I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

I'm looking at the react tutorial at https://github.com/amark/gun/wiki/React-Tutorial and noticed that the code examples are using depreciated lifecycle methods such as componentWillMount.

Issue Description

When I run the example cartpole with the default parameters, it can not converge to the max reward 200, I wonder what went wrong.

Version Information

Please indicate relevant versions, including, if relevant:

• Deeplearning4j versi

There seems to be conflicting data on what the "OriginGeopoint" is. In the documentation it's referenced as the location of the PlayerStart while in code it's commented as the coordinates of Unreal level at the coordinates 0,0,0.

Code:
https://github.com/microsoft/AirSim/blob/e24a11d5985c1d6f573735f28a3844bc634559db/AirLib/include/common/AirSimSettings.hpp#L350

Documentation:
https://micro

我发现examples/retinaface.cpp中，如果开启OMP加速的话似乎在检测到人脸时会发生内存泄漏，但我定位不了这个问题的具体原因。

值得注意的时，如果将qsort_descent_inplace函数中的OMP指令注释掉这个问题就会消失掉。

static void qsort_descent_inplace(std::vector<FaceObject>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;
    ...
    // #pragma omp parallel sections
    {
        // #pragma

A negative effect could potentially kill a unit. We need to check if the unit is still alive after executing the effects and return early if the unit died. Example: a "poisoned" effect (it doesn't exist, at least yet) that damages the unit turn by turn.

i'm a newbie in programming. I try to use this library. it's very useful for me.
I want to show centroid in K-means clustering. how to show it? thank u so much..

❓ Questions and Help

I followed the fine-tuning example described in here: https://github.com/pytorch/fairseq/blob/master/examples/mbart/README.md
However I didn't manage to reproduce the results described in the paper for EN-RO translation.

How to reproduce fine tuning with mbart?

1. Can you clarify where did you get the data and how did you preprocess it for training in more de

Description

Add Azure notebook to our SETUP doc.
I tested google colab and Azure notebook to run reco-repo without requiring creating any DSVM or compute by myself, and it works really well with simple tweaks to the notebooks (e.g. for some libs, should install manually).

I think it would be good to add at least Azure notebook to our SETUP doc, where users can easily test out our repo w/o

Documentation Is:

• Missing
• Needed
• Confusing
• Not Sure?

E

Upon environment timeout python client will only receive the error message "Environment in wrong status for call to observations()". Might be good to provide more information why the environment is not running anymore (due to timeout etc.)

if (!is_running(self)) {
  PyErr_SetString(PyExc_RuntimeError,
  "Environment in wrong status for call to observations()");
  return NULL;
}

There is nan value in multistepBucketLikelihoods, when I use my own dataset, and set _NUM_RECORDS as 6000. The error is listed as below.

multistepBucketLikelihoods = {1: {499: 1.0}, 5: {499: nan, 501: 0.0}}
File "D:\ProgramData\PythonWorkspace\nupic\docs\examples\opf\test.py", line 52, in runHotgym fiveStepConfidence = allPredictions[5][fiveStep]
File "D:\ProgramData\PythonWorkspace\nup

I understand that these two python files show two different methods to construct a model. The original n_epoch is 500 which works perfect for both python files. But if I change n_epoch to 20, only tutorial_mnist_mlp_static.py can achieve a high test accuracy (~0.97). The other file tutorial_mnist_mlp_static_2.py only get 0.47.

The models built from these two files looks the same for me (the s

Let's enable loading weights from a URL directly

Option 1:

Automate it with our current API

Trainer.load_from_checkpoint('http://')

Option 2:

Have a separate method

Trainer.load_from_checkpoint_at_url('http://')

Resources

We can use this under the hood:
(https://pytorch.org/docs/stable/hub.html#torch.hub.load_state_dict_from_url)

Any tho

I'm submitting a ... (check one with "x")

[ ] bug report
[ ] help wanted
[ ] feature request

Current behavior

Expected/desired behavior

Reproduction of the problem

If the current behavior is a bug or you can illustrate your feature request better with an example, please provide the steps to reproduce.

What is the expected behavior?

• Operating System: Windows
• Serpent.AI Version: not sure
• Game: (Cuphead) Executable
• Backend: GPU

i followed the hello world tutorial, created plug in for cuphead executable game when i launch the game i get this error

(AI) C:\Users\ANTONY\SerpentAI>serpent launch cuphead
Traceback (most recent call last):
  File "c:\programdata\anaconda3\envs\ai\lib\runpy

A more consistent and multi-functional global level of verbosity control,
suggest an enhancement that will see print(...) in project be converted to using the python logging. module

import logging
#Then instead of print() use either
logging.info(......)
#or
logging.debug(.....)
#or
logging.warning(....)
#or
#logging.error()

In that way verbosity can be globally

Is is possible to include the docs from https://carla.readthedocs.io/en/latest/python_api/#carla.Actor in the python library? I would prefer using the docs in my IDE rather than jumping into the browser when I have to look something up.

Thank you.