🐛 Bug

Tensor averaging not implemented for complex types

To Reproduce

>>> import torch
>>> x = torch.ones(4, dtype=torch.complex128).cuda()
>>> torch.mean(x)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-108-34af2dc3edea> in <module>
      1 impo

Improve navigation of the I/O how-to by prepending a decision tree/flowchart using an image or graphviz, as suggested by @mattip.

.join seems to not work properly with unicode arrays. Here is a reproducer:

In [16]: str_arr = np.array(["hi", "there"])

In [17]: ",".join(str_arr)
Out[17]: 'hi,there'
In [18]: numba.njit(lambda str_arr: ",".join(str_arr))(str_arr)
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
- Resolution failure for literal arguments:
No implementation of function 

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

The current version of the HANS dataset is missing the additional information provided for each example, including the sentence parses, heuristic and subcase.

Description

TVM support has been added in dmlc/gluon-nlp#1390. Also, we updated our benchmarking utility to support profiling the inference speed of TVM: https://github.com/dmlc/gluon-nlp/tree/master/scripts/benchmarks. We can further improve our document.

• Attach the benchmark numbers. (Good for beginners)
• Add a tutorial about how to convert GluonNLP mod

Support Series.median()

MCVE Code Sample

# Your code here
import numpy as np
import xarray as xr

data = np.zeros((10, 4))
example_xr = xr.DataArray(data, coords=[range(10), ["