h5py
Numba
h5py | Numba | |
---|---|---|
5 | 124 | |
2,003 | 9,452 | |
0.8% | 1.1% | |
8.8 | 9.9 | |
3 days ago | 9 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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h5py
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Working with data files too large for RAM
There's some good answers here, but another option I haven't seen suggested: Convert your txt file to HDF5 (Regardless if you follow my approach here, you should really consider converting your data to anything but a txt file). There's a nice library for working with it in python called h5py. The HDF format is designed specifically with working with very large sets of data (it even has compression options), often scientific in nature, but it's not a database. As far as how this fixes the specific issue you you've described, you can utilize numpy slicing to load one chunk your data at a time. Here's a stackoverflow answer which discusses a solution.
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How to combine multiple numpy arrays stored on disk which are too big to fit in RAM?
If it is a dataset, it should consist of individual instances. You could store these instances in separate files. Otherwise, HDF5 is a very convenient storage format. It allows random read/write access to elements of arrays stored on disk and has excellent Python support in form of the h5py package.
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Is Python really this slow?
If possible, try to monitor your memory usage during execution and if you see that you are consistently exceeding ~50% (my own rule of thumb, though you may want to discuss this with others as well) of what's available. If you are consistently using most of the available memory, then it's likely worth taking a moment to evaluate whether you can operate on subsets of the data from start to finish, and leave the rest of the data on disk until you are almost ready to use it. Tools like h5py are very helpful in these kinds of situations.
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Python packages as API end points.
Yea - I really struggled with getting the correct version on h5py to work with both tensorflow and allenai nlp modules. May be its about finding the right version of libraries. Github Issue. I ended up using pickle to save stuff, like John who commented on 26/03/2020 on the same(closed) issue.
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Tracing and visualizing the Python GIL with perf and VizTracer
Apply these to more issues, like in https://github.com/h5py/h5py/issues/1516
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
NetworkX - Network Analysis in Python
gil_load - Utility for measuring the fraction of time the CPython GIL is held
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
Dask - Parallel computing with task scheduling
CPython - The Python programming language
cupy - NumPy & SciPy for GPU
external-Merge-Sort - external Merge Sort in python.
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
per4m - Profiling and tracing information for Python using viztracer and perf, the GIL exposed.
SymPy - A computer algebra system written in pure Python