feat(Modal): ✨ New component

Resolves #587

Without going into to much the small stuff, is not possible to make this component a bit more similar to our other components?
If we utilise react context we can save some prop drilling (header), encourage component composability and be predictable.
I think least Card , Accordion, Modal and parts of Popover should share the same component anatomy when it comes to sub-components.
Example:
<Modal>
  <Modal.Header />
  <Divider/>
  <Modal.Content />
  <Divider />
  <Modal.Footer />
</Modal>

In addition, could we also not use the anchoEl and open pattern we have for Popover and planned Dropdown for this component?

I would advise against it.
A model does not need an anchorEl, and the function that are in use are native - meaning this component should be fully SSR friendly, if you want to open it without clickevents. Though, I have not tested if this is the case.

Without going into to much the small stuff, is not possible to make this component a bit more similar to our other components?
If we utilise react context we can save some prop drilling (header), encourage component composability and be predictable.
I think least Card , Accordion, Modal and parts of Popover should share the same component anatomy when it comes to sub-components.
Example:
<Modal>
  <Modal.Header />
  <Divider/>
  <Modal.Content />
  <Divider />
  <Modal.Footer />
</Modal>

In addition, could we also not use the anchoEl and open pattern we have for Popover and planned Dropdown for this component?

I would advise against it. A modal does not need an anchorEl, and the function that are in use are native - meaning this component should be fully SSR friendly, if you want to open it without clickevents. Though, I have not tested if this is the case. If you have dividers on the footer and header, it should change the padding as well.

Thanks god it was friday, I was thinking more in the lines of open (silly anchorEl, shush) 🙈 🤪

So using the useRef outside and having users call showModal is to support SSR an open Modal?

Was thinking this could be done with an open prop on Modal and calling this internally. This would make the component more uniform with our other components, as this is suddenly a very different way to open, compared to our other components 🤔

When it comes to context and SSR, I was thinking this is already and interactive component since its only visible once a user interacts with a trigger to open it. Using a context inside Modal and flagging it with "use client" shouldn't be much of a change then...

I guess one edge-case as you mention will be to SSR and already opened Modal, but is that an actual use-case? useMediaQuery might also cause a layout shift in this case?
We are not in a rush so suggest we test the different approaches here using NextJS and SSR/RSC?

Without going into to much the small stuff, is not possible to make this component a bit more similar to our other components?
If we utilise react context we can save some prop drilling (header), encourage component composability and be predictable.
I think least Card , Accordion, Modal and parts of Popover should share the same component anatomy when it comes to sub-components.
Example:
<Modal>
  <Modal.Header />
  <Divider/>
  <Modal.Content />
  <Divider />
  <Modal.Footer />
</Modal>

In addition, could we also not use the anchoEl and open pattern we have for Popover and planned Dropdown for this component?

I would advise against it. A modal does not need an anchorEl, and the function that are in use are native - meaning this component should be fully SSR friendly, if you want to open it without clickevents. Though, I have not tested if this is the case. If you have dividers on the footer and header, it should change the padding as well.

Thanks god it was friday, I was thinking more in the lines of open (silly anchorEl, shush) 🙈 🤪

So using the useRef outside and having users call showModal is to support SSR an open Modal?

Was thinking this could be done with an open prop on Modal and calling this internally. This would make the component more uniform with our other components, as this is suddenly a very different way to open, compared to our other components 🤔

When it comes to context and SSR, I was thinking this is already and interactive component since its only visible once a user interacts with a trigger to open it. Using a context inside Modal and flagging it with "use client" shouldn't be much of a change then...

I guess one edge-case as you mention will be to SSR and already opened Modal, but is that an actual use-case? useMediaQuery might also cause a layout shift in this case?
We are not in a rush so suggest we test the different approaches here using NextJS and SSR/RSC?

You can still pass an open prop to the modal, since it is a native prop.
However, since we are using the native dialog element, you also get to use the native functions. Yes, it is different than our other components, but our other components, such as Popover, does not use a native element - meaning we have to attach functions ourselves.
We could handle this internally, but I don't see why we should? Since the user gets access to the ref anyways, they can call the native functions and potentially break the internal state if we base it off the open prop (which also has an effect on the element).

As discussed on Slack, we'll have a session today experimenting with modal stuff.

Quick summary of our session.

We'll keep the open approach of having to use ref and native showModal()
Add better description for why this approach was chosen due to state being managed by native dialog element and we don't want to mess with that :)
We'll use React.Context (share close callback) to enable Modal.Header composability instead of props.
Inspired by Radix's onInterationOutside we'll adopt that instead of closeOnBackdropClick.


Update DG for import/export command and Fix bug for import

Updated

User stories
Implementation of Export/Import
SequenceDiagram for Import
Manual Testing for Export/Import
Use Cases for Export/Import
Bug fix for import erasing all data when user closes Trackermon after importing corrupted data

Codecov Report

Merging #200 (14bf1ac) into master (e79f35e) will decrease coverage by 0.16%.
The diff coverage is 0.00%.

@@             Coverage Diff              @@
##             master     #200      +/-   ##
============================================
- Coverage     47.38%   47.21%   -0.17%
  Complexity      368      368
============================================
  Files            91       91
  Lines          1718     1724       +6
  Branches        193      193
============================================
  Hits            814      814
- Misses          836      842       +6
  Partials         68       68

Impacted Files
Coverage Δ

...main/java/seedu/trackermon/logic/LogicManager.java
45.45% <0.00%> (-10.11%)
:arrow_down:

...seedu/trackermon/logic/commands/ExportCommand.java
0.00% <ø> (ø)

...seedu/trackermon/logic/commands/ImportCommand.java
0.00% <ø> (ø)

Continue to review full report at Codecov.

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Is there a graph.pbtxt and checkpoint file available after your imagenet training?

Thanks for your pre-trained model! I'd just like to ask if you have the graph.pbtxt and checkpoint file (not the data file you already uploaded) available as well. Because it seems that when you run checkpoint_file = tf.train.latest_checkpoint(logdir), where logdir is the folder storing the files you uploaded, there is an error of the correct checkpoint not getting found. I believe after training, there should be 5 files included:

model.ckpt.meta
model.ckpt.index
model.ckpt.data-00000-of-00001
checkpoint
graph.pbtxt

the checkpoint file should contain the latest checkpoint information in order for the get_latest_checkpoint function to work.

One can use --checkpoint_path to indicate a specify ckpt, so checkpoint is net necessary.
You can use freeze_graph.py to get the frozen graph (graph.pb).

Using the freeze_graph code you provided, I somehow got this error:
Traceback (most recent call last):
  File "freeze_graph.py", line 58, in <module>
    freeze_graph(args.model_folder)
  File "freeze_graph.py", line 15, in freeze_graph
    input_checkpoint = checkpoint.model_checkpoint_path
AttributeError: 'NoneType' object has no attribute 'model_checkpoint_path'

The error comes from the input_checkpoint and after I manually keyed in input_checkpoint it has worked.
Also, I have tried the same method of obtaining PB graph from that tutorial, but the result seem to vary from using the official freeze graph file obtained from TensorFlow: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py
In one of the StackOverflow questions I asked, it is suggested to use the official freeze_graph tool instead: http://stackoverflow.com/questions/43341970/tensorflow-dramatic-loss-of-accuracy-after-freezing-graph
It is certainly possible to obtain the frozen graph, but I believe one will require the graph.pbtxt and checkpoint file in order to fine-tune from your imagenet model. Would it be possible to upload those two files as well?

@kwotsin You can use these code to get the '.pbtxt'.
graph_filename = "./frozen_model.pb"

with tf.gfile.GFile(graph_filename, "rb") as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.train.write_graph(graph_def, './', 'graph.pbtxt', True)

I'm trying to make the frozen graph work, for now, it seem that we should build another graph that with direct image input op(eg.placeholder), for feeding image to it. Let's do it : )

@Zehaos I have tried your method of freeze graph and gotten a frozen model. I tested the pre-trained model on a cat image and seem to have gotten a very weird output, as it continually outputs 'buckle' (Label 464) for all images.
Did you experience any accuracy issues after freezing your model? Here is the code I used to evaluate your pretrained model:
https://gist.github.com/kwotsin/e7f3b38733e89c5cd4d0256263524173
Can you verify if I used the correct input and output nodes?

I got the same output, 'buckle' (Label 464) for all images on my machine.
It will be useful that you can have a python code which can classify an image with checkpoint.
We can use it to compare with frozen model
Thanks,

@kwotsin @kaishijeng
I also got '464', It seems that Morgan's script is out of date.
I will upload the original 'graph.pbtxt' tomorrow, since my server is no available now, and we can use the official script instead.
Thanks for your issues.

I have upload the original graph.pbtxt and checkpoint files, click here to download.

Hi @Zehaos, thank you so much for your files. The model is working perfectly now with very good accuracy (in comparison to inception v3). This architecture is indeed very good. The frozen size is just 17MB and when quantized, you get 4.5MB in model size but the accuracy remains very good.
Thank you for your files!! Excellent work!!

@kwotsin Good to hear that~

Kwotsin,
Can you share your frozen PB model and test program? I have used

freeze_graph.py to convert the latest ckpt from Zehao  and run the same
test, the result is still weird (always got buckle).
Thanks,
On Mon, May 8, 2017 at 4:07 AM, Zehao Shi<EMAIL_ADDRESS>wrote:

@kwotsin https://github.com/kwotsin Good to hear that~
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Below is my python test code:
import time
from datetime import datetime
import tensorflow as tf
from scipy.misc import imread, imresize
import numpy as np
img = imread("./images/image-1171.jpg")
#img = imread("./images/dog-cat.jpg")
img = imresize(img, (224,224,3))
img = img.astype(np.float32)
img = np.expand_dims(img, 0)
#Define the filename of the frozen graph
graph_filename = "./model/frozen_model.pb"
labels_file = './model/labels.txt'
labels_dict = [line.rstrip('\n') for line in open(labels_file)]
#Create a graph def object to read the graph
with tf.gfile.GFile(graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
#Construct the graph and import the graph from graphdef
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def)
for op in tf.get_default_graph().get_operations():
print str(op.name)
#We define the input and output node we will feed in
input_node = graph.get_tensor_by_name('import/clone_0/fifo_queue_Dequeue:0')
output_node = graph.get_tensor_by_name('import/clone_0/MobileNet/Predictions/Softmax:0')
with tf.Session() as sess:
    start_time = time.time()
    predictions = sess.run(output_node, feed_dict = {input_node: img})
    duration = time.time() - start_time
    print "Time: ", duration
    start_time = time.time()
    predictions = sess.run(output_node, feed_dict = {input_node: img})
    duration = time.time() - start_time
    print "Time: ", duration
    start_time = time.time()
    predictions = sess.run(output_node, feed_dict = {input_node: img})
    duration = time.time() - start_time
    print "Time: ", duration
    start_time = time.time()
    predictions = sess.run(output_node, feed_dict = {input_node: img})
    duration = time.time() - start_time
    print "Time: ", duration
    print predictions
    print len(predictions[0])
    test = predictions[0]

label_predicted = np.argmax(predictions[0])
    top_5_predictions = test.argsort()[::-1]
    print top_5_predictions[0], labels_dict[top_5_predictions[0]], labels_dict[top_5_predictions[0]+1]
    print top_5_predictions[1], labels_dict[top_5_predictions[1]], labels_dict[top_5_predictions[1]+1]
    print top_5_predictions[2], labels_dict[top_5_predictions[2]], labels_dict[top_5_predictions[2]+1]
    print  test[top_5_predictions[0]]
    print  test[top_5_predictions[1]]

@kaishijeng Please follow these steps:

Insert a placeholder op into the original graph.
Use insert_placeholder.py
Use the offical freeze graph script to freeze the new graph
Use freeze_graph.py
Test the frozen graph.
Use test_frozen_graph.py
Please let me know if there is any problem, thanks.

Zehaos,
I am able to to use insert_placeholder.py to convert original graph to the following new files:
checkpoint
graph.pbtxt
with_placeholder.data-00000-of-00001
with_placeholder.index
with_placeholder.meta
Can you provide me an instruction of how to use freeze_graph.py to freeze new graph on Step 2?
Thanks,

@kaishijeng
Run it as below:
python freeze_graph.py \
--input_graph=graph.pbtxt \
--input_checkpoint=with_placeholder \
--output_graph=/tmp/frozen_graph.pb \
--output_node_names=MobileNet/Predictions/Softmax

It works now.
I saw your roadmap which  plans to implement object detection with SSD.
Do you consider to use yolo(darknet) instead of SSD?
yolo-v2 has good accuracy at a faster speed than SSD.
Thanks,
On Tue, May 9, 2017 at 12:16 AM, Zehao Shi<EMAIL_ADDRESS>wrote:

@kaishijeng https://github.com/kaishijeng
Run it as below:
python freeze_graph.py
--input_graph=graph.pbtxt
--input_checkpoint=with_placeholder
--output_graph=/tmp/frozen_graph.pb
--output_node_names=MobileNet/Predictions/Softmax
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I'm interested in both of them.
I have test MobileNet in a yolo-like framework, it works fine.
I will clean the code and make it available soon.

I will wait for your yolo-like framework.
Thanks,
On Tue, May 9, 2017 at 12:52 AM, Zehao Shi<EMAIL_ADDRESS>wrote:

I'm interested in both of them.
I have test MobileNet in a yolo-like framework, it works fine.
I will clean the code and make it available soon.
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Any idea when mobilenet-based yolo-like detection will be released?
Thanks,

@kaishijeng Maybe next week.

Thanks,
On Tue, May 16, 2017 at 1:45 AM, Zehao Shi<EMAIL_ADDRESS>wrote:

@kaishijeng https://github.com/kaishijeng Maybe next week.
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@Zehaos  Very excited to see a yolo/ SSD like framework on mobilenet.

@itsayush Be aware that it is still under debugging.

@Zehaos  no worries. Will wait. Also, i am guessing you are going to allow training on YOLOv2 too. If you do, it would be great if you also provide a way to fold batch norm variables. That makes it it easy to run the model on mobile.

@Zehaos could you kindly tell me how you decided the output node names for running the freeze_graph.py script? I am trying to freeze a Inception model graph and I cannot understand what should be the input for the --output_node_names.

@abhiML I use Softmax activation to do the classification, so I use "MobileNet/Predictions/Softmax". The name is consisted of two name scope: 'MobileNet' & 'Predictions' and the node name 'Softmax'.
Is it clear?

So as far as I understand your final predicitions go through a softmax layer so you are using this. I checked your .pbtxt file, there are 2 instances of  "MobileNet/Predictions/Softmax".
Namely:
node {
  name: "clone_0/MobileNet/Predictions/Softmax".....

and
node {
  name: "clone_1/MobileNet/Predictions/Softmax"....

I checked my own .pbtxt file and there are 3 occurences of "softmax". So I am confused as to which node I should name as my output node.

Okay so I checked my file again and saw there is only one node whose
op: "softmax"

So I am guessing that is my output node.

@abhiML You can try the following code to see what should your output node be:
for op in graph.get_operations():
    print op.values()

This should let you identify the specific tensor name for the output, and you can verify it's the correct output node since it will produce the tensor shape as well.

Nice. Thanks a lot!

Hey @Zehaos have you updated all the code that is needed for Yolo to work on Mobilenet. Looking forward to experimenting with it.

@Zehaos Can you please provide the checkpoint file that you use in the insert_placeholder.py script. Thanks!

@Zehaos Look like the links you provided for insert_placeholder.py and 2 others are missing. Can you update them ?

One can use --checkpoint_path to indicate a specify ckpt, so checkpoint is net necessary.
You can use freeze_graph.py to get the frozen graph (graph.pb).

how to find output_node_name option?


Drag and Drop between Input fields

Selecting text from a redux-form connected input field, and then dragging and dropping to another input field does not currently work.

Do you know what event gets fired on a text drop?

Correct. The event is drop, the handler is onDrop.

They really need to put the "close and comment" button on the opposite side. You're the third person I've seen today make that error (and I do it often).
I don't suppose event.target.value is set when onDrop is triggered, is it? Otherwise you could just say onDrop={fields.myField.onBlur}.

Ha, I know I thought the "close and comment" meant close the preview.
Unfortunately event.target.value is not set.

How frustrating! From what I can tell in Chrome, the drop event has no information whatsoever about the data being dropped in, and it fires an onChange for with whatever was already in the recipient field.
Any ideas?


Azure Kinect Body Tracking SDK is not work well with RTX3080

windows 10,x64
i7-9700KF
32GB
NIVIDA GeForce RTX3080
run the Azure Kinect Body Tracking SDK ,but k4abt_simple_3d_viewer.exe    do not work
but the SDK is work well

related issue #1125

Who can tell me, has this problem been solved and what needs to be updated. I updated to cudn10.1, but it has no effect.
Do I need to move the CUDN   DLL library to bianary replacement of body tracking file ？？


pass requestor explicitly, instead of just using the URL instructor

Coverage decreased (-0.05%) to 85.385% when pulling d82f213fd52e9337d708663da206570728e574db on bug/graderoster-requestor into c4f7e25474c1f8f8387480e97187c08f38a4e59b on develop.


Feat: add option for admin.fetchOffsets to resolve the offsets

Closes #205
Had a stab at issue #205 as I don't think anyone was working on it any more.
Tweaking the admin to resolve the offsets I think was fairly quick, but let me know if you had other ideas for the implementation.
I might have gone a little OTT with some of the tests 🙈. Wanted to include test cases for:
(a) what happens when you fetch/resolve without having reset the consumer group offsets: it should return the consumer's latest offsets rather than the topic's highwatermark. That's where I'm sending two separate message batches and fetching the offsets before the second batch, and
(b) what happens when low watermark is greater than 0. Am simulating this by deleting some messages from the topic. I couldn't find that the kafkajs client implemented deleteRecords yet? (not sure if it's on the roadmap for the future, or a decision to leave it out, or I might have missed it), so I wrote a helper for the test to do it.
Let me know if you want me to simplify any of these.. (also, if you don't have deleteRecords but do want to implement it in the main client I'm happy to look into that as well)

Hi @artysidorenko, thanks for the PR. Adding this flag is something that I wanted for a while but end up forgetting about 😅, so it's definitely something we would like to have. We never had a need for deleteRecords and that's why we don't have it yet, but we would gladly review a PR adding the feature.

Great contribution, thanks a lot! Don't forget to update the type definition for Admin.fetchOffsets in types/index.d.ts

Cool thanks :) I'll have a go at building deleteRecords into the protocols, and made a note to re-use it in here/clean it up once that's done.


Remove throat to remove CommonJS dependency

Is your feature request related to a problem? Please describe.
Most build tools always give a warning for this package because it has a dependency on throat, which is a CommonJS module. The functionality provided by the library is also quite minimal.
Describe the solution you'd like
Just replace throat with normal promise chaining to mimic the queuing.
Describe alternatives you've considered
None.
Additional context
Included a PR for your consideration as well.

closed by #521


[Game Crash] Trying to edit stepstage chart and crash.

Describe the crash
I'm trying to make stepstage chart, but it crashed. Even with the fresh install.
info.txt
log.txt
How can you reproduce the error?

Try to edit Any map (twin)
Crash

Screenshots/Videos
No response
Game Mode
stepstage
Additional Information
stepstage bug report - UPDATE THE ISSUE FORM.

In Free (one-lane) Mode: 123 ▷ YBR
In Basic (twin) Mode: 123456 ▷ YBR|RBY

Fixed in 4.14


Adding dashboard health checks

See https://github.com/threefoldtech/js-sdk/issues/364

there should be a healthcheck for tokenservices along with the stellar health check too

Added


WIP: Added firebase rules testing [#171131781] (DO NOT MERGE)

@knowuh I had to update the rules to comment out the core membership check as it doesn't work when deployed.

Keeping this PR open as a WIP branch for next scheduled work on the WATERS app.

Closing to cleanup PR list.


[BUG] RichHandler: not able to click links that span over multiple lines

Before anything - just wow. Rich is so aesthetically pleasing to use - congrats on that!
Describe the bug
If you have a long url being printed to the console (longer than what can fit in a single line) then you can't easily open the link. You have to mark then whole url, copy it and fix issues around the line breaks.
To Reproduce
You can actually see the issue by simply using rich.print. The "fixing url" part is a bit more cumbersome with the RichHandler since you have other information mixed in there as well.
If I use the native print then it works fine.
Platform
Using both Mac and Linux and both hyper terminal as well as vscode terminal. Using rich 9.6.1

Clickable URLs is something your terminal does automatically. The hard wrapping in the RichHandler will unfortunately break it. You must have very large URLs or a small terminal window if this is happening.
One solution would be for Rich to generate ANSI links for URLS, which would work even if wrapped on to multiple lines. Unfortunately this type of links don't work on all terminals. Doesn't work on Hyper, but it does on iTerm. You can try it yourself with the following:
console.print(url, style=f"link {url}")

I could potentially add an options to do that automatically in RichHandler

Clickable URLs is something your terminal does automatically. The hard wrapping in the RichHandler will unfortunately break it. You must have very large URLs or a small terminal window if this is happening.
One solution would be for Rich to generate ANSI links for URLS, which would work even if wrapped on to multiple lines. Unfortunately this type of links don't work on all terminals. Doesn't work on Hyper, but it does on iTerm. You can try it yourself with the following:
console.print(url, style=f"link {url}")

I could potentially add an options to do that automatically in RichHandler

It is indeed a long URL - OAuth2 Code flow has a lot of query parameters.
I tried your suggestion but it still only takes the first line in both hyper and VSCodes integrated terminal. As expected I suppose.
It's not possible to disable hard-wrapping of links?

It is indeed a long URL - OAuth2 Code flow has a lot of query parameters.
I tried your suggestion but it still only takes the first line in both hyper and VSCodes integrated terminal. As expected I suppose.
It's not possible to disable hard-wrapping of links?

I tried your suggestion but it still only takes the first line in both hyper and VSCodes integrated terminal. As expected I suppose.

Yes, doesn't work on Hyper or vscode. Works on iTerm and gnome terminal. You could ask the Hyper developers to implement ansi links.

It's not possible to disable hard-wrapping of links?

Not in RichHandler which uses a table to render columns.
You could implement logging handler that simply prints the message to the console. You would lose the nice columns, but you could still have color. But at least your links would be clickable.

I tried your suggestion but it still only takes the first line in both hyper and VSCodes integrated terminal. As expected I suppose.

Yes, doesn't work on Hyper or vscode. Works on iTerm and gnome terminal. You could ask the Hyper developers to implement ansi links.

It's not possible to disable hard-wrapping of links?

Not in RichHandler which uses a table to render columns.
You could implement logging handler that simply prints the message to the console. You would lose the nice columns, but you could still have color. But at least your links would be clickable.

You could implement logging handler that simply prints the message to the console. You would lose the nice columns, but you could still have color. But at least your links would be clickable.

But it doesn't work if I just rich.print(url) either while print(url) works. Is that expected?

You could implement logging handler that simply prints the message to the console. You would lose the nice columns, but you could still have color. But at least your links would be clickable.

But it doesn't work if I just rich.print(url) either while print(url) works. Is that expected?

Yes, see the docs on soft wrapping.

Yes, see the docs on soft wrapping.

Ahh my bad. Thanks for your help - I'll close the issue.

Ahh my bad. Thanks for your help - I'll close the issue.


Point/fix health recording bugs

Fix the following bugs

Change wording in the Health Recordings Screen from "Add More Data + " to "Add Health Recording" and "เพิ่มบันทึกใหม่"
Missing analytics. No data returned...
Selected image can't be displayed on health edit screen if the original image is an invalid form.
Can't add data when switching between health records
Can't delete health record on caretaker device

LGTM. On a side note, should we change the translation of the add entry button in view/add health recording page from 'เพิ่มข้อมูล' to 'บันทึกข้อมูล'? My elderly was a bit confused by the translation.

Noted. Will do. Thanks for the suggestion


Add support for Moodle/LTI and email/password-based authentication

Description
This development is a joint work with @Aleridia and @agrn that completed their internship in Toulouse this summer.
This PR adds the following features to learn-ocaml:

the ability to send e-mails for account confirmation & password change/reset (assuming the new option use_passwd is enabled)
the ability to authenticate from a Moodle server (hosted apart, e.g. in one's university) using the so-called LTI protocol, allowing one to sign-up and sign-in very easily (assuming both options use_moodle and use_passwd have been enabled)
the ability to "upgrade" a legacy token-based account to a password-based and/or LTI-based account, while keeping the same underlying /sync architecture based on tokens, which are still "unique IDs" identifying the accounts, albeit login for new accounts and upgraded accounts is specifically performed with an e-mail address (or by a LTI request).

Important notes

This PR depends on #354, so these two PRs #354 and #362 should be merged in a row.
As usual, the *.opam* files (and dune-project) should be bumped (e.g. to 0.13) before the upcoming release, but I did not include this commit in this PR because it would make the PR unmergeable otherwise (due to a conflict with this already-integrated commit).
The server API has been modified/extended since 0.12 as needed by these features, but the /sync folders created with 0.12 are fully compatible, given all the new metadata is stored in /sync/data/, and great care has been put into making the migration smooth from 0.12 to (upcoming) 0.13, whatever is the chosen config:

use_passwd=false, use_moodle=false (backward-compatible choice)
use_passwd=true, use_moodle=false (email/pass auth)
use_passwd=true, use_moodle=true (email/pass auth + Moodle/LTI support)

How to test it?
Deploying a complete dev environment to test this PR is very easy using docker-compose.
First, create a file demo-repository/server_config.json containing:
{
    "use_passwd": true,
    "use_moodle": true
}

then you should just need to run:

cd .../learn-ocaml
git pr 362  # using this git alias
docker-compose up --build
xdg-open http://localhost:8080  # learn-ocaml application
xdg-open http://localhost:1080  # web-based, ephemeral mail user agent
xdg-open http://localhost:9090  # dockerized Moodle server (login=user, passwd=bitnami)

Anyway, note that if you don't want to rely on these features:

The usual ocaml-based deployment is still possible: make && make opaminstall && learn-ocaml --repo=demo-repository
likewise for the standard, single-container Docker deployment;
and static deployment (e.g. using GitHub Pages, without a backend) is still possible since #356
(documentation for #356 should be added though)

Status of the PR and checklist
All points that had been raised in the video meeting / review with @yurug, and subsequently in later tests have been addressed and the current version of the code has been successfully deployed (I hope it'll be further tested thanks to ~180 students in the upcoming days :)
Note that the docker-compose.yml file gathered in this PR only brings a "dev / test" environment.
Regarding the "prod" environment, a configuration template (without Moodle, but with a dockerized mail transfer agent) is available in the following repo: https://github.com/pfitaxel/docker-learn-ocaml
A couple of tasks remain though:

[ ] Rename/Make environment variables FROM_DOMAIN SMTPSERVER, EMAIL documented in Cmdliner
[ ] Add more documentation in the docs folder [mandatory]
[ ] Bump cohttp versions to relax a bit the "upward" (<) constraints [optional, advised by opam maintainers]
[ ] Investigate the perms warning that appeared here;
[ ] Check/fix/update compatibility with the emacs front-end: https://github.com/pfitaxel/learn-ocaml.el

Ideally, I guess that the first task should be performed as part of this PR (I'll have a look ASAP) and the three others could be done likewise, or possibly after the PR merge? just before the 0.13 release anyway… that might occur in October, maybe?
Cc @yurug FYI

Thanks for this PR! I will review it in a couple of days.

FYI @yurug, we deployed the oauth-moodle branch this week, but we experienced one major issue: when many people connect (via Moodle or a password), the server seems to become unresponsive…
The issue certainly comes from token_index.ml or from learnocaml_server.ml and we'll investigate further anyway, but unfortunately this is not straightforward to reproduce - as everything works very fine when there is only one person (the tester!) authenticating at the same time.
So for the moment, we've rolled-back to learn-ocaml 0.12 in prod for our students :-/

Autre remarque sur la résolution de conflit: il y a cette erreur dans le log du CI :
# File "src/server/learnocaml_server.ml", line 1169, characters 48-56:
# Error: Unbound value root_url

@erikmd What is the status of this PR?

FYI @yurug, we deployed the oauth-moodle branch this week, but we experienced one major issue: when many people connect (via Moodle or a password), the server seems to become unresponsive…

corroborates what I wrote in the review :thinking: ; if the scan indeed took minutes, you probably had all the requests trigger concurrent scans before the first one could finish, which would explain the blowup

@erikmd What is the status of this PR? Do you need more feedback?

Thanks @yurug ! so FYI:

the PR based on this branch is not the latest version of the feature (see the branch oauth-moodle-dev)
it had become slightly stalled because there was an important evolution that did require OCaml > 4.08,
but now that we are compatible with OCaml 4.12, I'll be able to work on it soon 👍
so I'll obviously let you know when I need more feedback, so that we can discuss acceptance tests (and I'll refactor the shape of the PR to make it easier to review it, as well as w.r.t. our release-please conventions…)

I see this has no milestone: I think it's among the top most-wanted features and should be prioritised :)

Hi @AltGr,
yes, thanks, this almost-done PR certainly is among the most important ones for teachers' UX.
Regarding the exact milestone, I'm not sure because we should probably discuss the roadmap at the next telco and prioritize w.r.t. the meta-issue https://github.com/ocaml-sf/learn-ocaml/issues/508.
Anyway at first sight, I'd say that 1.1 could integrate small features but technically important ones (e.g., https://github.com/ocaml-sf/learn-ocaml/issues/541 as well as fixes after the 1.0.0 release), and 1.2 could be dedicated to #362.
But we should certainly discuss more


ensure callback is called one time when worker process has an error

this PR ensures when the phantom worker has en error the _executeInWorker callback is only called one time.
a race condition existed before between the timeout check and the worker process callback

You are right. This was probably the intention. I will publish to npm in couple of minutes.

Published.

:+1:


Create .html for chat view

Implement an html file with a basic view for the chat (can be a very shallow version, just to show something through the endpoint). ;-)

done here 9c0f1971579abf56bcf0763a895db955c7f8eb93


Menus aren't available

I'm having trouble getting the menus to load.  I've confirmed that the procedure is running, but the menus are not showing up in the Ribbon.
I've looked all over in the Ribbon customization settings and can't find them there to turn on.
I looked at the Application.CommandBars(1).Controls and I can see VbaDeveloper as one of the controls, but they are not visible.
Any help is appreciated.

I have the same issue.  No menus.

The same problem.

Just got this to work in Excel 2013. Here's what I did:

Followed the instructions in Build.bas.
Made sure to save my version of 'vbaDeveloper.xlam' after testImport() ran successfully. You'll know it works if in the VBA Project Explorer -> VbaDeveloper (vbaDeveloper.xlam) -> Modules folder you see more than just "Build" (ErrorHandling, Formatter, Menu et al.).
Closed and relaunched Excel (in hindsight probably not necessary, but, this is what I did).
From Excel (not the VBA editor) navigated to: File -> Options -> Add-Ins selected "Excel Add-ins" in the drop-down menu at the bottom of the panel labeled "Manage:" and clicked the "Go..." button.
From the Add-Ins dialog (opened by the previous step), clicked the "Browse..." button and selected my "vbaDeveloper.xlam" file from step 1.
At this point, "Vbadeveloper" was now showing in the "Add-Ins available:" list menu back on the Add-Ins dialog. I simply ensured that it was checked, then clicked the "OK" button.
Once again closed and relaunched Excel. At this point I finally had a new "Menu Commands" group visible in the "ADD-INS" ribbon menu with a "VbaDeveloper" drop-down menu.

Good luck. :smile:


Does zectl update /efi/loader/entries/* ?

Hello!
Love this tool! I followed the systemboot guide to configure systemd-bootloader (https://github.com/johnramsden/zectl/blob/master/docs/plugins/systemdboot.md) and most things work as expected, but the 'options' line below doesn't update to point to the new boot environment after using zectl activate <newname>
Should this be updated by the tool or does this need to get manually updated?
cat /efi/loader/entries/org.zectl-default.conf
title     Arch Linux (Surface)
linux     /env/org.zectl-default/vmlinuz-linux-surface
initrd    /env/org.zectl-default/intel-ucode.img
initrd    /env/org.zectl-default/initramfs-linux-surface.img
options   zfs=zroot/ROOT/default rw

Yes, it should be auto-updated. Can you post:
zectl get

Also, to clarify it doesn't modify in-place it creates a new configuration on create.
So when you create:
zectl activate <newname>

You end up with:
<efi root>/loader/entries/org.zectl-<newname>.conf

Upon activating <newname>, <efi root>/loader/loader.conf is modified to look like:
default    org.zectl-<newname>.conf
# Other settings ....

Yes, it should be auto-updated.
Can you post the output from:
zectl get

PROPERTY                   VALUE
org.zectl.systemdboot:efi  /efi
org.zectl:bootloader
org.zectl:bootpoolroot
org.zectl:bootpoolprefix

Ah ok. No i don't get a new loader:
ryan in ﴱ home-cluster in cluster/default/zigbee2mqtt on  master ❯ sudo zectl list
Name          Active  Mountpoint  Creation
base_install  NR      /                 2020-12-22 15:31
swaywm                -                 2020-12-26 10:49
 ryan in ﴱ home-cluster in cluster/default/zigbee2mqtt on  master ❯
sudo zectl create test
 ryan in ﴱ home-cluster in cluster/default/zigbee2mqtt on  master ❯ sudo zectl activate test                                                                                [19:54:44]
 ryan in ﴱ home-cluster in cluster/default/zigbee2mqtt on  master ❯ sudo zectl list                                                                                [19:54:48]
Name          Active  Mountpoint  Creation
base_install  N       /                 2020-12-22 15:31
test          R       -                 2020-12-29 19:54
swaywm                -                 2020-12-26 10:49
 ryan in ﴱ home-cluster in cluster/default/zigbee2mqtt on  master ❯ ls -lh /efi/loader/entries/                                                                                [19:54:51]
total 4.0K
-rwxr-xr-x 1 root root 237 Dec 26 11:59 org.zectl-default.conf*

The issue is that you don't have a bootloader set
Once you have set a bootloader, you should see something similar to the following depending on what you have set:
$ zectl get

PROPERTY                                       VALUE
org.zectl.systemdboot:efi                      /efi
org.zectl:bootloader                           systemdboot
org.zectl:bootpoolroot
org.zectl:bootpoolprefix

@johnramsden -- That was the fix! Thank you! I had assumed that because there was only one bootloader plugin available and that the command wasn't listed out on the systemboot.md walkthrough that it would be a default command and therefore unneeded. Added that option and fixed up all my other configs to point to the current BE and it all works as expected.
Thanks again!


Reuse a single virtualenv for all ansible cases

Installing ansible and molecule inside each scenario/case it is not is a waste of disk, bandwidth and cpu. This change reconfigures all generated cases to use ".tox/ansible" folder, reducing the overall footprint considerably (one venv is >250MB).
This change is marked as major because it has the potential to break some use-cases.

@greg-hellings I seen lots of people doing a pre-run of tox with tox --notest before running tox. This assures that environments are installed serially before being run in parallel. In fact this is also likely to make it much faster because in parallel run pip may endup even compiling the same package multiple times. With serial, it will likely pickup the already build dependency from cache.

It will still cause the race condition of tox deleting/reinstalling the virtualenv when it encounters a new driver. But your PR from last night that appears to just install all the things will make that a moot point.
We should still guard this around discovery of a requirements.txt, as that will still cause a redeploy of the virtualenv.


[UI] Cluster Templates - UI says I must supply aadClientSecret even though is already supplied in my CT

Version: master-head (8/7/19)
What kind of request is this (question/bug/enhancement/feature request):
Bug
Steps to reproduce (least amount of steps as possible):

Create a CT. Pick "Azure" cloud option and provide all four required fields. Do not set any overrides.
Leverage the template to create an Azure RKE cluster.

Result:
The UI says we must supply the addClientSecret, yet this is impossible since it's not an override and it's also already in the CT so I should not have to supply this...

Version: master-head (8/7/19) (latest UI)
This is now fixed and confirmed working. I was able to create my cluster and leverage Azure Storage Class (disk) and bind it to a volume and write to the disk without any issues. This proves that 1) this is now working and 2) I have Azure Cloud access and that this worked correctly from end to end.


Add all available select and drop options to the help menu

can you add all the select and drop options to the help or more-help menu?
for example, the below segsMax (and segsMin) works to filter out unwanted streams but its not listed in the program help

Drop all subtitles less then 2 segments: -ds segsMax=2:for=all --auto-select
Originally posted by @nilaoda in https://github.com/nilaoda/N_m3u8DL-RE/issues/97#issuecomment-1372364043

N_m3u8DL-RE --morehelp select-video
drop-video has the opposite effect.

`>N_m3u8DL-RE.exe --morehelp select-video
More Help:
--select-video
Select video streams by regular expressions. OPTIONS is a colon separated list of:
id=REGEX:lang=REGEX:name=REGEX:codec=REGEX:res=REGEX
frame=REGEX:ch=REGEX:range=REGEX:url=REGEX:for=FOR

for=FOR: Select type. best[number], worst[number], all (Default: best)

Examples:
select best video
-sv best
select 4K+HEVC video
-sv res="3840*":codec=hvc1:for=best`
segsMax and segsMin arent mentioned in this help printout

@ElCap13 You should download the latest version.

N_m3u8DL-RE.exe --version
<IP_ADDRESS>

Well.... i forgot change English and Traditional Chinese help text
https://github.com/nilaoda/N_m3u8DL-RE/blob/7014cba7703b5b0dce869e142a0225640e94f3ec/src/N_m3u8DL-RE.Common/Resource/StaticText.cs#L373-L402

ah ok, that makes sense now.
thanks for a great program!


Update MultiLang.md

OPS Build status updates of commit 91b1410:
:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

windows-iotcore/develop-your-app/MultiLang.md
:white_check_mark:Succeeded

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


Fix bug with "every funk" in private class function

Here's a file that triggers the error.  Say "take every funk" when cursor is in private function at the bottom

import * as vscode from "vscode";
import { join } from "path";
import { COLORS } from "./constants";
import { SymbolColor } from "./constants";

export type DecorationMap = {
  [k in SymbolColor]?: vscode.TextEditorDecorationType;
};

export interface NamedDecoration {
  name: SymbolColor;
  decoration: vscode.TextEditorDecorationType;
}

export default class Decorations {
  decorations: NamedDecoration[];
  decorationMap: DecorationMap;

  constructor() {
    const iconWidth = "1rem";
    const iconPath = join(__dirname, "..", "images", "hat.svg");

    this.decorations = COLORS.map((color) => ({
      name: color,
      decoration: vscode.window.createTextEditorDecorationType({
        // borderStyle: "solid",
        // borderColor: new vscode.ThemeColor(`cursorless.${color}Border`),
        // borderWidth: "2px 0px 0px 0px",
        // borderRadius: "4px",
        // backgroundColor: new vscode.ThemeColor(`cursorless.${color}Background`),
        rangeBehavior: vscode.DecorationRangeBehavior.ClosedClosed,
        after: {
          margin: `-1rem 0 0 -${iconWidth}`,
          width: iconWidth,
          height: iconWidth,
          color: new vscode.ThemeColor(`cursorless.${color}Border`),
          contentIconPath: iconPath,
        },
      }),
    }));

    this.decorationMap = Object.fromEntries(
      this.decorations.map(({ name, decoration }) => [name, decoration])
    );
  }
  private constructWrapperSvg() {
    const svg = `<svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 0 24 24" width="24px" fill="currentColor"><path d="M0 0h24v24H0V0z" fill="none"/><path d="M19 13H5v-2h14v2z"/></svg>`;
    return vscode.Uri.parse(`data:image/svg+xml;utf8,${svg}`);
  }
}

Not sure if related but the following doesn't work:
class TestCase {
  talonCommand!: string;

  extractPrimitiveTargetKeys() {}
}

If you say "take funk" with your cursor in extractPrimitiveTargetKeys, you get an error Cannot read property 'type' of null

All of these things now work for me, so they were probably fixed when @AndreasArvidsson was hacking on our typescript pattern matchers 👍👍


Use Docker tag docbook-xsltng consistently

Fix some typos and make Docker tag consistent with README and in the guide

Thank you!


Training a model with --fp16_run does not work

Logs
2020-05-09 09:07:55.688678: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
2020-05-09 09:07:55.688739: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
2020-05-09 09:07:55.688748: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2020-05-09 09:07:56.202999: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
Using 16-bit float precision.
2020-05-09 09:07:56.205958: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-05-09 09:07:56.226625: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.227180: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:2d:00.0 name: GeForce RTX 2080 SUPER computeCapability: 7.5
coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 462.00GiB/s
2020-05-09 09:07:56.227205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-09 09:07:56.228990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-05-09 09:07:56.230509: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-05-09 09:07:56.230693: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-05-09 09:07:56.232219: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-05-09 09:07:56.232921: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-05-09 09:07:56.235715: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-09 09:07:56.235830: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.236162: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.236420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-05-09 09:07:56.236714: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-05-09 09:07:56.267004: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-05-09 09:07:56.268490: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x60950d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-05-09 09:07:56.268514: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-05-09 09:07:56.369166: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.370172: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x60fa9e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-05-09 09:07:56.370195: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5
2020-05-09 09:07:56.370362: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.371040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:2d:00.0 name: GeForce RTX 2080 SUPER computeCapability: 7.5
coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 462.00GiB/s
2020-05-09 09:07:56.371082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-09 09:07:56.371097: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-05-09 09:07:56.371109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-05-09 09:07:56.371121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-05-09 09:07:56.371133: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-05-09 09:07:56.371144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-05-09 09:07:56.371156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-09 09:07:56.371222: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.371764: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.372356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-05-09 09:07:56.373863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-05-09 09:07:56.373881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-05-09 09:07:56.373890: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-05-09 09:07:56.374008: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.374597: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-09 09:07:56.376374: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4858 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 SUPER, pci bus id: 0000:2d:00.0, compute capability: 7.5)
W0509 09:07:56.762424<PHONE_NUMBER>69088 run_common_voice.py:106] Physical devices cannot be modified after being initialized
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0509 09:07:56.765624<PHONE_NUMBER>69088 mirrored_strategy.py:435] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
WARNING:tensorflow:From /home/carson/Documents/code/other/rnnt-speech-recognition/model.py:57: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
W0509 09:07:57.166774<PHONE_NUMBER>69088 deprecation.py:323] From /home/carson/Documents/code/other/rnnt-speech-recognition/model.py:57: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb70059240>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.167124<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb70059240>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:From /home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py:958: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
W0509 09:07:57.169332<PHONE_NUMBER>69088 deprecation.py:323] From /home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py:958: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb700597f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.229205<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb700597f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb602a64a8>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.382876<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb602a64a8>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb60228c18>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.421375<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb60228c18>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb6019d198>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.460607<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb6019d198>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb601814e0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.556079<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb601814e0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb600cdc50>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.593875<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb600cdc50>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb600b3128>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.632421<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb600b3128>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb4c160080>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.943511<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb4c160080>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb0c15f0f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
W0509 09:07:57.987845<PHONE_NUMBER>69088 rnn_cell_impl.py:904] <tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7efb0c15f0f0>: Note that this cell is not optimized for performance. Please use tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU.
I0509 09:07:58.163432<PHONE_NUMBER>69088 run_common_voice.py:432] Using word-piece encoder with vocab size: 4088
Model: "encoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_1 (InputLayer)         [(None, None, 80)]        0
_________________________________________________________________
rnn (RNN)                    (None, None,<PHONE_NUMBER>
_________________________________________________________________
layer_normalization (LayerNo (None, None, 640)         1280
_________________________________________________________________
rnn_1 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_1 (Layer (None, None, 640)         1280
_________________________________________________________________
time_reduction (TimeReductio (None, None, 1280)        0
_________________________________________________________________
rnn_2 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_2 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_3 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_3 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_4 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_4 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_5 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_5 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_6 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_6 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_7 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_7 (Layer (None, None, 640)         1280
=================================================================
Total params: 95,102,976
Trainable params: 95,102,976
Non-trainable params: 0
_________________________________________________________________
Model: "prediction_network"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input_2 (InputLayer)         [(None, None)]            0
_________________________________________________________________
embedding (Embedding)        (None, None,<PHONE_NUMBER>
_________________________________________________________________
rnn_8 (RNN)                  (None, None,<PHONE_NUMBER>
_________________________________________________________________
layer_normalization_8 (Layer (None, None, 640)         1280
_________________________________________________________________
rnn_9 (RNN)                  (None, None,<PHONE_NUMBER>2
_________________________________________________________________
layer_normalization_9 (Layer (None, None, 640)         1280
=================================================================
Total params: 23,084,544
Trainable params: 23,084,544
Non-trainable params: 0
_________________________________________________________________
Model: "transducer"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
mel_specs (InputLayer)          [(None, None, 80)]   0
__________________________________________________________________________________________________
pred_inp (InputLayer)           [(None, None)]       0
__________________________________________________________________________________________________
encoder (Model)                 (None, None,<PHONE_NUMBER>6    mel_specs[0][0]
__________________________________________________________________________________________________
prediction_network (Model)      (None, None,<PHONE_NUMBER>4    pred_inp[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape (TensorFlowOp [(3,)]               0           prediction_network[1][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1 (TensorFlow [(3,)]               0           encoder[1][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice (Tens [()]                 0           tf_op_layer_Shape[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1 (Te [()]                 0           tf_op_layer_Shape_1[0][0]
__________________________________________________________________________________________________
tf_op_layer_ExpandDims (TensorF [(None, None, 1, 640 0           encoder[1][0]
__________________________________________________________________________________________________
tf_op_layer_stack_inp_enc (Tens [(4,)]               0           tf_op_layer_strided_slice[0][0]
__________________________________________________________________________________________________
tf_op_layer_ExpandDims_1 (Tenso [(None, 1, None, 640 0           prediction_network[1][0]
__________________________________________________________________________________________________
tf_op_layer_stack_pred_out (Ten [(4,)]               0           tf_op_layer_strided_slice_1[0][0]
__________________________________________________________________________________________________
tf_op_layer_Tile (TensorFlowOpL [(None, None, None,  0           tf_op_layer_ExpandDims[0][0]
                                                                 tf_op_layer_stack_inp_enc[0][0]
__________________________________________________________________________________________________
tf_op_layer_Tile_1 (TensorFlowO [(None, None, None,  0           tf_op_layer_ExpandDims_1[0][0]
                                                                 tf_op_layer_stack_pred_out[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, None, None, 1 0           tf_op_layer_Tile[0][0]
                                                                 tf_op_layer_Tile_1[0][0]
__________________________________________________________________________________________________
dense (Dense)                   (None, None, None, 6 819840      concatenate[0][0]
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, None, None, 4 2620408     dense[0][0]
==================================================================================================
Total params: 121,627,768
Trainable params: 121,627,768
Non-trainable params: 0
__________________________________________________________________________________________________
Starting training.
Performing evaluation.
WARNING:tensorflow:AutoGraph could not transform <function warp_rnnt at 0x7efb72f16730> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.
W0509 09:07:59.091631<PHONE_NUMBER>63520 ag_logging.py:146] AutoGraph could not transform <function warp_rnnt at 0x7efb72f16730> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.
INFO:tensorflow:Error reported to Coordinator: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.
Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 468, in _apply_op_helper
    preferred_dtype=default_dtype)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1290, in convert_to_tensor
    (dtype.name, value.dtype.name, value))
ValueError: Tensor conversion requested dtype float32 for Tensor with dtype float16: <tf.Tensor 'transducer/dense_1/BiasAdd:0' shape=(32, 162, 37, 4088) dtype=float16>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 522, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 332, in _call_unconverted
    return f(*args)
  File "<string>", line 81, in warp_rnnt
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 491, in _apply_op_helper
    (prefix, dtypes.as_dtype(input_arg.type).name))
TypeError: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 468, in _apply_op_helper
    preferred_dtype=default_dtype)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1290, in convert_to_tensor
    (dtype.name, value.dtype.name, value))
ValueError: Tensor conversion requested dtype float32 for Tensor with dtype float16: <tf.Tensor 'transducer/dense_1/BiasAdd:0' shape=(32, 162, 37, 4088) dtype=float16>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/training/coordinator.py", line 297, in stop_on_exception
    yield
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 917, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
  File "/tmp/tmpd1pqtii1.py", line 26, in step_fn
    loss = ag__.converted_call(loss_fn, (labels, outputs), dict(spec_lengths=spec_lengths, label_lengths=label_lengths), fscope_1)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 565, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmpv50tjgtx.py", line 29, in tf___loss_fn
    loss = ag__.converted_call(rnnt_loss, (y_pred, y_true, spec_lengths, label_lengths - 1), None, fscope)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 567, in converted_call
    result = converted_f(*effective_args)
  File "/tmp/tmpa6vi89_4.py", line 32, in tf__rnnt_loss
    loss, _ = ag__.converted_call(_warprnnt.warp_rnnt, (acts, labels, input_lengths, label_lengths, blank_label), None, fscope)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 560, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 332, in _call_unconverted
    return f(*args)
  File "<string>", line 81, in warp_rnnt
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 491, in _apply_op_helper
    (prefix, dtypes.as_dtype(input_arg.type).name))
TypeError: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.
I0509 09:07:59.092102<PHONE_NUMBER>63520 coordinator.py:219] Error reported to Coordinator: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.
Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 468, in _apply_op_helper
    preferred_dtype=default_dtype)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1290, in convert_to_tensor
    (dtype.name, value.dtype.name, value))
ValueError: Tensor conversion requested dtype float32 for Tensor with dtype float16: <tf.Tensor 'transducer/dense_1/BiasAdd:0' shape=(32, 162, 37, 4088) dtype=float16>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 522, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 332, in _call_unconverted
    return f(*args)
  File "<string>", line 81, in warp_rnnt
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 491, in _apply_op_helper
    (prefix, dtypes.as_dtype(input_arg.type).name))
TypeError: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 468, in _apply_op_helper
    preferred_dtype=default_dtype)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1290, in convert_to_tensor
    (dtype.name, value.dtype.name, value))
ValueError: Tensor conversion requested dtype float32 for Tensor with dtype float16: <tf.Tensor 'transducer/dense_1/BiasAdd:0' shape=(32, 162, 37, 4088) dtype=float16>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/training/coordinator.py", line 297, in stop_on_exception
    yield
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/distribute/mirrored_strategy.py", line 917, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
  File "/tmp/tmpd1pqtii1.py", line 26, in step_fn
    loss = ag__.converted_call(loss_fn, (labels, outputs), dict(spec_lengths=spec_lengths, label_lengths=label_lengths), fscope_1)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 565, in converted_call
    result = converted_f(*effective_args, **kwargs)
  File "/tmp/tmpv50tjgtx.py", line 29, in tf___loss_fn
    loss = ag__.converted_call(rnnt_loss, (y_pred, y_true, spec_lengths, label_lengths - 1), None, fscope)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 567, in converted_call
    result = converted_f(*effective_args)
  File "/tmp/tmpa6vi89_4.py", line 32, in tf__rnnt_loss
    loss, _ = ag__.converted_call(_warprnnt.warp_rnnt, (acts, labels, input_lengths, label_lengths, blank_label), None, fscope)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 560, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/autograph/impl/api.py", line 332, in _call_unconverted
    return f(*args)
  File "<string>", line 81, in warp_rnnt
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 491, in _apply_op_helper
    (prefix, dtypes.as_dtype(input_arg.type).name))
TypeError: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.
2020-05-09 09:07:59.108461: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
2020-05-09 09:07:59.109964: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
Traceback (most recent call last):
  File "run_common_voice.py", line 537, in <module>
    app.run(main)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/absl/app.py", line 299, in run
    _run_main(main, args)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/absl/app.py", line 250, in _run_main
    sys.exit(main(argv))
  File "run_common_voice.py", line 497, in main
    gpus=gpus)
  File "run_common_voice.py", line 253, in run_training
    train()
  File "run_common_voice.py", line 203, in train
    gpus=gpus)
  File "run_common_voice.py", line 307, in run_evaluate
    loss, metrics_results = eval_step(inputs)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 568, in __call__
    result = self._call(*args, **kwds)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 615, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 497, in _initialize
    *args, **kwds))
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2389, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2703, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2593, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 978, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 439, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 968, in wrapper
    raise e.ag_error_metadata.to_exception(e)
TypeError: in converted code:

    run_common_voice.py:277 step_fn  *
        loss = loss_fn(labels, outputs,
    /home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/distribute/distribute_lib.py:763 experimental_run_v2
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /home/carson/Documents/code/other/rnnt-speech-recognition/utils/loss.py:32 _loss_fn  *
        loss = rnnt_loss(y_pred, y_true,
    /home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/warprnnt_tensorflow-0.1-py3.6-linux-x86_64.egg/warprnnt_tensorflow/__init__.py:32 rnnt_loss  *
        loss, _ = _warprnnt.warp_rnnt(acts, labels, input_lengths,
    <string>:81 warp_rnnt

    /home/carson/Documents/code/other/rnnt-speech-recognition/.env/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py:491 _apply_op_helper
        (prefix, dtypes.as_dtype(input_arg.type).name))

    TypeError: Input 'acts' of 'WarpRNNT' Op has type float16 that does not match expected type of float32.

@dyc3 From what I can tell fp16_run doesn't work right now because the RNNT loss function does not support float16, I am not very good with C/C++ so if someone else would add float16 support to the warp-transducer package then I will gladly merge it.


[OTHER] Invitation to the github community organisation

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Great 👍  Please follow the steps to join our GitHub Org and to get started with the EddieHub community https://github.com/EddieHubCommunity?view_as=public#steps-to-get-started

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Create .env

oops

Make proper commit messages.

I just copied some boilerplate code and was trying to get it working. It now runs at https://openai-quickstart-node-iota.vercel.app/  I chose 2000 tokens because 4096 is the limit, 2000 is a balance between allowing for long responses while still being fast

Please fork the repo to make changes like this.


binary: improve tests for invalid numbers

We should have separate tests for:

alphabetic characters at the beginning of a valid binary number
alphabetic characters at the end of a valid binary number
alphabetic characters in the middle of an otherwise valid binary number
invalid digits (e.g. 2)

If the test suite for binary has test cases that cover these edge cases, this issue can safely be closed.
See https://github.com/exercism/x-common/issues/95

The Scala tests cover all these test cases other than

Invalid digits.

I will add a test case.


Basic Smoke Test

Make sure everything compiles happily.

@ros-pull-request-builder please retest this

Merging with errors intentionally.


RFP - sidebar-diagnostics

Checklist

[x] The package I am requesting does not already exist on https://community.chocolatey.org/packages;
[x] There is no open issue for this package;
[x] The issue title starts with 'RFP - ';
[x] The download URL is public and not locked behind a paywall / login;

Package Details
Software project URL : https://github.com/ArcadeRenegade/SidebarDiagnostics
Direct download URL for the software / installer : https://github.com/ArcadeRenegade/SidebarDiagnostics/releases/download/v3.6.2/Setup.msi
Software summary / short description: A simple sidebar for Windows desktop that displays hardware diagnostic information.

Status: Review
https://community.chocolatey.org/packages/sidebar-diagnostics


Implement PHP symbol search

VSCode Version: 1.4.0
OS Version: OSX 10.11.16

Steps to Reproduce:

Import a magento project
Quick search Mage_Catalog_Model_Product_Api_V2

Hello, I work on magento projects, and I would like to be able to search using class name.
Example : Mage_Catalog_Model_Product_Api_V2
In magento Mage_Catalog_Model_Product_Api_V2 => go to app/code/core/Mage/Catalog/Model/Product/Api/V2.php. So it will be able to find file if I replace "_" by "".
Can we do it by default or set a settings for that? Thx

@sebfie what language is this?
Moving to Martin as owner of symbol search but this could well be for an unsupported language.

It's just php with zend naming convention.
Sébastien fieloux
0698458781
<EMAIL_ADDRESS>http://www.sebfie.com

Le 1 sept. 2016 à 13:34, Benjamin Pasero<EMAIL_ADDRESS>a écrit :
@sebfie what language is this?
Moving to Martin as owner of symbol search but this could well be for an unsupported language.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

What do you call 'Quick search'? Can you add a screenshot?

@sebfie What exactly do you mean by 'Quick search'? Which keyboard shortcut. Can you maybe add a screenshot?

I mean cmd + p
Sébastien fieloux
0698458781
<EMAIL_ADDRESS>http://www.sebfie.com

Le 7 sept. 2016 à 20:55, Martin Aeschlimann<EMAIL_ADDRESS>a écrit :
@sebfie What exactly do you mean by 'Quick search'? Which keyboard shortcut. Can you maybe add a screenshot?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

@sebfie I assume you are using an extension for PHP to be able to find symbols? Is that true?

No I don't, in fact I just want that "underscore" can be ignored from search.
Searching Mage_Catalog_Model_Product_Api_V2  should be equal to MageCatalogModelProductApiV2  which works!

@sebfie that is a PHP symbol and not a file right?

@bpasero That's a file, look on left column.

@sebfie thanks for clarification, to rephrase: You want to open the class name of a PHP file (V2.php).
@aeschli @jrieken please triage accordingly, this is about opening a symbol from a PHP file, ignoring underscores.

Right, in fact in zend framework, class name correspond to file PATH, so I want to put class name on this search and it will be able to find the corresponding file.

You are looking for php symbol search which we didn't implement

PHP IntelliSense now supports workspace symbol search in v0.0.8
https://marketplace.visualstudio.com/items?itemName=felixfbecker.php-intellisense

I do not understand why everyone is speaking about symbol. My request is simple :
"Be able to ignore underscore in this "quicksearch" "

Because you asked to open a file by class name. That is workspace symbol search. But the underscore should already be ignored, the file search has had fuzzy search for several iterations now.

No it's not the case, it's not ignored.
In fast I asked for searching class name because in zend, class name is representing path :
App_Stats_Model_Stats will be in app/stats/model/stats.php
Do I need a configuration to make underscore ignored?

Yeah VS Code cannot just completely "ignore" a specific character because one (outdated!) autoloading standard in one language happens to map that specific character to the directory separator.
I suggest you try out PHP IntelliSense and if the workspace symbol search is not sufficient for your use case (PSR-0) open an issue

To be honest, I would rather be typing stats to list the file and then select it. I would rather NOT type App_Stats_Model_Stats.

In a magento product, you will have many stats files for exemple.

For the file name quickopen case, it should now be fuzzy enough to ignore underscores. For the symbol case, that would be handled by an extension.

Can you clarify and tell me how to configure vscode to ignore "underscore" ? Thx

It should work automatically:

I will also look at fuzzy searching once we have the new parser merged in

In fact, I need the other way :

Should find file : Mage/Tax/Model/Sales/Total/Quote/Tax.php

Why are you not simply omitting the underscores while typing? I don't understand why you want to type more characters just so VS Code ignores them

You can see my previous messages, because it's a php class respecting a naming convention, so, I search a php class, I copy/paste its name in quick search and find it. It works on atom for example.

But you are copy+pasting it into the file search. If you copy+pasted it into symbol search it would work.

You mean if I search using #Mage_Tax_Model_Sales_Total_Quote_Tax ?

Works for me.

Have you installed the PHP IntelliSense extension?

You are right, I forgot to install it, thx a lot !

mmm in fact magento project seems too big, it does not show any results

Finally, I just want to be able to use search file and ignore underscore, It does not seems to be a so hard feature to have. It can be a params in config : search_file_ignore_char

Imo it screws the semantics. You want to search for a symbol, but you want to search by it from file search. And you want VS Code to transform your query in a way that matches the naming conventions of a very specific language convention. PSR-0 (underscores) is legacy. If VS Code supports searching by PSR-0 convention, they should support searching by PSR-4 convention (backslashes) too. If they support PHP naming conventions, they should support Java conventions (dots) too. VS Code can't special case all the language conventions in file search. That's what language servers are for, and the symbol search they provide.
If you have a specific problem with magento please open an issue at https://github.com/felixfbecker/php-language-server.

That's why I suggest an option to ignore some char, users can use or not.
Thx anyway but I can not use your editor for magento/zend projects.

@sebfie Please open an issue at the LS repo. If you have magento or zend as a Composer dependency the index will be cached after the first time.


k3s segmentation fault Rpi 4 4GB

Version:
v1.0.0 (18bd921c)
Installed with:
curl -fL https://get.k3s.io | K3S_TOKEN=Randompassword sh -s - server --cluster-init
Describe the bug
K3s is segfaulting in Ubuntu 19.10
To Reproduce
After deploying a multimaster (3 nodes) cluster and deployed my first pod (NFS Provisioner), loaded an storage class and deployed a Pihole using PVC, the first master began to segfault.
Expected behavior
K3s keeps running.
Actual behavior
Segmentation Fault
Additional context
Logs from the Debug.
k3s_segv.log

This doesn't appear to be the same issue but (maybe) related: https://github.com/rancher/k3s/issues/1115

In this case the OS is ARM64 and not armfh

I saw this yesterday.  3 raspberry 4s with Ubuntu 19.10 arm64 and k3s 1.0.  I've just recreated the cluster since I had tried to install a mountain of stuff all at once.  I'll try adding things back one at a time and attach another log if it crashes again.


Project dashboard main

This new branch can be merged now.

This new branch can be merged now

How about the previous pr?
Should I merge this one and ignore it?


Pulling feels too.... lightweight now?

Description

Ctrl + right clicking objects used to be kind of slow and heavy, now it's so light that everything feels like it's on rollerskates. Rotation of objects as a result of pulling in a certain direction is basically broken as well now, and pulling just feels... way less responsive?
List of issues off the top of my head:

Feels a lot less responsive despite cooldown being intended (you can't change direction mid-drag anymore)
You can never get the object to get dragged to the exact spot you want (it overshoots and never lands on your mouse)
Object rotation can no longer be controlled at-will by dragging the object away from you
The object you're dragging has almost no resistance letting you yeet people and objects at will like it's zero-grav
Things like janitor buckets, glasses, bowls, bottles get spilled now
You can now drag people into trash cans way faster than the actual action of clicking and dragging them in

Reproduction

Ctrl + right click to move things around while pulling it

Yeah, I can vouch. Pulling/pushing is incredibly weird and is slightly delayed, it never puts an object where you want it to be

I found out as a borg that if the gravity is off, you can grab a body, drag it forward quickly, and use it to propel yourself rather quickly, and results in a silly effect of the body spinning faster and faster

I found out as a borg that if the gravity is off, you can grab a body, drag it forward quickly, and use it to propel yourself rather quickly, and results in a silly effect of the body spinning faster and faster. Felt like quickly swimming through the station lol. Not a huge fan

this has always kinda been an issue, tbf.

Might be related, but I had an Artifact just tweak out due to a physics bug. Just getting thrown around as if someone was carrying it and ctrl+right clicking

I hope this gets fixed soon because having the mop bucket spill any time you push it is one of the most annoying things I've ever experienced in this game ever.

it also kinda feels like whatever you're dragging overshoots where you want it to go

You can easily toss people into space, and even toss things like the gravity gen into space with no friction now. They're essentially weightless.