Performance of Hybrid MPI+OpenMP Setups

Hi everyone,
I found some weird performance issue when combining MPI and OpenMP. My CMake configuration is
Hypre

HYPRE_WITH_CALIPER=ON
HYPRE_WITH_OPENMP=ON
HYPRE_BUILD_TYPE=Release

Caliper:

WITH_MPI=ON
WITH_OMPT=ON

and configured mfem via its make script as follows
make config MFEM_USE_MPI=YES MFEM_USE_METIS_5=YES MFEM_USE_CALIPER=YES MFEM_USE_LIBUNWIND=YES MFEM_USE_GSLIB=YES MFEM_USE_ZLIB=YES MFEM_USE_OPENMP=YES

Now on the caliper example I can observe the following:
ex1 works as expected.

Serial PA O1
$ CALI_CONFIG=openmp-report(output=serial-pa-o1.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 ./ex1 -d omp -o 1 -pa

Path                       #Threads Time (thread) (avg) Time (total) Thread Work % (min) Work % (avg) Barrier % (avg) Barrier % (max) Time (work) (avg) Time (barrier) (avg) Time (work) (total) Time (barrier) (total)
main
 |-                                            0.276795     0.333397
 |-                              16            0.485097     0.485097      0    91.870463    91.870463        8.129537        8.129537          0.000037             0.000030            0.000037               0.000030
  Save the results                             0.040139     0.040139
  Solve A X=B (PA)
   |-                                          0.027722     0.027722
   |-                            16            0.462600     0.462600      0    92.928770    92.928770        7.071230        7.071230          0.003141             0.000019            0.003141               0.000019
    PCG
     |-                                        0.027670     0.027670
     |-                          16            0.459418     0.459418      0    92.883375    92.883375        7.116625        7.116625          0.418330             0.032052            0.418330               0.032052
  Set up the bilinear form
   |-                                          0.105811     0.105811
   |-                            16            0.012252     0.012252      0    88.507634    88.507634       11.492366       11.492366          0.010782             0.001400            0.010782               0.001400
  Set up the linear form
   |-                                          0.092316     0.092903
   |-                            16            0.010169     0.010169      0     0.481474     0.481474       99.518526       99.518526          0.000023             0.004754            0.000023               0.004754

Serial PA O2
$ CALI_CONFIG=openmp-report(output=serial-pa-o2.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 ./ex1 -d omp -o 2 -pa

Path                       #Threads Time (thread) (avg) Time (total) Thread Work % (min) Work % (avg) Barrier % (avg) Barrier % (max) Time (work) (avg) Time (barrier) (avg) Time (work) (total) Time (barrier) (total)
main
 |-                                            0.578963     0.666955
 |-                              16            2.657075     2.657075      0    85.262635    85.262635       14.737365       14.737365          0.000065             0.000162            0.000065               0.000162
  Save the results                             0.071830     0.071830
  Solve A X=B (PA)
   |-                                          0.148412     0.148412
   |-                            16            2.624499     2.624499      0    85.503914    85.503914       14.496086       14.496086          0.006388             0.003524            0.006388               0.003524
    PCG
     |-                                        0.148140     0.148140
     |-                          16            2.614568     2.614568      0    85.584756    85.584756       14.415244       14.415244          2.209593             0.372167            2.209593               0.372167
  Set up the bilinear form
   |-                                          0.117506     0.117506
   |-                            16            0.021877     0.021877      0    75.989543    75.989543       24.010457       24.010457          0.016568             0.005235            0.016568               0.005235
  Set up the linear form
   |-                                          0.225398     0.225975
   |-                            16            0.010446     0.010446      0     2.344382     2.344382       97.655618       97.655618          0.000116             0.004832            0.000116               0.004832

Serial Assembled O1
$ CALI_CONFIG=openmp-report(output=serial-assembled-o1.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 ./ex1 -d omp -o 1

Path                       #Threads Time (thread) (avg) Time (total) Thread Work % (min) Work % (avg) Barrier % (avg) Barrier % (max) Time (work) (avg) Time (barrier) (avg) Time (work) (total) Time (barrier) (total)
main
 |-                                            0.569429     0.626631
 |-                              16            0.058298     0.058298      0    55.341837    55.341837       44.658163       44.658163          0.000023             0.000050            0.000023               0.000050
  Save the results                             0.040128     0.040128
  Solve A X=B (FA)
   |-                                          0.208715     0.208715
   |-                                          0.047168     0.047168      0    63.231074    63.231074       36.768926       36.768926
    PCG
     |-                                        0.208698     0.208698
     |-                          16            0.047168     0.047168      0    63.231074    63.231074       36.768926       36.768926          0.027104             0.015761            0.027104               0.015761
  Set up the bilinear form
   |-                                          0.217370     0.217370
   |-                            16            0.000469     0.000469      0    24.565757    24.565757       75.434243       75.434243          0.000099             0.000304            0.000099               0.000304
  Set up the linear form
   |-                                          0.093189     0.093767
   |-                            16            0.010578     0.010578      0     0.356355     0.356355       99.643645       99.643645          0.000021             0.005872            0.000021               0.005872

Serial Assembled O2
$ CALI_CONFIG=openmp-report(output=serial-assembled-o2.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 ./ex1 -d omp -o 2

Path                       #Threads Time (thread) (avg) Time (total) Thread Work % (min) Work % (avg) Barrier % (avg) Barrier % (max) Time (work) (avg) Time (barrier) (avg) Time (work) (total) Time (barrier) (total)
main
 |-                                            3.694145     3.780781
 |-                              16            0.338554     0.338554      0    74.518249    74.518249       25.481751       25.481751          0.000064             0.000138            0.000064               0.000138
  Save the results                             0.070249     0.070249
  Solve A X=B (FA)
   |-                                          2.537043     2.537043
   |-                                          0.326605     0.326605      0    75.847333    75.847333       24.152667       24.152667
    PCG
     |-                                        2.536965     2.536965
     |-                          16            0.326605     0.326605      0    75.847333    75.847333       24.152667       24.152667          0.235173             0.074888            0.235173               0.074888
  Set up the bilinear form
   |-                                          0.859797     0.859797
   |-                            16            0.000724     0.000724      0    37.894737    37.894737       62.105263       62.105263          0.000252             0.000413            0.000252               0.000413
  Set up the linear form
   |-                                          0.211163     0.211746
   |-                            16            0.011002     0.011002      0     3.180279     3.180279       96.819721       96.819721          0.000169             0.005145            0.000169               0.005145

ex1p with 1 process and partial assembly is really slow compared to the serial version.

Parallel PA O1
$ CALI_CONFIG=openmp-report(output=parallel-pa-o1.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 mpirun -np 1 ./ex1p -d omp -o 1 -pa

Path                             #Threads Time (thread) (avg) Time (total) Thread Work % (min) Work % (avg) Barrier % (avg) Barrier % (max) Time (work) (avg) Time (barrier) (avg) Time (work) (total) Time (barrier) (total)
main
 |-                                                  1.425465     1.663397
 |-                                    16           55.905275    55.905275      0    65.105857    65.105857       34.894143       34.894143          0.000166             0.110564            0.000166               0.110564
  Save the results                                   0.343997     0.343997
  Solve A X=B
   |-                                                0.140432     0.140432
   |-                                  16           55.376816    55.376816      0    65.106723    65.106723       34.893277       34.893277         36.001183            19.294463           36.001183              19.294463
  Set up the bilinear form
   |-                                                0.499783     0.499783
   |-                                  16            0.396433     0.396433      0    88.505168    88.505168       11.494832       11.494832          0.294924             0.038304            0.294924               0.038304
  Set up the linear form
   |-                                                0.439408     0.439408
   |-                                  16            0.010102     0.010102      0     0.465531     0.465531       99.534469       99.534469          0.000047             0.010049            0.000047               0.010049
  hypre_MatvecCommPkgCreate                                       0.000040
    hypre_LocateAssumedPartition                                  0.000010

This problem seems to at least partially vanish with higher order.

Parallel PA O2
$ CALI_CONFIG=openmp-report(output=parallel-pa-o2.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 mpirun -np 1 ./ex1p -d omp -o 2 -pa

Path                             #Threads Time (thread) (avg) Time (total) Thread Work % (min) Work % (avg) Barrier % (avg) Barrier % (max) Time (work) (avg) Time (barrier) (avg) Time (work) (total) Time (barrier) (total)
main
 |-                                                  3.180665     3.561150
 |-                                    16          391.793439   391.793439      0    77.017562    77.017562       22.982438       22.982438          0.003492             0.077510            0.003492               0.077510
  Save the results                                   0.484027     0.484027
  Solve A X=B
   |-                                                1.323858     1.323858
   |-                                  16          390.864742   390.864742      0    77.014106    77.014106       22.985894       22.985894        300.536611            89.699189          300.536611              89.699189
  Set up the bilinear form
   |-                                                0.487542     0.487542
   |-                                  16            0.753386     0.753386      0    88.836055    88.836055       11.163945       11.163945          0.603442             0.075834            0.603442               0.075834
  Set up the linear form
   |-                                                0.839649     0.839649
   |-                                  16            0.010561     0.010561      0     2.179475     2.179475       97.820525       97.820525          0.000230             0.010323            0.000230               0.010323
  hypre_MatvecCommPkgCreate                                       0.000047
    hypre_LocateAssumedPartition                                  0.000010

The hypre version shares the same issue

Parallel Hypre O1
$ CALI_CONFIG=openmp-report(output=parallel-hypre-o1.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 mpirun -np 1 ./ex1p -d omp -o 1

parallel-hypre-o1.txt

Parallel Hypre O2
$ CALI_CONFIG=openmp-report(output=parallel-hypre-o2.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 mpirun -np 1 ./ex1p -d omp -o 2

parallel-hypre-o2.txt

Parallel Hypre O3
$ CALI_CONFIG=openmp-report(output=parallel-hypre-o3.txt),show_threads=1,show_regions=true OMP_NUM_THREADS=16 mpirun -np 1 ./ex1p -d omp -o 3

parallel-hypre-o3.txt

I observe the same issue when using raja. Is this issue known or at least reproducible? Or is this issue on my side, having something misconfigured?
Thanks in advance!

ping @v-dobrev @tzanio any idea if this is reproducible or what the culprit could be?

The parallel version refines the mesh more -- with the default mesh (star.mesh) that results in 4x more elements. Also, when using PA, the preconditioner used in the example is not optimal, so on the larger mesh one gets more PCG iterations too -- 161 vs 701. Just running these on 1 processor without OpenMP results in total run time of 1.37 seconds (ex1 -pa) vs 19.72 seconds (ex1p -pa) on my laptop.
There are probably some additional inefficiencies with -d omp that I did not explore. We usually use MPI parallelism and not OpenMP, so the latter is not well optimized. That said, there seems to be something else going on in the Parallel PA runs you posted -- 55 sec using 16 threads does not seem right to me.

I am getting rather confident that the blocks which I see in the measurements come from some kind of interaction with the omp instrumentation, because building everything without caliper does not have the same issue. There still remains a performance gap between pure mpi and pure omp for pure partial assembly+cg performance for the diffusion problem as set up in ex1. The omp device ("serial problem as in ex1) is 40-50% slower than mpi ("parallel problem as in ex1p), because the CPU utilization never really peaks - which I think can be attributed to having a not optimized implementation of the opm code.
I think this "resolves" the issue. Thanks for taking some time to also take a look into this Veselin!

Thanks for the detailed investigation, Dennis.


Convert Parser to a class

Addresses #368.
I considered defining the dynamic methods in the constructor method but decided against it because that is not a concern of instantiating the class. Going the prototype route also allows us to add the dynamic methods once instead of every time we instantiate.

@chancancode I think this is good to go unless you have any other comments.


com.couchbase.lite.CouchbaseLiteException: Cannot index view student_view: no map block registered, Status: 400 (HTTP 400 bad_request)

 public void generateViewForMiltipleFilter(final String keyDocumentType, final String valueDocumentType, final List<Object> keys, String viewName, String viewVersionNo) {
        View view = null;
        if (database != null) {
            view = database.getExistingView(viewName);
        } else {
            Log.d(TAG, "Database does not exist.");
        }
        if (view == null) {
            view = database.getView(viewName);
            view.setMap(new Mapper() {
                @Override
                public void map(Map<String, Object> document, Emitter emitter) {
                    Object docType = document.get(keyDocumentType);
                    List<Object> keysList = new ArrayList<Object>();
                    for (Object mKey : keys) {
                        keysList.add(document.get(mKey));
                    }
                        emitter.emit(keysList, document);
                }
            }, viewVersionNo);
        }
    }

Hi @kundan7560,
Your codes look fine. Can you provide more information? or Can you debug further by yourself?

Hi @hideki At first run its working fine . now i close the app again open it . insert some records. now on run the query its not showing results ,showing exception mentioned above.

@kundan7560,
After reopening the app, does the app set the map function again?

@hideki No I get the existing view that is already exist.

@kundan7560,
Can you write an unit-test or a simple sample app to reproduce the issue?

@hideki After calling the map function every time its working. but why not with existing map function that has been already exist. why i am getting no map block registered exception?

Once registered the map function, the indexing should use the registered map function. Currently, I don't know how you encounter the problem. We need more information.

@hideki Hi how to register map function is there any method to register map function in couchbase lite?

@hideki Hi When i remove the below line its working...
if (database != null) {
view = database.getExistingView(viewName);
} else {
Log.d(TAG, "Database does not exist.");
}

Hi @kundan7560,
Here is a guide for View. If the app follows it, the app should not have the problem.
https://developer.couchbase.com/documentation/mobile/1.4/guides/couchbase-lite/native-api/view/index.html
However, CBL could have the bug in the code. To investigate the problem, we need more info.

Hi@hideki thanks lot for support. i have figured out the problem.


Multiple function in the same consumer group

Now I have two functions doing different works registered in the same consumer group for a event hub. As https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features#consumer-groups points out, they should work independently. But the result is that each function consumers exeact half of the events.
Does it means a function should monopolize a consumer group？

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

ID: 0b24ff8d-a59e-01e0-205d-ac777f06bb7a
Version Independent ID: f2106a8a-28fe-ebdf-525e-1d93013778fa
Content: Azure Event Hubs bindings for Azure Functions
Content Source: articles/azure-functions/functions-bindings-event-hubs.md
Service: azure-functions
GitHub Login: @craigshoemaker
Microsoft Alias: cshoe

@smartczh Thank you for your feedback! We will review and provide an update as appropriate.

@smartczh Yes. To quote the event hub doc,

In a stream processing architecture, each downstream application equates to a consumer group.

So, each of your event hub triggered function would require it to have its own consumer group.

@smartczh Just following up here... Hope my previous comment clears things up.

Thank you Pramod,
There is no problem any more and you can close this.
At 2019-08-09 19:08:23, "Pramod Valavala"<EMAIL_ADDRESS>wrote:
@smartczh Just following up here... Hope my previous comment clears things up.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

@smartczh Glad we could help!
We will now proceed to close this thread. If there are further questions regarding this matter, please tag me in your reply. We will gladly continue the discussion and we will reopen the issue.


Export doc to pdf

Hi,
I try to export the whole doc to a pdf (or a single html page that can be converted to pdf).
Are there any features or workarounds or code bases already?
Or is it possible to show a clean single html page which can be used as step in between (ranento -> single html page -> pdf)?
Would be nice if you can point me to a correct/possible direction.
Bye Darian

Please tell us more about what you're looking to accomplish.

Are you looking to print the entire documentation base?
Is this for you and your team or for public users of Raneto?

Some options:

"Print" to PDF with many popular web browsers. If there are design elements that show up, we can tweak the CSS styles to make it just the content.
You can print all the Markdown files with a markdown viewer or something like this Chrome extension

Hi and thank you for your answer.
Its for public users for raneto.
I forked raneto and added the pdf support already.
Because lack of time the project is currently freezed.
But maybe your "Print to PDF" idea could solve our issue as well. Thanks for the hint.
Thx Darian


Questions about environment and package version.

I tried to run your code, but it seems incompatible. First of all, I run under python3.7: pip install deep-alignment-pytorch.
But I got the following error：

Then I went back to 3.6 and anaconda to try to install, but it also failed.
Finally I tried to use google colab, it worked miraculously.
But I still encountered many problems, including TensorFlow version problems, path loss problems, etc., but I have solved them. But in the end, during training, I got a problem I couldn't solve:

Can you give me some suggestions, I will be very grateful!

I'm really sorry, you encountered theses issues. Regarding your python errors: I've never seen them and for me the setup works, sorry!
Regarding your tensorflow error: this is strange since the code is purely PyTorch so you shouldn't need tf at all.
If I find some time, I'll do a rewrite of this.


Extending basic graph formation

Description
add_nodes should be renamed to add_nodes_from and add_edges should be made add_edges_from to provide familiarity to NetworkX users migrating from Python. Additionally, support for remove_node, remove_nodes_from, remove_edge and remove_edges_from should be provided.
However, the graph format must be finalised first (please see the issue referenced).

Relevant Issues : https://github.com/athityakumar/networkx.rb/issues/8
Relevant PRs : (optional)
Type of issue :

User mode :

[ ] Installation
[x] Feature request
[ ] Bug in existing feature

Developer mode :

[ ] Code quality
[ ] Tests
[ ] Documentation

I'd like to work on this issue, once the basic graph format has been decided.

Done!
I added alias (remove_nodes_from and remove_edges_from)
Graph has also add_nodes_from and add_edges_from.


pr_check: use the new combined iqe plugin

We have merged the frontend and backend plugins to one.

/retest


GitLab Code Quality report artifact formatter

Thanks for this nice utility!
I started playing around with it to integrate it into our pre-commit and our GitLab CI pipeline.
As a result, I ended up writing a custom formatter that generates the required code quality report artifact (see docs) so that any issues show up in the merge request and the diff.
Instead of leaving this buried in a private repo (or publishing it separately), would there be any interest in integrating this into this repo?

Sure! Can you point to the code so I can get an idea of the scope of it?

Here it is: https://github.com/mschoettle/markdownlint-cli2/commit/6bf34774d98a45db4f7b3c7675930c016ff7db3f

This looks very reasonable! I will have some questions/feedback, but not too much. Happy to see a PR. Please review CONTRIBUTING.md first. Thanks!!

One thing I’d like to clarify before you do any work here: if this gets merged into the CLI2 project, I will treat it as my responsibility/problem and publish updates along with the other packages which will happen under my NPM account. I absolutely understand if you want to maintain more control/visibility over your work, and if so I think you should publish it directly. I’m happy to help with that. Otherwise, I would leave the author field pointing to you, and I would be happy to take patches and updates in the future, but I will assume I might never see you again and handle things accordingly. I hope that’s clear and make sense – I’m happy to chat more if you have concerns.

Thanks for being clear about this. I think that makes a lot of sense. Given the small size of this formatter I feel it would be a big maintenance overhead to publish it separately. But I also understand if you don't want to start collecting a lot of different formatters in your project.
I am working on a PR and might have a few open issues left where I need some help that I will list in the PR.

Sounds good! I'm happy to have formatters in this repo because it's easier to do testing, but wanted to be sure there weren't any surprises. Regarding your PR, I will eventually want a README and integration with tests, but I'm also OK accepting your change without that and adding it myself later.


Unrecoverable authorization error

IMPORTANT: Before you create an issue, please take a look at our Issue Reporting Guide.
Briefly summarize your issue:
I am developing an alexa sample application for ubuntu linux.at the time building i received an error called unrecoverable authorization error and unrecoverable capabilities API error
What is the expected behavior?
to receive an authorization  #code
What behavior are you observing?

Provide the steps to reproduce the issue, if applicable:
``
Tell us about your environment:
ubuntu linux
What version of the AVS Device SDK are you using?
  <x.y.z>

Tell us what hardware you're using:

lenovo 320p
[ ] Raspberry Pi
[ ] Other - tell us more:

Tell us about your OS (Type & version):

[ ] Linux
``

Hi @ANUSUYA-1996, can you please run the sample application with DEBUG9 and paste the logs for us? Thanks


Feature/cicd

add cicd publish to pypi

Addresses https://github.com/dataops-tk/tap-powerbi-metadata/issues/18


Tag set join, feature request or doable?

I would like to join geoJSON to an Influx metric where the Series GeoID Tag would actually be a pair of group by tags, aka tag set.
Since there is only one field in GeoLoop for Series GeoID Tag I'm guessing I'd have to use an Alias By in order to create a compound reference.
Thoughts?

interesting! so, to clarify, if your influx data were schematized as such:
temperature,units=c,asset_type=building,asset=front_office
... and your geojson has a polygon labelled as such:
{"type":"Feature","properties":{"location":"building_front_office"},"geometry":{"type":"Polygon","coordinates": ...
... then it should in theory be possible to use the alias $m {location:$tag_asset_type_$tag_asset} to generate the correct lookup value joining asset_type=building,asset=front_office to {"location":"building_front_office"}. the problem is I don't think Grafana will let us assemble the string this way. The real solution would be to either re-do the influx tags, or add some kind of more sophisticated matching engine in this plugin.
GeoLoop right now uses regex capture group to find the metric tag it needs to map into the GeoJSON:
https://github.com/CitiLogics/citilogics-geoloop-panel/blob/55585bf1a6b4549308b12f9bb8bddfc6ddfa0332/src/geoloop_ctrl.js#L317
... so we could possibly expose a pair of regex replacement strings to the user, to allow an arbitrary transformation from the tag-space into the geo-space. For the example above, something like
input: /asset=([^,}]+),asset_type=([^,}]+)/
output: "$1_$2"

I've got it working with the Alias By for now. Eventually I'll provide the metric a cleaner, single tag which will represent the compound tags so that it doesn't have to do it during runtime when I redo the schema (considering moving to TimescaleDB).
But yes, you got the idea. And seriously, this is the coolest Grafana plugin ever!


Export data and the executed SQL in the same user action.

When we are extracting data as evidente to be sent to other functional areas we need to export the data and the SQL that we used to get tha data,
This makes the process unnecessarly two phased.
my idea is to include a checkbox in the "data transfer" window indicating that we want it to create an aditional  .sql file with the same file name containing the executed sql command.

thanks for suggestion


Fixed #24499 -- Dropped support for PostGIS 1.5.

https://code.djangoproject.com/ticket/24499#ticket

Postponed to Django 2.0 per Claude's request.

@claudep, are you okay to proceed with this? If so, could you review?

Tim, the patch looks good. My only reluctance currently is very selfish! My main dev system is still on Debian stable where 1.5 is the installed Postgis version. The next stable is not far away, but meanwhile I would be unable to work on GIS, unless I setup external repositories or docker-style configs (as I need to keep 1.5 to match some production installs).

As you are the main contributor to GIS these days, that's a good reason for me. I'll see if I can use this as motivation to put something together with docker based on our Jenkins playbooks to ease testing.

Feel free to rebase and commit this at your convenience.


Issue while building from source isaac_ros_nvblox

Hi,
I am trying to build isaac_ros_nvblox from source. I am following the instructions from (https://nvidia-isaac-ros.github.io/repositories_and_packages/isaac_ros_nvblox/isaac_ros_nvblox/index.html#quickstart) I find myself stuck with this issue for past couple of days. I tried different releases until 3.0.1, its still the same. The fact that surprises me the most is Until Nov 27th I didnt have issues with the build. I tried with multiple machines, still its the same. It would be great if someone can help me out.
Thanks.
Processing: nvblox_ros]
--- stderr: nvblox_ros
/workspaces/isaac_ros-dev/src/isaac_ros_nvblox/nvblox_ros/src/lib/nvblox_node.cpp: In member function ‘void nvblox::NvbloxNode::subscribeToTopics()’:
/workspaces/isaac_ros-dev/src/isaac_ros_nvblox/nvblox_ros/src/lib/nvblox_node.cpp:179:38: error: ‘NitrosStatisticsConfig’ is not a member of ‘nvidia::isaac_ros::nitros’; did you mean ‘NitrosDiagnosticsConfig’?
  179 |           nvidia::isaac_ros::nitros::NitrosStatisticsConfig(), input_qos));
      |                                      ^~~~~~~~~~~~~~~~~~~~~~
      |                                      NitrosDiagnosticsConfig
/workspaces/isaac_ros-dev/src/isaac_ros_nvblox/nvblox_ros/src/lib/nvblox_node.cpp:195:38: error: ‘NitrosStatisticsConfig’ is not a member of ‘nvidia::isaac_ros::nitros’; did you mean ‘NitrosDiagnosticsConfig’?
  195 |           nvidia::isaac_ros::nitros::NitrosStatisticsConfig(), input_qos));
      |                                      ^~~~~~~~~~~~~~~~~~~~~~
      |                                      NitrosDiagnosticsConfig
gmake[2]: *** [CMakeFiles/nvblox_ros_lib.dir/build.make:230: CMakeFiles/nvblox_ros_lib.dir/src/lib/nvblox_node.cpp.o] Error 1
gmake[2]: *** Waiting for unfinished jobs....
gmake[1]: *** [CMakeFiles/Makefile2:535: CMakeFiles/nvblox_ros_lib.dir/all] Error 2
gmake[1]: *** Waiting for unfinished jobs....
gmake: *** [Makefile:146: all] Error 2

I just tried and am unable to repro this issue.
Could you try again with the latest release? You should have two repos checked out: isaac_ros_common and isaac_ros_nvblox both on the release-3.2 branch. Under that condition, I was able to build.


Import header from brainglobe-utils

Description
What is this PR

[ ] Bug fix
[ ] Addition of a new feature
[X] Other

Why is this PR needed?
brainglobe-registration currently contains a copy of the html headers from brainglobe_utils
What does this PR do?
This PR imports the headers from brainglobe_utils and removes any duplicates in this repo.
References
For https://github.com/brainglobe/cellfinder/issues/327
How has this PR been tested?
All tests pass locally.
Is this a breaking change?
No
Does this PR require an update to the documentation?
No
Checklist:

[X] The code has been tested locally
[X] Tests have been added to cover all new functionality (unit & integration)
[X] The documentation has been updated to reflect any changes
[X] The code has been formatted with pre-commit

We've seen this napari error before in brainreg: https://brainglobe.zulipchat.com/#narrow/stream/408839-dev-private/topic/.E2.9C.94.20Brainreg.20test.20failure/near/431276552
Though it was supposed to be fixed by https://github.com/napari/napari/pull/6799 on napari's end, and it looks like someone in the last 3 days has commented on the PR.

Thanks @willGraham01 - I guess the fix just hasn't made it into a new napari release yet? I was getting around this locally by using pip install lxml_html_clean in my environment.


Datepicker disabled days not working

I have tried both version 1.0.11 and 1.0.10 and neither one work. I am trying to disable days based on the selection above the datepicker. Basically if they chose a value that is a sunday I want everything else to be disabled. Everything I have tried it passes in the array of disabled days but it won't disable those days to make it where the user can only choose that day of the week.
My Vue file:
<template>

        Skate session preference:

        <select id="session" name="session" class="form-control" v-model="selectedSessionId">
            <option value="">Select a Session</option>
            <option :value="session.id" v-for="session in sessions">{{session.session_description}}</option>
        </select>
        <datepicker
                :value.sync="selectedDate"
                :disabled-days-of-Week="getDays"
                format="yyyy-MM-dd"
        >
        </datepicker>

</template>

Here is a screen capture of the data being passed in and the datepicker itself where you can see that it is not disabling days.

@yuche or anyone else have you encountered this problem at all?

You wrote disabled-days-of-Week, html is not case sensitive but this is vue, put all in lowercase.

I did some tests and work right...
You tested the last version?


DepthExpandConv is not defined

DepthExpandConv is not defined.
Although he was not used.

Thanks for letting me know the issue of the code. Actually, this function is only used for ablation and not a part of MicroNet. I just removed this condition and its corresponding functions.


correction in functional test to display etcd logs

PR for resolving #837

@salamaniibm This needs a rebase then I can merge

Logs are no longer displayed on failure


Explore using Smoothie Charts in the Electron App

Using Smoothie Charts

[x]  Add a smoothie chart to main app page
[x]   Have it load test data through an interface to the main process

This experiment is documented in the branch issues-1. I'd say ultimately it wasn't successful as the chart didn't render well (very pixelated) and suffered from some issues with resizing of the electron window.


Use genType to compile component as React.FunctionComponent in Typescript.

Currently, when we compile a<EMAIL_ADDRESS>to Typescript, it generates a React.ComponentType.
I'm not familiar with genType's configuration, but is it possible to generate a React.FunctionComponent instead ?
We are currently using the addon-docs storybook addon to display our components. But it seems it can't find the component types when using React.ComponentType (works fine with React.FunctionComponent).
Thanks for your feedback :)

In fact, this could be set somehow in the config no ?

Tried before, but still have not found a way to use FunctionComponent without some issue popping up somewhere, either with importing components from JS, or with components requiring conversion.
See e.g. https://github.com/reason-association/genType/blob/master/Changes.md#2330

No problem, thanks @cristianoc ! I'll find another way to manage that with storybook :)


Version constraint too tight

Version constraint is set to "3.4.0" which means 3.4.x-dev or 3.4.1 is unsupported. Could that constraint be loosened?

Hi there,
Have been using this on SS 4.0 so yes it can. I am unable to make it to my PC for a few days, more than happy to merge a pull request if you are able to fix this
Thanks
Sajan

👍  Thanks Sajan

Pleasure :)


bug: TypeScript files (.ts ext) not in compile context

From @danbucholtz on February 10, 2016 20:33
Type: bug
Ionic Version: 2.x
Platform: all
When I create a new .ts file, for example, "Todo.ts" that exports a class called Todo, VSCode and Atom are giving me errors about how "Todo.ts" is not included in the TypeScript compilation context.  The tsconfig.json file glob is not set-up correctly.

Copied from original issue: driftyco/ionic#5397

From @danbucholtz on February 11, 2016 0:2
in app/Todo.ts
export class Todo{ }

The TypeScript compiler is only aware of the files in the files array in tsconfig.json and those files' dependencies. The Ionic starters by default only include app/app.ts, whose dependencies include all of the .ts files in the starters.  So because todo.ts is not imported anywhere and therefore not a part of the app's dependency tree, it isn't visible to the TypeScript compiler used by the IDE.
One solution I believe is to make the files array empty, since the default behavior for the TypeScript compiler is to include all .ts and .d.ts files.  The reason we specify app/app.ts explicitly is for the webpack typescript loader, which we want to only compile the files that webpack feeds it as webpack does its own crawl of the dependency tree, starting from the app entry point (app/app.ts), since any extraneous files being compiled that are not a part of the dependency tree would slow down the build unnecessarily.
This is somewhat confusing because the TypeScript compiler is being used by both the IDE for typechecking and also the webpack loader when bundling your app, and both refer to the same tsconfig.json for guidance.
There are 3 possible solutions to making that error in the IDE go away:

import {Todo} from 'todo'; somewhere in your project, it will be part of the dependency tree and visible to the TypeScript compiler and the error will go away.

Add app/todo.ts to your files array in tsconfig.json, or make the array empty.

If you are using Atom, you could use the non-standard filesGlob property: filesGlob: [ '**/*.ts'] to indicate to Atom which files should be part of the compilation context of the editor.  Awesome-typescript-loader, the webpack typescript loader used by the starters, also supports this property, so if you don't want it to include files that match the glob pattern in addition to the files in the dependency tree, you'll need to set resolveGlobs to false

I'm going to close this as intended behavior, but please comment if you have any issues or feel this is incorrect.

Yep - this was really more of a "bring to the team's attention" issue.  Would be great if the glob approach was enabled by default because otherwise I can see where users will be displeased when they create new Typescript files and are immediately getting compile errors.  I don't think too many users will have a flow of importing a class that doesn't yet exist then creating it.  Just my $0.02.  Kind of a bummer than VSCode does not support the glob.

Ah thanks, and yeah I don't think it's unreasonable to have filesGlob in there by default.
Re-opening, I'll close when I've updated the starters.

This has been a big problem for me as well in starting with TS. Many Google searches lead me here. Now I know the fix, though. Thanks.

Moved to exclude in tsconfig, so all new files should be in the compilation context from now on.

That seems like a solid approach.  Thanks!

@danbucholtz although I realize that was only the conference app starter, so closing was a little premature. Updating the other starters now!


Multiple Validation Errors For DAPP

The activity file for DAPP http://www.aidstream.org/files/xml/dapp-activities.xml is currently failing validation with a number of errors
Element 'legacy-data', attribute 'iati-equivalent': 'DAPP Malawi' is not a valid value of the atomic type 'xs:NMTOKEN'.
Element 'result': Missing child element(s). Expected is ( title ).
Element 'point': Missing child element(s). Expected is ( pos ).
Please can you investigate and amend Aidstream if required.
The other error reported by the validator is similar to that reported via issue #137
Element 'value': '42,166.72' is not a valid value of the atomic type 'xs:decimal'.

Same as https://github.com/younginnovations/aidstream/issues/135#issuecomment-167989277


[enhancement request] add 'Create New Deck with this ID' link

When viewing the card/ page for an Identity, it would be nice to have a link right there to start building a new deck using that Identity.

ooh, these are great suggestions.  I think they may wait until NRDB v2, but we'll see....


HttpAbstractions - QueryString - the leading "?" does not conform to OWIN spec

http://owin.org/
Both 1.0.0 and the 1.1.0 draft say the owin.RequestQueryString must NOT include the leading "?" and the value may be an empty string.
This one:
https://github.com/aspnet/HttpAbstractions/blob/dev/src/Microsoft.AspNet.Http/QueryString.cs
throws in the constructor if the leading "?" is not there.
Which way do we go?

RFC 3986 doesn't consider the question mark to be part of the query string.  Is it included for consistency with previous versions of ASP.NET?

It's primarily for convenience. This value us often used to generate new Uris via a simple concatenation with PathString:
pathBase + path + query, so having the ? built in prevents you from having to do this:
pathBase + path + (query.HasValue : "?" + query.Value : "")

We should move away from that kind of logic. There's a reason we have Path class and UriBuilder.
var uriBuilder = new UriBuilder(path);
uriBuilder.Query = query;
uriBuilder.Uri.ToString();

Or if that is too verbose, something new like
UrlPath.AddPath(pathBase).AddQuery(query)

Anything is better than string concatenation.
Even, adding a secondary property PrefixedQueryString or something would be better.
Sticking to specs is preferable so we can keep things consistent. I hate it when I have to think, "Wait, does this property start with the question mark or not?". Consistency means I can just assume the standard.

Or using this code
path + Utilities.AddQuestionMark(query)


updaete blog title

I prefer to remove this document, how do you say? @Poor12
This document high light the features of v1.3, a little bit outdated now.

I prefer to remove this document, how do you say? @Poor12
This document high light the features of v1.3, a little bit outdated now.

I think the key question is whether we need to maintain and update the blog content for a long time.  Blogs are updated less frequently and with less content now.

So, what's your opinion?

I prefer to remove this document, how do you say? @Poor12
This document high light the features of v1.3, a little bit outdated now.

how to deal with this @RainbowMango

I think it'ok to delete. But we should decide a time point to update the blog.

Is it okay to follow my translation?

Let's remove this document instead of translating it.

Let's remove this document instead of translating it.

delete blog this document mom

fine. let's move forward for now.

fine. let's move forward for now.

ok


use different CRS in stage_ldas_ts?

@jread-usgs, in light of  https://github.com/USGS-CIDA/stream_metab_usa/pull/121#discussion_r70883034

there's a column in get_meta('basic') called coord_datum. The values there are almost always NAD83 (723 of those, 1 of WGS84). does that mean we should be using EPSG:4269 for most sites and EPSG: 4326 just for that one WGS84 site? (i'm getting my info from https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf)

should/can we specify the CRS when creating the site stencil in stage_ldas_ts at https://github.com/USGS-R/mda.streams/blob/3e9791c0d6ce407d53b989f0cc60614e20ce89ad/R/stage_ldas_ts.R#L55?
stencil <- simplegeom(lon_lat_df)

yes, what I think we should do here is do a spatial transform to get all of them into the datum and proj that geoknife uses for the simplegeom class. Good catch.


feat: actually parse Cargo.lock for vendor normalization

@bors merge

bors merge

bors r-

bors merge


How to use --user-config-filename from Docker container

How to use the howfairis Docker image is described on https://github.com/fair-software/howfairis#docker .
It does not describe how to use a local user config file. Can that be described?

It fails when I do:
docker run --rm fairsoftware/howfairis --user-config-filename .custom.howfairis.yml https://github.com/fair-software/howfairis
FileNotFoundError: .custom.howfairis.yml doesn't exist.
Checking compliance with fair-software.eu...
url: https://github.com/fair-software/howfairis
Local configuration file: .custom.howfairis.yml

However following works:
docker run --rm -v $PWD/.custom.howfairis.yml:/.custom.howfairis.yml fairsoftware/howfairis \
  --user-config-filename /.custom.howfairis.yml https://github.com/fair-software/howfairis
Checking compliance with fair-software.eu...
url: https://github.com/fair-software/howfairis
Local configuration file: /.custom.howfairis.yml
(1/5) repository
      ✓ has_open_repository
(2/5) license
      ✓ has_license
(3/5) registry
      × has_ascl_badge
      × has_bintray_badge
      × has_conda_badge
      × has_cran_badge
      × has_crates_badge
      × has_maven_badge
      × has_npm_badge
      ✓ has_pypi_badge
      × has_rsd_badge
      × is_on_github_marketplace
(4/5) citation
      × has_citation_file
      ✓ has_citationcff_file
      × has_codemeta_file
      ✓ has_zenodo_badge
      ✓ has_zenodo_metadata_file
(5/5) checklist
      ✓ has_core_infrastructures_badge

Calculated compliance: ● ● ● ● ●

Expected badge is equal to the actual badge. It's all good.


i keyboard shortcut doesn't work in SSA view

Switching to LLIL SSA form does not allow you to swap back to disassembly via the i keyboard shortcut. space and h still work, but i does not.

Closing this issue and adding reference/TODO in #92.


using a different bucket of an artifact repository in different steps/tasks ?

Summary
at the moment, you can crate a ConfigMap named artifact-repositories with multiple repositories
then, inside each workflow, you can specify the repo to use with artifactRepositoryRef
let's assume you need step1 to read from the bucket1 and to write to bucket2
in this case, you need to choose one of the 2 repos as a "default" (and to repeat the repository info into the other one)
It all comes down to the fact that the "bucket" is ignored when you specify artifactRepositoryRef
kind: Workflow
metadata:
  generateName: input-artifact-s3-
spec:
  artifactRepositoryRef: repo1
  entrypoint: input-artifact-s3-example
  templates:
  - name: input-artifact-s3-example
    inputs:
      artifacts:
      - name: my-art
        path: /my-artifact
        s3:
          bucket: my-other-bucket #here, this should "override" the bucket configured in repo1
          key: path/in/bucket
    container:
      image: debian:latest
      command: [sh, -c]
      args: ["ls -l /my-artifact"]

Of course, in the example, I could use repo2 (which would be configured to my-other-bucket) but the point is to have another template that is using my-bucket.
Of course (and this is what I am doing right now), I can repeat the whole s3 config inline but the point is to have the configuration of repositories stored in configmaps
is this a bug or by design ?
See #6065 to extend the problem to multiple repositories
do not hesitate to ask me more details

Message from the maintainers:
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

This would be an extremely useful feature,  allowing the "key only" functionality to also select a specific configuration in the artifactRepository configmap, via a "refKey".  Our use case separates the buckets used for default artifact passing from specific inputs and outputs of a computational pipeline, which causes us to inline s3/bucket configurations in many places in the workflow.


Use Math.cosh of ES6/2015 if available

This PR suggests two smaller refactorings with regard to ol.math.cosh:

449131a516f38e4816905c7db433e92287e20e08 refactors the implementation in a way which saves us one call to Math.exp. This variant performs better for me, and the code is taken from the MDN reference implmentation
cd99d44704946519a82d22c89c08aac08e49263b then changes the code again, so that the native method Math.cosh (standardized in ES6 / 2015) is used if it is available (Which naturally gives us best prefromance).

Here is a jsperf compairing the implementations: http://jsperf.com/cosh-num-calls-math-exp
Please review.

Thanks for any input here.

Thanks for any review.

Looks good to me. Please merge.

Thanks for the review, Andreas.


Guess the complementary dimension when only one is passed to pcolormesh

[x] closes #1290

Seems pretty safe to me, thanks! Could you add some tests, too? (at least for proper code coverage)
A good place to put a test would be here:
https://github.com/pydata/xarray/blob/master/xarray/tests/test_plot.py#L638
(I suspect your PR brakes these tests, btw)

Does anyone know why Travis doesn't start?

I have no experience with how tests are implemented in xarray. Are there pointers somewhere? How do I know the tests pass?

I have added a couple of exceptions to the _infer_xy_labels function and updated the tests as best I could

thanks ! Do we need to worry about the "appveyor" build fails? The failure seems to happen in somewhere unrelated

Can anyone knowledgeable tell me how to fix the appveyor build? It's complaining of a failing test_dask_distributed_integration_test...

I think I managed to restart the Travis-CI test. Maybe try pushing again or rebasing to see if that trigger them again.
See https://github.com/pydata/xarray/issues/1292 for the failing dask distibuted tests. Those can be safely ignored here.

Tests should pass after a rebase or merge thanks to #1293.

I hope everything's ok now. Thanks for the hand-holding

Good, thanks!


[1.12.0-RCx] Review consistency of CLI reference with the code

Review consistency of CLI reference with the code on the new commands:

docker node
docker service
docker swarm

Ping @sfsmithcha: are you available to take a pass a this?

@icecrime I'll take a look

The docker service subcommands do not have cli reference pages yet.

docker node updates part of https://github.com/docker/docker/pull/23656
@thaJeztah working on docker service commands

docker service added here https://github.com/docker/docker/pull/23702

Looks like these were all done, so closing


How to train a image-condition model with my custom dataset?

Hello
@justinpinkney
I want to email to you but failed.
Thank for your great work in image-condition model, and I am very interesting in it.
Can you give me an example about how to train a image-condition model with my custom dataset??
I have found a config sd-image.yaml, but it seems to train on lion dataset.

I'm also interested in doing this.

Ok nice. What have you tried so far?

Ok nice. What have you tried so far?

Hello, may be we can chat with wechat or twiter or other  chatting style.
I just using FrozenImageCilpEmbedding mudule, and i do not have any progress

Ok nice. What have you tried so far?

@BenjaminIrwin

Hey for local data try taking the image variations config and then replace the data part with something like this:
data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 2
    num_workers: 2
    num_val_workers: 0 # Avoid a weird val dataloader issue
    train:
      target: ldm.data.simple.FolderData
      params:
        root_dir: /data/ffhq/images1024x1024
        ext: png
        image_transforms:
        - target: torchvision.transforms.Resize
          params:
            size: 512
            interpolation: 3
        - target: torchvision.transforms.RandomHorizontalFlip
    validation:
      target: ldm.data.simple.FolderData
      params:
        root_dir: /data/celeba1000/HQ
        ext: jpg
        image_transforms:
        - target: torchvision.transforms.Resize
          params:
            size: 512
            interpolation: 3

Depending what you're doing you might want to avoid doing validation which you can do with:
trainer:
    check_val_every_n_epoch: 100000

hope that helps. If you have issues feel free to post your config and any error. I'm curious what sort of dataset you're planning on using if you're willing to share details!

Hey for local data try taking the image variations config and then replace the data part with something like this:
data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 2
    num_workers: 2
    num_val_workers: 0 # Avoid a weird val dataloader issue
    train:
      target: ldm.data.simple.FolderData
      params:
        root_dir: /data/ffhq/images1024x1024
        ext: png
        image_transforms:
        - target: torchvision.transforms.Resize
          params:
            size: 512
            interpolation: 3
        - target: torchvision.transforms.RandomHorizontalFlip
    validation:
      target: ldm.data.simple.FolderData
      params:
        root_dir: /data/celeba1000/HQ
        ext: jpg
        image_transforms:
        - target: torchvision.transforms.Resize
          params:
            size: 512
            interpolation: 3

The above is assuming square images, but you can add a random or centre crop to the image_transforms if yours aren't square already
Depending what you're doing you might want to avoid doing validation which you can do with:
trainer:
    check_val_every_n_epoch: 100000

hope that helps. If you have issues feel free to post your config and any error. I'm curious what sort of dataset you're planning on using if you're willing to share details!

Thanks!
I have run the program, but something was wrong :
the condition image is :

the input image is:

but the reconstruction_gs is

andsamplesis :

Why i have this problem?
This is my config:
`model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: image
image_size: 64
channels: 4
cond_stage_trainable: false   # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
scheduler_config: # 10000 warmup steps
  target: ldm.lr_scheduler.LambdaLinearScheduler
  params:
    warm_up_steps: [ 1000 ]
    cycle_lengths: [<PHONE_NUMBER>0000 ] # incredibly large number to prevent corner cases
    f_start: [ 1.e-6 ]
    f_max: [ 1. ]
    f_min: [ 1. ]

unet_config:
  target: ldm.modules.diffusionmodules.openaimodel.UNetModel
  params:
    image_size: 32 # unused
    in_channels: 4
    out_channels: 4
    model_channels: 320
    attention_resolutions: [ 4, 2, 1 ]
    num_res_blocks: 2
    channel_mult: [ 1, 2, 4, 4 ]
    num_heads: 8
    use_spatial_transformer: True
    transformer_depth: 1
    context_dim: 768
    use_checkpoint: True
    legacy: False

first_stage_config:
  target:  ldm.models.autoencoder.AutoencoderKL #ldm.models.autoencoder.VQModelInterface    #ldm.models.autoencoder.AutoencoderKL
  params:
    embed_dim: 4
    monitor: val/rec_loss
    ddconfig:
      double_z: true
      z_channels: 4
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult:
      - 1
      - 2
      - 4
      - 4
      num_res_blocks: 2
      attn_resolutions: []
      dropout: 0.0
    lossconfig:
      target: torch.nn.Identity

cond_stage_config:
  target: ldm.modules.encoders.modules.FrozenCLIPImageEmbedder

data:
target: main.DataModuleFromConfig
params:
batch_size: 2
num_workers: 2
num_val_workers: 0 # Avoid a weird val dataloader issue
train:
target: ldm.data.simple.FolderData
params:
root_dir: /home/share/movie_dataset/fanghua/png
ext: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 256
interpolation: 3
- target: torchvision.transforms.RandomHorizontalFlip
validation:
target: ldm.data.simple.FolderData
params:
root_dir: /home/share/movie_dataset/fanghua/png
ext: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 256
interpolation: 3
lightning:
find_unused_parameters: false
modelcheckpoint:
params:
every_n_train_steps: 5000
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 1000
max_images: 8
increase_log_steps: False
log_first_step: True
log_images_kwargs:
use_ema_scope: False
inpaint: False
plot_progressive_rows: False
plot_diffusion_rows: False
N: 8
unconditional_guidance_scale: 3.0
unconditional_guidance_label: [""]
trainer:
benchmark: True
# val_check_interval: 5000000 # really sorry
num_sanity_val_steps: 0
accumulate_grad_batches: 1
`
My dataset is fanghua.
@justinpinkney

And I want to know, where is the condition image feeded into and where is the input feeded into?
What are the requirements for condition image?
@justinpinkney

f you have issues feel free to post your config and any error. I'm curious what sort of dataset you're planning on using if you're willing to share details!

I want to set RGB image as my image condition and gray image as inputs, so how can i set the config.yaml?

@justinpinkney  hello bro

Sorry, I'm paying attention now. Remind me what you r issue is on this? The reason for the reconstructions looking like total noise are probably because the vae weights aren't being loaded, try specifying the ckpt_path to load for these like this: https://github.com/justinpinkney/stable-diffusion/blob/main/configs/stable-diffusion/pokemon.yaml#L46

@justinpinkney
hello，I have reloaded the vae weights, but the results are also like this :
condition input:

input:

reconstruction:

samples:

These images above are sampling after 600 iters
This my config:
`model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: image_rgb
image_size: 64
channels: 4
cond_stage_trainable: False   # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
scheduler_config: # 10000 warmup steps
  target: ldm.lr_scheduler.LambdaLinearScheduler
  params:
    warm_up_steps: [ 1000 ]
    cycle_lengths: [<PHONE_NUMBER>0000 ] # incredibly large number to prevent corner cases
    f_start: [ 1.e-6 ]
    f_max: [ 1. ]
    f_min: [ 1. ]

unet_config:
  target: ldm.modules.diffusionmodules.openaimodel.UNetModel
  params:
    image_size: 32 # unused
    in_channels: 4
    out_channels: 4
    model_channels: 320
    attention_resolutions: [ 4, 2, 1 ]
    num_res_blocks: 2
    channel_mult: [ 1, 2, 4, 4 ]
    num_heads: 8
    use_spatial_transformer: True
    transformer_depth: 1
    context_dim: 768
    use_checkpoint: True
    legacy: False

first_stage_config:
  target:  ldm.models.autoencoder.AutoencoderKL #ldm.models.autoencoder.VQModelInterface    #ldm.models.autoencoder.AutoencoderKL
  ckpt_path: models/first_stage_models/kl-f8/model.ckpt
  params:
    embed_dim: 4
    monitor: val/rec_loss
    ddconfig:
      double_z: true
      z_channels: 4
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult:
      - 1
      - 2
      - 4
      - 4
      num_res_blocks: 2
      attn_resolutions: []
      dropout: 0.0
    lossconfig:
      target: torch.nn.Identity

cond_stage_config:
  target: ldm.modules.encoders.modules.FrozenCLIPImageEmbedder

data:
target: main.DataModuleFromConfig
params:
batch_size: 3
num_workers: 6
num_val_workers: 0 # Avoid a weird val dataloader issue
train:
target: ldm.data.simple.FolderData
params:
root_dir: /home/share/movie_dataset/fanghua/png
ext: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 256
interpolation: 3
- target: torchvision.transforms.RandomHorizontalFlip
validation:
target: ldm.data.simple.FolderData
params:
root_dir: /home/share/movie_dataset/fanghua/png
ext: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 256
interpolation: 3
lightning:
find_unused_parameters: false
modelcheckpoint:
params:
every_n_train_steps: 500
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 100
max_images: 4
increase_log_steps: False
log_first_step: True
log_images_kwargs:
use_ema_scope: False
inpaint: False
plot_progressive_rows: False
plot_diffusion_rows: False
N: 8
unconditional_guidance_scale: 3.0
unconditional_guidance_label: [""]
trainer:
benchmark: True
# val_check_interval: 5000000 # really sorry
num_sanity_val_steps: 0
accumulate_grad_batches: 1
`

@justinpinkney
hello
the vae checkpoints are these right?

now  the reconstruction is like this:

samples are like this:

@justinpinkney

@justinpinkney hello the vae checkpoints are these right?

Yes those are the zips (and assuming you extracted the checkpoint from inside them) then those should contain the weights. The reconstructions literally only touch the vae encoder/decoder, so if those don't look right something is wrong with that part of the model.
what's the output of ls -lah models/first_stage_models/kl-f8/model.ckpt? is it the right size, might it be corrupted?

@justinpinkney
hello  the output is 1.1G

The size is right, and i do not have any resolution to solve my problem, canyou give me a example  configure abut the con_stage_key is image and the conditional input is image?
Thanks!!

In that can I don't know why the vae reconstructions aren't working. What is the command line argument you are using to launch training?

This is my  command line :
CUDA_VISIBLE_DEVICES=7 python main.py --base stable-diffusion/configs/fanghua_cond.yaml -t --gpus 0,
and my config is:
`model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: image
cond_stage_key: image_rgb
image_size: 64
channels: 4
cond_stage_trainable: False   # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
scheduler_config: # 10000 warmup steps
  target: ldm.lr_scheduler.LambdaLinearScheduler
  params:
    warm_up_steps: [ 1000 ]
    cycle_lengths: [<PHONE_NUMBER>0000 ] # incredibly large number to prevent corner cases
    f_start: [ 1.e-6 ]
    f_max: [ 1. ]
    f_min: [ 1. ]

unet_config:
  target: ldm.modules.diffusionmodules.openaimodel.UNetModel
  params:
    image_size: 32 # unused
    in_channels: 4
    out_channels: 4
    model_channels: 320
    attention_resolutions: [ 4, 2, 1 ]
    num_res_blocks: 2
    channel_mult: [ 1, 2, 4, 4 ]
    num_heads: 8
    use_spatial_transformer: True
    transformer_depth: 1
    context_dim: 768
    use_checkpoint: True
    legacy: False

first_stage_config:
  target:  ldm.models.autoencoder.AutoencoderKL #ldm.models.autoencoder.VQModelInterface    #ldm.models.autoencoder.AutoencoderKL
  ckpt_path: stable-diffusion-main/models/first_stage_models/kl-f8/model.ckpt
  params:
    embed_dim: 4
    monitor: val/rec_loss
    ddconfig:
      double_z: true
      z_channels: 4
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult:
      - 1
      - 2
      - 4
      - 4
      num_res_blocks: 2
      attn_resolutions: []
      dropout: 0.0
    lossconfig:
      target: torch.nn.Identity

cond_stage_config:
  target: ldm.modules.encoders.modules.FrozenCLIPImageEmbedder

data:
target: main.DataModuleFromConfig
params:
batch_size: 3
num_workers: 6
num_val_workers: 0 # Avoid a weird val dataloader issue
train:
target: ldm.data.simple.FolderData
params:
root_dir: /home/share/movie_dataset/fanghua/png
ext: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 256
interpolation: 3
- target: torchvision.transforms.RandomHorizontalFlip
validation:
target: ldm.data.simple.FolderData
params:
root_dir: /home/share/movie_dataset/fanghua/png
ext: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 256
interpolation: 3
lightning:
find_unused_parameters: false
modelcheckpoint:
params:
every_n_train_steps: 500
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 100
max_images: 4
increase_log_steps: False
log_first_step: True
log_images_kwargs:
use_ema_scope: False
inpaint: False
plot_progressive_rows: False
plot_diffusion_rows: False
N: 8
unconditional_guidance_scale: 3.0
unconditional_guidance_label: [""]
trainer:
benchmark: True
# val_check_interval: 5000000 # really sorry
num_sanity_val_steps: 0
accumulate_grad_batches: 1
`
@justinpinkney

@justinpinkney
hello can i ask you some questions?

@justinpinkney
Do i need to use this ckpt?
sd-clip-vit-l14-img-embed_ema_only.ckpt?
like this:
model:
base_learning_rate: 1.0e-04
ckpt_path:sd-clip-vit-l14-img-embed_ema_only.ckpt
target: ldm.models.diffusion.ddpm.LatentDiffusion

There is an extra argument to main.py to load from an existing checkpoint you should use. Let me check

Use --finetune_from and pass the checkpoint path. This should load the existing model so the initial set of logs should just look like the output of that model

But why the reconstruction is not work ？I want to train a new model
@justinpinkney

I have no solution for it.  when i load the weight ckpt the reconstruction is also not work
@justinpinkney

nt to main.py to load from an existing checkpoint you should use. Let me check

大佬，能加下你的微信，一起交流下？？

可以 我加你
繁华落尽
@.***

------------------ 原始邮件 ------------------
发件人:                                                                                "justinpinkney/stable-diffusion"                                                                                @.>;
发送时间: 2023年4月3日(星期一) 下午5:04
@.>;
<EMAIL_ADDRESS>主题: Re: [justinpinkney/stable-diffusion] How to train a image-condition model with my custom dataset? (Issue #49)
nt to main.py to load from an existing checkpoint you should use. Let me check
大佬，能加下你的微信，一起交流下？？
—
Reply to this email directly, view it on GitHub, or unsubscribe.
You are receiving this because you were mentioned.Message ID: @.***>

可以 我加你 繁华落尽 @.***
…
------------------ 原始邮件 ------------------ 发件人: "justinpinkney/stable-diffusion" @.>; 发送时间: 2023年4月3日(星期一) 下午5:04 @.><EMAIL_ADDRESS>主题: Re: [justinpinkney/stable-diffusion] How to train a image-condition model with my custom dataset? (Issue #49) nt to main.py to load from an existing checkpoint you should use. Let me check 大佬，能加下你的微信，一起交流下？？ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>

微信<PHONE_NUMBER>3

可以 我加你 繁华落尽 @.***
…
------------------ 原始邮件 ------------------ 发件人: "justinpinkney/stable-diffusion" @.>; 发送时间: 2023年4月3日(星期一) 下午5:04 @.><EMAIL_ADDRESS>主题: Re: [justinpinkney/stable-diffusion] How to train a image-condition model with my custom dataset? (Issue #49) nt to main.py to load from an existing checkpoint you should use. Let me check 大佬，能加下你的微信，一起交流下？？ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>

Hi， may I add your wechat for further discussing? I am also trying to train image condition model on my data. Thanks!
My wechat: wtliao

@justinpinkney thanks for sharing this nice code base. I have a question for training the image condition model. Stable diffusion model is trained by randomly dropping 10% prompt (which means the condition prompt is set as empty "") to guarantee the performance of unconditional model, because it utilize the class-free guidance. When training image condition one, is there also such setting? Thanks!

Yep. I do the same thing but replace with zeros 10% of the time

Yep. I do the same thing but replace with zeros 10% of the time

I see the code in https://github.com/justinpinkney/stable-diffusion/blob/4ac995b6f663b74dfe65400285e193d4167d259c/ldm/models/diffusion/ddpm.py#L397 to set it as none condition input
and in https://github.com/justinpinkney/stable-diffusion/blob/4ac995b6f663b74dfe65400285e193d4167d259c/ldm/modules/encoders/modules.py#L425 to set the return as zero.
But I dont know how to trigger this setting. I add the following ucg_training setting in config file.
    ucg_training:
      image:
        p: 0.1
        val: []

But it doesn't work, could you help me how to set it in the image variant training (batch>1). Thanks!

可以 我加你 繁华落尽 @.***
…
------------------ 原始邮件 ------------------ 发件人: "justinpinkney/stable-diffusion" @.>; 发送时间: 2023年4月3日(星期一) 下午5:04 @.><EMAIL_ADDRESS>主题: Re: [justinpinkney/stable-diffusion] How to train a image-condition model with my custom dataset? (Issue #49) nt to main.py to load from an existing checkpoint you should use. Let me check 大佬，能加下你的微信，一起交流下？？ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>

大佬，可以加下微信不，有些问题想交流下：18623454317

可以 我加你 繁华落尽 @.***
…
------------------ 原始邮件 ------------------ 发件人: "justinpinkney/stable-diffusion" @.>; 发送时间: 2023年4月3日(星期一) 下午5:04 @.><EMAIL_ADDRESS>主题: Re: [justinpinkney/stable-diffusion] How to train a image-condition model with my custom dataset? (Issue #49) nt to main.py to load from an existing checkpoint you should use. Let me check 大佬，能加下你的微信，一起交流下？？ — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you were mentioned.Message ID: @.***>

@pokameng 大佬，可否交流下


Rollback on failure

Added support to rollback service if the upgrade failed.
The rollback will be done if you pass the option --rollback-on-error with --wait-for-upgrade-to-finish.
After the rollback, the program ends with code 1 to be sure that our CI job fails.
Just share if you have any suggestions to improve the feature 👍

Great 👍  thanks for the PR


Fix: Add config for mac/arm64

I've seen a similar PR for a more recent SDK version but sadly I still have an old Couchbase server version 4.1.0 running for some data. That isn't scheduled to be updated anytime soon.

I already went and signed the individual agreement, the page shows

Agreement already submitted.

Just wanted to check in and see if you were able to sort this out?

Nope, I don't understand what more is needed of me. I understand keeping support for such an old version of couchbase SDK is not top priority but this sure is a pain to deal with.
If you are completely unable to install this version on a M1 mac, what works for me is to install a specific npm version through nvm using a terminal with rosetta and then using that specific version to npm install and run my node app afterwards. I also used brew to install this brew install libcouchbase@2 at some point which might help.

@jchris, @brett19 Could someone do something?

I finally took the time to jump through all the hoops to get this into the Gerrit system but sadly the bot is not happy and I have no idea what to do next
https://review.couchbase.org/c/couchnode/+/207927


logarithmic axes support

I don't need it yet but should be taken into account when (re-)designing like in #68 I think.
If it already works somehow even better ;)

this should be done easly with 0.6, will work on it after 0.6 is ready

with 0.6 this is really easy, here is an example
https://github.com/beto-rodriguez/Live-Charts/tree/master/ChartsTest/Scatter Examples/logarithmicScatter
I will update example as soon as possible to display labels without log, I will use a label formatter for this.


Example

Hello, good morning, I would like to know if there is any project that can be shared where django-simple-history is used, since I am trying to implement it in my application but I can not access historical information. The lio mio comes at the moment of registering the model, since when I consult the admin, I see the model as is but I do not see who modifies it, nor the date.
Sorry, my english is not good, I use google translate

We don't have a demo project right now. What you may be missing, regarding the admin, is that you need to opt-in to seeing history in your admin by using simple_history.admin.SimpleHistoryAdmin.


Remove unused code

Fixes #10418
The PR removes symbols which are not referenced at all (either on desktop or web).

[x] Pull request represents a single change (i.e. not fixing disparate/unrelated things in a single PR).
[x] Title summarizes what is changing.
[ ] Has a news entry file (remember to thank yourself!).
[ ] Appropriate comments and documentation strings in the code.
[ ] Has sufficient logging.
[ ] Has telemetry for feature-requests.
[ ] Unit tests & system/integration tests are added/updated.
[ ] Test plan is updated as appropriate.
[ ] package-lock.json has been regenerated by running npm install (if dependencies have changed).

Codecov Report

Merging #10426 (dd80acc) into main (1208b19) will increase coverage by 1%.
The diff coverage is 78%.

@@           Coverage Diff           @@
##            main   #10426    +/-   ##
=======================================
+ Coverage     69%      71%    +1%
=======================================
  Files        479      478     -1
  Lines      29452    28676   -776
  Branches    5039     4856   -183
=======================================
- Hits       20575    20415   -160
+ Misses      6987     6362   -625
- Partials    1890     1899     +9

Impacted Files
Coverage Δ

src/intellisense/notebookPythonPathService.node.ts
45% <ø> (ø)

src/kernels/execution/helpers.ts
69% <ø> (+1%)
:arrow_up:

src/platform/common/platform/errors.ts
100% <ø> (+11%)
:arrow_up:

src/platform/common/platform/types.ts
100% <ø> (ø)

src/platform/common/utils.ts
69% <ø> (-1%)
:arrow_down:

src/platform/common/utils/async.ts
51% <ø> (+<1%)
:arrow_up:

src/platform/common/utils/decorators.ts
91% <ø> (+6%)
:arrow_up:

src/platform/common/utils/misc.ts
82% <ø> (+10%)
:arrow_up:

src/platform/common/utils/sysTypes.ts
73% <ø> (+16%)
:arrow_up:

src/platform/vscode-path/map.ts
10% <ø> (+<1%)
:arrow_up:

... and 30 more

I'm pro most of the removals, but did wonder a bit about the vscode-path removals. Wasn't that a direct directory copy of existing code that @rchiodo added for web? Seems a bit odd to have only a partial implementation of the API.

@IanMatthewHuff you are correct, the vscode-path was borrowed for enabling path on Web. The catch here is we want a web enabled path but that depends on strings and a few other base modules from VS Code, though only a few functions are used, we copied them all over. The modules are pretty stable in VS Code (normally no changes to existing functions anymore, only additions), and strings contains tons of logic to handle unicode segmentation and emojis, which are not relevant to us.
If we could export path module from vscode, it might help so we don't have to manually tree shake the modules. But I'm also not too concerned about removing dead code from those modules, as we are very unlikely to modify them.

@rebornix Thanks for explanation, I was worried about having to update it all the time doing "partial" updates of only some functions. Sounds good to remove then.

I'm going to merge this because it's affecting one of the test failures I'm working on. I think we're not parsing semvers correctly and that's messing up conda detection.


Randomly getting : AssertionError: Region not allocated

Describe the bug
Hi! During the execution of a code, I randomly have an "AssertionError: Region not allocated" exception.
It seems to be related to this old issue: https://github.com/pyglet/pyglet/issues/160
This error happens when I attribute a sprite to a batch.
traceback:
  File ((mycode.py))
    "spr2.batch = my_batch"

  File pyglet\sprite.py:435 in batch
    self._vertex_list.delete()

  File pyglet\graphics\vertexdomain.py:267 in delete
    super().delete()

  File pyglet\graphics\vertexdomain.py:157 in delete
    self.domain.allocator.dealloc(self.start, self.count)

  File pyglet\graphics\allocation.py:264 in dealloc
    assert p >= 0 and size <= alloc_size - p, 'Region not allocated'  # noqa: PT018

AssertionError: Region not allocated

How To Reproduce
I made the following code to reproduce the error. This code doesn't make sense being presented like that but it's a simplified version of larger codes which are useful for my project but not for reproducing this issue.
Minimal code to reproduce:

put a debug.png near the script
run the code for a bit of time (<5minutes?, <100k iterations?, very inconsistent)
AssertionError: Region not allocated will happen randomly after some time

Script:
from pyglet.gl import *
import pyglet

#init
window = pyglet.window.Window(800, 600, resizable=True)
batch = pyglet.graphics.Batch()

#MySprite
class MySprite:
    cache_e_to_imgs = {}
    def __init__(self, e):

        if not e in MySprite.cache_e_to_imgs:
            MySprite.cache_e_to_imgs[e] = [pyglet.resource.texture(f'{e}.png')]

        imgs = MySprite.cache_e_to_imgs[e]
        anim = [pyglet.sprite.Sprite(img=img) for img in imgs]
        self.anim, self.frames = anim, imgs

#Visual component of an object
class MyVisualObject:
    def __init__(self, obj):
        self.object = obj

    #init sprite or skeleon
    def init(self, e=None):
        self.v = MySprite(self.object.name)

#An object
class MyObject:
    def __init__(self,name):
        self.name = name
        self.v = MyVisualObject(self)
        self.v.init()

data = {'i':0}
@window.event
def on_draw():
    glClearColor(.7,.7,.7,1)
    window.clear()

    #new_obj
    new_obj = MyObject('debug')
    img = new_obj.v.v.frames[0]

    print(data['i'], 'id(texture):', id(img))
    spr = pyglet.sprite.Sprite(img=img)
    spr.batch = batch

    data['i'] += 1
    batch.draw()

pyglet.app.run()

Someone else managed to reproduce this issue on his setup, I'm using the latest version of pyglet (2.0.16).
Thansk for trying to understand it!

It seems that the issue is related to how the garbage collector interacts with pyglet.
Explicitly deleting ( sprite.delete( ) ) all sprites I create ("spr" and "new_obj.v.v.anim") instead of relying on gc seems to be a way to solve the issue. Without explicit deletion, the issue remains.
Thanks to caffeinepills and square789 for helping with this issue on Discord!

No problem! I will close this for now as Whiax has resolved the issue via manual deletions.


[PE-D][Tester A] Cross-listed modules are treated as separate modules

Some modules have two different module codes but are functionally the same module, being taught as one class under the same professor, e.g. MA3238/ST3236. Other modules are cross-listed as they are taught by different departments depending on the semester, e.g. MA2116/ST2131. But these are treated as separate modules in the app.

However, this is unlikely to be much of an issue because they are preclusions anyway.

Labels: severity.Low type.FeatureFlaw
original: Prearo/ped#2

Closed since the team decided that there would not be a student that is taking both in the same semester. Additionally, we want to cater to any NUS student in the future which is why we allow both to be added.


fix(json-config-loading): Fix handling Invalid AutoConfigs and fix config writing.

Contains

fix invalid json config writing
add logging when config invalid
use defaults when config invalid
Fixes #4642

How to test
Chapter 1 (fix writing, require default settings):

Open TS
Open Settings -> autoconfig
change one of setting (set non-default)
exit game
Open TS
Open Settings -> autoconfig
change same settings to default
exit game
check config file - content should be {} (all defaults)

Chapter 2 (logging and invalid config):

make Config file as invalid - to corrupt json structure
Open TS
Check Logs - you should see error log entry about config loading error
Check how renders invalid config in autoconfig - should be default
exit game
check config file - content should be {} (all defaults) if you don't change settings at (4)

I guess keeping the broken one is okay as long as settings are not changed.
If they are changed I'd say we should clean the broken config up before.

It isn't easy. fast way - easy. right way - not so easy.
For example: if you choose equals to default values - then you will have wrong case with changed values will stay, when you returns default values by hand - they not saves.


Update readme to add examples

Change 4th script to output unique values for an attribute key

@m-yosefpor can you please review this?

Going to merge so new contributors can use this, @m-yosefpor / @sashman plz leave any comments here so I can keep tweaking.

❤  thanks very much @alikhajeh1 @aliscott!


Use std.http.Method for Request.method

This PR does the following:

Request.method becomes an std.http.Method enum so developers can use switch statements on it
Request.method_str is the original string representation of the http method

Why both? Quality of life. Since there is no easy and concise way of printing Enum in Zig, one will make request easier to compare and the other will be easier to print. Examples also have been updated accordingly. This decision was made based on my personal need without approval of anyone, so please do feel free to reject this PR, request changes, or make criticisms if this change doesn't feel fit for the project.
This has been tested as part of my own project, and everything is working as intended. A test package is available, use at your own risk.
Closes #67

that's my guy

LGTM!

I made changes accordingly, please take a look.

potentially unused import

I think it is used, check the reply.

I'll have to make a new release

I don't mind, I can use my own alpha build for the time being.

THANK YOU for this great contribution

No, THANK YOU for this amazing package, glad I can help.

Merged on the phone 😎

😎


Refactor of global state

This uses context API to allow for global cart state and actions. This handles some of the out of sync behavior of the cart and cart icon depending on the view.

not sure what check this is failing, but it works on my machine :)


add keybind O to toggle output

For #5425

[x] Pull request represents a single change (i.e. not fixing disparate/unrelated things in a single PR).
[x] Title summarizes what is changing.
[x] Has a news entry file (remember to thank yourself!).
[ ] Appropriate comments and documentation strings in the code.
[ ] Has sufficient logging.
[ ] Has telemetry for enhancements.
[ ] Unit tests & system/integration tests are added/updated.
[ ] Test plan is updated as appropriate.
[ ] package-lock.json has been regenerated by running npm install (if dependencies have changed).

Question for @claudiaregio: do we want this in the cell or editor context?

Codecov Report

Merging #5512 (0bc1589) into main (f536e12) will decrease coverage by 0%.
The diff coverage is 11%.

:exclamation: Current head 0bc1589 differs from pull request most recent head b88c501. Consider uploading reports for the commit b88c501 to get more accurate results

@@          Coverage Diff          @@
##            main   #5512   +/-   ##
=====================================
- Coverage     73%     72%   -1%
=====================================
  Files        401     401
  Lines      26599   26617   +18
  Branches    3884    3887    +3
=====================================
- Hits       19418   19371   -47
- Misses      5582    5634   +52
- Partials    1599    1612   +13

Impacted Files
Coverage Δ

...rc/client/datascience/commands/notebookCommands.ts
63% <0%> (-5%)
:arrow_down:

...ient/datascience/interactive-ipynb/nativeEditor.ts
69% <0%> (-2%)
:arrow_down:

src/client/datascience/types.ts
100% <ø> (ø)

src/client/datascience/notebook/notebookEditor.ts
32% <8%> (-2%)
:arrow_down:

src/client/datascience/constants.ts
99% <100%> (+<1%)
:arrow_up:

...nt/datascience/jupyter/kernels/kernelSelections.ts
50% <0%> (-45%)
:arrow_down:

...rc/client/datascience/notebookStorage/baseModel.ts
74% <0%> (-6%)
:arrow_down:

src/client/common/process/pythonDaemonPool.ts
75% <0%> (-5%)
:arrow_down:

...datascience/jupyter/liveshare/hostJupyterServer.ts
63% <0%> (-3%)
:arrow_down:

...ctive-common/intellisense/languageServerWrapper.ts
73% <0%> (-2%)
:arrow_down:

... and 12 more


FancyZones layout keeps resetting away from the Custom Layout I set

Microsoft PowerToys version
0.68.1
Installation method
Microsoft Store
Running as admin
None
Area(s) with issue?
FancyZones Editor
Steps to reproduce

I set a layout for both my monitors
I do nothing (The computer is left running)
The bug happens

✔ Expected Behavior
The layout does not change
❌ Actual Behavior
Randomly changes back to either column or priority column layout (I don't remember which)
Other Software
No response

Do you use different virtual monitors?
/needinfo


use async/await in the preHandler hook

As titled, it should simplify a lot of the code.

@cemremengu , is it not though (implicitly), by virtue of the fact that it returns a Promise?


Update deck building rules

[ ] core deeds do not count towards starting posse limit in new rules
[ ] jokers should not be restricted in new restricted list
[ ] rename restricted lists (Old Timer, Weird West)

Adding to list another deck building rule from issue townteki/townsquare#943


Add logistic regression example with NUTS

This PR adds an example of a logistic regression model, similar to PyMC3's, and samples from its posterior with NUTS.

@rlouf Anything that's missing here?

Yep, correct results.

What's causing the current issue with the logistic regression results?

The mass matrix + step size that is used by PyMC3 leads to only divergent transitions; actually the largest step size at which we get an acceptance rate > .5 is orders of magnitude smaller than that used by PyMC3. Investigating why that's the case.

@rlouf Which mass matrix initialization method is this using? PyMC3's or Stan's? If PyMC3, @ricardoV94 actually found a problem there and @aseyboldt has worked on a new one that works much better.

https://github.com/pymc-devs/pymc3/pull/5004

Ah thanks, I'll check this out. I decided to check against numpyro's returned parameters in the end.
Nevertheless since PyMC3 samples fine with the parameters it obtains from warmup I shouldn't have any issue on my end.

@rlouf Can we add reviewing here as well?

Do you mean NBReview by reviewing?

Yeah.
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I guess that's @brandonwillard's decision to make.

Do we need to add an application to the aesara-devs org or something?


⚠ Bofrost.be has degraded performance

In 11f415f, Bofrost.be (https://www.bofrost.be) experienced degraded performance:

HTTP code: 200
Response time: 2834 ms

Resolved: Bofrost.be performance has improved in 341a565.