"Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easi

Hi, I am trying to run the inspect_model.py notebook on a custom dataset, and am running into an AssertionError with block 1b. RPN Predictions. I have seen previous threads related to this issue (#23, #848, #893, #92) however these are all closed and it seems the issue was fixed in a merge previous to me downloading the code.

The specific code causing the issue is:

p

Judging by the logic in https://github.com/horovod/horovod/blob/38e91bee84efbb5b563a4928027a75dc3974633b/setup.py#L1369 it is clear, that before installing Horovod one needs to install the underlying framework(s) (TensorFlow, PyTorch, ...).

This is not mentioned in the installation instructions which made me think, I can install Horovod and then any framework I like (or switch between them) and

Support for storing large tensor values in external files was introduced in #678, but AFAICT is undocumented.

This is a pretty important feature, functionally, but it's also important for end users who may not realise that they need to move around more than just the *.onnx file.

I would suggest it should be documented in IR.md, and perhaps there are other locations from which it could be s

We have a big todo list of classes/functions to type (add type hints, see PEP484 for more infos about type hints) and the docs: https://docs.python.org/3/library/typing.html

Functions of the list should be typed and for class

Here, I don't understand why we need to multiply the input image by the label. Don't we need to concat the two part in CGAN? I'll be really appreciate if anyone can help me out!

It might be useful to write the ReadME of the repo in English for ease of understanding.
It looks pretty useful.

Sample on front page:

const model = new KerasJS.Model({
  filepaths: {

filepaths in plural.
code for Model:

if (!filepath) {
      throw new Error('[Model] path to protobuf-serialized model definition file is missing.')
    }

Platform (like ubuntu 16.04/win10): Windows 10
Python version: 3.7.4, mmdnn==0.2.5

Running scripts: mmconvert -f caffe -df keras -om test

I know that this command is not supposed to run without passing an input file, but the error message is incorrect and should be improved:

mmconvert: error: argument --srcFramework/-f: invalid choice: 'None' (choose from 'caffe', 'caffe2', 'cn

Priority

Low

Issue Description

In the screenshot below, LAST MODIFIED shows Sat, 26 Oct 1985 08:15:00 GMT,which is incorrect.

Expected Results

LAST MODIFIED shows correct timestamp, so that users know when this file is modified.

Screenshots/Related files

![image](https://user-images.githubusercontent.com/25629006/52917615-75906980-32a2-11e9-8825-10885a5955d2

Link for the dataset in documentation to train FAQ bot in Russian is broken:
Origin in docs: https://github.com/deepmipt/DeepPavlov/blob/master/docs/features/skills/faq.rst
Broken Link: http://www.ftl.name/page/989

As well as following links for files:
http://files.deeppavlov.ai/faq/school/faq_tfidf_cos_model.pkl

A good example is here.

Basically, collect something in a FAQ page on Github wiki or in a Markdown directory in the repo itself.

text after a graph

_Alternatively, if context labels are provided with each text document, the model can be trained in a contextual mode, where the model learns the text given the context so the recurrent layers learn the decontextualized language. The text-only path can piggy-back off the decontextualized layers; in all, this results in much faster training and better quantitative and qualitat

Spark 2.3 officially support run on kubernetes. While our guide of "Run on Kubernetes" is still based on a special version of Spark 2.2, which is out of date. We need to:

1. update that document to Spark 2.3
2. release the corresponding docker images.