on the original, and embedding a figure in two places & closing one causes a crash issue w/ blitting an imshow in matplotlib (but not scatterplots). In order to do this I’m using pickle
Tag: pickle
sending pickled data via UDP, unpickle fails with UnicodeDecodeError: ‘ascii’ codec can’t decode byte 0xff in position 0: ordinal not in range(128)
This is my code snippet that sends data over ethernet from one PC to another which is working fine. At the receiver end, I am not able to decode it. It says: This is the snippet at the receiver end Answer Why are you even pickling the data? You can send binary data via UDP anyway: Then at the receiving
AttributeError: Can’t pickle local object ‘.’
I am trying to pickle a nested dictionary which is created using: My simplified code goes like this: However it gives error: After I print dictionary it shows: I try to make the dictionary global within that function but the error is the same. I appreciate any solution or insight to this problem. Thanks! Answer pickle records references to functions
cannot load pickle files for xgboost images of version > 1.2-2 in sagemaker – UnpicklingError
I can train a XGBoost model using Sagemaker images like so: This work for all versions 1.2-2, 1.3-1 and 1.5-1. Unfortunately the following code only works for version 1.2-2: Otherwise I get a: Am I missing something? Is my “pickle loading code wrong”? The version of xgboost is 1.6.0 where I run the pickle code. Answer I found the solution
How to persist and load all attributes of a dataclass
I want to persist all attributes of an object which is an instance of a dataclass. Then I want to load back that object from the files that I persisted. Here it is an example that fullfills the task: As you can see I need to repeat the same code for every attribute, what is a better way to do
Unable to load pickle file in streamlit
I have some code to deploy model in streamlit. I just upload all file to github and share it in streamlit app. Here is some code It runs perfect in local. But in streamlit it has some bug It’s the first time that I work on streamlit. So, thank you for reading! Have a nice day! Answer I had the
Dill doesn’t seem to respect metaclass
I have recently begun working with dill. I have a metaclass, which I use to create a Singleton pattern. Always have one object at any instant. I am using dill to serialise.The problem is once the object is loaded back, it doesn’t respect the Singleton pattern (enforced by metaclass) and __init__ gets called. Here is the code which can reproduce
TensorFlow TextVectorization producing Ragged Tensor with no padding after loading it from pickle
I have a TensorFlow TextVectorization layer named “eng_vectorization”: and I saved it in a pickle file, using this code: Then I load that pickle file properly as new_eng_vectorization: Now I am expecting, both previous vectorization eng_vectorization and newly loaded vectorization new_eng_vectorization to work the same, but they are not. The output of original vectorization, eng_vectorization([‘Hello people’]) is a Tensor: And
Unpickling and decrypting a file in memory in Python 3.7
I have a pickled .pkl file that I encrypted using the following encrpytion: I now want to decrypt and unpickle the file in memory. This is because I don’t want to alter the actual file in the storage. I tried the following: The original file is written as a pickled .pkl and then encrypted. So I figured I could just
Cache only a single step in sklearn’s Pipeline
I want to use UMAP in my sklearn’s Pipeline, and I would like to cache that step to speed things up. However, since I have custom Transformer, the suggested method doesn’t work. Example code: If you run this, you will get a PicklingError, saying it cannot pickle the custom transformer. But I only need to cache the UMAP step. Any