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#### Description
Unfortunately, probably due to the stratification balancing, the train and test size can vary between iteration. This is pretty highly undesirable and it should always rebalance to …
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### Description
Some background, I'm trying to parallelize a CPU-intensive computation using a callback to some `scipy.optimize` routines using `jax.pure_callback` across the available CPUs on my m…
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Would like to change the parallel python example to the code below
```python
import numpy as np
import sys
import datetime
from mpi4py import MPI
def inside_circle(total_count):
x = n…
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Hello, I have large vectors of lat, lon, alt that I would like to convert to a local coordinate system some "enu" frame such that I can do calculations the ecef.Cartesian and the ecef.EcefKarney are d…
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#### Description
When passed a pandas.Series instead of the expected float value for effect_size, the function will return a pandas.Series. While I realize that my input type is incorrect, I feel t…
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`pybind11:vectorize()` is very convenient, it allows us to provide Numpy-like universal functions (broadcasting, fast-loop call) with minimal effort. However, it seems to be problematic when releasing…
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We should follow sklearn's example and use RandomState for any random variable or random process generation.
makes it easier to initialize with seed and will work better with parallel processing.
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| | |
| --- | --- |
| Bugzilla Link | [31677](https://llvm.org/bz31677) |
| Version | 3.9 |
| OS | Linux |
| CC | @alexey-bataev,@lesshaste,@hfinkel,@joker-eph,@TNorthover |
## Extended Description…
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One of the quite compelling new additions to the torch ecosystem is NestedTensors, see https://pytorch.org/docs/stable/nested.html.
Basically, this is a new primitive in the Torch ecosystem which a…
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Nice to have some pytorch-like operations very useful with tensor (arrays) manipulations.
e.g. tensor.unique().. which returns an array of unique numbers with or without counting them.
or - tensor.u…