Allow for selection of Terraform Cloud project ID other than 'Default Project'

Describe the outcome you'd like
I would like to be able to manage my AFT workspaces in a Terraform Cloud project other than Default Project.
Is your feature request related to a problem you are currently experiencing? If so, please describe.
The module does not seem to support a variable where I can specify a project_id to ensure the workspaces managed by AFT get created under a particular Terraform Cloud project of my choosing.
This would be a nice feature because If the Default Project space is used for other (non-AFT) purposes, existing workspaces can get drowned out and hard to find once AFT starts creating a lot of customizations workspaces.
Additional context
Based on my understanding of the AFT workflow, it may make sense to:

Store the TFC project_id as an SSM parameter (sourced from an option module variable input - which could hopefully default to null).
Retrieve the parameter in workspace_manager.py.
Add support for data.relationships.project.data.id in the create workspace request body.

See:

https://www.hashicorp.com/blog/terraform-cloud-adds-projects-to-organize-workspaces-at-scale
https://developer.hashicorp.com/terraform/cloud-docs/api-docs/workspaces

Other considerations
Assume AFT workspaces are already being created/used in Default Project. If this feature were to be added, will AFT simply correct the course? Or, would the AFT user need to perform any sort of migration to transition any pre-existing AFT  workspaces to a different project?

Do you see value of specifying the project as part of account request? so that admins can manage workspaces by group of projects and perhaps use it to drive other things (i.e. variable sets)

@wellsiau-aws In my case I am primarily using AFT for its global-customizations feature as a means of being able to deploy baseline (usually compliance-related) resources to all of my accounts.
For now, I have chosen to keep terraform code related to my actual application-related infrastructure outside of the AFT workflow (i.e. I'm not using account-customizations for anything meaningful at the moment). So from my personal use-case/perspective, AFT's relationship with TFC workspaces is quite simple. I just need a separate TFC project space to manage all of my customizations workspaces.
I suppose from your perspective you need to consider that account-customizations might be set for application-A, application-B and application-C and that end-users may wish to manage account-customizations-related workspaces in separate TFC project folders (mapped to each application).
So to answer your question directly:
No, I don't see value in that for my use case. However, I think thats largely because of how I'm making use of AFT. If I were making more use of account-customizations, I could see why the account request section of the workflow might make more sense as the place where project_id is defined.

One other interesting pattern I'm exploring in terms of the relationship between a TFC workspace and an AWS account is the authentication piece.
I notice today that you guys use workspace variables with session credentials to allow the workspace to apply customizations (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_SESSION_TOKEN).
I wonder if you may be able to create a more permanent (but still session-driven) relationship between the customizations TFC workspaces and the AWS account by making use of an IAM OIDC provider. This has recently been supported by TFC.

Thanks for sharing your perspective, this is very useful!
regarding dynamic provider crednetials / OIDC-based variable, we have open request about it (#318)

Thanks for sharing your perspective, this is very useful!
regarding dynamic provider crednetials / OIDC-based variable, we have open request about it (#318)

No problem. Good to see you're looking at OIDC providers!

@rybons thank you for reaching out.
I have created a backlog to explore this feature request.

+1 on this idea. I would like to make use of project-scoped permissions in TFC, but manually moving new workspaces into the project is clunky. Adding this feature would be awesome

My workaround for the time being is to rename the default project and leverage it just for AFT


[FEATURE] allow job posters to see list of applications on their job page

in edit.html and manage.html these are two views used to manage a job post
in apply_token table we have applications for each job post (can join using job_id key)
not all jobs have apply_tokens as these are only when the application is done via CV (email) and not via outside link (url)
when apply_tokens exist for a job please display a table with the applications, <created_at, email, cv (create download link)>
display non-confirmed applications as well, but rank them at the bottom of the table

/bounty $50

/bounty $50

Heya, I'm happy to pick up this issue :)


Non-sense text

Hi
This text "Replace  with the path to EuStockMarkets_NA.csv on your local file system, for example, C:/Users/admin/Documents/Microsoft/EuStockMarkets_NA.csv." doesn´t make sense since script dont have file path.
Regards,

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

ID: a4253d0e-7a9f-a0a7-edee-4cbd387ec98d
Version Independent ID: 14a7818e-43ac-86ca-a4a1-d4da38441e42
Content: Use R in Power Query Editor - Power BI
Content Source: powerbi-docs/connect-data/desktop-r-in-query-editor.md
Service: powerbi
Sub-service: powerbi-ai
GitHub Login: @davidiseminger
Microsoft Alias: davidi

Thanks for your comment, @marciotex. This issue has been assigned to the doc author for review.

Good point - I've removed that passage. Thanks!


No module named caffe2_pybind11_state_gpu

System information

Operating system: Mac OS
Compiler version:
CMake version:
CMake arguments:
Relevant libraries/versions (e.g. CUDA):No cuda

CMake summary output
******** Summary ********
I  build Caffe2 without Anaconda. But when I run

cd ~ && python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" \|\| echo "Failure"

It shows Failure. When I run a Python interpreter in the caffe2/build directory and then trying from caffe2.python import core, it tells me that

WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu

Is there any way to improve this? It's a very confusing message. I just got it myself.

I can add some documentation on the installation page, but I've found this warning to be very useful when using Caffe2 on Ubuntu boxes with GPUs.

@pjh5 I also encountered this issue. But I indeed set the USE_CUDA flag to True in CMakeList.txt. And the machine indeed has GPUs. So how can I fix this?

@Yuliang-Zou if cmake cannot find CUDA then it will turn USE_CUDA to OFF, no matter what you set it to. Look through your cmake output for an error message and search through our issues for a fix. If you can't find what you need, then please open a new issue with your cmake output.

@pjh5 I set LD_LIBRARY_PATH=/path/to/cuda/lib64, is it not enough for caffe2 to find CUDA?


CVE-2007-4559 Patch

Patching CVE-2007-4559
Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.
If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

Codecov Report
Base: 66.95% // Head: 67.28% // Increases project coverage by +0.32% :tada:

Coverage data is based on head (139a2b1) compared to base (709bd78).
Patch coverage: 91.66% of modified lines in pull request are covered.

Additional details and impacted files
@@            Coverage Diff             @@
##           master      #53      +/-   ##
==========================================
+ Coverage   66.95%   67.28%   +0.32%
==========================================
  Files          10       10
  Lines         802      813      +11
==========================================
+ Hits          537      547      +10
- Misses        265      266       +1

Impacted Files
Coverage Δ

stixpy/data/test.py
93.75% <91.66%> (-1.49%)
:arrow_down:

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.

:umbrella: View full report at Codecov.
:loudspeaker: Do you have feedback about the report comment? Let us know in this issue.

Nice, spot this has been closed in #58.


Auto-detect and raise error if CSI is required for BAM indexing

While running the rnaseq pipeline with barley genome (~4GB) and 90 single end samples, I ran into an issue with BAM index size.
Error:
$ more work/bc/3ce96a9e1f5824ca661081f18a50b5/.command.err
[E::hts_idx_check_range] Region 536886600..536886700 cannot be stored in a bai index. Try using a csi index
[E::sam_index] Read 'SRR5819886.10734788' with ref_name='chr2H', ref_length=665585731, flags=272, pos=536886601 cannot be indexed
samtools index: failed to create index for "rar3_t0_rep2.sorted.bam": Numerical result out of range

From StdOut:
-[nf-core/rnaseq] Pipeline completed with errors-
Error executing process > 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:BAM_SORT_SAMTOOLS:SAMTOOLS_INDEX (rar3_t0_rep2)'

Caused by:
  Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:BAM_SORT_SAMTOOLS:SAMTOOLS_INDEX (rar3_t0_rep2)` terminated with an error exit status (1)

Command executed:

  samtools index  rar3_t0_rep2.sorted.bam
  cat <<-END_VERSIONS > versions.yml
  SAMTOOLS_INDEX:
      samtools: $(echo $(samtools --version 2>&1) | sed 's/^.*samtools //; s/Using.*$//')
  END_VERSIONS

Command exit status:
  1

Command output:
  (empty)

Command error:
  [E::hts_idx_check_range] Region 536886600..536886700 cannot be stored in a bai index. Try using a csi index
  [E::sam_index] Read 'SRR5819886.10734788' with ref_name='chr2H', ref_length=665585731, flags=272, pos=536886601 cannot be indexed
  samtools index: failed to create index for "rar3_t0_rep2.sorted.bam": Numerical result out of range

Work dir:
  /lustre/scratch123/tol/teams/tolit/users/ps22/work/bc/3ce96a9e1f5824ca661081f18a50b5

Tip: view the complete command output by changing to the process work dir and entering the command `cat .command.out`

Command:
bsub -q oversubscribed -R 'select[mem>16000] rusage[mem=16000] span[hosts=1]' -M 16000 -J rnaseq -o rnaseq.%J.o -e rnaseq.%J.e "nextflow run nf-core/rnaseq --input samplesheet.csv --fasta Barley_MorexV3_pseudomolecules.fasta --gff Hv_Morex_pgsb_Jul2020.gff --outdir rnaseq --skip_multiqc -resume -profile singularity,sanger -r 3.4 -c tol.config"

From samtools author Robert Davies:

Yes, BAI has a limit of 512 Mbases. CSI was created to fix this. I'd recommend using CSI for everything if you can, so you don't have to worry about it.

One other problem you may hit with Tree of Life is a reference length limit of about 2 Gbases in the BAM format. If you're planning to make anything that long, you should ask samtools to output the .sam.gz format which can handle longer references. To get everything to work properly you'll also need to use samtools 1.14 or later; and beware that other programs may not be able to read the file if they can't accept the very large position values.

Another concern is compatibility issue with downstream software. Further comments on that:

You could have problems with some software, see for example https://github.com/samtools/htsjdk/issues/900

However, as bai doesn't work for longer references, there's not much choice for them. For maximum compatibility you could check the length of the longest reference and use that to select .bai or .csi as appropriate.

Hi @priyanka-surana ! It is going to be a little tricky trying to auto-detect this and switch from using BAI -> CSI in the pipeline. Mainly because this would also need to be auto-detected somehow in modules.config when setting the appropriate configuration based on the value of params.bam_csi_index.
The next best solution was to auto-detect this at the beginning of the pipeline based on the sizes of the contigs in the FAI file and to raise an error recommending the usage of --bam_csi_index as shown in the screenshot below. At least the pipeline will fail gracefully right at the beginning rather than getting quite far in and then failing.

Be great if you are able to test this on the wheat genome when https://github.com/nf-core/rnaseq/pull/770 is merged into dev.

@drpatelh Sounds good.

Thank you! Merged.
nextflow pull nf-core/rnaseq
nextflow run nf-core/rnaseq <YOUR_PARAMETERS> -r dev

It gives the error as expected.
From .nextflow.log
....
Feb-21 10:49:40.741 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[jobId: 908221; id: 2; name: NFCORE_RNASEQ:RNASEQ:PREPARE_GENOME:CUSTOM_GE
TCHROMSIZES (Barley_MorexV3_pseudomolecules.fasta); status: COMPLETED; exit: 0; error: -; workDir: /lustre/scratch123/tol/teams/tolit/users/ps22/work/e9/ab796c20d1064b1b1339545
69aad6e started:<PHONE_NUMBER>844; exited: 2022-02-21T10:49:39Z; ]
Feb-21 10:49:40.755 [Task submitter] DEBUG nextflow.executor.GridTaskHandler - [LSF] submitted process NFCORE_RNASEQ:RNASEQ:FASTQC_UMITOOLS_TRIMGALORE:TRIMGALORE (mla6_t16_rep1
) > jobId: 908302; workDir: /lustre/scratch123/tol/teams/tolit/users/ps22/work/6a/0a2d50302dd180623d8543fc1bb833
Feb-21 10:49:40.755 [Task submitter] INFO  nextflow.Session - [6a/0a2d50] Submitted process > NFCORE_RNASEQ:RNASEQ:FASTQC_UMITOOLS_TRIMGALORE:TRIMGALORE (mla6_t16_rep1)
Feb-21 10:49:40.756 [Actor Thread 42] ERROR nextflow.Nextflow - =============================================================================
  Contig longer than 512000000bp found in reference genome!

  chr1H: 516505932

  Provide the '--bam_csi_index' parameter to use a CSI instead of BAI index.

  Please see:
  https://github.com/nf-core/rnaseq/issues/744
=============================================================================

Awesome! 🚀


Add MPI cluster resolver and update documentation of SLURM cluster resolver

@frankchn As discussed I updated the documentation for the Slurm cluster resolver enhanced in #36159 which became outdated.
I also added the MPI cluster resolver mentioned there and included it in the documentation update to avoid conflicts or dependencies between those 2 related changes.
2 design decisions of the MPI cluster resolver I wanted to highlight:

import of mpi4py only on constructor (only place where it is needed) to allow importing the file even when the package is not installed. Can be useful when conditionally switching on the used resolver and can reduce program startup time.
removed parameter tasks_per_node (actually just didn't add it). As I highlight in the Slurm doc file using it is usually not a good idea as the resolver is able to do that itself and it is very easy to use wrong and hard to use right. I don't see a usecase for that, especially when using MPI

@frankchn The error is

do_check_futures_test: Check that python files have certain future imports

I omitted those present in other files as they are only required for Python 2 (AFAIK) and TF announced dropping Python 2 after 2.1(?) Isn't that true? Should the check be changed or shall I add the future imports? That would be
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

Although nothing of that is used...

@Flamefire For consistency can you just add it in? I think all our files have it since we have some code internally still on Py2 so we need to maintain compatibility right now. Thanks!

I think all our files have it since we have some code internally still on Py2 so we need to maintain compatibility right now

Oh, so what is about that announcement:

TensorFlow will also stop supporting Python 2 starting January 1, 2020

Do I also have to adapt the super() call to the Python 2 version? Added the future imports for now to make CI happy.

Yeah for the open source builds we are not supporting Python 2 any more, but internally within Google the migration to Python 3 isn't complete, so that's why the pre-submit checks are still around to enforce the compatibility headers.
Your code won't be run internally, so I think it would be fine to leave it as is otherwise.

I see, thanks for the explanation. Then this is ready (again) I believe

@frankchn Seeing a (possibly unrelated) failure:

//tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef:tf_add.mlir.test FAILED

Is this good to go anyway? Maybe even for 2.2 as https://github.com/tensorflow/tensorflow/pull/36159 is part of 2.2 and this is the corresponding docu update.
Maybe one final thing: There is a question https://github.com/tensorflow/tensorflow/issues/36094#issuecomment-609973779 about how to use this and indeed there is no documentation on how to use a ClusterResolver with a Strategy in an example. I could add it here, but I think it would be even better to add it to the main docu. So probably out of scope of this PR.

Yeah that test failure is unrelated.
I think we are getting some internal tests done and this will be merged in the next day or two. Not sure if this will make it into 2.2 though since there has already been RC cuts and they are only really willing to accept bug fixes at this point. Will definitely be in 2.3 (cut in ~May-ish) though.

Hi @Flamefire, just got updated on an internal discussion and we are actually thinking of moving some of the cluster resolvers out of the TPU repo into a repo owned by a SIG instead (maybe SIG-Networking).
Still happy to accept the documentation update though, so can you remove the mpi_cluster_resolver.py from the PR, and create a new Issue re: mpi_cluster_resolver and assign it to me and @jhseu?
We'll update you on whether we decide to move the cluster resolvers soon. Thanks!

Sorry about the back and forth!

Moved the documentation update to a new PR with an appropriate branch and updated commit and readme. --> https://github.com/tensorflow/tensorflow/pull/38355
Issue Re MPI resolver added: https://github.com/tensorflow/tensorflow/issues/38356
Closing this.


Issue when using Facenet model for face recognition

I want to build pipeline for face detection and recognition. I have successfully converted the publicly available pre-trained  facenet model to IR (.xml and .bin) and using it to extract face embedding (feature vector). Also, I am using Intel's model "face-detection-retail-0004" IR model to detect faces on frames. Basically, I want the pipeline to do something like the following:
detect face(s) on the frame ==> pass the detected face to facenet model to obtain face embedding ==> pass the embedding feature vector to SVM classifier to recognize the person in the frame
I already have trained SVM classsifer and have the .pkl model file.
My pipeline is set up something like the following:
PIPELINE="gst-launch-1.0 \
$SOURCE_ELEMENT ! decodebin ! \
gvadetect model=face-detection-retail-0004.xml device=cpu ! queue ! \
gvainference model=facenet.xml device=cpu! queue ! \
gvapython module=$PYTHON_SCRIPT1 ! \
gvawatermark ! videoconvert ! fpsdisplaysink video-sink=xvimagesink sync=false"

In $PYTHON_SCRIPT1, I am doing post-processing by retrieving raw tensor data which is the face embedding and doing the classification using the classifier .pkl model. Something like this:
with open("classifier.pkl", 'rb') as infile:
    (model, class_names) = pickle.load(infile)

def process_frame(frame: VideoFrame, threshold: float = DETECT_THRESHOLD) -> bool:
    for roi in frame.regions():
        for tensor in roi.tensors():
            if tensor.layer_name() == 'embeddings':
                predictions = model.predict_proba([tensor.data()])
                best_class_indices = np.argmax(predictions, axis=1)
                tensor.set_label(class_names[best_class_indices[0]])

    return True

The predictions made by the above implementation are wrong. It is able to retrieve facial embeddings but I don't think they are correct. My guess is the input to gvainferece facenet model is not right. We might not be passing the input in proper format for the model to process which is the detected face images in rgb format in fixed dimension 160x160. If this is the case how do I do it exactly? I did try messing around with model_proc to gvainference to pre-process model input but no luck.
Is there something that I am doing wrong? Would this be recommended way to solve face detection and recognition problem with dlstreamer gst pipeline?

I think I see the issue, can you try using gvaclassify instead of gvainference. gvaclassify will work on the detected ROIs by default, gvainference will work accross the original image by default.
For a working example using the model from open model zoo (using simple distance calculation instead of SVM):
https://github.com/intel/video-analytics-serving/issues/19#issuecomment-688582265
https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/face-recognition-resnet100-arcface/face-recognition-resnet100-arcface.md

@nnshah1 : Unfortunately, I did try using gvaclassify but still the same issue. I did try gvainference and added "inference-region"= 1 to target only ROI found by gvadetect but couldn't get it working properly.  Facenet embeddings are obtained  in proper format  as I could see it by printing tensor.data() but the inference is wrong. The SVM model is correct and so is facenet model as it works perfectly fine in a separate python program without pipeline implementation.  I believe the appropriate input is not being passed to gvainference facenet model after gvadetect and it just calculates the wrong face embeddings. Should I do model input processing for gvainference facenet model as it accepts input image of fixed dimension 160x160 in rgb? If so, what would be input prepossessing in json file?
Is there anyway I can see what is being passed as region of interest by gvadetect?  like get the buffer blob or image for region of interest?
I went through the example of face recognition in video analytic serving you supplied and tried to follow it. But, I couldn't make it work.

@sanshr Thanks for the update - what issue did you have with the face recognition video analytics serving sample? Can you provide more details? That should work with no changes.
The color space for the facenet model might need to be specified as I believe the default will be BGR, try a model-proc file like the following:
{
  "json_schema_version": "2.0.0",
  "input_preproc": [
    {
      "format": "image",
      "layer_name": "0",
      "params": {
        "color_space": "RGB"
      }
    }
  ]
}

@nnshah1 Thank you for your prompt response. The issue I faced  with the example you sent was converting "face-recognition-resnet100-arcface" mxnet model to IR using openvino model optimizer. It complained that I needed to supply input shape of the model and I did. Then, again, when using it - the pipeline didn't work. It just freezes after first few frames. Not sure if model-proc version json 1.0.0 had anything to do with it.
I tried the model-proc on  facenet model but still it gives me incorrect inference result while still  producing 512 feature vector embeddings in tensor.data()

Thanks, I will take a look at the va serving sample - I should be able to get that to work - might need some updates for the latest version of the model.
For the facenet model - is it possible to share model IR along with some expected input / output pairs?
I would start by removing the detection phase and sending in a pre-cropped image, use full frame inference / classification - and ensure that is giving the results you expect compared to a known input /output pair

@nnshah1 I did try removing the detect and directly supplying cropped image to gvainferece but still the results the same.
IR Facenet model converted using model optimizer supplied with OpenVino: https://drive.google.com/file/d/1H3Ty8ZEo5_4XNoMUkU7P4FqznhEeXXyL/view?usp=sharing
Original Facenet TF model  in frozen graph .pb format: https://drive.google.com/file/d/1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-/view
Trained SVM classifier with two classes Joe BIden and Barack Obama in .pkl format: https://drive.google.com/file/d/1pcUHtiq6fpvItB-eFYsrJxZTyC6aFcYD/view?usp=sharing
Expected flow that I was planning is something like the following:
Input image or frame:
/home/san/Desktop/datasets/custom/Joe_Biden/Joe_Biden_0005.jpg
face detection  (done via gvadetect)
/home/san/Desktop/datasets/custom_160/Joe_Biden/Joe_Biden_0005.png
input to gvainference facenet model is the detected face with dimension 160x160 in RGB that outputs face feature embedding in [1, 512]  dimension.
Using gvapython to do post-processing to detect the face based on the embedding and classifier with something like
with open("classifier.pkl", 'rb') as infile:
    (model, class_names) = pickle.load(infile)

def process_frame(frame: VideoFrame, threshold: float = DETECT_THRESHOLD) -> bool:
    for roi in frame.regions():
        for tensor in roi.tensors():
            if tensor.layer_name() == 'embeddings':
                predictions = model.predict_proba([tensor.data()])
                best_class_indices = np.argmax(predictions, axis=1)
                tensor.set_label(class_names[best_class_indices[0]])

    return True

@nnshah1 Closing the issue as I just decided with the solution you provided (which uses ArcFace model) and everything is working as expected. Thanks for the help!

@sanshr Sorry that the facenet model didn't work out - but glad you are unblocked. Please let us know if you need anything else / have issues moving forward.

@nnshah1 : I need to store detected face in disk in .png or .jpg format. Basically, storing detected face. My approach  I applied something like the following:
frame_w = frame.video_meta().width
frame_h = frame.video_meta().height
with frame.data() as frame_img:
    frame_img = frame_img.reshape( (frame_h, frame_w, 1))
    // croping the image to ROI like this
    // cv::Rect myROI(10, 10, 100, 100);
    // cv::Mat frame_img = frame_img(myROI);
    cv.imwrite("test.png", frame_img)

Not sure if this works as I tried just saving the frame as image, I got distorted image in black and white like this:

Height and width are correct. However, it's in black and white.
Examining frame info,
frame.video_info().finfo.format is GST_VIDEO_FORMAT_I420
frame.video_info().finfo.pixel_stride is [1, 1, 1, 0]
How would I save region of interest detected photo in process_frame function without using jpegenc (video convert) in pipeline?

The frame.data() will return a numpy array with the correct shape for the video format (I420), so you should not have to reshape it. Reshaping it will infact cause the artifacts you are seeing. Try removing the reshape and doing the png save -

@nnshah1 Thank you again for your prompt response. I did try it without reshaping it like this:
with frame.data() as frame_img:
                cv.imwrite("/home/san/Desktop/sdw.png", frame_img)

Same distorted image got saved. Not sure why though. As, my pipeline is working with all correct inference output.

What happens when you convert to bgr fcolor space first - and then same the bgr image as png?
# Convert YUV420 to BGR (for testing), applies BT.601 "Limited Range" conversion.
    bgr = cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR_I420)

It appears to have the same distorted with a color tint on it.
Result: https://ibb.co/McY39BS

Can you share the latest pipeline you have? if I get time I can try a few things towards the end of the week

@nnshah1 Hi again! Thank you for the response. I did manage to solve the issue. It was related with the pipeline set up and I have fixed it. I know this is irrelevant to the issue but can you suggest me how I can change  planar 4:2:0 YUV (I420) to RGB? The frame data retrieved with frame.data() as numpy 8bit array is in YUV 420 format and saving the array with cv.imwrite  causes it to be stored in 8bit grayscale image. Can you advise on how I can save it as rgb image and not grayscale without use of other additional  elements in pipeline such as videoconvert? My pipeline is optimized and introduction of other elements especially video convert will likely cause performance issues. Thanks again!

@nnshah1 solved the issue with COLOR_YUV420p2RGB instead of COLOR_YUV2BGR_I420. Thanks anyway!

That's great! Glad it glad it got solved. Do you mind adding here your final pipeline - it may help others in the future and we can look to add something similar into our internal testing -


Fix origin issue#2834; submit existing subnets as in-use

Do not rely on InitMinions implementation to know if subnets already exist or not.
InitMinions always returns nil error with OpenShift integration e.g.
@mrunalp Review please
Fixes: https://github.com/openshift/origin/issues/2834

@rajatchopra Were you able to test with a new cluster as well?

Vagrant up works. So I guess it is good to go. I will test some more in a while and we can revert/add-another-fix if I find something off.

SGTM.


[ NEW PAGE ] What is OpenQASM

What the new page should cover:
Focus on Import/Export & interoperability
SME collaborators (involve for planning and review):
jake Lishman, Luciano Bello

Is https://github.com/Qiskit/qiskit/pull/10557 helpful? If so, we should make sure to add Alberto as a co-author.


Adapt to changed mqtt.async_publish in HA core 2021.12 #166

mqtt.async_publish is a couroutine in HA core 2021.12: home-assistant/core#58441
This requires minimum version of HA to 2021.12.0.

Tested and no issues here.

All fine here too in this regard.

fixed in 7a22d8e070cc6621ecf9a53b80b0d1c94e99285a


Update Malay Translation

Translation from free to Percuma

Coverage increased (+0.007%) to 53.675% when pulling 8ff6b4bf6278619efb321f783006a7912c975681 on HelloSeaNation:patch-6 into 0491f927e7d4746b6e1e52c18353f8a874ee9338 on codecombat:master.


Roadmap for Version 1.10

Hi Shiwen, your project is amazing.
I wonder if you have an estimated date for the adaptation of version 1.10.
Thank you and congratulations again for the project

Thats will be great !!! I need version 1.10 too  !!

Please refer to Development Plans. The v1.10.4-c1.2.0 will be released shortly after v1.8.10-c1.2.0 is released, and the estimated date is at the end of this year. Additionally, there is a high probability that v1.9.12-c1.2.0 will be skipped directly.

Any new release plan? @chengshiwen

I really hope this project was not abandonned <3

I really hope this project was not abandonned <3

Not abandoned. There will be some new version updates soon.


Scrolling and hover together does't work

When user is scrolling, hover event disable.
And the animation (CSS) stop and start again, only after user move the cursor.

Which browser?

Google Chrome - version 56.0.2924.87 - Windows 7 x64

can you provide an example page?


Advanced search results. Sort alphabetically

How easy would this be to implement?
It would be  a nice option as I see lots of named queries in categories. Alphabetical sorting would allow them to be grouped easily.

Sorting the queries won't be difficult.  Would you like the list to always be alphabetical, or just as as option?

just as an option.
Many people will want them to remain in the order that they do them.

The history table can now be sorted by query name if you click on "Query history" in the header.  Let me know if you'd like any tweaks.  The icon changes when you click.  Currently the sort order switches back to the default when you make a new query.  That's so that the most recent query appears at the top of the table.

I meant to say, it won't be live for 10 minutes or so.

works for me!

works for me!

OK, thanks.


[FLINK-32856][JUnit5 Migration] Migrate the testutils, throughput and throwable packages of flink-runtime module to junit5

[FLINK-32856][JUnit5 Migration] Migrate the testutils, throughput and throwable packages of flink-runtime module to junit5

Thanks for your contribution! Overall LGTM with minor comments.

Thanks, LGTM


Long runtime for simulating spatial data

Hi scDesign3 developers,
Thank you for maintaining the amazing tool!
When I was simulating the data from https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-2-sagittal-anterior-1-standard-1-1-0, scDesign3 seems to stuck in 'Marginal Fitting' for a long time. Doesn't matter if I just simulate for 10 genes, it stucks at Marginal Fitting. I will very much appreciate your help!! Thanks :)
Best,
Chit Tong
Here is the code I used:
print("read reference data")
brain <- Load10X_Spatial(data.dir="/anterior_data/",
                        filename="filtered_feature_bc_matrix.h5",
                        assay="Spatial",
                        slice="slice1"
)
brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)
brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)
brain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)
SpatialFeaturePlot(brain, features = "nCount_Spatial") + theme(legend.position = "right")

brain[['spatial1']] <- GetTissueCoordinates(brain)$imagerow
brain[['spatial2']] <- GetTissueCoordinates(brain)$imagecol
brain.sce <- as.SingleCellExperiment(brain)

brain.sce <- brain.sce[1:10]

print("simulation")
set.seed(123)

example_simu <- scdesign3(
    sce = brain.sce,
    assay_use = "counts",
    celltype = "ident", ##ident or cell_type
    pseudotime = NULL,
    spatial = c("spatial1", "spatial2"),
    other_covariates = NULL,
    mu_formula = "s(spatial1, spatial2, bs = 'gp', k= 400)",
    sigma_formula = "1",
    family_use = "nb",
    n_cores = 30,
    usebam = FALSE,
    corr_formula = "1",
    copula = "gaussian",
    DT = TRUE,
    pseudo_obs = FALSE,
    return_model = FALSE,
    nonzerovar = FALSE,
    parallelization = "pbmcapply"
  )

Update, when I run for only 10 genes, it works. It is only taking longer than I expected.  For the full dataset, it was running for four days and stopped by our server due to the time limit.

Hi,
Sorry for the delay in response. Unfortunately the estimation of spatial data can be time consuming. Two solutions you can try:

Set k = 100 instead of 400. The time increases almost quadratically with k. Smaller k may result in slight under-fit but usually 100 is large enough.
Set usebam = TRUE. It will use a faster estimation.

Please let me know if it works for you!
Best,
Dongyuan

Hi,
When I try to run the tutorial code with the changes you mentioned I get the following warning?
Warning in estimate.theta(theta, family, y, mu, scale = scale1, wt = G$w,  :
step failure in theta estimation
Is this a problem?
Best,
Lucas

@lucas02061 Hi Lucas,
This is an issue with bam: https://stats.stackexchange.com/questions/401278/step-failure-in-theta-estimation-using-bam-in-mgcv
Based on my experience, the function does not converge, but usually, it will not be too bad. I think you are probably OK with the result.
Best,
Dongyuan

Hi @SONGDONGYUAN1994,
Are there any plans of adding a progress bar to scDesign3? The Marginal Fitting step is very slow on spatial transcriptomics data even with the settings you mentioned earlier. It is hard to know if it is unfeasible to do a run on a given dataset or it is almost done running without a progress bar.
Best,
Lucas

@lucas02061 Thanks for raising this problem. Are you still using Windows right now? On Linux, you can use pbmcapply, but on Windows, I am not sure (there should be one); @qw130 Qingyang, do you have any idea?
Unfortunately, I am traveling in the next two weeks but I will begin on improving the speed after that. Could you first subsample ~ 100 genes and tell me the running time? Thanks!
Best,
Dongyuan

For only a 100 genes using the following settings it takes me approximatly15 minutes to run. Once I start going higher on genes though it starts taking many hours.
example_simu <- scdesign3(
sce = SCD_Input,
assay_use = "counts",
celltype = "cell_type",
pseudotime = NULL,
spatial = c("X", "Y"),
other_covariates = NULL,
mu_formula = "s(X, Y, bs = 'gp', k= 100)", # should be 100-400
sigma_formula = "1",
family_use = "nb",
n_cores = 2,
usebam = TRUE,
corr_formula = "1",
copula = "gaussian",
DT = TRUE,
pseudo_obs = FALSE,
return_model = FALSE,
nonzerovar = FALSE,
parallelization = "bpmapply"
)
Thanks for your help.
Best,
Lucas

Hi @SONGDONGYUAN1994,
I tried running it on our server (which is quite heavy) for 24 hours and it is still stuck on marginal fitting. It still seems incredibly slow with the settings provided in this thread.
I used the following dataset:
class: SingleCellExperiment
dim: 18078 684
metadata(0):
assays(1): counts
rownames(18078): Xkr4 Sox17 ... CAAA01118383.1 CAAA01147332.1
rowData names(1): gene_id
colnames(684): AAAGACCCAAGTCGCG-1 AAAGGGATGTAGCAAG-1 ... TTGTGGCCCTGACAGT-1 TTGTTCAGTGTGCTAC-1
colData names(3): X Y cell_type
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
class: SingleCellExperiment
dim: 18078 684
metadata(0):
assays(1): counts
rownames(18078): Xkr4 Sox17 ... CAAA01118383.1 CAAA01147332.1
rowData names(1): gene_id
colnames(684): AAAGACCCAAGTCGCG-1 AAAGGGATGTAGCAAG-1 ... TTGTGGCCCTGACAGT-1 TTGTTCAGTGTGCTAC-1
colData names(3): X Y cell_type
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
And I used the following settings.
example_simu <- scdesign3(
sce = SCD_Input,
assay_use = "counts",
celltype = "cell_type",
pseudotime = NULL,
spatial = c("X", "Y"),
other_covariates = NULL,
mu_formula = "s(X, Y, bs = 'gp', k= 100)", # should be 100-400
sigma_formula = "1",
family_use = "nb",
n_cores = 2,
usebam = TRUE,
corr_formula = "1",
copula = "gaussian",
DT = TRUE,
pseudo_obs = FALSE,
return_model = FALSE,
nonzerovar = FALSE,
parallelization = "pbmcapply",
BPPARAM=NULL
)
Am I doing something wrong?
Best regards,
Lucas

Hi Lucas,
@lucas02061 I feel that it is probably caused by some very sparse genes; models on those genes might be hard to converge. That is not your fault - could you check if the running time is related to this? I can fix it later. @qw130 Hi Qingyang, could you help Lucas on this? Sorry that I am traveling next week.

Dear Dongyuan,
I also have the same issue in fitting spatial data. Can I feed in 10 genes each time, and repeat the fitting multiple times in independent runs to get the result?
Best,
Feng

Hi Feng,
If you ignore the gene-gene correlation structure (or you believe that all variations should be explained by spatial coordinates), this is totally OK. It would be better that you can first divide genes into gene modules/groups then do this fitting rather than randomly split genes into 10-gene groups.

Thank you. That is a great point. I will use gene module groups instead.

Hi developers, I want to add scDesign3 to a benchmarking paper that my research group is working on, but I also have trouble with running the tool for spatial data on a decently beefy linux server, where the server gets stuck on marginal fitting. It took approximately one hour and 24 minutes to run on the dataset from the tutorial for spatial data, is it expected for the tool to take a long time running on the this dataset?
I make use of the following parameters with multiple cores to speed it up.
example_simu <- scdesign3(           sce = SCD_Input,           assay_use = "counts",           celltype = "cell_type",           pseudotime = NULL,           spatial = c("X", "Y"),           other_covariates = NULL,           mu_formula = "s(X, Y, bs = 'gp', k= 400)",           sigma_formula = "1",           family_use = "nb",           n_cores = 16,           usebam = FALSE,           corr_formula = "1",           copula = "gaussian",           DT = TRUE,           pseudo_obs = FALSE,           return_model = FALSE,           nonzerovar = FALSE,           parallelization = "pbmcapply"       ))
Best Regards Nikolaj.


Compiling recursive functions fails in nopython mode

>>> @jit(int_(int_), nopython=True)
... def fac(n):
...     if n == 1:
...         return 1
...     else:
...         return n * fac(n - 1)
...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "numba\decorators.py", line 128, in wrapper
    disp.compile(sig)
  File "numba\dispatcher.py", line 108, in compile
    flags=flags, locals=locs)
  File "numba\compiler.py", line 94, in compile_extra
    return_type, flags, locals)
  File "numba\compiler.py", line 120, in compile_bytecode
    locals)
  File "numba\compiler.py", line 283, in type_inference_stage
    infer.propagate()
  File "numba\typeinfer.py", line 318, in propagate
    self.constrains.propagate(self.context, self.typevars)
  File "numba\typeinfer.py", line 116, in propagate
    constrain(context, typevars)
  File "numba\typeinfer.py", line 168, in __call__
    self.resolve(context, typevars, fnty)
  File "numba\typeinfer.py", line 177, in resolve
    sig = context.resolve_function_type(fnty, args, ())
  File "numba\typing\context.py", line 51, in resolve_function_type
    fnobj = func.overloaded.compile(tuple(args))
  File "numba\dispatcher.py", line 108, in compile
    flags=flags, locals=locs)
  File "numba\compiler.py", line 94, in compile_extra
    return_type, flags, locals)
  File "numba\compiler.py", line 120, in compile_bytecode
    locals)
  File "numba\compiler.py", line 283, in type_inference_stage
    infer.propagate()
  File "numba\typeinfer.py", line 318, in propagate
    self.constrains.propagate(self.context, self.typevars)
  File "numba\typeinfer.py", line 116, in propagate
    constrain(context, typevars)
  File "numba\typeinfer.py", line 168, in __call__
    self.resolve(context, typevars, fnty)
  File "numba\typeinfer.py", line 180, in resolve
    raise TypingError(msg, loc=self.loc)
numba.typeinfer.TypingError: Undeclared Dispatcher(CPUOverloaded(<function fac at 0x07548B30>))(int32,)
File "<stdin>", line 6

In #991 @seibert mentioned that recursion is not a high priority because it's only really used in trivial things like fibonacci numbers.
I have a use case that I think would justify work on this: tree-based exact nearest neighbors queries. The scikit-learn BallTree/KDTree is great, but one issue is that users can't easily define their own distance metrics for use in them, because this must be done at the Cython level. It would be nice to have a numba implementation of the tree code, because then a user could JIT-compile their custom metric and use it for fast neighbors queries. The most straightforward tree query code uses a recursive algorithm, and so it currently can't be run quickly with Numba. It is of course possible to replace the recursion with a manual call stack, but I don't really want to do that :smile:
I hope that gives some motivation to fix the recursion issue!

@jakevdp: Thanks for the example!  I agree this is a compelling reason to support recursion.  Is this direct recursion?
After the May release of Numba, we are going to spend the next release working on refactoring some internals of Numba that have gotten overly complicated with the feature-blitz of the last 5 releases.  I'm hoping during this work, we can try to address some of the reasons why supporting recursion is complicated in Numba, and make it easier to implement.

It is direct recursion; you can take a look at the code in this gist: https://gist.github.com/jakevdp/5216193

Basic direct recursion works on master right now.  Indirect recursion will work with https://github.com/numba/numba/pull/2130 merged.

#2130 was merged, can this and #1845 be closed?

Good catch!  Thanks.


Pcm 1459

Codecov Report

Merging #350 (19e4909) into master (0048b0d) will not change coverage.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##            master      #350   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files           11        11
  Lines         1129      1136    +7
  Branches       235       237    +2
=========================================
+ Hits          1129      1136    +7

Impacted Files
Coverage Δ

src/media-utils.ts
100.00% <100.00%> (ø)

src/sessions/session-manager.ts
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 0048b0d...19e4909. Read the comment docs.


Add support for directional layout margins

Add support for new directionalLayoutMargins property/NSDirectionalEdgeInsets structure in iOS 11.

Added new layoutMarginLeading and layoutMarginTrailing properties to UIView. In iOS 11 and later, these map directly to directionalLayoutMargins. In iOS 10 and earlier, the current text direction (left-to-right or right-to-left) is used to dynamically apply the values.


feat: update dataset editor modal

SUMMARY

reuse dataset editing modal from explore
convert ChangeDatasourceModal and DatasourceModal  to use hooks
test has failed after convert to hooks but I will update test after this pr merge

Future improvement:

convert DatasourceEditor to use hooks

BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF

TEST PLAN

ADDITIONAL INFORMATION

[ ] Has associated issue:
[x] Changes UI
[ ] Requires DB Migration.
[ ] Confirm DB Migration upgrade and downgrade tested.
[ ] Introduces new feature or API
[ ] Removes existing feature or API

Codecov Report

Merging #10347 into master will decrease coverage by 0.72%.
The diff coverage is 83.33%.

@@            Coverage Diff             @@
##           master   #10347      +/-   ##
==========================================
- Coverage   70.61%   69.89%   -0.73%
==========================================
  Files         601      196     -405
  Lines       32225    18952   -13273
  Branches     3257        0    -3257
==========================================
- Hits        22757    13247    -9510
+ Misses       9365     5705    -3660
+ Partials      103        0     -103

Flag
Coverage Δ

#cypress
?

#javascript
?

#python
69.89% <83.33%> (-0.04%)
:arrow_down:

Impacted Files
Coverage Δ

superset/datasets/api.py
92.02% <ø> (ø)

superset/connectors/druid/models.py
82.48% <66.66%> (-0.06%)
:arrow_down:

superset/connectors/sqla/models.py
89.18% <100.00%> (-0.70%)
:arrow_down:

superset/db_engine_specs/sqlite.py
64.51% <0.00%> (-9.68%)
:arrow_down:

superset/db_engine_specs/postgres.py
97.36% <0.00%> (-2.64%)
:arrow_down:

superset/result_set.py
96.63% <0.00%> (-1.69%)
:arrow_down:

superset/sql_lab.py
77.92% <0.00%> (-0.44%)
:arrow_down:

superset/sql_parse.py
99.30% <0.00%> (-0.01%)
:arrow_down:

superset/errors.py
100.00% <0.00%> (ø)

... and 402 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update e4c9c73...079b221. Read the comment docs.

@lilykuang Looks like cypress is failing on this test: https://github.com/preset-io/incubator-superset/blob/3445710267cef7cbbee28b2a49785723fc6d3247/superset-frontend/cypress-base/cypress/integration/explore/control.test.ts#L34
It looks like something that could have been broken with this change

@lilykuang Looks like cypress is failing on this test: https://github.com/preset-io/incubator-superset/blob/3445710267cef7cbbee28b2a49785723fc6d3247/superset-frontend/cypress-base/cypress/integration/explore/control.test.ts#L34
It looks like something that could have been broken with this change
@nytai The superset/explore page was broken after I updated put endpoint to use api/v1/. It have to be changed back to old endpoint for both dataset list and explore to work.


Rerank jobs in waiting list

What would you like to be added:
Rerank jobs in waiting list
Why is this needed:
Image we have limited nodes, and someone may submit too many jobs, if any guys want to run there job, they have to wait for a very long time
Without this feature, how does the current module work:
Just Waiting~
Components that may involve changes:

If you are using hived scheduler, you could set the field extras.hivedScheduler.jobPriorityClass to a higher priority so it will preempt previous jobs instead of waiting. The default job priority is "test".

@abuccts if i use prod for extras.hivedScheduler.jobPriorityClass
The oldest job would be killed and get following error codes

code: -101
--
200 | phrase: PodExternalDeleted

@abuccts  I just want make my job in the first place in waiting list, not to preempt previous job instead of waiting

we can introduce a per-user throttling to limit the max num of jobs one user can submit in a period of time.

OR inside the k8s pod priority bits, we can leave some user configurable FIFO bits between Hived priority bits (high bits) and timestamp bits (low bits).
So user can use it to adjust scheduling order without changing the preemption priority.

waiting for the new feature~

OR inside the k8s pod priority bits, we can leave some user configurable FIFO bits between Hived priority bits (high bits) and timestamp bits (low bits).
So user (should be VC or whole cluster Admin) can use it to adjust scheduling order without changing the preemption priority.

here is the fair-share scheduling and dynamic priority of LSF for reference. By default, LSF calculates the dynamic priority for each user based on:

The number of shares assigned to the user
The resources used by jobs belonging to the user:

Number of job slots reserved and in use
Run time of running jobs
Cumulative actual CPU time (not normalized), adjusted so that recently used CPU time is weighted more heavily than CPU time used in the distant past

When enabled, the scheduler could also involve additional resources used by jobs belonging to the user.

Thanks for the info. The dynamic priority may be in our long term plan, as it is not easy to update priority across the whole system (K8s+Hived).
However, the user manually adjusted FIFO bits as mentioned above, should be very easy to implememt :)

Or if you mean the job priority is dynamic before submission, but fixed after submission. This should be easy, and its extreme case is Fan's "per-user throttling"


zal and zle tags are not working

Confirmed.
Implementation required.

They will be seeing implementation in parts and pieces.


IRGen,Serialization: account for transitive dynamic linkage

When building complex projects, there may cases of static libraries which expose @inlinable functions which reference functions from dynamically linked dependencies. In such a case, we need to consider the provenance of the function_ref when determining the DLL storage for linkage. We would previously use the deserialised metadata on the SILFunction as there are entities where the DeclContext may not be deserialised. However, this leaves us in a state where we are unable to determine the actual provenance correctly in some cases. By simply accessing the parent module directly from the SILFunction we ensure that we properly identify the origin of the function allowing us to query the DLL storage property. This further allows us to remove the extra storage required for whether the SILFunction is statically linked.

CC: @xymus

@swift-ci please test

@swift-ci please build toolchain Windows platform


In which path should I put this ZoeD_M12_N.pt file for comfyUI?

I'm trying to use controlnet depth node in comfyUI, and I couldn't able to download the ZoeD_M12_N.pt file by .bat, so I downloaded it manually, but I don't know In which path should I put this ZoeD_M12_N.pt file. plz help

There are two pull requests that want to solve it.
Before author makes a change, you can modify the code like the pull requests so that you can find all models in ckpts.

There are two pull requests that want to solve it. Before author makes a change, you can modify the code like the pull requests so that you can find all models in ckpts.

executed git pull, said already up to date, empty in ckpts folder still.crying


Use Github Actions over CircleCI

unfortunately circleci sucks but it has the best ui out of any CI i've ever used; huge shame honestly

the appveyor check can be ignored since these commits were added before i fixed it being built but if it really matters you can rerun it over the website itself


Tabular: Added 'excluded_model_types' fit() argument

Issue #, if available:
Description of changes:
Added 'excluded_model_types' fit() argument
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

Example Code:

import pandas as pd
import sklearn.datasets

from autogluon.utils.tabular.ml.utils import generate_train_test_split
from autogluon.task.tabular_prediction import TabularPrediction as task

LABEL = 'target'

if __name__ == '__main__':
    cancer_data = sklearn.datasets.load_breast_cancer(return_X_y=False)
    X = pd.DataFrame(cancer_data['data'], columns=cancer_data['feature_names'])
    X[LABEL] = cancer_data[LABEL]

    X_train, X_test, y_train, y_test = generate_train_test_split(X=X.drop(LABEL, axis=1), y=X[LABEL], problem_type='binary', test_size=0.2)

    X_train[LABEL] = y_train
    X_test[LABEL] = y_test

    predictor = task.fit(
        train_data=X_train,
        label=LABEL,
        excluded_model_types=['KNN', 'custom', 'NN'],
        verbosity=2,
    )

    predictor.leaderboard(X_test)

Example Output:
No output_directory specified. Models will be saved in: AutogluonModels/ag-20200629_020324/
Beginning AutoGluon training ...
AutoGluon will save models to AutogluonModels/ag-20200629_020324/
AutoGluon Version:  0.0.1+256ca96
Train Data Rows:    455
Train Data Columns: 31
Preprocessing data ...
Here are the 2 unique label values in your data:  [0, 1]
AutoGluon infers your prediction problem is: binary  (because only two unique label-values observed).
If this is wrong, please specify `problem_type` argument in fit() instead (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
Selected class <--> label mapping:  class 1 = 1, class 0 = 0
Train Data Class Count: 2
Feature Generator processed 455 data points with 30 features
Original Features:
	float features: 30
	object features: 0
Generated Features:
	int features: 0
All Features:
	float features: 30
	object features: 0
	int features: 0
	Data preprocessing and feature engineering runtime = 0.09s ...
AutoGluon will gauge predictive performance using evaluation metric: accuracy
To change this, specify the eval_metric argument of fit()
AutoGluon will early stop models using evaluation metric: accuracy
Excluded Model Types: ['KNN', 'custom', 'NN']
	Found 'NN' model in hyperparameters, but 'NN' is present in `excluded_model_types` and will be removed.
	Found 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.
	Found 'custom' model in hyperparameters, but 'custom' is present in `excluded_model_types` and will be removed.
Fitting model: RandomForestClassifierGini ...
	0.956	 = Validation accuracy score
	0.69s	 = Training runtime
	0.12s	 = Validation runtime
Fitting model: RandomForestClassifierEntr ...
	0.956	 = Validation accuracy score
	0.67s	 = Training runtime
	0.11s	 = Validation runtime
Fitting model: ExtraTreesClassifierGini ...
	0.9451	 = Validation accuracy score
	0.44s	 = Training runtime
	0.12s	 = Validation runtime
Fitting model: ExtraTreesClassifierEntr ...
	0.967	 = Validation accuracy score
	0.43s	 = Training runtime
	0.11s	 = Validation runtime
Fitting model: LightGBMClassifier ...
	0.989	 = Validation accuracy score
	0.35s	 = Training runtime
	0.01s	 = Validation runtime
Fitting model: CatboostClassifier ...
	0.989	 = Validation accuracy score
	1.21s	 = Training runtime
	0.01s	 = Validation runtime
Fitting model: weighted_ensemble_k0_l1 ...
	0.989	 = Validation accuracy score
	0.37s	 = Training runtime
	0.0s	 = Validation runtime
AutoGluon training complete, total runtime = 5.37s ...
                        model  score_test  score_val  pred_time_test  pred_time_val  fit_time  pred_time_test_marginal  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer
0  RandomForestClassifierEntr    0.947368   0.956044        0.121879       0.113781  0.665862                 0.121879                0.113781           0.665862            0       True
1          CatboostClassifier    0.938596   0.989011        0.008576       0.007551  1.211811                 0.008576                0.007551           1.211811            0       True
2          LightGBMClassifier    0.938596   0.989011        0.014692       0.010993  0.351359                 0.014692                0.010993           0.351359            0       True
3     weighted_ensemble_k0_l1    0.938596   0.989011        0.017389       0.012017  0.719750                 0.002697                0.001024           0.368391            1       True
4    ExtraTreesClassifierGini    0.938596   0.945055        0.122306       0.116379  0.436087                 0.122306                0.116379           0.436087            0       True
5  RandomForestClassifierGini    0.938596   0.956044        0.134387       0.120344  0.692904                 0.134387                0.120344           0.692904            0       True
6    ExtraTreesClassifierEntr    0.929825   0.967033        0.119529       0.113273  0.430941                 0.119529                0.113273           0.430941            0       True

left some docs comments, will review code once CI passes

Job PR-543-2 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-543/2/index.html


refactor: global function into inGame and preGame

All the functions in global have different implementation for in game state and for pre game state anyway so there is no need for global functions

Done! I also added edge case for when the player that 'exit' the game is the current player


Create age of agents with short interval as an agent state

We want to track the birth spacing and the age of each agent at second order and higher live births in FPsim.
First step:

[ ] Create short_interval_age as an agent state in sim.py
https://github.com/fpsim/fpsim/pull/107/commits/57662ec344de503d91b162ab88c7eca238a0e71a

Let's close out the age-specific short interval issues for now, they have been resolved but may resurface if needed as we refactor the age specific tracking code. @nnoori-IDM @avictorious

Closed with PR #107


Shallow cloning on travis

According to https://docs.travis-ci.com/user/customizing-the-build#git-clone-depth, Travis CI provide a way to shallow clone a repository. This has the obvious benefit of speed, since you only need to download a small number of commits.

please stop opening PR on projects just to increase your activity.
On all my maven projects, I use jgitver which uses the git history to compute the version.
It is thus intended to checkout the whole history (or on some project a decent part of the history).


postInstall step not running?

Hey,
I saw another issue on this -- I'm encountering the same problem.
I install cssnext via jspm: jspm install npm:cssnext. It installs without error, but when I try to leverage cssnext I encounter an error stating that features.js does not exist.
If I navigate to the caniuse-api directory and run node generator.js the file is generated successfully and all is well.
Node v5.3.0
NPM v3.5.3
Windows 10 x64

Does jspm run postinstall hook?
What output do you got when you install it? (you should got some logs from caniuse-api).

Just leaving it here for reference https://github.com/jspm/npm/issues/36

Does #41 fix this?

I'm having the same issue.

After this mornings closed pull request, this is the only barrier to JSPM support

See #42.

Closed by #47


Queries sidebar no longer scrollable

After upgrading to sqlpad 6.x I'm no longer able to scroll through the list of saved queries (if it exceeds what's visible).  This worked previously on 5.x.
I'm able to use the search box to find a query, so I know the queries are still present on the backend.

Hi @sirianni - which version of SQLPad, browser, and OS are you running? On SQLPad 6.7, latest Chrome, windows 10, queries list is scrollable.

I am running SQLPad 6.7.1 with Chrome 93.0.4577.63 on Linux.  I see the same issue on the latest Firefox.
It looks like the XHR only requests 20 queries, so I'm assuming there's some lazy pagination going on behind the scenes...?
I do see scroll bars if I shrink the height so that the 20 queries are shown, but it still doesn't fetch more queries if I scroll to the bottom.  Since there are only 20 elements in the DOM, I can understand why the browser is not showing scroll bars if they all fit.

We've upgraded to 6.11 and this seems to have been fixed.


⚠ ClamAV has degraded performance

In b5d4734, ClamAV (https://spamassassin.apache.org/) experienced degraded performance:

HTTP code: 200
Response time: 53 ms

Resolved: ClamAV performance has improved in 906dad3.


Add the CreateLedger channel to the Wallet API

Fixes #3656
Adds CreateLedgerChannel to the wallet API.
Essentially we're exposing the existing CreateLedgerChannel on the engine via the wallet.

Note that there are unhandled errors reported by the test suite:
I believe these are caused by aborting the message service at the end of the test. Since we're dropping and delaying messages the test message service may have more messages queued to send when we go to destroy it.

We should handle this error though and not just log it out. I will create an issue for that.

PASS src/__chain-test__/directly-funded-channel.test.ts (115.853 s)
@statechannels/server-wallet:   ● Console
@statechannels/server-wallet:     console.error
@statechannels/server-wallet:       Unhandled error
@statechannels/server-wallet:       at process.uncaught (../../node_modules/jest-jasmine2/build/jasmine/Env.js:248:21)
@statechannels/server-wallet:     console.error
@statechannels/server-wallet:       AbortError: Delay aborted
@statechannels/server-wallet:           at createAbortError (/home/circleci/project/node_modules/delay/index.js:7:16)
@statechannels/server-wallet:           at AbortSignal.signalListener (/home/circleci/project/node_modules/delay/index.js:24:12)
@statechannels/server-wallet:           at AbortSignal.dispatchEvent (/home/circleci/project/node_modules/event-target-shim/src/event-target.mjs:337:35)
@statechannels/server-wallet:           at abortSignal (/home/circleci/project/node_modules/abort-controller/src/abort-signal.ts:68:12)
@statechannels/server-wallet:           at AbortController.abort (/home/circleci/project/node_modules/abort-controller/src/abort-controller.ts:26:9)
@statechannels/server-wallet:           at TestMessageService.destroy (/home/circleci/project/packages/server-wallet/src/message-service/test-message-service.ts:133:31)
@statechannels/server-wallet:           at Wallet.destroy (/home/circleci/project/packages/server-wallet/src/wallet/wallet.ts:399:36)
@statechannels/server-wallet:           at async Promise.all (index 0)
@statechannels/server-wallet:           at Object.<anonymous> (/home/circleci/project/packages/server-wallet/src/__chain-test__/directly-funded-channel.test.ts:102:5)
@statechannels/server-wallet:       at process.uncaught (../../node_modules/jest-jasmine2/build/jasmine/Env.js:249:21)
@statechannels/server-wallet:     console.error
@statechannels/server-wallet:       Unhandled error
@statechannels/server-wallet:       at process.uncaught (../../node_modules/jest-jasmine2/build/jasmine/Env.js:248:21)
@statechannels/server-wallet:     console.error
@statechannels/server-wallet:       AbortError: Delay aborted
@statechannels/server-wallet:           at createAbortError (/home/circleci/project/node_modules/delay/index.js:7:16)
@statechannels/server-wallet:           at AbortSignal.signalListener (/home/circleci/project/node_modules/delay/index.js:24:12)
@statechannels/server-wallet:           at AbortSignal.dispatchEvent (/home/circleci/project/node_modules/event-target-shim/src/event-target.mjs:337:35)
@statechannels/server-wallet:           at abortSignal (/home/circleci/project/node_modules/abort-controller/src/abort-signal.ts:68:12)
@statechannels/server-wallet:           at AbortController.abort (/home/circleci/project/node_modules/abort-controller/src/abort-controller.ts:26:9)
@statechannels/server-wallet:           at TestMessageService.destroy (/home/circleci/project/packages/server-wallet/src/message-service/test-message-service.ts:133:31)
@statechannels/server-wallet:           at Wallet.destroy (/home/circleci/project/packages/server-wallet/src/wallet/wallet.ts:399:36)
@statechannels/server-wallet:           at runMicrotasks (<anonymous>)
@statechannels/server-wallet:           at runNextTicks (internal/process/task_queues.js:62:5)
@statechannels/server-wallet:           at processTimers (internal/timers.js:489:9)
@statechannels/server-wallet:           at async Promise.all (index 1)
@statechannels/server-wallet:           at Object.<anonymous> (/home/circleci/project/packages/server-wallet/src/__chain-test__/directly-funded-channel.test.ts:102:5)
@statechannels/server-wallet:       at process.uncaught (../../node_modules/jest-jasmine2/build/jasmine/Env.js:249:21)
@statechannels/server-wallet: PASS src/engine/__test__/add-my-state.test.ts

@statechannels/server-wallet: PASS src/__chain-test__/socket-io-directly-funded-channel.test.ts (33.19 s)

Also, I noticed this test. Do we rely on socket.io in our test suite? That makes me wary. Should our test suite rely on external services?

There's one test that uses the socket.io message service. Socket.io is just using websockets (or http polling) behind the scene so I don't think that's an external service.

Also, I noticed this test. Do we rely on socket.io in our test suite? That makes me wary. Should our test suite rely on external services?

There's one test that uses the socket.io message service. Socket.io is just using websockets (or http polling) behind the scene so I don't think that's an external service? It's not phoning home to some central server (like firebase did)

I think I misunderstood something about socket.io, possibly based on some critical commentary on it from many months ago. I thought it did phone home to a central server, but that doesn't seem to be true! (The comment might have been talking about socket.io - based browser <-> browser communication, which would require a central server.)
websockets that fall back to http polling seems pretty nice!


fix: tamagui website issue on windows

certified Windows moment
DATA_PATH has backslashes on Windows and slashes on Linux. Tried the fix and it works both on Windows and Linux.
Closes #625

ty


Adjust plural forms for translations

These plural forms were re-added in https://github.com/bitcoin-core/gui/pull/557.
The following make -C src translate also caught a new last-minute added string.

The following sections might be updated with supplementary metadata relevant to reviewers and maintainers.

Reviews
See the guideline for information on the review process.
A summary of reviews will appear here.

cc @jarolrod @luke-jr

concept ack

Closed in favour of #724.


Add cloudprovider config for AWS / Fix local dev script

Fixes #214
by adding in the controller manager config for AWS cloud provider. Also fixes small nit with local dev script by updating how the S3 bucket gets created.
Signed-off-by: Steve Sloka<EMAIL_ADDRESS>

Seeing this error in Controller manager:
route_controller.go:120] Couldn't reconcile node routes: error listing routes: unable to find route table for AWS cluster: kubernetes```

It looks like we aren't tagging the route table with the correct tags in the cloud formation template. Should have tag-key=kubernetes.io/cluster/<cluster-id>...am I reading that code right?
https://github.com/kubernetes/kubernetes/blob/master/pkg/cloudprovider/providers/aws/aws.go#L744
from
https://github.com/kubernetes/kubernetes/blob/master/pkg/cloudprovider/providers/aws/tags.go#L251
from
https://github.com/kubernetes/kubernetes/blob/master/pkg/cloudprovider/providers/aws/aws_routes.go#L44
I'd need to spend more time on it though to make sure it's doing what I think it's doing.

@chuckha where do I add that? To the cloudformation template?

oh i was thinking directly in the aws console

+1 to tag in the cloudformation template, although I'm not sure how this was working before, the code @chuckha linked has been there for a while.

@stevesloka can you add steps to reproduce so I can try this out when I get a moment?

@chuckha all I did was deploy the quickstart, then try and create a service type "LoadBalancer". What I got was pending for the state of it as well as errors in Controller manager stating the cloud provider was not yet defined. So I added that (In this PR) and the ELB gets created, but the instances weren't added and they did not have proper security groups to allow traffic to the nodes.

@stevesloka @chuckha I just wanted to add that we have a customer experiencing the same error. They have bootstrapped with just kubeadm.
route_controller.go:120] Couldn't reconcile node routes: error listing routes: unable to find route table for AWS cluster: kubernetes


LaTeX in name in key_dimensions of HoloMap

This works:
hv.HoloMap({i: hv.Image(np.random.rand(25,25)) for i in range(10)}, key_dimensions=['$\phi$'])

This doesn't
hv.HoloMap({(i, j): hv.Image(np.random.rand(25,25)) for i in range(5) for j in range(5)}, key_dimensions=['$\phi$', '$\theta$'])

Javascript error adding output!
SyntaxError: Unexpected token ILLEGAL
See your browser Javascript console for more details.

Two things, I've escaped slashes in widget labels. That probably needs a more robust fix, so I'll leave the issue open for now. Secondly you'll have to use either r'$\theta$' or $\\theta$ since Python treats \t as a horizontal tab, which means you have to declare a raw string or escape it with another backslash.

I will close now as it seems to be working. If any more issues crop up we'll have to come up with a scheme to sanitize the dimension labels passed to the Jinja template.


Incorrect resource name

This page specifies that the resource name should be https://eventhubs.azure.net when in fact it should be "<namespace>.servicebus.windows.net".
I discovered this from perusing a tutorial elsewhere on Azure.
Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: a7b89aee-8938-c033-2815-03c543702e39
Version Independent ID: d6c48306-31c5-e050-c22d-37895f362fbb
Content: Authorize access with Azure Active Directory
Content Source: articles/event-hubs/authorize-access-azure-active-directory.md
Service: event-hubs
GitHub Login: @spelluru
Microsoft Alias: spelluru

Thank you for your detailed feedback.  We are looking into the issue, and we will respond when we have more information.

@malthe Thanks again for the feedback. The PR for the fix has been created and it will be merged soon. I am closing the issue based on this update. If there are further questions regarding this matter, please tag me in your reply and reopen the issue. We will gladly continue the discussion.


Run GUI and CLI mode at the same time for a single container

What problem or use case are you trying to solve?
I have developed a Discord bot capable of initializing and executing OpenHands Docker containers. My intention is to enable simultaneous execution of graphical user interface (GUI) and command-line interface (CLI) functionalities. This will allow users to interact with the GUI independently, while the bot can simultaneously fulfill tasks through CLI commands, even with voice commands. By incorporating both GUI and CLI interfaces, the functionality of OpenHands can be extended to accommodate these diverse use cases.
Describe the UX of the solution you'd like
Users can utilize speech-to-text or text-to-speech flows to command OpenHands through layers, such as my Discord bot. Subsequently, the layer can execute tasks via CLI, which will impact the GUI itself. This approach enables a genuine collaboration experience.
Do you have thoughts on the technical implementation?
This really shouldn't be a problem with current implementation.
Describe alternatives you've considered
Currently, I am executing two concurrent containers: one for graphical user interface (GUI) and another for command-line interface (CLI). However, these containers lack communication, which may potentially lead to confusion for the end users.
Additional context

I had a look at the codebase and tried to keep a simpler approach by connecting to websocket via a rust exec.
However I see when a repo is selected, i mean nevermind, long story short, websocket connection didn't work as I thought it would cuz there are lots of other processes that needs to run (another container is being run when a repo is selected, i must handle that too with rust workaround.
I'll have another look later on to make this happen with my initial approach.

I’ve made it! I’ll be sharing more details and creating a PR with additional setup instructions on how to run OpenHands like this. Right now, it’s like two agents are controlled from a single one, and even the CLI is running from the first container, so it’s super smooth.
I’ll be working on letting the GUI work with the CLI together and updating the GUI based on the CLI output and states, etc.


Merge next into master

We missed some commits from next, because the PRs went in after the last reset of master.

Thanks, I was on my way out the door and tried to rush.  I guess I didn't think it through.


Vendor rpc library and update imports

Also vendor cldr library.
Remove unused go.iced script.

I built the protocol files with a local version with https://github.com/keybase/node-avdl-compiler/pull/8 . Then I guess when you do the npm push we could peg to that version?

Okay, also bumped avdl-compiler to 1.3.9.

👍

circleci failure is expected (since it depends on kbfs)


Recommendations for frame rate

20 fps?  10 - 30 fps?

Having some recommendations like:

feature to set/limit transmitter rate
feature to get the maximum receiving rate of renderer
default/typical rate examples (s-ADM frame rate ? 20-50 ms)
recommend strategies to manage rate (too high : avoid flooding by decimating,
Too low: interpolate...)

The Dolby Atmos ADM profile says:

The sampling period should be less than 20 milliseconds. Sampling is optional if the metadata state
remains unchanged.

20 ms seems like a decent recommendation.
It's tempting to add some support for interpolation to ADM-OSC, but the Dolby profile does not support setting the interpolation value. The interpolate over 250 samples at 48k, or 500 samples at 96k.

The 1.0 spec recommends 20ms.


invalid ELF header

ERROR: node_modules/better-sqlite3/build/Release/better_sqlite3.node: invalid ELF header
Trying to node index.js on my Amazon Web Server and it's throwing me this issue. Looked up some threads on the issue but found no fixes. Need help ASAP really.
Node Version: v15.5.0
Full Error Log:
/home/ubuntu/odin/node_modules/bindings/bindings.js:121
throw e;
^
Error: /home/ubuntu/user/node_modules/better-sqlite3/build/Release/better_sqlite3.node: invalid ELF header
at Object.Module._extensions..node (node:internal/modules/cjs/loader:1167:18)
at Module.load (node:internal/modules/cjs/loader:973:32)
at Function.Module._load (node:internal/modules/cjs/loader:813:14)
at Module.require (node:internal/modules/cjs/loader:997:19)
at require (node:internal/modules/cjs/helpers:92:18)
at bindings (/home/ubuntu/odin/node_modules/bindings/bindings.js:112:48)
at Object. (/home/ubuntu/odin/node_modules/better-sqlite3/lib/database.js:9:24)
at Module._compile (node:internal/modules/cjs/loader:1108:14)
at Object.Module._extensions..js (node:internal/modules/cjs/loader:1137:10)
at Module.load (node:internal/modules/cjs/loader:973:32) {
code: 'ERR_DLOPEN_FAILED'
}

Have you tried deleting node_modules and running npm install again (on said Amazon Web Server) to make sure you have the appropriate binary for you system?

Have you tried deleting node_modules and running npm install again (on said Amazon Web Server) to make sure you have the appropriate binary for you system?

did you manage to find a fix for this?
i attempted to run an app using better-sqlite3 on my local machine and it worked just fine, but when i ran it on my VPS it threw the same error.

I am also having this error, i saw somewhere on #146 that twigz mentioned to use v14.15.0 of node -  I tried that but it still fails to run using nvm. After changing however, I get the invalid ELF header error, and so I am running npm install once more. For some strange reason, npm install takes 400% longer to run on my pi than on my pc - connected to the same switch - I was wondering if it was connected in some way?

Closing this as @3Proto found a fix.

Hi,
I'm trying to host my bot on Replit and this is the error I get. Can anyone help? That fix didn't work.

I use the Pterodactyl panel and whenever I run my bot the same error appears

I use the Pterodactyl panel and whenever I run my bot the same error appears

dide u found issues or fix ?

Delete better_sqlite3 folder in node_modules and install better_sqlite3 package again.

This is now happening to me....

I got it working using yarn instead of npm and v16 of Node.JS on Ubuntu

how tho

how tho

yarn add better-sqlite3

Então rapaziada que ainda esta com esse erro , funciona para hosbedar bot discord para vps da Amazon
1  Eu removi o node.js  instalei a versão "v14.15.0
2 usei curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.35.3/install.sh | bash
3 usei  nvm install 14.15.0 | depois você apaga sua pasta node_modulus , usa o npm install para instalar novamente  os pacotes
isso vai evitar de dar bugs na versão da vps da amazon que estiver ultilizando...

encountered the same error on hosting
/home/container/node_modules/bindings/bindings.js:121
        throw e;
        ^
Error: /home/container/node_modules/better-sqlite3/build/Release/better_sqlite3.node: invalid ELF header
    at Object.Module._extensions..node (node:internal/modules/cjs/loader:1179:18)
    at Module.load (node:internal/modules/cjs/loader:975:32)
    at Function.Module._load (node:internal/modules/cjs/loader:822:12)
    at Module.require (node:internal/modules/cjs/loader:999:19)
    at require (node:internal/modules/cjs/helpers:102:18)
    at bindings (/home/container/node_modules/bindings/bindings.js:112:48)
    at new Database (/home/container/node_modules/better-sqlite3/lib/database.js:48:64)
    at Database (/home/container/node_modules/better-sqlite3/lib/database.js:11:10)
    at new SqliteDriver (/home/container/node_modules/quick.db/out/drivers/SqliteDriver.js:8:25)
    at new QuickDB (/home/container/node_modules/quick.db/out/index.js:17:28) {
  code: 'ERR_DLOPEN_FAILED'
}
Node.js v17.3.0

@3Proto

this worked for me

Still present.
I am using Mac (M1 Pro) as dev machine (NodeJS 16) and I am trying to deploy it to AWS Lambda function (with AWS Amplify NodeJS 16).
Even if I am installing the the package with platform specific instruction,
npm install --arch=x64 --platform=linux better-sqlite3
all I get back from Lambda is "invalid ELF Header".

Can you share the link to a fix ?

I think it may help https://github.com/kelektiv/node.bcrypt.js/issues/578

I got it working using yarn instead of npm and v16 of Node.JS on Ubuntu installed from the snap store

I got it working by replacing the npm commands with yarn as well.

this is happening because you're using your dev device node_modules while on different VPS/Server architecture
all you need is to delete node_modules on the server/prod


Should improve keys insertion success rate

Hey!
As you may know, inserting the secure boot keys into UEFI tends to fail a lot from userspace (my guess is that UEFI implementations suck). Personally I had to manually insert them from the firmware setup.
This PR is a proposal based on the green tip from the ArchLinux wiki.
I'm hoping that this way your tool will work out of the box for most vendors.

Very interesting, thanks! Looks good to me, I'll merge as soon as I have chance to make release and update AUR pkg at the same time, not to forget :wink:


Homekit Air-conditioner Layout

Hey Guys,
I'm just getting started with the Air Conditioning control. Any idea how to get pretty layouts like below to show up on Homekit?

It's quite easy - within this plugin, just define the "type" as air conditioner and home kit will you show you this nice interface. You can check the documentation and sample config.json for reference. Helped me a lot to get started :)

Thank you. I will give it a try.


Update chainlink

The team had supplied information in other places, so mixed it together for this resource page. While I'm here, they created the bounty https://github.com/ethberlinzwei/Bounties/issues/6, but I wonder if we/they should split it into 3?

thanks! merged - they can split it afterwards if they want


ZeroNet site

I found a Fastcast site on ZeroNet with the name Fascast. It works pretty well, I was just wondering who created it? It would be cool to make it dynamic so it uses a database and people can add videos!
http://<IP_ADDRESS>:43110/1qJ3EM5rTa2wPTBvJuCFjdLXjrBCgBkR8

sadly the site hasn't been updated ever since april, but someone could clone it and register a namecoin domain. i am up for the hosting challenge, but i barely know any web development coding.

Hi @emuNAND, thanks for the information! That would be great if you're keen to set it up! It would be cool to create a repository maybe called ZeroFascast with the code and details for the zeronet site so people can find it.

@jakefb actually, there is a ZeroNetwork Wiki that exists. perhaps i could edit it and lead the dead link to the new one?

@emuNAND, yeah that sounds like a good idea! Since the old site is misspelt as Fascast you should have no problem registering it. Let me know how it goes and I'd love to help with the coding too!

@jakefb alright, edited! im going to register the namecoin now.

@emuNAND awesome! How's it going? Can you send me the link so I can seed?

@jakefb it's in the first comment. sadly the blockchain got corrupted so I have to restart.

added the favicon onto the zite!

@jakefb how would you feel if we used this as a backup git?

Hi @emuNAND sorry about the really late reply, I've been busy with work. I was able to clone the zeronet git, it's pretty cool so all we need to do is push the project files to it. I have been unable to access http://<IP_ADDRESS>:43110/1DzteF7ea3PaaxArGWdh1CLPwQKoB2mrP4/ however it says there are no peers. Thanks for all your help!


Add coverage test

Summary
Under the pytest framework with pytest-cov module, the output will be like the following message
---------- coverage: platform linux, python 3.8.10-final-0 -----------
Name                         Stmts   Miss  Cover
------------------------------------------------
proxybroker/__init__.py         22      0   100%
proxybroker/api.py             229    196    14%
proxybroker/checker.py         209    185    11%
proxybroker/errors.py           24      0   100%
proxybroker/judge.py            78     60    23%
proxybroker/negotiators.py      96      4    96%
proxybroker/providers.py       358    215    40%
proxybroker/proxy.py           217     58    73%
proxybroker/resolver.py         93     12    87%
proxybroker/server.py          245    218    11%
proxybroker/utils.py            83     22    73%
tests/__init__.py                0      0   100%
tests/conftest.py               49      6    88%
tests/test_negotiators.py       58      0   100%
tests/test_proxy.py            173      0   100%
tests/test_resolver.py          65      0   100%
tests/test_utils.py             24      0   100%
tests/utils.py                   8      0   100%
------------------------------------------------
TOTAL                         2031    976    52%

I figure out that the CI flow all depends on poetry.lock, which should be define in CONTRIBUTE.md. The Ci failed because I updated the requirements/ only

oops tests are failing https://github.com/bluet/proxybroker2/runs/7989566629?check_suite_focus=true#step:9:14
pytest: error: unrecognized arguments: --cov

LGTM

I figure out that the CI flow all depends on poetry.lock, which should be define in CONTRIBUTE.md. The Ci failed because I updated the requirements/ only

related https://github.com/bluet/proxybroker2/issues/93#issuecomment-1225179188


docfix for minimize in TrainResult

thanks for the quick reply on slack, I was also thinking that maybe it can be renamed to something more intuitive like backprop

test is failing on master - https://github.com/PyTorchLightning/pytorch-lightning/commit/be0438bb47596447b9485f0bf507b474660f0f00#commitcomment-41807345

@s-rog I don't think backprop is more intuitive in this case. Minimize makes more sense in way that you want to pass it a loss tensor that you wish to minimize.


Repackage and reingest packages into Archivematica for Miten

See https://wellcome.slack.com/archives/C01B83N9NMP/p1607521287000700

Done!


[docs] Main "Base UI" website still using old Unstyled sample code

Duplicates

[X] I have searched the existing issues

Related page
https://mui.com/base-ui/
Kind of issue
Broken demonstration
Issue description
I found the unstyled Tabs component I wanted on the main Base UI site. So I copied the code into my app and… it failed. This turned out to be because the TabsUnstyled format was removed about a year ago. But the example code still says to use it:
import { TabsUnstyled } from '@mui/base/Tabs';
import { TabsListUnstyled } from '@mui/base/TabsList';
import { TabPanelUnstyled } from '@mui/base/TabPanel';
import { TabUnstyled } from '@mui/base/Tab';
import { styled } from '@mui/system';

[… etc]

Context 🔦

To anyone wishing to open a PR for this issue, replacing all occurrences of TabsUnstyled, TabsListUnstyled, TabPanelUnstyled, TabUnstyled to Tabs, TabsList, TabPanel, Tab from below file should be good.
/docs/src/components/productBaseUI/components/BaseTabsDemo.tsx

There also seems to be a forgotten file with a TabsUnstyled example. I suppose it should be deleted
https://github.com/mui/material-ui/blob/master/docs/src/pages/components/tabs/UnstyledTabsCustomized.preview


Camera pose

Thank you for your amazing work!
I'm new to the 3d reconstruction work.
I have read your paper and find that the camera pose is extremly important to the reconstruction task.

Could you please tell me that
how do I get a 3-dimensional reconstruction if I input an image and do not know the corresponding camera pose when the image was taken?
Thank you

Hey,
in this work we assumed a fixed camera height, with a fixed camera tilt and no inplane rotation, to reduce the solution space. We used the value presented in the SUNCGToolbox.
This is designed to work with robots, which already have a desired height and tilt, of course one can train a method with more range. This will likely reduce the performance.
Best,
Max

Thank you for your relpy. I am trying to construct a model which can estimate camera pose.

发件人: Maximilian Denninger @.>
发送时间: 2022年9月10日 13:37
收件人: DLR-RM/SingleViewReconstruction @.>
抄送: 文翊涵 @.>; Author @.>
主题: Re: [DLR-RM/SingleViewReconstruction] Camera pose (Issue #11)
Hey,
in this work we assumed a fixed camera height, with a fixed camera tilt and no inplane rotation, to reduce the solution space. We used the value presented in the SUNCGToolbox.
This is designed to work with robots, which already have a desired height and tilt, of course one can train a method with more range. This will likely reduce the performance.
Best,
Max
―
Reply to this email directly, view it on GitHubhttps://github.com/DLR-RM/SingleViewReconstruction/issues/11#issuecomment-1242637161, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ARBAJK3UWSIZ2MR2KK46F5LV5QNCDANCNFSM6AAAAAAQJEVFCA.
You are receiving this because you authored the thread.Message ID: @.***>

Hey,
that is not done in this work.
Best,
Max

Hey,
Thank you for your reply! I mean I will try to construct a model by myself to finish my project.😄
Hope I can get some progress
Best regards,
yihan


Roblox has Incorrect Listing

Backstory
I often use the Roblox platform.
Data

[ ] name: Roblox
[ ] url: https://en.help.roblox.com/hc/en-us/articles/203313050-How-Do-I-Delete-My-Account-
[x] difficulty: hard
[x] notes: Have to live in a place with legal regulations to delete account.
[x] email: https://roblox.com/support
[ ] domains: https://www.roblox.com/

Tended to on #814


[BUG] SAML authentication not working

Trying to download report using SAML auth is not working:
opensearch-reporting-cli -u 'https://opensearch.domain/_dashboards/goto/534bf220622bbb1095cdf24e6bd447d9?security_tenant=global' -a saml -t global -n report.pdf -c username:password
Error:
✖ Downloading report failed. Error: No element found for selector: [name="identifier"]

I'm using AWS OpenSearch Service with SAML configuration from AzureAD.

Thanks for reporting this issue. I will try to reproduce this and provide update.

Hello,
Any updates on this issue, please? I encounter the same exact problem.
The report generation works ok for basic authentication, but not for SAML.
Thanks

Hello,
Could someone please assist me with downloading the report using any available authentication method?
I'd like to confirm if my approach to passing the URL, username, and password within quotes is correct:
opensearch-reporting-cli -u 'https://<domain>/goto/52b219fb76f754216f8037d1211ac3a5' -a basic -c 'some_username':'some_password_with_special_characters' -f png

When I open the link in incognito mode, I'm directed to the following page:

If I remove the -a authentication type from the CLI command, the same page prompting for an Identity Provider selection is downloaded as a PNG.
How can I bypass the Identity Provider selection so that the provided credentials work properly?
Regards

Hi Team,
Any update? I hace the same error while using basic auth. I am running dashboards 2.8

I am getting same error on SAML.


Base aepp should support tokens

show balances for standardized token contract
transfer these tokens using contract
DEPENDS on standardised tokens by core team

@thepiwo please help me describe this in simple english text

clarification: supporting tokens working on top of ae (not eth or other networks)

I don't think there is anything to add here @edwarddikgale


Separate api and collaboration documentation

This PR splits documentation into two separate parts, one for API and the other emphasising collaboration.
Closes: #72

:tada: This PR is included in version 3.0.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Refactor: Move XyzModules to new Modules directory

Checklist

[x] the contributor license agreement is signed
[x] commit message follows commit guidelines
[x] tests are included

Description of change

We recently established the new tsrc/modules directory, this PR is an effort to consolidate the majority of the modules here. I did shy away from some (such as cloudprovider) if I was unsure I could adequately validate them in short order. But for all the others, you'll notice I did a commit for each. That was the output of a process of:

Move module (via IntelliJ - and thereby it's refactor tool) to the modules directory;
Run npm test
Run npm dev:build
Manually validate in oEQ running locally
Commit
Repeat for next module......

I've not really addressed any issues in the module - or moved comms logic out to the REST API Module - as I didn't want to further blur the basic move. Those tasks can be subsequent refactors.
I've also done this in the component/new-search-page due to that's where we've created the modules directory, and I wanted to do that now before we add more code.

Thanks @PenghaiZhang :+1:


GitHub repo

Activities to be done on the repo

@AliMeer Can you please verify this ticket on your side and update the status.

Done


[MuseCoCo] attribute2music generate error

Thank you very much for your team's contribution for this great works!
I follow your instruction in musecoco/README.md,
when i run this code
    # II. Attribute-to-Music Generation
    # The following script takes "data/infer_input/infer_test.bin" as input.
    bash interactive_1billion.sh 0 200
    # bash interactive.sh start_idx end_idx input_name

i got a error like this
    File "/usr/local/lib/python3.8/site-packages/fast_transformers/causal_product/__init__.py", line 44, in forward
        CausalDotProduct.dot[device.type](
    TypeError: 'NoneType' object is not callable

The original error message is as follows
Traceback (most recent call last):
  File "interactive_dict_v5_1billion.py", line 433, in <module>
    cli_main()
  File "interactive_dict_v5_1billion.py", line 423, in cli_main
    attributes(args)
  File "interactive_dict_v5_1billion.py", line 352, in attributes
    translations = task.inference_step(
  File "/usr/local/lib/python3.8/site-packages/fairseq/tasks/language_modeling.py", line 313, in inference_step
    return generator.generate(
  File "/usr/local/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/command_seq_generator.py", line 179, in generate
    return self._generate(sample, **kwargs)
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/command_seq_generator.py", line 315, in _generate
    lprobs, avg_attn_scores = self.model.forward_decoder(
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/command_seq_generator.py", line 837, in forward_decoder
    decoder_out = model.decoder.forward(tokens, sep_pos, encoder_out=encoder_out)
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/transformer.py", line 193, in forward
    x, extra = self.extract_features(
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/transformer.py", line 216, in extract_features
    return self.extract_features_scriptable(
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/transformer.py", line 363, in extract_features_scriptable
    x, layer_attn, _ = layer(
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/transformer_layer.py", line 58, in forward
    x, attn = self.run_self_attn(
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/transformer_layer.py", line 117, in run_self_attn
    r = self.self_attn(query, query, query, attn_mask=None, key_padding_mask=key_padding_mask)
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/attention_layer.py", line 114, in forward
    new_values = self.inner_attention(
  File "/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/causal_linear_attention.py", line 70, in forward
    V = causal_linear(
  File "/content/drive/.shortcut-targets-by-id/1jpYMr2ZASgh-ocgmVjIxEo4ckn5kGPvN/muzic/musecoco/2-attribute2music_model/linear_mask/linear/causal_linear_attention.py", line 23, in causal_linear
    V_new = causal_dot_product(Q, K, V)
  File "/usr/local/lib/python3.8/site-packages/fast_transformers/causal_product/__init__.py", line 44, in forward
    CausalDotProduct.dot[device.type](
TypeError: 'NoneType' object is not callable

Have you ever encountered this problem?
I'd appreciate it if you could help me out
Again, Thank your greatest contribution for this great works!

I get the same problem and believe that there is something wrong with the installed package pytorch-fast-transformers
idiap/fast-transformers#125

Agree with u, but it seems that this repo and pytorch-fast-transformers repo are not looking at issues. :(

I have solved the issue. Please check idiap/fast-transformers#125

@Jackl-o-o-l  thank you a lot :)   it work now