Document best practices for Airflow

specifically, answer these common questions:
How do i create and deploy a spark job?
How do i create and deploy a TensorFlow job?
Where should I put the source code for the jobs (both airflow dag and spark/tensorflow)?
How do i trigger the airflow dag upon github commit?

good questions/ flows to consider :-)

Me too

How to add DAG, can you please guide me to the right way?

As far as I'm understanding, right now I'm only able to upload files through copy to the container?

@Hronom correct. and i just spoke with one of the airflow committers.  he confirmed that editable airflow DAGs are not on the near-term roadmap.
stability and scale are their primary concerns at the moment - which is a good thing, of course!
closing this for now as an adequate workaround exists.


[Mhlo] Rewrite Softmax with decomposition, add lowering of Element-wise, Reduction and GlobalAvgPool ops

This PR accomplishes the following tasks:

We remove the original lowering of Softmax. Instead, we decompose the ONNX Softmax operator into other ONNX operators, and then lowering the corresponding operators separately. In this way, we can reuse the lowering of basic operators (Add, ReduceSum, etc.). This pattern rewriting may be reused if applicable.
We add the lowering of more Element-wise operators.
We add the lowering of Reduction ops (ReduceMax, ReduceMin, ReduceSum, ReduceMean).
We add the lowering of GlobalAveragePool.

Can one of the admins verify this patch?

FYI @yaochengji @chenchongsong

Can one of the admins verify this patch?

@jenkins-droid test this please

Can one of the admins verify this patch?

@jenkins-droid test this please

Instead, we decompose the ONNX Softmax operator into other ONNX operators, and then lowering the corresponding operators separately. In this way, we can reuse the lowering of basic operators (Add, ReduceSum, etc.). This pattern rewriting may be reused if applicable.

While I agree on decomposing ONNX ops into other ONNX ops, decomposing Softmax into Add, ReduceSum, ReduceMax etc. here is a bit concerning because some accelerators, e.g. NNPA, directly support Softmax. Is it ok to move the softmax decomposition into --convert-onnx-to-mhlo?

Also, libMhloInferShapeEqualityOpInterface.so is still not being installed by make install

Is it ok to move the softmax decomposition into --convert-onnx-to-mhlo?

@tungld Here the softmax lowering pattern doesn't fall into the lowering pass, instead it only falls into the populateLoweringONNXDecomposeOpToONNXPattern util function. I suggest we could still put softmax decomposing patten in current decompose files, and different backends could select and reuse the lowering patterns through a util function. Ex. populateDecomposingONNXBeforeMhloPatterns, populateDecomposingONNXBeforeNNPAPatterns. Do you think it's acceptable?
@Connor-XY Could you rename the util function to populateDecomposingONNXBeforeMhloPatterns to let reviewers know it is dedicated for Mhlo? And add populateWithGenerated(patterns); in the util implementation?

Can one of the admins verify this patch?

Also, libMhloInferShapeEqualityOpInterface.so is still not being installed by make install

Do we need to install other dependencies besides MhloDialect and MhloInferShapeEqualityOpInterface?

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Can one of the admins verify this patch?

@jenkins-droid test this please

PS: after you send a few PR successfully, we can add you to the list of authorized ids to launch Jenkins. Think we did this already for one of your collegues.

Can one of the admins verify this patch?

PS: after you send a few PR successfully, we can add you to the list of authorized ids to launch Jenkins. Think we did this already for one of your collegues.

Thanks! I would appreciate that.

Can one of the admins verify this patch?

Also, libMhloInferShapeEqualityOpInterface.so is still not being installed by make install

Do we need to install other dependencies besides MhloDialect and MhloInferShapeEqualityOpInterface?

# ldd libOMONNXToMhlo.so |grep 'not found'|sort|uniq -d
	libHloOpsCommon.so.15git => not found
	libMLIRMhloUtils.so.15git => not found
	libMhloInferShapeEqualityOpInterface.so.15git => not found

Can one of the admins verify this patch?

Also, libMhloInferShapeEqualityOpInterface.so is still not being installed by make install

Do we need to install other dependencies besides MhloDialect and MhloInferShapeEqualityOpInterface?

# ldd libOMONNXToMhlo.so |grep 'not found'|sort|uniq -d
	libHloOpsCommon.so.15git => not found
	libMLIRMhloUtils.so.15git => not found
	libMhloInferShapeEqualityOpInterface.so.15git => not found

Note that the missing libraries themselves can have other dependencies.

Thanks for your help! I should have installed all the required libraries now.

I suggest we could still put softmax decomposing patten in current decompose files, and different backends could select and reuse the lowering patterns through a util function. Ex. populateDecomposingONNXBeforeMhloPatterns, populateDecomposingONNXBeforeNNPAPatterns. Do you think it's acceptable?

Sounds reasonable. I like the idea that patterns can be called on-demand depending on the target dialect. How about creating an option in the decomposition pass, say --target=mhlo and conditionally add populateDecomposingONNXBeforeMhloPatterns? so that everything about decomposition will be managed/called in a single place.

(sometimes the power jenkins is a bit flaky... restarted the testing by merging to the latest)

Sounds reasonable. I like the idea that patterns can be called on-demand depending on the target dialect. How about creating an option in the decomposition pass, say --target=mhlo and conditionally add populateDecomposingONNXBeforeMhloPatterns? so that everything about decomposition will be managed/called in a single place.
Sounds also a good way to handle this.

@Connor-XY Could you modify it as the above way and resolve the conflicts?

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Can one of the admins verify this patch?

@jenkins-droid test this please

Can one of the admins verify this patch?

I will simply update the branch to the latest. Note that you will have to update your local repo from the GitHub version. Hope its fine by you.

Sounds reasonable. I like the idea that patterns can be called on-demand depending on the target dialect. How about creating an option in the decomposition pass, say --target=mhlo and conditionally add populateDecomposingONNXBeforeMhloPatterns? so that everything about decomposition will be managed/called in a single place.

is that done, or would we merge the PR asap to fix the lib issue, and then act on the suggestion from @tungld ? Please advise @Connor-XY . Tx

Sounds reasonable. I like the idea that patterns can be called on-demand depending on the target dialect. How about creating an option in the decomposition pass, say --target=mhlo and conditionally add populateDecomposingONNXBeforeMhloPatterns? so that everything about decomposition will be managed/called in a single place.

is that done, or would we merge the PR asap to fix the lib issue, and then act on the suggestion from @tungld ? Please advise @Connor-XY . Tx

That is done. You could merge the PR if it passes the tests.

Any update on when this will be merged? I'm getting an error loading libMhloDialect.so.15 and it was mentioned on issue https://github.com/onnx/onnx-mlir/issues/1549 that this PR can fix it. Thanks!

will merge as soon as the latest CIs pass, sorry for the delay.

Jenkins Linux ppc64le Build #5919 [push] [Mhlo] Rewrite Softmax w... started at 11:50

Jenkins Linux amd64 Build #6807 [push] [Mhlo] Rewrite Softmax w... started at 10:49

Jenkins Linux s390x Build #6822 [push] [Mhlo] Rewrite Softmax w... started at 11:49

Jenkins Linux amd64 Build #6807 [push] [Mhlo] Rewrite Softmax w... passed after 1 hr 3 min

Jenkins Linux ppc64le Build #5919 [push] [Mhlo] Rewrite Softmax w... passed after 1 hr 37 min

Jenkins Linux s390x Build #6822 [push] [Mhlo] Rewrite Softmax w... passed after 1 hr 38 min


New App icon. Looks great in macOS Sierra.

I've changed the defaultAction.icns so it looks great in macOS Sierra UI.

Does this have high a high resolution set built in as well? I'm assuming from the filesize (70kb) it doesn't

I've made a new one, it's almost the same size but it's sharp and looks good on retina displays

I don’t like the current icon, but if we’re replacing it, we should quit using one or more gears. That never made sense to me. It should be some kind of arrow, or a person running, or something having to do with “action”. Gears have come to be associated with settings.

Gears have come to be associated with settings.

Good point, I like the idea of an arrow or a running man!

On 19 Sep 2016, at 21:25, Rob McBroom<EMAIL_ADDRESS>wrote:
I don’t like the current icon, but if we’re replacing it, we should quit using one or more gears. That never made sense to me. It should be some kind of arrow, or a person running, or something having to do with “action”. Gears have come to be associated with settings.
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub https://github.com/quicksilver/Quicksilver/pull/2274#issuecomment-247991893, or mute the thread https://github.com/notifications/unsubscribe-auth/AAJLn72eaN6VcyQruDJQkQM6CqwhhL9gks5qro0zgaJpZM4J_i_5.

OK, we'll approach this by a different angle. Let's update the default icon. Then we will go from there. I'm proposing a design:

Now that looks more promising. I’ll have to look at it in context, though.

@skurfer any thoughts about merging this? does it require more work to get the icon referenced correctly?

I think this looks like a good update, but the icns file format/sizes seems incorrect.
See this Apple doc on the image sizes and then this blog post on how to create the ICNS file

I like the idea of inverting the icon colors — make purple what is current gray and vice versa. This could result in a more vibrant icon. But the implementation here is just too bland, it lacks contrast and depth. So instead of a potentially richer icon, it goes the opposite way.
To illustrate what I'm talking about, I did a sketch using ideas from the proposed icon but targeting something bolder, lively:

About the suggested action icon, although sharper and retina-friendly, it looks too complex. And I agree with @skurfer, an arrow would be much more appropriate icon for "run" than a gear.

@vadimrob  and @fturcheti  - it's been a while, but both your icon suggestions are great. Let's give Quicksilver a facelift!
If either of you still has your files, please can you provide the icon in the following format then we'll merge this in!

Let's bring QS into the 202s :D

Hello! I'll check and see what I've got but I think if you really want to update it now we should modify to a square type of icon design that is better integrated to the current macOS design language.

@pjrobertson

Unclear if this will be any help, but I whipped it up yesterday for something entirely unrelated, and it seemed relevant.
I don't really know what I'm supposed to do with the @2x thing, but this seems to make an .app happy.
#!/bin/bash
# icons.sh
# Quick and dirty script to make an icon package based on a single higher
# resolution image.
# Usage: `./icons.sh MyLargeIcon.jpg`

set -Eeuf -o pipefail
set -x

main() {
  img=$1

  sips -s format png --out tmp.png "${img}"
  mkdir icon.iconset

  sz=16
  while [[ "${sz}" -le 512 ]]; do
    for multiplier in "" "@2x"; do
      sips -z "${sz}" "${sz}" tmp.png --out "icon.iconset/icon_${sz}x${sz}${multiplier}.png"
    done
    ((sz *= 2))
  done
  iconutil -c icns icon.iconset
  rm -r icon.iconset tmp.png
}
main "$@"

@vadimrob - looks great! I will comment on the other pull request.

We already have one of the new style square icons on master, but this one’s fine if you like it better.


adding preco college

Добавление Уральского Регионального Колледжа

@Task110001 I am sorry, I have to close the request without merging, because you made a mistake in a request and requested to merge too many files, most of them do not relate to the organization which you would like to add to this repository. Please ensure that you are familiar with this repository rules located at the bottom of this page and submit a new request with the correct information. Thank you for your understanding!


Using sigma.js canvas renderer for high quality image?

I'm using sigma.js canvas renderer to render my image with snapshot plugin. The resolution is the size of the web browser e.g. 300x300. I'm looking to turn my graph visualization into a higher resolution e.g. a 300dpi equivalent, for printing on a book. What would be the steps required to take?
I have tried upscaling the canvas and then taking a snapshot but the generated image remained of the same resolution e.g.:
var original_width = 1000;
var canvas = document.getElementById('sigma-container');
canvas.width = original_width;
canvas.style.width = original_width / (300/96);

How can I go about solving my problem?

What if you set the style width to the same, rather than dividing it by a ratio?

I get a 1000 wide image but the image doesn't scale in any way, in other words, a lot of white/blank space.

Did you refresh sigma's instance after scaling the canvas? Did you try scaling the canvas before instantiating sigma?

Tried both but could see no change.


verify github webhook signature

Required items, please complete
Change(s):

Currently this connector ain't supporting any authentication of incoming webhooks. Github provides a mechanism of webhook authentication with HMAC signature of request's body. I added validation of x-hub-signature header following Github's guildlines. Connector is still working without presence of signature header for backward compatibility. Secret on Connector side is stored the same way as WorkspaceKey:

     sentinel_shared_key = os.environ.get('WorkspaceKey')
     github_webhook_secret = os.environ.get('GithubWebhookSecret')

Also i have updated presented zip archive with azure function accordingly. After this PR will be accepted i'm going to create PR in Azure Docs describing steps required to store secret on both Azure and Github sides.
Reason for Change(s):

Enhancing security of webhook calls.

Version Updated:

N/A

Testing Completed:

Yes, i have tested it my organization's Azure environment.

Checked that the validations are passing and have addressed any issues that are present:

No schema-related changes was made.

@microsoft-github-policy-service agree

Hi @ansereb we are checking on this, will update you till next on 17. 07. 2023, thanks!

Hi @v-vdixit. Here is a screenshot of successfull delivery of payload from Github to Azure webhook with X-Hub-Signature header:

Github delivery

I'm not sure what kind of proof of work can be made on Azure side but here is Function App with my code and successfull invocations:

Azure portal


Fixing erroneous installation instructions

When MINIFY_HTML is set to True, but htmlmin is not installed,
the following warning is printed:
MINIFY_HTML is `True`, but minify extra could not be imported. Install with `unicorn[minify]`.
MINIFY_HTML is `True`, but minify extra could not be imported. Install with `unicorn[minify]`.

Users should be instructed to install django-unicorn[minify] instead of unicorn[minify]`

@all-contributors add @rhymiz for code

Thanks for the PR and improving the logging message! 👍


When using Telnet in QoS, the idle connect may not be closed in time, despite using the idle check.

[x] I have searched the issues of this repository and believe that this is not a duplicate.
[x] I have checked the FAQ of this repository and believe that this is not a duplicate.

Bug Specification
When using Telnet in QoS, the idle connect may not be closed in time, despite using the idle check.
When using IdleStateHandler with TelnetProcessHandler in the QosProcessHandler, the timeout is set to  5*60 seconds, but NO handler deals with the IdleStateEvent. Maybe the TelnetProcessHandler should override the userEventTriggered method to handle this Event, and close the idle connect.

close #7281 via #7282


Library plugins

[x] New feature (non-breaking change which adds functionality)

Proposed changes
This is an initial implementation of Library source plugins as outlined in the design note: https://github.com/node-red/designs/pull/41
This PR is against the plugins branch (see #2779).
It adds support for custom library plugins that add new sources to the import/export library dialog in the editor.
This PR includes a stubbed out library plugin (although the fine details of the API is still evolving). See the design note linked above for more details.
This is a draft PR as there is a lot more work to be done. I'm working on this in parallel to the plugins PR as it helps to have a real use case for plugins as it helps flush out requirements for the base functionality.
Checklist

[ ] I have read the contribution guidelines
[ ] For non-bugfix PRs, I have discussed this change on the forum/slack team.
[ ] I have run grunt to verify the unit tests pass
[ ] I have added suitable unit tests to cover the new/changed functionality

Coverage decreased (-0.09%) to 67.537% when pulling b41c7962c29e131d9a35c3f2d28d5a2b55ad7aaa on library-plugins into f96ce2fd834ab2c670863006eece8c93969a80b4 on dev.

This is now ready to merge.
I have created a file system library plugin and pushed it here: https://github.com/node-red/node-red-library-file-store
It will be published to npm as @node-red/library-file-store as part of the 1.3 release.


"cmd.exe /c start" is not a command

must be slice. Do you prefer opnr.cmdArgsFor(url)?

Thanks! No, I think it's ok like it is for now.

Can you please help me with vim? I'm trying to create command to call mdopen on file I'm editing by :MDOpen. I'm tried to write command at .vimrc but it's not working. Something like:
command! MDOpen execute 'mdopen' expand('%:p')

You can put below into your vimrc
augroup MDOpen
  au!
  autocmd FileType markdown command! -buffer MDOpen silent !mdopen %
augroup END

Or put following file into your ~/.vim/ftplugin/markdown/mdopen.vim
command! -buffer MDOpen silent !mdopen %

Cool, thanks a lot!


AttributeError in bind_user with invalid username

When passing an invalid username I'm getting an exception rather than None:
Traceback (most recent call last):
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask/app.py", line 2292, in wsgi_app
    response = self.full_dispatch_request()
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask/app.py", line 1815, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask/app.py", line 1718, in handle_user_exception
    reraise(exc_type, exc_value, tb)
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask/_compat.py", line 35, in reraise
    raise value
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask/app.py", line 1813, in full_dispatch_request
    rv = self.dispatch_request()
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask/app.py", line 1799, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "/Users/$USER/dev/myproject/myproject.py", line 98, in login
    test = ldap.bind_user(user, passwd)
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask_simpleldap/__init__.py", line 147, in bind_user
    user_dn = self.get_object_details(user=username, dn_only=True)
  File "/Users/$USER/.local/share/virtualenvs/myproject-A47Vo0O9/lib/python3.6/site-packages/flask_simpleldap/__init__.py", line 197, in get_object_details
    for k, v in list(records[0][1].items()):
AttributeError: 'list' object has no attribute 'items'

Fixed in v1.3.0


Please remove header() calls

Thanks for the library. Works well, except the following lines cause issues for me:
// Helps with the CORS issues.
header( 'Access-Control-Allow-Origin: *' );
header( 'Access-Control-Allow-Methods: POST, GET' );
header( 'Access-Control-Allow-Credentials: true' );

If this library is invoked after output has already occurred, then this warning will appear:

Warning: Cannot modify header information - headers already sent by (...) in /app/vendor/ahmadawais/sendy-php-api/src/API.php

Obviously there are ways I could work around that, but given that this is a client API, it's not appropriate for it to force server headers on me. (These headers do absolutely nothing unless the Sendy server is setting them.) Also it's quite possible that this library is used via CLI where there is not even the concept of headers to send.
In the meantime I have removed these lines locally but I prefer to not need to use a modified version of the library.

breaks with https://github.com/nelmio/NelmioCorsBundle


code with document.querySelector() is throwing error.

Code snippets where document.querySelector() being used, is throwing an error.
For e.g. in case of Logic, below error is shown,
Cannot set properties of null (setting 'innerText').
Would like to update the code for such changes.

I don't see any problems.


Limit noise of unmanaged child resources

Description
By working on adding support for aws_s3_bucket_public_access_block and idea came to my mind.

A child resource basically mean that a child resource cannot exist without any parent (no orphans).
We have started to define those hierarchy rules in driftctl in pkg/resource/resource_types.go and it look like that:
	"aws_security_group": {children: []ResourceType{
		"aws_security_group_rule",
	}},
	"aws_security_group_rule": {},
	"aws_sns_topic": {children: []ResourceType{
		"aws_sns_topic_policy",
	}},
	"aws_sns_topic_policy":       {},
	"aws_sns_topic_subscription": {},
	"aws_sqs_queue": {children: []ResourceType{
		"aws_sqs_queue_policy",
	}},

We have done that initially to be able to be consistent when for example we are ignoring aws_sqs_queue from the drift ignore but we still want to report aws_sqs_queue_policy (maybe not the right example).
The idea was to being able to avoid ignoring some resources that are created in expander middlewares (understand directly created from their parents, not coming from their enumerator).
More details about that can be found in that PR https://github.com/snyk/driftctl/pull/1131

Now we have that context, I think it can make sense to omit from scan results child resources if their parent is unmanaged.
Example
In the example below, I don't find that it makes a lot of sense to show the aws_s3_bucket_public_access_block resource.
Found resources not covered by IaC:
  aws_s3_bucket:
    - test-elie-driftctl
  aws_s3_bucket_public_access_block:
    - test-elie-driftctl

A single middleware could maybe be responsible of doing that if we can provide a way to identify the children-parent relation.
Maybe we could also introduce an opt-out flag for that behavior, or reuse the --strict one ? I'm afraid that the strict flag will became an to big holdall in the future.

One thing to consider is the experience of using driftctl to import all unmanaged resources into terraform. If by default the child resources are not displayed, it may be confusing to the user that suddenly more unmanaged resources are discover after they imported the parent. That nuance I think has to be somehow visible in the output (some sort of notification that children are filtered out).

One thing to consider is the experience of using driftctl to import all unmanaged resources into terraform

This is really a good catch, I didn't have that use-case in mind while writing the issues and this is definitely something that we need to consider.
As you said, maybe a notice in the output to say that some resource have been ignored, combined with an opt-out or --strict flag can maybe compensate that


How to train WaveRNN

Your WaveRNN repository(master-barnch) excludes preprocess.py modules, how can I train the  WaveRNN model? @begeekmyfriend

It is quite easy to understand that you only need two directories called gta (or mel for ground truth) and quant to each speaker for DataLoader according to dataset.py. The file format is all npy and the sizes of the first (or maybe last that I do not remember) dimension of npy must match with each other.

It is quite easy to understand that you only need two directories called gta (or mel for ground truth) and quant to each speaker for DataLoader according to dataset.py. The file format is all npy and the sizes of the first (or maybe last that I do not remember) dimension of npy must match with each other.

It is quite easy to understand that you only need two directories called gta (or mel for ground truth) and quant (for wav clip) to each speaker for DataLoader according to dataset.py. The file format is all npy and the sizes of the first (or maybe last that I do not remember) dimension of npy must match with each other. For instance, you might just copy the wav clips into quant directory generated in preprocessor.py in tacotron2. And the GTA mel can be generated through scripts/gta_synth.sh.

It is quite easy to understand that you only need two directories called gta (or mel for ground truth) and quant (for wav clip) to each speaker for DataLoader according to dataset.py. The file format is all npy and the sizes of the first (or maybe last that I do not remember) dimension of npy must match with each other. For instance, you might just copy the wav clips into quant directory generated in preprocessor.py in tacotron2. And the GTA mel can be generated through scripts/gta_synth.sh.

@begeekmyfriend It can be considered that we can use the results(wav, mels, gta) generated through the preprocessing of this tacotron2 repository. It's a simple issue, but it can be helpful to automate the pipeline between the two repository directory structures. I will try and PR. Thank you for the quick answer.

@begeekmyfriend It can be considered that we can use the results(wav, mels, gta) generated through the preprocessing of this tacotron2 repository. It's a simple issue, but it can be helpful to automate the pipeline between the two repository directory structures. I will try and PR. Thank you for the quick answer.


How to enforce model to output SQL code only?

Hello, I noticed that the model spent the majority of time doing code explanation and only output the SQL query at the end. Is it possible to tell model to directly generate sql query so users don't need to spend time waiting for the actual code. Thanks!

Hi there, are you using the prompt in prompt.md? If you are and if you're using sqlcoder2, it should only output the SQL and not the explanation.


Make site navigation clearer and more user-friendly

"Some of the pages are quite long, they all have the same look and feel, and when they have lots of links - like Theme 1 - and once you scroll down a bit it's not obvious what theme's page your on or what else is on the page." - H
"Better navigation between pages would be great as well - like getting to the list of themes from an individual theme's page." - H
Proposed solution:

Add a sticky header the stays in sight as you scroll that contains the type of entity you're browsing and buttons (w/ dropdowns where appropriate) for the different collections.

e.g. "Themes > Biodiversity" .................... Themes | Prizes | Data | Mentors | Stuff

Have a sticky Wikipedia-style table of contents on the left-hand side so its clear (on the long pages) what's available. Have it highlight sections as you scroll so your position within the page is clear.

Foundation vs Bootstrap vs Hack on the existing theme?
Foundation for Sites
Some templates
Some more templates
A theme

Made a start on improving navigation...by porting the whole thing to Bootstrap! Still lots of polishing todo, but it's good enough for a POC.
e.g.

[ ] Properly respect and acknowledge the license of Bootstrap and the template we're using
[ ] Think about how breadcrumbs can be made more useful (e.g. Showing the collection)
[ ] Make the column width used consistent across all pages and resolutions
[ ] Generally apply polish to the tablet/mobile view
[ ] Is the table of contents nav well designed? (visually and from a usability POV)
[ ] Add a sponsors aside
[ ] Polish the theme cards
[ ] Finish the footer
[ ] Make the tiger stripe effect on sections happen in CSS-land, not markup (because sections won't always appear if there's no content)
[ ] Hookup the social icons for all cards that have them

@jesserobertson et al.
New design is at http://govhackaustralia.github.io, and the old one is forked to http://keithamoss.github.io for posterity and reference (in case I forgot to port anything, or did so oddly).
Comments most welcome!

Yus, no more requests to help people with Jekyll. :100:

Tell Helen I won't take her criticisms of my l33t w3bs1t3 sklz0rs personally...

Some thoughts:
Got some issues with the dropdown menu/ToC on the right not disappearing on the home page. it sticks around and interferes with the theme boxes.
It would be nice to have a slightly thinner layout for large windows (pretty sure there's another bootstrap layout to steal for that)

Why is there the drop-down menu on the left and the scrolling menu bar at the top? I'd personally go for the scrolling bar and move everything into that...

Ta for the feedback!
Helen's comments were referring to my l33t w3bs1t3 sklz0rs on my fork of govhack-science :)
The dropdown menu/ToC is there for in-page navigation, and the scrolling menubar for in-site navigation. Could probably make that a little clearer in the positioning and styling of it.
Re the positioning of the sticky ToC: Yeah, I need to tweak the number of Bootstrap columns used so they're consistent on all pages to avoid that. I want to bring them back to the same width of the layout you had for GovHack-Science (or maybe just a smidgen wider).

Bootstrap Mega Menus
Yamm 3
Bootstrap Mega Menu (Bootsnipp)
Andrew Pougher's (jsFiddle)
FH Mega Menu (Code Canyon - $5 Demo)
Mega Dropdown (Cody House)
Horizontal Drop Down Menu (Codrops)

Hacked together a megamenu based on this jsFiddle.


The validation in the profile manager fields is enabled before anything is entered

Description
The validation in the profile manager fields is enabled before anything is entered in these fields.
Untitled_ Jul 26, 2023 4_23 PM.webm
The affected fields are Password, Mnemonic, Confirm Password
Steps to reproduce

Navigate to https://dashboard.dev.grid.tf/
Click outside of the password field when it asks you to enter it.
Switch to the Connect Your Wallet tab.
Click outside of the mnemonic field.

Environment

Network: devnet
Dashboard Version: 2.1.0-rc3

Verified, Devnet 2.1.0-rc6.
Input are no longer enabled when entered.

But the Password input in the login tab is still connected to the confirm password input.
Untitled_ Aug 7, 2023 4_26 PM.webm

Verified, 2.1.0-rc8.


Update composer.json

Update composer file for illuminate/database & illuminate/support V6.0

Thanks, but will need to update travis as well.


Update Android Gradle Plugin to 2.3.2, support libs to 25.3.1.

I suspect it'll cause Travis Lint failure but I'll try to fix.

closed to prevent accidental merge


Extend ConnectivityStatus with UNKNOWN

Problem description
When the service tries to call the network to get some information, but the network cannot deliver either information about DATA nor SMS, then we cannot respond with "NOT_CONNECTED", but we need to communicate to the consumer that the network was not able to deliver a current status of connection.
Possible evolution
Add a new connectivity-status UNKNOWN

It seems, we have a similar issue against issue-138

hmm..mm @maxl2287 why wouldn't the API return a temporary error if it is unable to determine the connectivity status?
cc: @NoelWirzius @gmuratk

There could be some potential use-cases for "Unknown" status:

The network is partly down, so some services like voice calls work, but others like data or SMS don't work properly.
When there is too much network traffic, some requests like data or SMS might be delayed or fail.
The service provider's systems have problems, so they can't provide certain information.

Tricky question...
I checked on Device location...

In Location Retrieval if we are not able to locate the mobile within the required freshness we send back a 422
In location Verification if we're not able to check the mobile location we send back a 200 with a "verificationResult": "UNKNOWN"

I tend to prefer 422 for our API as easier for my perspective to differentiate  "business" valuable response.

Thanks @bigludo7 for more insight on this and I would also prefer 422 in this case where it explicitly indicates a failure to process the request due to some unknown network issue.

I also tend to agree with the 422. It should not be a successful response and we should not charge for something that is not client's fault.

@jlurien @bigludo7 I guess we can transform this issue into:
"Respond with HTTP-422 when the service is unable to provide information about the current connectivity".
Wdyt?

@jlurien @bigludo7 I guess we can transform this issue into: "Respond with HTTP-422 when the service is unable to provide information about the current connectivity".
Wdyt?

Yes !
Seems that we have a consensus to send back a 422 when we're not able to get the connectivity status.
We could probably make a PR for that and update this. What do you think @akoshunyadi ? are we still in time to do that for this release?

We have already GENERIC_422_UNABLE_TO_PROVIDE_REACHABILITY_STATUS , that's for that case, isn't it?

We have already GENERIC_422_UNABLE_TO_PROVIDE_REACHABILITY_STATUS , that's for that case, isn't it?

oh ! yes we have it already. Thanks.
So nothing to do on the yaml - we're good.

Can we close then this issue here?

Can we close then this issue here?

+1


Add test for #389 Skip having non-zero value on reused pages

Would you be able to accept the CLA to allow this to be pulled in?

@mspielberg ping


Add ActionBar to About screen

Before

After

Definitely looks better with the action bar to me, and would be more convenient. Not sure why the original devs decided to hide it?


Options in Image.build_from_dir

I have a use case where I need to set the no-cache option when calling build_from_dir. I have tried passing it as a hash but it just gets ignored and the cached results are used. How can I override the behaviour? It might be worth updating the doc if its unclear.
Thanks in advance!

@photosojourn can you include a code snippet?


Improve workflow for themes created externally

I see that both Color Themes and Color Theme Editor points to this repo. I’m working on a theme named Dainty for Visual Studio. I would love to get its default configuration merged into Color Themes at some point if you’re interested, but I think it needs more work.
Now, regarding the Color Theme Editor – the way my theme is built might be a bit different than usual. Instead of setting colors from the Color Themes page, it is built from a Node.js script using a combination of search–replace, and setting specific colors for categories afterwards.
My workflow consists of compiling the theme, importing into Color Themes by clicking “Import Theme”. After some changes it’s not unusual to have 150 themes in the list. I realize that this might be a bit of a different workflow than usual, and deleting the themes requires lots of pointing and clicking – and this is the biggest pain point.
So I’m just wondering if there is a better way. I would love to open one of the command windows and type something similar to:
Import-ColorTheme path
Delete-ColorTheme -Custom -All

Or even better:
Update-ColorTheme path

I’m not sure if it’s necessary, but I currently assign a unique UUID to each compiled theme. I had some issues that might have been caused by reusing the same UUID and doing many imports, although I find the issue hard to reproduce. With an update command it might be preferable to have one UUID for the theme.
If this is not something you plan to implement then I would appreciate if you told me how to delete the theme files from the file system. I’ve looked in a few different directories in AppData, but I can’t find them. I’m not that familiar with Visual Studio extensions.

Here is an update.
Because my theme supports configuration, I’m not sure if I wish to bundle my theme with Color Themes – and from what I’ve picked up in various issues, it seems like you want to keep the list as small as possible.
While it would be helpful with a button to delete all custom themes, it is no longer a huge issue for me. I just delete them before the grows large.
What I’d love to see is a way to import themes from a path using some kind of CLI command. An Update-ColorTheme path would be perfect.


Edit save - accepts additional FFMPEG commads

Save() now accepts an array of additional commands which defaults to [] if not given.  This is added onto $commands[].
inputing ['profile:v', 'baseline'] would save the video with constraint baseline instead of HIGH

@beeseenmedia What is your use case of additional commands to ffmpeg? Could this be solved through more features in the formats or anywhere else?
It seems a bit dangerous to pass unfiltered commands to ffmpeg.

@romainneutron, shouldn't we close this PR?
It hasn't moved since more than a year + it doesn't pass the tests...

Sorry, you can close it.
The use case was that i needed certain parameters in order for the output video to be compatible with android devices and instagram.

Hi @spearmootz,
Thanks for this feedback.
Could you create an issue with the parameters you would need? I will consider creating a filter.
Thanks
Romain


Forced bits infrastructure improvements

Just noticed that a test is failing. Don't merge.

Test seems to pass locally, will figure out what's going wrong here.

LGTM thanks


Conflict with phonegap-plugin-push

Please update to Firebase (ios 7.13.1 and later and managed by gradle in android)
As it is causing conflicts when using phonegap-plugin-push and read that is because both uses the google services which is causing the issue - please see https://github.com/phonegap/phonegap-plugin-push/issues/283.
If i use this plugin with the push plugin, the app crashes

I have the same issue. Do you have an idea on how to solve it or give it a turnaround?

I've implemented another version to work with firebase, with same API.
Try this one: cordova-plugin-admobpro-firebase.


SQL database with metadata exceeding 4000 token limit of Open AI.

Issue you'd like to raise.
I am trying to query databases with multiple tables. Because of DB metadata, the prompt length exceeds the max token limit of 4000 of OpenAI . Do you have any ideas on how to solve this problem for databases with medium to large metadata? I have tried it for Postgrsql and Presto.
InvalidRequestError: This model's maximum context length is 4097 tokens, however you requested 4995 tokens (4739 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.
Suggestion:
No response

I found the SQLDatabaseSequentialChain method, so closing this issue.
https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html#sqldatabasesequentialchain


Front-end

Do you plan on supporting other front-end like Boost MSM does? I am using the functor front-end right now and it would ease the transition if SML had something similar.

Other front-ends are supported out of the box. The only requirement to be able to use the backend is that a front-end has to produce a list of transitional types. Only one front-end is currently implemented in the core, though.


Move vsphere specific methods out of vmpooler

vmpooler has the vsphere provider taken out, moving some vsphere related
methods to the provider:

pool_folders
get_base_folders

And the related spec tests.
At the same time renaming some configuration and code items
to remove harmful terminology.
Note this version of the vsphere provider needs to run on vmpooler
that also contain the renaming changes (version >2.1)
depends on https://github.com/puppetlabs/vmpooler/pull/467
and a release of vmpooler >2.1

This needs all the _vms_and_folders bits in the pool_manager spec test renamed to _vms_and_resources like was done in VMPooler


xcode12.3  'libjpush-ios-3.2.8' is missing

xcode12.3  'libjpush-ios-3.2.8' is missing
Description
React Native version:
rn 0.57.8
Steps To Reproduce
1.xcode启动ios
2.
Expected Results
xcode12.3  'libjpush-ios-3.2.8' is missing

Please test your code with latest version of RN, 0.57 is too old and unmaintained.

Please test your code with latest version of RN, 0.57 is too old and unmaintained.


chore: polish ssrManifestPlugin.ts

Description
improve code in ssrManifestPlugin.ts:

reduce nesting level.
remove a redundant array length check.
replace index iteration with for-of.

What is the purpose of this pull request?

[ ] Bug fix
[ ] New Feature
[ ] Documentation update
[x] Other

Before submitting the PR, please make sure you do the following

[x] Read the Contributing Guidelines.
[x] Read the Pull Request Guidelines and follow the Commit Convention.
[x] Check that there isn't already a PR that solves the problem the same way to avoid creating a duplicate.
[x] Provide a description in this PR that addresses what the PR is solving, or reference the issue that it solves (e.g. fixes #123).
[x] Ideally, include relevant tests that fail without this PR but pass with it.

Thanks for the reply. Maybe the CONTRIBUTING.md could be updated for this.

I think we should merge this PR, Im waiting to see what other maintainers think

I think the import.length removal is good, but I'm not sure about the rest. They're just different ways of doing the same thing.

Thanks for the PR @Kaciras, as Blu and Poyoho said, better to avoid these changes to keep the repo history clean.


What is exp_info.json?

examples/estimator/analyze_from_estimator
with CUR_DIR.joinpath(estimator_name, 'exp_info.json').open() as fp:
    latest_id = json.load(fp)['id']

Thanks

Hi, your feedback is very welcome. Please refer to the Estimator documentation(https://qlib.readthedocs.io/en/latest/component/estimator.html#experiment-section) to know more about exp_info.json, the content in exp_info_path will help you a lot.

Thanks.

@pan-cai  We will design a more user-friendly interface to training model soon.
Please stay tuned for our next release :)


can't run the example demo

Hi , I clone the code and try to run the example.
I have made my node version 5.10.0 and run "npm install", but when I run "npm start"

I think it error because lack of the .babelrc file, so I add the .babelrc under the example folder.
Then I try to run "npm start " again , it still has error and I don't know how to fix it, pls help me . Thx!
The error shows below:

closing, since this project is a joke


Refine jenkins Email Check approach

Trello: https://trello.com/c/xeq75b8T/128-update-email-alert-checks-to-not-call-people-on-http-issues
This changes the Email Check jobs that run in Jenkins so that they are flagged as unstable if they exit with a code of 1 and are fails if they exit with 2 (which is the code used in https://github.com/alphagov/email-alert-monitoring).
This then sets the freshness threshold to return a critical when these have not had a passive check for 90 minutes.

I can see where the process exits with 2 for a failure, where do you see unstable builds exiting as 1?

@elliotcm Sure, they're here: https://github.com/alphagov/email-alert-monitoring/blob/master/Rakefile#L68-L87

That's the exit 2s, I'm looking for the exit 1s

Oh sorry Elliot I misread. The exit 1s are if the rake task exits on a Ruby exception.


Test for empty subdir when searching for stacks

I was looking to see if it was possible to default the stack ID to an environment variable and found #60 but also found #125 had recently been merged so it should figure it out from the underlying cloned git repository. A new release hadn't been cut with this functionality so I tried building from source. I found spacectl still couldn't figure out the stack ID automatically.
All of my stacks map 1:1 with their own separate repository so the project root is unset. I found I needed to apply this small patch in order for the search to return any results.
Also, it currently always prompts me to pick the stack from the search results, even if there's only ever 1 stack returned. This looks to be caused by this condition:
https://github.com/spacelift-io/spacectl/blob/d36ea753da14b0bbfcc6f5c3ff2003fbb2800ce6/internal/cmd/stack/stack_selector.go#L68
forcePrompt is always true so it doesn't matter how many search results have been returned.

Sorry for the late reply.

Also, it currently always prompts me to pick the stack from the search results, even if there's only ever 1 stack returned. This looks to be caused by this condition

that is done on purpose, so you don't accidentally execute an action on a wrong stack. This PR however is alright and if you would solve the conflicts, I'm happy to approve it.

that is done on purpose, so you don't accidentally execute an action on a wrong stack.

It would be nice if there was a way to turn that off, either a flag or by setting an environment variable maybe?

This PR however is alright and if you would solve the conflicts, I'm happy to approve it.

Conflicts fixed.

It would be nice if there was a way to turn that off, either a flag or by setting an environment variable maybe?

Feel free to open an issue for that.


Calculated values

What are you guys thinking about calculated values? Something like Behaviors in FRP terms or spreadsheet-like dataflow.
Main question is why do we need them?
Ok - let's explain we have transitions between scenes, and main value for all transition is progress - this is value changed by time for 0 to 1. In scene we want to change text opacity and some position.
const Scene = (width, height, progress) =>
    <View style={{position: "absolute",
                           height: height,
                           width: width,
                           backgroundColor "blue"}}
        <Text style={{fontSize: 16,
                      opacity: progress < 0.5 ? (1 - (progress * 2)) : 0,
                      marginLeft: progress < 0.5 ? (progress * 2 * 250 * -1) : 0}} />
    </View>

But I want to use calculated values, smth like this:
class AnimatedCell {
    constructor() {
        this._subscribers = [];
    }

    push(value) {
       this._subscribers.forEach(next => next.call(null, value));
    }

    subscribe(next) {
       this._subscribers.push(next);
    }
}

const bind = (cell, fn) => {
    const newCell = new AnimatedCell();
    cell.subscribe(val => newCell.push(fn.call(null, val))
}

const bindToAnimated = (cell, initialValue) =>
    const newAnimated = new AnimatedValue(initialValue || 0);
    cell.subscribe(val => newAnimated.setValue(val));
    return newAnimated;
}

And use it in scene:
const Scene = (width, height, progress) => {
    const opacity = bind(progress, progress => progress < 0.5 ? (1 - (progress * 2)) : 0);
    const marginLeft = bind(progress, progress => progress < 0.5 ? (progress * 2 * 250 * -1) : 0);
    return (
    <View style={{position: "absolute",
                           height: height,
                           width: width,
                           backgroundColor "blue"}}
        <Text style={{fontSize: 16,
                          opacity: bindToAnimated(opacity, 1),
                          marginLeft: bindToAnimated(marginLeft, 0)}} />
    </View>
    );
}

Every time when progress is changed, opacity and margin-left recalculated and refresh Animated.
What do you think?

If you guys familiar with ClojureScript you can see it in action on my experiment with react native navigation https://github.com/savelichalex/experiment-navigation-react-native/tree/cell-proposal

I also think being able to implement this bind() more powerful and interesting (myself, I prefer to call it map() but whatever). Also allow to extend Animated in third party library.
I remember we had a discussion here in the past: https://github.com/animatedjs/interactive-docs/issues/5
is this ~ the same topic or am I out of subject?

I think the main problem we evoked, is that it is hard for React Native to implement Animations in an another thread (like in ObjC / Java side) if animated object are not serializable, which is the case when you use newValue = bind(value, aJSFunction) .

Yeah, this is possible to extend in third party library, but maybe we can create smth interesting if it'll be in core. I'm trying to say, that we also can extend behavior of few core methods. For example if Animated.timing get computed value, then they just push on it and update all dependent values.


GeoField widget type

Would love to see that feature! Mainly because I use GeoAlchemy2

Ok  - started to develop it

Started to develop it  - second attempt


TF CNN benchmark resnet-50 not freeing up GPU memory after being terminated

Hi
I've ran some of the cnn benchmarks on an cluster that uses SLURM, on a Nvidia XP. One of my jobs got stuck. When I cancelled the script, it seems not to free up the GPU memory anymore after that.
totalMemory: 11.90GiB freeMemory: 365.94MiB
Now I can't run any more scripts. How can I free up my GPU memory again?
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): cnn_benchmarks
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Cluster node with amd cpu and NVidia XP
TensorFlow installed from (source or binary): source
TensorFlow version (use command below): 1.4
Python version: 2.7
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source): GCC version:  gcc version 4.8.5 20150623 (Red Hat 4.8.5-16) (GCC)
CUDA/cuDNN version: Cuda8, Cudnn 6
GPU model and memory: Nvidia XP
Exact command to reproduce: python tf_cnn_benchmarks.py --model=resnet50 --num_gpus=1 --local_parameter_device=gpu

Source code / logs
2017-11-06 21:45:35.243801: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX FMA
2017-11-06 21:45:35.832723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties:
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:41:00.0
totalMemory: 11.90GiB freeMemory: 365.94MiB
2017-11-06 21:45:35.832780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: TITAN Xp, pci bus id: 0000:41:00.0, compute capability: 6.1)
2017-11-06 21:45:46.271894: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 144.00MiB.  Current allocation summary follows.
2017-11-06 21:45:46.271976: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (256): 	Total Chunks: 18, Chunks in use: 18. 4.5KiB allocated for chunks. 4.5KiB in use in bin. 576B client-requested in use in bin.
2017-11-06 21:45:46.271992: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (512): 	Total Chunks: 2, Chunks in use: 2. 1.5KiB allocated for chunks. 1.5KiB in use in bin. 1.5KiB client-requested in use in bin.
2017-11-06 21:45:46.272005: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (1024): 	Total Chunks: 7, Chunks in use: 7. 9.2KiB allocated for chunks. 9.2KiB in use in bin. 9.0KiB client-requested in use in bin.
2017-11-06 21:45:46.272016: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (2048): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272029: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (4096): 	Total Chunks: 1, Chunks in use: 1. 4.0KiB allocated for chunks. 4.0KiB in use in bin. 3.9KiB client-requested in use in bin.
2017-11-06 21:45:46.272040: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (8192): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272052: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (16384): 	Total Chunks: 2, Chunks in use: 2. 32.0KiB allocated for chunks. 32.0KiB in use in bin. 32.0KiB client-requested in use in bin.
2017-11-06 21:45:46.272063: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (32768): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272076: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (65536): 	Total Chunks: 1, Chunks in use: 1. 90.8KiB allocated for chunks. 90.8KiB in use in bin. 90.8KiB client-requested in use in bin.
2017-11-06 21:45:46.272087: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (131072): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272097: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (262144): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272108: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (524288): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272120: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (1048576): 	Total Chunks: 1, Chunks in use: 1. 1.17MiB allocated for chunks. 1.17MiB in use in bin. 1.17MiB client-requested in use in bin.
2017-11-06 21:45:46.272131: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (2097152): 	Total Chunks: 2, Chunks in use: 2. 5.91MiB allocated for chunks. 5.91MiB in use in bin. 5.91MiB client-requested in use in bin.
2017-11-06 21:45:46.272143: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (4194304): 	Total Chunks: 1, Chunks in use: 1. 5.06MiB allocated for chunks. 5.06MiB in use in bin. 5.06MiB client-requested in use in bin.
2017-11-06 21:45:46.272153: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (8388608): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272164: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272176: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272186: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272197: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (134217728): 	Total Chunks: 1, Chunks in use: 0. 128.66MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272208: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (268435456): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:46.272220: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin for 144.00MiB was 128.00MiB, Chunk State:
2017-11-06 21:45:46.272296: I tensorflow/core/common_runtime/bfc_allocator.cc:649]   Size: 128.66MiB | Requested Size: 0B | in_use: 0, prev:   Size: 1.0KiB | Requested Size: 1.0KiB | in_use: 1
2017-11-06 21:45:46.272309: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400000 of size 1280
2017-11-06 21:45:46.272318: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400500 of size 256
2017-11-06 21:45:46.272326: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400600 of size 256
2017-11-06 21:45:46.272341: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400700 of size 256
2017-11-06 21:45:46.272350: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400800 of size 256
2017-11-06 21:45:46.272358: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400900 of size 256
2017-11-06 21:45:46.272368: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400a00 of size 768
2017-11-06 21:45:46.272376: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400d00 of size 256
2017-11-06 21:45:46.272385: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400e00 of size 256
2017-11-06 21:45:46.272393: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400f00 of size 1536
2017-11-06 21:45:46.272402: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401500 of size 256
2017-11-06 21:45:46.272411: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401600 of size 256
2017-11-06 21:45:46.272420: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401700 of size 1536
2017-11-06 21:45:46.272428: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401d00 of size 256
2017-11-06 21:45:46.272438: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401e00 of size 256
2017-11-06 21:45:46.272447: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401f00 of size 1024
2017-11-06 21:45:46.272455: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e402300 of size 256
2017-11-06 21:45:46.272465: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e402400 of size 256
2017-11-06 21:45:46.272474: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e402500 of size 16384
2017-11-06 21:45:46.272482: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e406500 of size 256
2017-11-06 21:45:46.272491: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e406600 of size 256
2017-11-06 21:45:46.272499: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e406700 of size 16384
2017-11-06 21:45:46.272507: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e40a700 of size 256
2017-11-06 21:45:46.272516: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e40a800 of size 256
2017-11-06 21:45:46.272528: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e40a900 of size 4096
2017-11-06 21:45:46.272537: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e40b900 of size 92928
2017-11-06 21:45:46.272546: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e422400 of size 256
2017-11-06 21:45:46.272554: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e422500 of size 1228800
2017-11-06 21:45:46.272562: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e54e500 of size 768
2017-11-06 21:45:46.272571: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e54e800 of size 2654208
2017-11-06 21:45:46.272580: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e7d6800 of size 1536
2017-11-06 21:45:46.272589: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e7d6e00 of size 5308416
2017-11-06 21:45:46.272597: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020ece6e00 of size 1536
2017-11-06 21:45:46.272606: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020ece7400 of size 3538944
2017-11-06 21:45:46.272614: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020f047400 of size 1024
2017-11-06 21:45:46.272623: I tensorflow/core/common_runtime/bfc_allocator.cc:670] Free at 0x1020f047800 of size 134907904
2017-11-06 21:45:46.272631: I tensorflow/core/common_runtime/bfc_allocator.cc:676]      Summary of in-use Chunks by size:
2017-11-06 21:45:46.272645: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 18 Chunks of size 256 totalling 4.5KiB
2017-11-06 21:45:46.272655: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 2 Chunks of size 768 totalling 1.5KiB
2017-11-06 21:45:46.272665: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 2 Chunks of size 1024 totalling 2.0KiB
2017-11-06 21:45:46.272675: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 1280 totalling 1.2KiB
2017-11-06 21:45:46.272686: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 4 Chunks of size 1536 totalling 6.0KiB
2017-11-06 21:45:46.272695: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 4096 totalling 4.0KiB
2017-11-06 21:45:46.272706: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 2 Chunks of size 16384 totalling 32.0KiB
2017-11-06 21:45:46.272716: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 92928 totalling 90.8KiB
2017-11-06 21:45:46.272725: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 1228800 totalling 1.17MiB
2017-11-06 21:45:46.272735: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 2654208 totalling 2.53MiB
2017-11-06 21:45:46.272749: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 3538944 totalling 3.38MiB
2017-11-06 21:45:46.272760: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 5308416 totalling 5.06MiB
2017-11-06 21:45:46.272772: I tensorflow/core/common_runtime/bfc_allocator.cc:683] Sum Total of in-use chunks: 12.28MiB
2017-11-06 21:45:46.272786: I tensorflow/core/common_runtime/bfc_allocator.cc:685] Stats:
Limit:                   147783680
InUse:                    12875776
MaxInUse:                 12875776
NumAllocs:                      35
MaxAllocSize:              5308416
2017-11-06 21:45:46.272799: W tensorflow/core/common_runtime/bfc_allocator.cc:277] *********___________________________________________________________________________________________
2017-11-06 21:45:46.272823: W tensorflow/core/framework/op_kernel.cc:1192] Resource exhausted: OOM when allocating tensor with shape[9216,4096]
2017-11-06 21:45:56.273596: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 64.00MiB.  Current allocation summary follows.
2017-11-06 21:45:56.273635: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (256): 	Total Chunks: 18, Chunks in use: 18. 4.5KiB allocated for chunks. 4.5KiB in use in bin. 576B client-requested in use in bin.
2017-11-06 21:45:56.273649: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (512): 	Total Chunks: 2, Chunks in use: 2. 1.5KiB allocated for chunks. 1.5KiB in use in bin. 1.5KiB client-requested in use in bin.
2017-11-06 21:45:56.273661: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (1024): 	Total Chunks: 7, Chunks in use: 7. 9.2KiB allocated for chunks. 9.2KiB in use in bin. 9.0KiB client-requested in use in bin.
2017-11-06 21:45:56.273672: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (2048): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273684: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (4096): 	Total Chunks: 2, Chunks in use: 2. 8.0KiB allocated for chunks. 8.0KiB in use in bin. 7.8KiB client-requested in use in bin.
2017-11-06 21:45:56.273695: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (8192): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273707: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (16384): 	Total Chunks: 4, Chunks in use: 4. 64.0KiB allocated for chunks. 64.0KiB in use in bin. 64.0KiB client-requested in use in bin.
2017-11-06 21:45:56.273718: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (32768): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273731: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (65536): 	Total Chunks: 2, Chunks in use: 1. 181.5KiB allocated for chunks. 90.8KiB in use in bin. 90.8KiB client-requested in use in bin.
2017-11-06 21:45:56.273742: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (131072): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273754: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (262144): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273765: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (524288): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273777: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (1048576): 	Total Chunks: 2, Chunks in use: 1. 2.34MiB allocated for chunks. 1.17MiB in use in bin. 1.17MiB client-requested in use in bin.
2017-11-06 21:45:56.273788: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (2097152): 	Total Chunks: 3, Chunks in use: 1. 8.44MiB allocated for chunks. 2.53MiB in use in bin. 2.53MiB client-requested in use in bin.
2017-11-06 21:45:56.273800: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (4194304): 	Total Chunks: 2, Chunks in use: 2. 10.12MiB allocated for chunks. 10.12MiB in use in bin. 8.44MiB client-requested in use in bin.
2017-11-06 21:45:56.273813: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (8388608): 	Total Chunks: 1, Chunks in use: 1. 15.64MiB allocated for chunks. 15.64MiB in use in bin. 15.64MiB client-requested in use in bin.
2017-11-06 21:45:56.273823: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (16777216): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273834: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (33554432): 	Total Chunks: 1, Chunks in use: 0. 40.13MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273852: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (67108864): 	Total Chunks: 1, Chunks in use: 1. 64.00MiB allocated for chunks. 64.00MiB in use in bin. 64.00MiB client-requested in use in bin.
2017-11-06 21:45:56.273863: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (134217728): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273874: I tensorflow/core/common_runtime/bfc_allocator.cc:627] Bin (268435456): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2017-11-06 21:45:56.273884: I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin for 64.00MiB was 64.00MiB, Chunk State:
2017-11-06 21:45:56.273895: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400000 of size 1280
2017-11-06 21:45:56.273904: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400500 of size 256
2017-11-06 21:45:56.273913: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400600 of size 256
2017-11-06 21:45:56.273921: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400700 of size 256
2017-11-06 21:45:56.273929: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400800 of size 256
2017-11-06 21:45:56.273937: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400900 of size 256
2017-11-06 21:45:56.273946: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400a00 of size 768
2017-11-06 21:45:56.273955: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400d00 of size 256
2017-11-06 21:45:56.273963: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400e00 of size 256
2017-11-06 21:45:56.273972: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e400f00 of size 1536
2017-11-06 21:45:56.273981: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401500 of size 256
2017-11-06 21:45:56.273989: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401600 of size 256
2017-11-06 21:45:56.273997: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401700 of size 1536
2017-11-06 21:45:56.274005: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401d00 of size 256
2017-11-06 21:45:56.274014: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401e00 of size 256
2017-11-06 21:45:56.274029: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e401f00 of size 1024
2017-11-06 21:45:56.274038: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e402300 of size 256
2017-11-06 21:45:56.274046: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e402400 of size 256
2017-11-06 21:45:56.274056: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e402500 of size 16384
2017-11-06 21:45:56.274065: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e406500 of size 256
2017-11-06 21:45:56.274074: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e406600 of size 256
2017-11-06 21:45:56.274084: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e406700 of size 16384
2017-11-06 21:45:56.274093: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e40a700 of size 256
2017-11-06 21:45:56.274101: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e40a800 of size 256
2017-11-06 21:45:56.274109: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e40a900 of size 4096
2017-11-06 21:45:56.274117: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e422400 of size 256
2017-11-06 21:45:56.274126: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e54e500 of size 768
2017-11-06 21:45:56.274138: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e7d6800 of size 1536
2017-11-06 21:45:56.274149: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020e7d6e00 of size 5308416
2017-11-06 21:45:56.274157: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020ece6e00 of size 1536
2017-11-06 21:45:56.274166: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020f047400 of size 1024
2017-11-06 21:45:56.274174: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020f047800 of size 16384
2017-11-06 21:45:56.274182: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1020f04b800 of size 67108864
2017-11-06 21:45:56.274191: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1021304b800 of size 16384
2017-11-06 21:45:56.274199: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1021304f800 of size 16400384
2017-11-06 21:45:56.274207: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x10213ff3800 of size 4096
2017-11-06 21:45:56.274216: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x10213ff4800 of size 92928
2017-11-06 21:45:56.274225: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x1021400b300 of size 1228800
2017-11-06 21:45:56.274299: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x10214137300 of size 2654208
2017-11-06 21:45:56.274308: I tensorflow/core/common_runtime/bfc_allocator.cc:661] Chunk at 0x102143bf300 of size 5308416
2017-11-06 21:45:56.274317: I tensorflow/core/common_runtime/bfc_allocator.cc:670] Free at 0x1020e40b900 of size 92928
2017-11-06 21:45:56.274325: I tensorflow/core/common_runtime/bfc_allocator.cc:670] Free at 0x1020e422500 of size 1228800
2017-11-06 21:45:56.274333: I tensorflow/core/common_runtime/bfc_allocator.cc:670] Free at 0x1020e54e800 of size 2654208
2017-11-06 21:45:56.274342: I tensorflow/core/common_runtime/bfc_allocator.cc:670] Free at 0x1020ece7400 of size 3538944
2017-11-06 21:45:56.274351: I tensorflow/core/common_runtime/bfc_allocator.cc:670] Free at 0x102148cf300 of size 42077440
2017-11-06 21:45:56.274359: I tensorflow/core/common_runtime/bfc_allocator.cc:676]      Summary of in-use Chunks by size:
2017-11-06 21:45:56.274369: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 18 Chunks of size 256 totalling 4.5KiB
2017-11-06 21:45:56.274381: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 2 Chunks of size 768 totalling 1.5KiB
2017-11-06 21:45:56.274391: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 2 Chunks of size 1024 totalling 2.0KiB
2017-11-06 21:45:56.274401: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 1280 totalling 1.2KiB
2017-11-06 21:45:56.274412: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 4 Chunks of size 1536 totalling 6.0KiB
2017-11-06 21:45:56.274422: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 2 Chunks of size 4096 totalling 8.0KiB
2017-11-06 21:45:56.274433: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 4 Chunks of size 16384 totalling 64.0KiB
2017-11-06 21:45:56.274443: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 92928 totalling 90.8KiB
2017-11-06 21:45:56.274454: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 1228800 totalling 1.17MiB
2017-11-06 21:45:56.274463: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 2654208 totalling 2.53MiB
2017-11-06 21:45:56.274474: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 2 Chunks of size 5308416 totalling 10.12MiB
2017-11-06 21:45:56.274489: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 16400384 totalling 15.64MiB
2017-11-06 21:45:56.274500: I tensorflow/core/common_runtime/bfc_allocator.cc:679] 1 Chunks of size 67108864 totalling 64.00MiB
2017-11-06 21:45:56.274510: I tensorflow/core/common_runtime/bfc_allocator.cc:683] Sum Total of in-use chunks: 93.64MiB
2017-11-06 21:45:56.274522: I tensorflow/core/common_runtime/bfc_allocator.cc:685] Stats:
Limit:                   147783680
InUse:                    98191360
MaxInUse:                101730304
NumAllocs:                      45
MaxAllocSize:             67108864
2017-11-06 21:45:56.274536: W tensorflow/core/common_runtime/bfc_allocator.cc:277] ********************************************************************____________________________
2017-11-06 21:45:56.274550: W tensorflow/core/framework/op_kernel.cc:1192] Resource exhausted: OOM when allocating tensor with shape[4096,4096]
TensorFlow:  1.4
Model:       alexnet
Mode:        training
SingleSess:  False
Batch size:  32 global
32 per device
Devices:     ['/gpu:0']
Data format: NCHW
Optimizer:   sgd
Variables:   parameter_server
Generating model
Traceback (most recent call last):
File "tf_cnn_benchmarks.py", line 46, in
tf.app.run()
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "tf_cnn_benchmarks.py", line 42, in main
bench.run()
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", line 892, in run
return self._benchmark_cnn()
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", line 1068, in _benchmark_cnn
start_standard_services=start_standard_services) as sess:
File "/usr/lib64/python2.7/contextlib.py", line 17, in enter
return self.gen.next()
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 964, in managed_session
self.stop(close_summary_writer=close_summary_writer)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 792, in stop
stop_grace_period_secs=self._stop_grace_secs)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 389, in join
six.reraise(*self._exc_info_to_raise)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 953, in managed_session
start_standard_services=start_standard_services)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/supervisor.py", line 708, in prepare_or_wait_for_session
init_feed_dict=self._init_feed_dict, init_fn=self._init_fn)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/training/session_manager.py", line 279, in prepare_session
sess.run(init_op, feed_dict=init_feed_dict)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 889, in run
run_metadata_ptr)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1317, in _do_run
options, run_metadata)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[9216,4096]
[[Node: v/cg/affine0/weights/Initializer/truncated_normal/TruncatedNormal = TruncatedNormalT=DT_INT32, _class=["loc:@v/cg/affine0/weights"], dtype=DT_FLOAT, seed=1234, seed2=149, _device="/job:localhost/replica:0/task:0/device:GPU:0"]]
Caused by op u'v/cg/affine0/weights/Initializer/truncated_normal/TruncatedNormal', defined at:
File "tf_cnn_benchmarks.py", line 46, in
tf.app.run()
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "tf_cnn_benchmarks.py", line 42, in main
bench.run()
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", line 892, in run
return self._benchmark_cnn()
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", line 986, in _benchmark_cnn
(image_producer_ops, enqueue_ops, fetches) = self._build_model()
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", line 1236, in _build_model
gpu_compute_stage_ops, gpu_grad_stage_ops)
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", line 1524, in add_forward_pass_and_gradients
self.model.add_inference(network)
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/models/alexnet_model.py", line 45, in add_inference
cnn.affine(4096)
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/convnet_builder.py", line 311, in affine
initializer=tf.truncated_normal_initializer(stddev=stddev))
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/convnet_builder.py", line 117, in get_variable
var = tf.get_variable(name, shape, dtype, *args, **kwargs)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 1203, in get_variable
constraint=constraint)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 1092, in get_variable
constraint=constraint)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 417, in get_variable
return custom_getter(**custom_getter_kwargs)
File "/home/kklein/benchmarks/scripts/tf_cnn_benchmarks/convnet_builder.py", line 86, in inner_custom_getter
var = getter(*args, **kwargs)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 394, in _true_getter
use_resource=use_resource, constraint=constraint)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 805, in _get_single_variable
constraint=constraint)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 213, in init
constraint=constraint)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 303, in _init_from_args
initial_value(), name="initial_value", dtype=dtype)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 779, in
shape.as_list(), dtype=dtype, partition_info=partition_info)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/init_ops.py", line 309, in call
shape, self.mean, self.stddev, dtype, seed=self.seed)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/random_ops.py", line 172, in truncated_normal
shape_tensor, dtype, seed=seed1, seed2=seed2)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/gen_random_ops.py", line 588, in _truncated_normal
name=name)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/home/kklein/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1470, in init
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[9216,4096]
[[Node: v/cg/affine0/weights/Initializer/truncated_normal/TruncatedNormal = TruncatedNormalT=DT_INT32, _class=["loc:@v/cg/affine0/weights"], dtype=DT_FLOAT, seed=1234, seed2=149, _device="/job:localhost/replica:0/task:0/device:GPU:0"]]
srun: error: gpu08: task 0: Exited with exit code 1

Is there anything else running on the same XP gpu?
To free up GPU memory, you can try approaches recommended on stackoverflow, for example:
https://stackoverflow.com/questions/15197286/how-can-i-flush-gpu-memory-using-cuda-physical-reset-is-unavailable

The admin had to relaunch the node on the cluster. My script turned into a zombie a threw the gpu off the bus :dagger:
I'm now running my stuff again.
Thanks

If there is no process running on the GPU but memory is not freed, this is a known bug of nvidia driver.

It has been 14 days with no activity and this issue has an assignee.Please update the label and/or status accordingly.

Nagging Assignee: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @bignamehyp: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Close this issue due to inactivity.


Stop multiple modules on Box.Application.stop

Developer can try stopping multiple modules on stop method:
Stopping the specific modules :
App.stop(['testModule1', 'testModule1']);

or
App.stopOnly(['testModule1', 'testModule1'])

What do you think?
Thanks.

Closing for now, we will try to keep the interface as straight forward as possible.


Fixed logic in LargestTwinPrime operator

LargestTwinPrime should check if current twin prime is largest only among other twin primes not just primes. In a set 1..23 largest twin prime is 19, but error in definition leads to situation that there is no largest twin prime, because the largest twin prime 19 is checked with prime 23 and is thrown away and the next prime - 23 is not twin prime.

Thanks for catching this! Will merge after I check how it renders: slashes aren't handled properly in {{% %}} shortcodes, so I might have to switch them to {{< >}} first.

Thank you for such a good site!

@hwayne I also see another bugs - what should I do with them? Create a separate pull request for each?

That works; if it'd be easier for you feel free to just list the bugs in an
issue and I'll fix them all tomorrow. Slated the afternoon for learntla
work and will get this pr (and other prs and fixes) in!
On Wed, Jun 28, 2017, 3:49 PM ka4eli<EMAIL_ADDRESS>wrote:

@hwayne https://github.com/hwayne I also see another bugs - what should
I do with them? Create a separate pull request for each?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/hwayne/learntla/pull/18#issuecomment-311785484, or mute
the thread
https://github.com/notifications/unsubscribe-auth/ACiXdLwmnNCwXxHUNTP-QXWiGJgXf9QSks5sIrw6gaJpZM4OF9pF
.

FullTower[n \in 1..TSIZE] == n \* <<1, 2, 3, ...>>
Board[n \in 1..TSPACES] == IF n = 1 THEN FullTower ELSE <<>>

(* --algorithm hanoi
variables tower = Board;

define
  D == DOMAIN tower
end define;

begin
while TRUE do
  assert tower[TSPACES] /= <<1, 2, 3>>;
  \* rest is the same

should be, I think
CONSTANTS TSIZE, TSPACES

FullTower[n \in 1..TSIZE] == n \* <<1, 2, 3, ...>>
Board[n \in 1..TSPACES] == IF n = 1 THEN FullTower ELSE <<>>

(* --algorithm hanoi
variables tower = Board;

define
  D == DOMAIN tower
end define;

begin
while TRUE do
  assert tower[TSPACES] /= FullTower;
  \* rest is the same

I think it'll be easier to merge this in, check the server rendering is correct, then make any adjustments. So looks good!


Chrome it is not working

This is working in IE but in chrome it is not working.
Also will this work for any pages apart from list/documents?

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 5a9a92b8-9e9e-e314-d549-c8e39b6bc68b
Version Independent ID: 9a905f1b-4aa8-a72d-efbd-9022bf7e331f
Content: Build your first SharePoint Framework Extension (Hello World part 1)
Content Source: docs/spfx/extensions/get-started/build-a-hello-world-extension.md
Service: unspecified
GitHub Login: @spdevdocs
Microsoft Alias: spdevdocs

Verified it again and it is working. Closing this issue.

I am experiencing this issue as well, I am unable to load this test extension in Chrome or Firefox (Mac user so I do not have IE/Edge).  When I use the URL Below (Tennant and sub site modified) the site does not even hit the local server.  I have verified I can load localhost:4321 in the browser and I see the request logs in the console.  I have not modified the boilerplate extension.  I am unable to prevent URL encoding, when I review the url in browser I see quotes and spaces, when I copy the url I get what you see below (Encoded with %22's and %20's)
Note: I have already installed dev cert by running gulp trust-dev-cert.
Url I get when I copy from the browser URL Field: (Encoded)
https://Tennant.sharepoint.com/Subsite/Lists/TestList/AllItems.aspx?loadSPFX="true"&debugManifestsFile=https://localhost:4321/temp/manifests.js&customActions={"19ac98d4-405e-426b-9774-3fec0accf376":{"location":"ClientSideExtension.ApplicationCustomizer","properties":{"testMessage":"Hello as property!"}}}
What url I am providing my browser: (Not Encoded)
https://Tennant.sharepoint.com/Subsite/Lists/TestList/AllItems.aspx?loadSPFX="true"&debugManifestsFile=https://localhost:4321/temp/manifests.js&customActions={"19ac98d4-405e-426b-9774-3fec0accf376":{"location":"ClientSideExtension.ApplicationCustomizer","properties":{"testMessage":"Hello as property!"}}}

you guys most probably have self signed ssl certs, open the https://localhost:4321/temp/manifests.js in new browser tab, you get warning about certificate, accept it, reload sharepoint page with the debugging url  and voila..

FYI for others - my first mistake was not creating the myPage.aspx file in the target site.  The page referenced in the serve.json file needs to exist in order for the demo to work.

This still isn't working for me on Chrome or Firefox. When I run gulp serve, it opens the correct page with the debug query parameters all set, but I am never prompted to allow localhost debugging. No requests are made to localhost at all. I did what @michalhainc suggested, accepted the warning about the cert, and still nothing. Any help would be greatly appreciated! Thanks :)


API is down (500 Internal Server Error)

Hello! The API was working just fine a couple of hours ago, but now the root of the API gives:
{"error":{"code":404,"message":"Not Found"}}
Attempts to access other endpoints all resulted in:
{"error":{"code":500,"message":"Internal Server Error"}}

Hello,
The api should be available now. The server had some issues and a reboot fixed the problems.
Best Regards,
Benoît Frisch


dataset link / citation

Hi, I work with the TCIA team that is hosting your dataset.  In the README.md for the "TCIA Link" you are pointing to a URL that is going to stop working in the next couple of months (wiki.cancerimagingarchive.net).  The DOI URL in your dataset citation will continue to work even after our migrate to our new data management system.
Wang, Y., Duggar, W.N., Caballero, D.M., Vengaloor Thomas, T., Adari, N., Mundra, E.K., Wang, H. (2023) Brain Tumor Recurrence Prediction after Gamma Knife Radiotherapy from MRI and Related DICOM-RT: An Open Annotated Dataset and Baseline Algorithm (Brain-TR-GammaKnife) [Dataset]. The Cancer Imaging Archive. DOI:  https://doi.org/10.7937/xb6d-py67
Also, you might want to leverage https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_Linux_Data_Retriever_App.ipynb or https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_REST_API_Downloads.ipynb as a way to help people download the data without leaving the command line and verify that your code still works based on the way that the directories are named.

Thank you for reaching out. We'll update these based on the feedback.


Use a Pipenv to manage dependencies

This allows anyone to run pipenv install and does not interfere with package managers or other pip dependencies.
The traditional pip install -r requirements.txt stays available but pypa.io seems to be moving away from package installation without a virtual python environment.

Thanks.


Updated footer to update with filepath change

Closes #109

Coverage decreased (-0.3%) to 86.491% when pulling 5647c7f9cf96d9cb3c1fcf9ea93bc0fece620257 on debug_save_footer into 335c78e5806be4a221a6324b546731a9b44d8988 on V0.4-MainBranch.

Coverage decreased (-0.3%) to 86.491% when pulling 414d1d318640ecef5961ee84de533481957b9499 on debug_save_footer into 335c78e5806be4a221a6324b546731a9b44d8988 on V0.4-MainBranch.


Add note about npm warnings for IP1

Feedback welcome on the wording.
Give me a few minutes and I'll add a screenshot.

Updated the handout code (to not require npm 9), both the zip and on https://github.com/neu-se/covey.town-private/pull/5.
Might be a good idea for someone on Windows or Mac to test the updated zip.

If students will still need to use NPM 9 for IP2 (which will include the frontend components) - I would prefer to stick with NPM 9 for both - could you update the PR to just include a note about this, rather than a suggestion to use NPM 10?

Okay, so I redid this PR. I did not update the handout code (since there may be issues with IP2, which I will now go investigate), but I added a note and screenshot about the version warnings and vulnerability warnings.
So this is now ready for review/merge.