Float32 data promoted to Float64

I used rio-cogeo to convert a geotiff with 1 band of Type=Float32 geotiff.   To my surprise, the output generated was Type=Float64 geotiff.  Any idea why that happened and how to keep the data as Type=Float32?
Here's the actual rio-cogeo command I used:
rio cogeo create --cog-profile lzw Southern_California_Topobathy_DEM_1m.tif Southern_California_Topobathy_DEM_1m_COG.tif

And here's the gdalinfo for the INPUT file:
Driver: GTiff/GeoTIFF
Files: Southern_California_Topobathy_DEM_1m.tif
       Southern_California_Topobathy_DEM_1m.tif.ovr
       Southern_California_Topobathy_DEM_1m.tif.aux.xml
Size is 316120, 225217
Coordinate System is:
PROJCS["NAD_1983_NSRS2007_UTM_Zone_11N",
    GEOGCS["GCS_NAD_1983_NSRS2007",
        DATUM["NAD_1983_NSRS2007",
            SPHEROID["GRS_1980",6378137,298.257222101]],
        PRIMEM["Greenwich",0],
        UNIT["degree",0.0174532925199433]],
    PROJECTION["Transverse_Mercator"],
    PARAMETER["latitude_of_origin",0],
    PARAMETER["central_meridian",-117],
    PARAMETER["scale_factor",0.9996],
    PARAMETER["false_easting",500000],
    PARAMETER["false_northing",0],
    UNIT["metre",1,
        AUTHORITY["EPSG","9001"]]]
Origin =<PHONE_NUMBER>99998224666,3824832.000000000000000)
Pixel Size = (1.000000000000000,-1.000000000000000)
Metadata:
  AREA_OR_POINT=Area
  DataType=Generic
Image Structure Metadata:
  COMPRESSION=LZW
  INTERLEAVE=BAND
Corner Coordinates:
Upper Left  (  179524.000, 3824832.000) (120d29'26.74"W, 34d30'55.16"N)
Lower Left  (  179524.000, 3599615.000) (120d24'36.73"W, 32d29'15.49"N)
Upper Right (  495644.000, 3824832.000) (117d 2'50.95"W, 34d33'54.85"N)
Lower Right (  495644.000, 3599615.000) (117d 2'47.00"W, 32d32' 1.94"N)
Center      (  337584.000, 3712223.500) (118d44'57.15"W, 33d32'14.24"N)
Band 1 Block=128x128 Type=Float32, ColorInterp=Gray
  Min=-3857.312 Max=3067.828
  Minimum=-3857.312, Maximum=3067.828, Mean=-353.493, StdDev=804.656
  NoData Value=-3.40282306073709653e+38
  Overviews: 79030x56305, 39515x28153, 19758x14077, 9879x7039, 4940x3520, 2470x1760, 1235x880, 618x440, 309x220
  Metadata:
    RepresentationType=ATHEMATIC
    STATISTICS_COVARIANCES=647471.7255957157
    STATISTICS_MAXIMUM=3067.8276367188
    STATISTICS_MEAN=-353.49345874696
    STATISTICS_MINIMUM=-3857.3120117188
    STATISTICS_SKIPFACTORX=1
    STATISTICS_SKIPFACTORY=1
    STATISTICS_STDDEV=804.65627791978

And the gdalinfo for the OUTPUT file:
 Driver: GTiff/GeoTIFF
Files: tmp2cd_5blz.tif
Size is 316120, 225217
Coordinate System is:
PROJCS["NAD_1983_NSRS2007_UTM_Zone_11N",
    GEOGCS["GCS_NAD_1983_NSRS2007",
        DATUM["NAD_1983_NSRS2007",
            SPHEROID["GRS_1980",6378137,298.257222101]],
        PRIMEM["Greenwich",0],
        UNIT["degree",0.0174532925199433]],
    PROJECTION["Transverse_Mercator"],
    PARAMETER["latitude_of_origin",0],
    PARAMETER["central_meridian",-117],
    PARAMETER["scale_factor",0.9996],
    PARAMETER["false_easting",500000],
    PARAMETER["false_northing",0],
    UNIT["metre",1,
        AUTHORITY["EPSG","9001"]]]
Origin =<PHONE_NUMBER>99998224666,3824832.000000000000000)
Pixel Size = (1.000000000000000,-1.000000000000000)
Metadata:
  AREA_OR_POINT=Area
Image Structure Metadata:
  INTERLEAVE=BAND
Corner Coordinates:
Upper Left  (  179524.000, 3824832.000) (120d29'26.74"W, 34d30'55.16"N)
Lower Left  (  179524.000, 3599615.000) (120d24'36.73"W, 32d29'15.49"N)
Upper Right (  495644.000, 3824832.000) (117d 2'50.95"W, 34d33'54.85"N)
Lower Right (  495644.000, 3599615.000) (117d 2'47.00"W, 32d32' 1.94"N)
Center      (  337584.000, 3712223.500) (118d44'57.15"W, 33d32'14.24"N)
Band 1 Block=512x512 Type=Float64, ColorInterp=Gray
  NoData Value=-3.40282306073709653e+38
  Overviews: 158060x112609, 79030x56305, 39515x28153, 19758x14077, 9879x7039, 4940x3520, 2470x1760, 1235x880, 618x440

I've experienced the same. I'll dig up the details of my input file as well.

👋 @rsignell-usgs @DanSchoppe
I wasn't able to reproduce the issue on my mac with rasterio from wheels (gdal 2.4.1)
can you share your config please ?  To be honest this is mostly a rasterio/gdal bug, but I'll dig up

I've also got gdal=2.4.1, but I'm on linux, and I installed rio-cogeo=1.1.0 using conda install -c conda-forge rio-cogeo into an existing conda environment which sometimes can be problematic.  I'll try creating a new environment and see if that helps.

not sure conda has anything to do with that, maybe linux 🤔

@rsignell-usgs could you check what happen when you do `gdal_translate Southern_California_Topobathy_DEM_1m.tif  Southern_California_Topobathy_DEM_1m_lzw.tif -co COMPRESS=LZW"

I'm pretty sure the change appears here https://github.com/cogeotiff/rio-cogeo/blob/a6e8a2bc41d930ee211a86c3a6c375414fe4b623/rio_cogeo/cogeo.py#L262 but I've not been able to re-reproduce the error.
@rsignell-usgs could you share the data ?

The overviews are definitely different:
Source:
Overviews: 79030x56305, 39515x28153, 19758x14077, 9879x7039, 4940x3520, 2470x1760, 1235x880, 618x440, 309x220
Destination:
  Overviews: 158060x112609, 79030x56305, 39515x28153, 19758x14077, 9879x7039, 4940x3520, 2470x1760, 1235x880, 618x440

I'm guessing this is why rio-cogeo is still crunching away.  The source file is 230GB, and the destination is currently 600GB and still growing.

The source file is 230GB, and the destination is currently 600GB and still growing.
😱

@rsignell-usgs

The overviews are definitely different

The overviews are different because the overview level is calculated from the internal tile size (by default 512) and the size of your raster.
If you have already overviews for your input file you don't need to use rio-cogeo but should use GDAL directly

gdal_translate in.tif out.tif -co TILED=YES -co COPY_SRC_OVERVIEWS=YES -co COMPRESS=LZW

Because rio-cogeo is going to reprocess the overviews, the process is going time consumming.
On another note, can I ask you for what purpose are you creating such a huge COG ?
Sadly the schema we use for COG creation is not ideal for such a big raster. @rouault has made a nice COG driver (will be shipped in gdal 3.1 I think) which will work best with a file like yours : https://gdal.org/drivers/raster/cog.html
If you can't wait for gdal3.1 I'll definitely cut this raster in pieces and if it's for dynamic tiling purpose maybe have a look at: https://github.com/cogeotiff/rio-tiler-mosaic

I'm interested in this huge COG because I want to show how effective it is to access huge COG from S3 using rasterio with dask on AWS.   Basically use a workflow like the one in this excellent blog post by @scottyhq.
Unfortunately I'm out the door for a week of vacation, but will pick this up when I return.  Thanks for the help!

I'm interested in this huge COG because I want to show how effective it is to access huge COG from S3 using rasterio with dask on AWS. Basically use a workflow like the one in this excellent blog post by @scottyhq.

Well because the header of your file will be enormous (> 16ko) I guess it won't be as performant as it should (this is why the new COG driver was created). It will still work but every time dask will try to read some data it will do multiple S3 get_range calls to be able to know where to read within the raster.
Because we couldn't wait for GDAL3.1 (and we didn't know about the COG driver at the time) we created https://github.com/developmentseed/mosaicjson-spec and https://github.com/cogeotiff/rio-tiler-mosaic to be able to easily create a virtual mosaic of COGs... but maybe this is more adapted to dynamic tilling.

Oh. Well maybe I’ll try Zarr then!

I've snipped a tiny piece out and made it available here: https://esip-pangeo-uswest2.s3-us-west-2.amazonaws.com/sciencebase/tiny.tif
If I convert it using:
rio cogeo create -p lzw tiny.tif output.tif

the data gets promoted from Float32 to Float64

thanks @rsignell-usgs I'll have a look

@rsignell-usgs
I was able to recreate the issue and I think this is on GDAL side
$ gdalinfo tiny.tif -json | jq -r '.bands[0].type'
Float32

$ gdalwarp tiny.tif tiny_cog.tif
Processing tiny.tif [1/1] : 0Using internal nodata values (e.g. -3.40282e+38) for image tiny.tif.
...10...20...30...40...50...60...70...80...90...100 - done.

$ gdalinfo tiny_cog.tif -json | jq -r '.bands[0].type'
Float64

This only happens when using Warp (or WarpedVRT in rasterio). We may want to open an issue on GDAL
If I try to force the output file to be in Float32 (as the input file) rasterio errors with ValueError: the array's dtype 'float64' does not match the file's dtype 'float32' because the WarpedVRT creates a float64 array
🤔

@vincentsarago , where should I raise this GDAL issue?

It is not obvious there is a GDAL issue to me. @vincentsarago In your gdalwarp tiny.tif tiny_cog.tif please add a -overwrite flag so as to make sure you don't warp into an existing output file that would happen to be Float64. gdalwarp in_float32.tif out.tif -overwrite leads to out.tif being Float32 for me

Thanks @rouault, you are right, using -overwrite lead to a file with Float32.
I guess this is more on the rasterio side then. I'll try to dig deeper.

@rsignell-usgs this is a pure rasterio problem. I've opened a ticket over https://github.com/mapbox/rasterio/issues/1744 and hope this get resolved. I'm going to closes this issue but will follow on the rasterio repo

@rsignell-usgs I've seen that you succeed in you COG creation (ref: https://nbviewer.jupyter.org/gist/rsignell-usgs/0f96bb9c0ca34a5dd0fc8131a7bbae1c) I wonder if you ended up using pure GDAL commands ?
BTW the data looks great https://remotepixel.ca/cogeo.html?url=https://esip-pangeo-uswest2.s3-us-west-2.amazonaws.com/sciencebase/Southern_California_Topobathy_DEM_1m_cog.tif&rescale=-50,2000&color_map=terrain#12.91/33.75184/-117.57867

@vincentsarago @rouault I think I found the root over in https://github.com/mapbox/rasterio/issues/1744#issuecomment-528486687.

@vincentsarago , yes, in fact I just used gdal_translate as you suggested:
gdal_translate Southern_California_Topobathy_DEM_1m.tif   \
                       Southern_California_Topobathy_DEM_1m_cog.tif   \
                       -co TILED=YES -co COPY_SRC_OVERVIEWS=YES -co COMPRESS=LZW -co BIGTIFF=YES

Followed of course by:
rio cogeo validate Southern_California_Topobathy_DEM_1m_cog.tif

😸
BTW, I didn't know about the super cool service here:
https://remotepixel.ca/cogeo.html?url=https://esip-pangeo-uswest2.s3-us-west-2.amazonaws.com/sciencebase/Southern_California_Topobathy_DEM_1m_cog.tif&rescale=-50,2000&color_map=terrain#12.91/33.75184/-117.57867
Two questions:

Is it using the different overlays as you zoom in?
Where can I find the documentation for this RESTful API (what the parameter options are)?

BTW, I didn't know about the super cool service here:
https://remotepixel.ca/cogeo.html?url=https://esip-pangeo-uswest2.s3-us-west-2.amazonaws.com/sciencebase/Southern_California_Topobathy_DEM_1m_cog.tif&rescale=-50,2000&color_map=terrain#12.91/33.75184/-117.57867
Two questions:

Is it using the different overlays as you zoom in?

Yes, this use https://github.com/cogeotiff/rio-tiler, and will fetch the correct overview (I think when you say overlays you mean internal overview pyramid).

Where can I find the documentation for this RESTful API (what the parameter options are)?

The remotepixel tiler is opensource and code is over https://github.com/RemotePixel/remotepixel-tiler but the part to read any COG is from https://github.com/vincentsarago/lambda-tiler
lambda-tiler provide automatic documentation like https://cogeo.remotepixel.ca/docs
Note: RemotePixel is a personal project so it's not really made to be used as a Production endpoint. Deploying your own endpoint is pretty easy using https://github.com/vincentsarago/lambda-tiler


Add support for running tests using Renode for 32-bit RISC-V architecture

This PR adds support for running TFLM tests on a 32-bit RISC-V platform based on HiFive 1 simulated with Renode.
Issue #545 contains a summary of the changes.

FYI @petewarden this is what we've been working on recently. Of course this PR touches several different aspects of TFLM, but all of them are required to get things up and running.

Thanks for this! Can you also file a GitHub issue and mention it in the description, to help us keep track?

Also, can you break up the pull request a bit? At least having the RISC-V and other target changes be separate.

@petewarden I've collected the changes affecting other targets into #570. Note that this PR depends on #570, and includes changes from it. You can view the diff between #570 and this PR here: https://github.com/antmicro/tflite-micro/compare/riscv-support-prep...riscv-support?expand=1


[service discovery] fix minor python module use cases

What does this PR do?
when using python my/module/something.py , if my/module is a valid module, it's equivalent to have python -m my.module.something` hence the filename (without ext) should be part of that reconstructed module name
This PR fixes the service name we discover for this specific use case

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

/merge

/merge


Add normalizer

A min max clip and normalise method instead of defaulting to standard scaling.

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Files with missing lines
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src/rschip/image_chip.py
56.66%
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@@            Coverage Diff             @@
##             main       #6      +/-   ##
==========================================
- Coverage   95.39%   93.80%   -1.60%
==========================================
  Files           7        7
  Lines         478      500      +22
==========================================
+ Hits          456      469      +13
- Misses         22       31       +9

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Add functionality to visualization commands

The empty class we call Visualization needs functionality added to the slash commands.
Visualization Commands

/status (Gimpy)
/schema -> prints out the schema (Gimpy)
/query -> prints out a query(Gimpy)
/table -> prints out the entire table (Skye)
/column -> prints out a column of values (Skye)

Done with #82 .


I using NodeJS Ver6 : addRow() and insertRow() is not function. I can install exceljs ver ? to use

From Personal-experience I can tell you that i haven't got it going with node v6 and we therefore moved up to node 14 with our app.


Fallback if nerdfonts is not enabled

[x] I have read CONTRIBUTING.md
[x] I have read CODE_OF_CONDUCT.md

Thank you for the great project. According to the documentation, Nerd Fonts is an option for statusline. However, the current default behavior requires Nerd Fonts and moreover, it is a bit hard to disable Nerd Fonts. Hence, we add the new option 'nerdfonts' to disable easily. Cheers,

Hi! Thanks for contribution, it looks relevant.
Is there a reason of why not use Git and LSP as custom icons in args? Like MiniStatusline.section_git({ trunc_width = 75, icon = 'Git' }). I understand that this will need custom active content function (possibly quite verbose), but anything else besides this?
I am more curious about MiniStatusline.section_fileinfo() with nvim-web-devicons installed. Does it work incorrectly without Nerd font (shows weird square in place of an actual icon)?

Is there a reason of why not use Git and LSP as custom icons in args? Like MiniStatusline.section_git({ trunc_width = 75, icon = 'Git' }). I understand that this will need custom active content function (possibly quite verbose), but anything else besides this?

True. This is the reason why I created it. Currently, I am using MiniStatusline.section_git to suppress NerdFonts. Let me introduce my problem more.
MiniStatusline has  fantastic optional parameters to decorate statusline.

git status: it will be disabled if gitsign. nvim is disabled.
fileinfo status: it will fall back if kyazdani42/nvim-web-devicons is disabled.

The above option provides the fallback-mechanism if they are disabled.
However, the NerdFonts has no fallback-mechanism, that is, we need to manually make
statusline function to fallback. This is harder than others. Of course, I know that font detection is impossible from the terminal version of Neovim. Instead, I propose an easier way to disable it manually to fill the gap.
Further, we do not change any lines about section_fileinfo. Thus, it should work well.

Thanks for explanation. I understand your reasoning more clear now.

Further, we do not change any lines about section_fileinfo. Thus, it should work well.

I understand that. What I tried to ask you (as at the moment I can't reproduce "non-nerd-font" environment), is how it works right now under following conditions:

Non-nerd-font.
'nvim-web-devicons' installed.
section_fileinfo() active.

I imagine, it should act similarly to icons in git and diagnostic sections, but not sure.
All in all, at the moment I am more inclined to add this option, but in more general use_icons form. If false:

In section_git() fallback to "Git" (as you implemented).
In section_diagnostic() fallback to some short string indicating diagnostic. "LSP" is fine, but something more targeted would be better.
In section_fileinfo() fallback to as if 'nvim-web-devicons' is not installed.

I'll think about it a little bit more and get back to you, hopefully, tomorrow.

Noted. For the above situation (Non NerdFonts, 'nvim-web-devicons' is installed, section_fileinfo active), it should be disabled because the double-width icons breaks the window rendering for some neovim frontend. We need to disable it for such a frontend.
Thus, I change the behavior to return a blank string in the latest commits. Further, I agree the option name (nerdfonts) is not so general name. Hence, I have changed it to use_icons. Please check it.

Thank you for adopting my suggestion. I'm very happy. I'm sorry about the lack of testing and documentation, but I don't know enough about them, so please work on them yourself.

Thank you for adopting my suggestion. I'm very happy. I'm sorry about the lack of testing and documentation, but I don't know enough about them, so please work on them yourself.

Already done in latest main.
It was not that complex, rather tedious with current testing setup. And yeah, implementing new option is especially hard on documentation and implementation. Probably should document it in full somewhere.


Multi Database Support

Is this best practice for multi database for our source code

Connection String of Service

"ConnectionStrings": {
"DBType": "MSSQLSERVER", // MSSQLSERVER, POSTGRESQL, MYSQL
"BSuit": "Server=.;Database=BSuit.Microservices.Product;User Id=sa;Password=sqladmin123!@#;MultipleActiveResultSets=true"
},

App Settings

public class ConnectionStrings
{
public string DBType { get; set; } // New Field
public string BSuit { get; set; }
    public string MigrationsAssembly { get; set; }
}

xxxxxxxxxModuleServiceCollectionExtensions

if (appSettings.ConnectionStrings.DBType == "MSSQLSERVER")
{
services.AddDbContext(options => options.UseSqlServer(appSettings.ConnectionStrings.BSuit, sql =>
{
if (!string.IsNullOrEmpty(appSettings.ConnectionStrings.MigrationsAssembly))
{
sql.MigrationsAssembly(appSettings.ConnectionStrings.MigrationsAssembly);
}
}));
}
else if (appSettings.ConnectionStrings.DBType == "POSTGRESQL")
{
services.AddDbContext(options => options.UseNpgsql(appSettings.ConnectionStrings.BSuit, sql =>
{
if (!string.IsNullOrEmpty(appSettings.ConnectionStrings.MigrationsAssembly))
{
sql.MigrationsAssembly(appSettings.ConnectionStrings.MigrationsAssembly);
}
}));
}

Thanks @aneesanp123, however, the database provider (MSSQL/POSTGRESQL) is considered something we should configure explicitly rather than switching back and forth by value in the configuration.


rename_column sql syntax is incorrect

please see http://dev.mysql.com/doc/refman/5.1/en/alter-table.html
#dm-migrations / lib / dm-migrations / sql / table_modifier.rb
you need to change the sql syntax to look like this:
"ALTER TABLE #{quoted_table_name} CHANGE #{quote_column_name(name)} #{quote_column_name(new_name)}"
According to the docs this is correct syntax, also try running the current syntax via a ui or command line and you will get an error.

+1

+1
We need to add an MySQL adapter specific method for this, similar to change_column_type_statement. I might try to fix this.


[Rules] Update link

Summary
Links to a different section in the target page.

Preview:
https://pedro-2024-11-13-rules-updat.cloudflare-docs-7ou.pages.dev/rules/transform/


MangaDex 1.2.104

Update

Is there any reason to maintain this anymore?

@thu2468 answered in discord:

since mangadex still has issue can i push a pull request for the latest mangadex extension update


build failed when jsx syntax added

I tried to add minio extention to the mozaik by running yarn add mozaik-ext-minio
I see the following error.
Module parse failed: /Users/xyz/Desktop/m2/mozaik-demo/node_modules/mozaik-ext-minio/src/components/Bucket.js Unexpected token (16:12)
You may need an appropriate loader to handle this file type.
 return (

I observed this issue even in my custom extension.
Ex: React.createElement("div",null,"Hello world") is working fine but when i change to
 Hello world
 is not working.

I have given wrong entry for "module".
once i changed to "module": "es/components/index.js",. it started working.


Enable assertions.

Enable assertions.

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The diff coverage is n/a.

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=========================================
  Coverage     63.55%   63.55%
  Complexity      384      384
=========================================
  Files            79       79
  Lines          1391     1391
  Branches        142      142
=========================================
  Hits            884      884
  Misses          451      451
  Partials         56       56

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mlnet not compatible with .NET Core 6.0.1

Operating system:  Linux Mint 20.2 based on Ubuntu 20.04
Installed mlnet:
$ dotnet tool install -g mlnet
You can invoke the tool using the following command: mlnet
Tool 'mlnet' (version '16.2.0') was successfully installed.

Try to run mlnet:
$ mlnet
It was not possible to find any compatible framework version
The framework 'Microsoft.NETCore.App', version '3.1.0' (x64) was not found.
  - The following frameworks were found:
      5.0.5 at [/usr/share/dotnet/shared/Microsoft.NETCore.App]
      6.0.1 at [/usr/share/dotnet/shared/Microsoft.NETCore.App]

You can resolve the problem by installing the specified framework and/or SDK.

The specified framework can be found at:
  - https://aka.ms/dotnet-core-applaunch?framework=Microsoft.NETCore.App&framework_version=3.1.0&arch=x64&rid=linuxmint.20.2-x64

Possibly related to #640

@LittleLittleCloud  any thought on this?

@LittleLittleCloud Can you link to installation instruction here?

New version of the CLI has been released with support for .NET 6. Here are the install instructions. Closing this issue.
https://docs.microsoft.com/dotnet/machine-learning/how-to-guides/install-ml-net-cli?tabs=windows


CLI should take advantage of v2 API plugin form info

Because writing JSON on the command line with no arrow key support or validity checking is inhumane.
This is a stretch goal for v7 and may need to be pushed until later into v7 support.

This is still pending the completion of the agent API and the updating of the plugins

Plugin Metadata is complete and implemented on the backend (Agent → Core).  We'll be shoring up the /v2/ API this week and next to expose the information on a per-agent basis.

As in the daemon can gather the info and expose it through the API?

The code is currently gathering the information from the agents, and as of d306ec0, the agents are able to supply it to the core.  I don't believe we have an API for retrieving it for front-end uses yet.

To get it off the agents, would you need the ssh key?

No.  It's in the database, in core.  But there is no /v2/... API call to get it out of the database, so CLI can't use it (yet)

Spin up ./bin/testdev, wait about 2 minutes, and then issue the following SQL query: SELECT * FROM agents and you'll see what I mean.

I'm assuming there's a polling interval that causes the 2 minute wait?

Yeah.  The agents register something akin to a "ping me!", and the core only checks on the agents every X minutes, to keep spam down.

Buckler CLI supports v2 API natively.


Add vit_l_lm to model registry?

Hello, I have the following code:
model_type = 'vit_l_lm'
sam_model = get_trainable_sam_model(model_type=model_type, device=device)

When I run this, I get an error. It appears that vit_l_lm is not part of the model_registry, whereas other models like vit_b_lm and vit_l are. I confirmed this by calling get_model_names().
Just wondering if there is a reason vit_l_lm is not included, or if it is possible to add it. Thanks!

Hi @nicoleaudia,
Thanks for your interest in micro-sam.
Our latest version does support vit_l_lm. I am a bit confused why you get the error. Could you run the following script in terminal and share with us the outputs: python -c "from micro_sam.util import get_model_names; print(get_model_names())"
To remove another suspicion, could you also share the terminal outputs by running python -c "import micro_sam; print(micro_sam.__version__)"?

This seems to be resolved. Please reopen if the issue persists.

Thanks for suggesting to check the version -- one of my virtual environments was using a different version than the others! Fixed that and no longer having this issue.


fix(upgrade-job): upgrade path validation

use --set flags to set thin provisioning flags for 2.0.x releases, instead of 2.1.x release.
allow 'no-rollback' rule to be overridden by the --skip-upgrade-path-validation flag. This is not necessary for the openebs/openebs (Umbrella) chart because this upgrade-job does not support upgrade for the openebs chart, so might as well move that check to be a Core chart exclusive check.

why is this required --app-tag?

why is this required --app-tag?

Oh, that's not supposted to be there..

bors try

is the code for set flag removed now ?

bors merge


Backport of improvements to temporary file handling

Prerequisite for implementation of issue #4759

Backport of https://github.com/ibissource/iaf/pull/4717/commits/f42ff013cd4229aeba3763e10402ee95a24458d3 (from #4717)


Failed to find matching artist ({meta_artist}) and album ({meta_album})

I notice in 1.5.5 in verbose mode the following over and over for albums that lack existing embedded album art:
Failed to find matching artist ({meta_artist}) and album ({meta_album})
I don't know whether that is simply a cosmetic bug in what's written to the screen or whether get_cover_art is actually trying to search Apple Music for the strings ({meta_artist}) and ({meta_album})

Oops, thanks for catching this!  I switch our string conventions to use template strings and broke the convention in a couple places.  Please try again with the latest version (1.5.6)
In your case it should still print those errors, but this time it will display the actual artist and album names that it's searching with, based on the meta tags of your files.

That fixes the issue I reported.
Thank you.


min: Add 32bit version

Some updates for min:

add 32bit (ia32) architecture;

move extract_dir to higher level (since it equals for both 64bit/32bit versions);

update homepage.

[x] I have read the Contributing Guide.

/verify


(PDB-1590) Support two previous versions of all commands (for upgrades)

Note these previous versions are not supported at the API level. The
intent here is if the user has upgraded from PuppetDB 2.0.0 to 3.0.0 and
there are still persisted messages in the queue (POSTed, but not
processed) those messages should be consumable by PuppetDB 3.0.0.
This commit takes a previous version of the command and upgrades it,
filling in anything missing, changing the names of keys as necessary to
conform it to the current (supported) command version.

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://jenkins.puppetlabs.com/job/platform_puppetdb_intn-sys_pr/1511/

this is confirmed and was previously reviewed and approved of -- will merge when the pipeline is clear


Add an option to clear the deploy directory on deploy

The way I see it this could be done here with something a prop which could look something like.
./gradlew deploy -Pdestructive

This could also be done with a seperate task to wipe artifacts currently on the roboRIO, something like
./gradlew clearrio

How to do it in your own build.gradle if desired is a 1-line change: https://www.chiefdelphi.com/t/clearing-the-deploy-directory-on-a-roborio-running-commands-on-the-roborio/458273/10?u=peter_johnson

Maybe a good idea to document that somewhere as I've seen a lot of teams asking about it


!seen breaks if it's never seen a user.

Throws an exception and dies if a user doesn't exist.  That's bad.

Closing as the issue was already fixed in cf39ba16c2f1493029cda716966a89eaf3b29454


Scheduling RepeatedJob with first time in the past

Describe the bug
See discussion here
User wants a Repeated job to be executed forever starting from 2022/01/01 every 1h. What time should be placed as scheduled_time in this case? If you put there, for example, a very distant day in the future (e.g. 2050/01/01) the django-rq-scheduler will create a job, but RQ scheduler will be waiting till the 2050/01/01 to get it executed for the first time.
To Reproduce
Schedule a RepeatedJob to run every 1h starting from a time in the past.
Expected behavior
Should be scheduled if there is an expected run in the future.

@Cybernisk,
The logic in self._prevent_duplicate_runs() aims to handle the case you are describing.
So, let's say I schedule at 2022-01-01 00:10 to run a job every 1 hours for 10 times starting on 2022-01-01 00:10,
i.e., the first run had expired already.
self._prevent_duplicate_runs() will calculate the next scheduled time and reduce the number of expected runs.


How can i disable not required metrics?

Hello, i see that opencost support metrics disabling https://github.com/opencost/opencost/blob/c324846753beda858a1ea18a00fc50b5ca509309/pkg/metrics/metricsconfig.go#L24
But i don't see how this can be done using chart. Is there a way to do it?

I haven't seen that documented before but I'd love to get it added to both the documentation and Helm chart.

Any updates on this?
In kubecost helm chart i was able to specify something like this
  prometheus:
    nodeExporter:
      enabled: false
    kubeStateMetrics:
      enabled: false
    kube-state-metrics:
      disabled: true

  kubecostMetrics:
    emitKsmV1Metrics: false
    emitKsmV1MetricsOnly: true

Which disable metrics emitting. Is there a way to do the same here?

Would that go in the Prometheus configuration? The OpenCost Helm chart doesn't currently deploy Prometheus (Kubecost does), so maybe we'd need to alternate configs for Prometheus?
https://raw.githubusercontent.com/opencost/opencost/develop/kubernetes/prometheus/extraScrapeConfigs.yaml

I was able to do it by setting this environment variables
        EMIT_KSM_V1_METRICS: "false"
        EMIT_KSM_V1_METRICS_ONLY: "true"

It could be adapted from the cost-analyzer-helm-chart:
{{- if .Values.kubecostMetrics }}
- name: EMIT_POD_ANNOTATIONS_METRIC
  value: {{ (quote .Values.kubecostMetrics.emitPodAnnotations) | default (quote false) }}
- name: EMIT_NAMESPACE_ANNOTATIONS_METRIC
  value: {{ (quote .Values.kubecostMetrics.emitNamespaceAnnotations) | default (quote false) }}
{{- end }}
{{- if .Values.kubecostMetrics }}
- name: EMIT_KSM_V1_METRICS
  value: {{ (quote .Values.kubecostMetrics.emitKsmV1Metrics) | default (quote true) }}
{{- end }}
{{- if .Values.kubecostMetrics }}
- name: EMIT_KSM_V1_METRICS_ONLY # ONLY emit KSM v1 metrics that do not exist in KSM 2 by default
  value: {{ (quote .Values.kubecostMetrics.emitKsmV1MetricsOnly) | default (quote false) }}
{{- end }}

I didn't realize those were in the OpenCost codebase. You should be able to add those to opencost.exporter.env in your values.yaml, but a separate PR for specifying them would be worthwhile too.

If this configuration is desired via helm chart, I can add it to the deployment template. So it would be configurable like this:
opencostMetrics:
  emitPodAnnotations: false
  emitNamespaceAnnotations: false
  emitKsmV1Metrics: true
  emitKsmV1MetricsOnly: false

I'd approve it and add it to the docs.

I just created a draft PR.

@mpostument the change got released in 1.32.0.
You can now configure it like this:
opencost:
  metrics:
    kubeStateMetrics:
      emitPodAnnotations: ""
      emitNamespaceAnnotations: ""
      emitKsmV1Metrics: ""
      emitKsmV1MetricsOnly: ""


fix(CanvasForm): Expression field shows [object Object]

Context
When a new expression-aware step is generated, the expression property is created as follows:
        setHeader:
          id: setHeader-1234
          expression:
            simple:
              expression: {}

Notice the expression: {} bit which causes for the expression TextField to render [object Object]
The fix is to replace the {} with "", so we add an empty string.
Note
This comes from this PR https://github.com/KaotoIO/kaoto/pull/1068, in which I suggested to use expression: {} for the newly created step.
fix: https://github.com/KaotoIO/kaoto/issues/1076

if I understand well, for the setBody, when inserting the element, it is setting the body to empty string ad there won't be any message to tell user to configure the field.
It sounds better to not have any values in this case. Because I fear that a lot of users will forget to modify it and will not understand why the body is "lost".

Although we did not yet implement any check if the expression is filled, the behavior now is that just simple is selected and the expression is empty. This will make it easier to spot the missing bit once we have the validation ready.

current behavior is that:

when opening textual file, there is a validation error spotted
when running, for instance with setBody, the body is set null at runtime.

Slightly better than empty string but still not ideal.


Document how to interface with BETYdb

API for programmers

Embed documentation with source code
Have process that runs over it and generates document
Talk to @robkooper

@gsrohde what is the status of this issue?

There is now documentation in GitBook.  Closing this issue.  Will make new issues if and when specific documentation shortcomings become apparent.


EC2 add hosts does not work

Rancher Version:
v 1.1.4
Docker Version:
1.12.3 and API version 1.24
OS and where are the hosts located? (cloud, bare metal, etc):
Ubuntu 16.04.1 LTS (Xenial Xerus) on AWS
Setup Details: (single node rancher vs. HA rancher, internal DB vs. external DB)
Single Node Rancher master, internal DB
Environment Type: (Cattle/Kubernetes/Swarm/Mesos)
Kubernetes
Steps to Reproduce:
From kubernetes environment, add hosts with "Amazon EC2" as option and enter all required details.
Results:

With "rancher" as user:
Error checking the host: Error checking and/or regenerating the certs: There was an error validating certificates for host "<IP_ADDRESS>:2376": tls: DialWithDialer timed out

With Ubuntu user:
Error detecting OS: Too many retries waiting for SSH to be available. Last error: Maximum number of retries (60) exceeded

The logs with rancher user:
time="2016-11-15T07:00:24Z" level=info msg="stdout: (kube01) Launching instance..." resourceId: =1ph15
time="2016-11-15T07:00:48Z" level=info msg="stdout: Waiting for machine to be running, this may take a few minutes..." resourceId: =1ph15
time="2016-11-15T07:00:48Z" level=info msg="stdout: Detecting operating system of created instance..." resourceId: =1ph15
time="2016-11-15T07:00:48Z" level=info msg="stdout: Waiting for SSH to be available..." resourceId: =1ph15
time="2016-11-15T07:02:49Z" level=info msg="stdout: Detecting the provisioner..." resourceId: =1ph15
time="2016-11-15T07:02:49Z" level=info msg="stdout: Provisioning with rancheros..." resourceId: =1ph15
time="2016-11-15T07:02:50Z" level=info msg="stdout: Copying certs to the local machine directory..." resourceId: =1ph15
time="2016-11-15T07:02:52Z" level=info msg="stdout: Copying certs to the remote machine..." resourceId: =1ph15
time="2016-11-15T07:02:53Z" level=info msg="stdout: Setting Docker configuration on the remote daemon..." resourceId: =1ph15
time="2016-11-15T07:03:10Z" level=info msg="stdout: Checking connection to Docker..." resourceId: =1ph15
time="2016-11-15T07:03:14Z" level=info msg="stdout: The default lines below are for a sh/bash shell, you can specify the shell you're using, with the --shell flag." resourceId: =1ph15
time="2016-11-15T07:03:14Z" level=info msg="stdout: " resourceId: =1ph15
time="2016-11-15T07:03:14Z" level=info msg="stderr: Error creating machine: Error checking the host: Error checking and/or regenerating the certs: There was an error validating certificates for host \"<IP_ADDRESS>:2376\": tls: DialWithDialer timed out" resourceId=1ph15
time="2016-11-15T07:03:14Z" level=info msg="stderr: You can attempt to regenerate them using 'docker-machine regenerate-certs [name]'." resourceId=1ph15
time="2016-11-15T07:03:14Z" level=info msg="starting cleanup..." machine name=kube01

The logs with Ubuntu user:
time="2016-11-15T07:20:20Z" level=info msg="stdout: (kube01) Launching instance..." resourceId: =1ph16
time="2016-11-15T07:20:44Z" level=info msg="stdout: Waiting for machine to be running, this may take a few minutes..." resourceId: =1ph16
time="2016-11-15T07:20:44Z" level=info msg="stdout: Detecting operating system of created instance..." resourceId: =1ph16
time="2016-11-15T07:20:44Z" level=info msg="stdout: Waiting for SSH to be available..." resourceId: =1ph16
time="2016-11-15T07:25:40Z" level=info msg="stdout: The default lines below are for a sh/bash shell, you can specify the shell you're using, with the --shell flag." resourceId: =1ph16
time="2016-11-15T07:25:40Z" level=info msg="stdout: " resourceId: =1ph16
time="2016-11-15T07:25:40Z" level=info msg="stderr: Error creating machine: Error detecting OS: Too many retries waiting for SSH to be available.  Last error: Maximum number of retries (60) exceeded" resourceId=1ph16
time="2016-11-15T07:25:40Z" level=info msg="starting cleanup..." machine name=kube01
time="2016-11-15T07:25:40Z" level=info msg="cleanup successful" machine name=kube01
time="2016-11-15T07:25:40Z" level=error msg="Error processing event" err="Error detecting OS: Too many retries waiting for SSH to be available.  Last error: Maximum number of retries (60) exceeded" eventId=77c88c29-6014-4487-af38-349245540e87 eventName="physicalhost.create;handler=goMachineService" resourceId=1ph16

Expected:
EC2 hosts should be added seamlessley and should not time out. Once I tried adding 12-15 hosts and only 2-3 got added eventually

can you share the zone and ami youre using? Also are you using 'ubuntu' or 'Ubuntu' as the user? Can you also confirm the security group youre using? Are your rancher server AND rancher agents in AWS, or only agents?

I'm having the exact same issue.
AMI ID
ubuntu/images/hvm-ssd/ubuntu-wily-15.10-amd64-server-20151116.1 (ami-16b1a077)
Availability zone
us-west-2a
User
ubuntu
Security groups
rancher-machine
Also opened all traffic on all ports for both servers (both in same zone in AWS)
Thanks for the help.

@nano2yk I did a quick deployment from rancher using the EC2 machine driver with that AMI and was able to provision. Can you confirm that the outbound traffic on the security groups are open? Does the outbound sec group use anywhere (<IP_ADDRESS>/0) as the destination? Was the host stuck on 'waiting to ssh' from the rancher UI?

@aemneina
region us-east-1
I used "ubuntu" as user
Security group on source (Rancher server) as well as target (Agent) machines was kept all ports open from all sources. Used AMI ami-dfdff3c8. Also rancher server and agent all are in same region and in AWS.

@aemneina can you please confirm your settings and I can try with same settings!

These seem like they are  all docker-machine related issues, nothing directly to Rancher.
Are you able to use the same docker-machine command to create hosts  using Docker Machine? In the logs, you should be able to get the docker machien command, it starts something like "cmd slice ["create ..."].
If you can try using the to do `docker-machine create..." to see if it works locally, that'd be the first step.

@vishal-biyani the user for rancheros is rancher... can you also try provisioning with a rancheros v0.4.5 as a sanity check.

@aemneina Thanks, tried with 0.4.9 AMI (us-east-1 and ami-812ec0ec) and also rancheros-v0.7.1-hvm-1 (ami-dfdff3c8) and both worked with rancher user. Appreciate it.

Also worked without specifying the AMI and using ubuntu user.


COR-333: add week field type

Introduces a new field type week which uses the input type week and degrades to a regular text input if there is no browser support

I've added some unit tests to the WeekFieldEditor and MonthFieldEditor components, but I am still struggling to get the jest.fn() to behave as expected. Other tests are passing.
@ludydoo could you take a look and advise?

[] rebase on main
[] add logic in the recordStore.Create to coerce the Week values into a valid sql Date. The incoming payload will be like {"values":{"my_week_field": "2021W23"}}. The "2021W23" needs to be converted to a valid date (2021-06-07)
[] add logic in the recordStore.Get/List to convert the date retrieved from the sql database into 2021W23 format.

[] rebase on main
[] add logic in the recordStore.Create to coerce the Week values into a valid sql Date. The incoming payload will be like {"values":{"my_week_field": "2021W23"}}. The "2021W23" needs to be converted to a valid date (2021-06-07)
[] add logic in the recordStore.Get/List to convert the date retrieved from the sql database into 2021W23 format.

@ludydoo I'm not sure your comment is still true, as far as I can see the date in the database is a proper sql date, please review again

@internetti the payload from the api is a string
values: {"my-week-field":"2021W52"}

Though, the database expects a data
INSERT into myTable (myWeekField) values ("2021W52") <- I don't think this will work, since it's not a valid date

I think we need
here
https://github.com/nrc-no/core/blob/badb3e81092856f363282d82c2074d7742e9d8db/pkg/store/record.go#L323
to convert "2021W52" (payload sent by the js client) to "2021-12-23" (or whatever the first day of the 2021W52 is)
and then, when we retrieve "2021-12-23" from the Database, we need to convert it back to a format that the js client expects ("2021W52")

@ludydoo as far as I can see the payload being sent to the api is "2021-12-16T23:00:00.000Z"

and the column is a date column, and there is no error when entering a new value by sending a post from the app

@internetti then we have 2 choices

Make the client actually send a proper week like "2021W23" (that's what I thought was the process here..)
Ensure that the date stored in the db "2021-10-10T23...." actually is like.. the first day of the week, at 00:00:00 UTC. Because the client might send something like Wednesday at 11:23:40... as a week field value. Which is invalid. It will pose problems if the user defines the week field as a key field.

If a user defines a field as a week fields, and sets this as a key field, the user expects that no 2 records can exist for the same week.
But if the js client sends a "week" value of "Wednesday January the 15th, 2021 at 12:23:22PM" the week is "2021W2".
Then, the js client sends a second "week" of "Wednesday January the 17th, 2021 at 12:23:22PM". The week is still "2021W2"
But, we should throw here, because there would effectively be 2 records for the same week. Because we didn't "force" the field values to correspond to the first day of the week, like "Jan 03 2021, 00:00:00", we will break the "key" functionality where 2 records cannot have the same week value.

That's why I would personally prefer that the client sends a proper "2021W21", and that the record store converts this to a sql date such as 2021-06-05 (or whatever the first day of that week is). The record store would also have to do the conversion the other way around, when a user retrieves the record from the db, so that 2021-06-05 is converted back to 2021W21

/lgtm

/lgtm

/lgtm


ROX-26052: Create a cached client

Description
Caching policies in the controller so that it doesn't put too much of a load on Central. The cache will be updated on the first reconciliation that happens after the cache age hits five minutes.
Note that the cache is a map, keyed off the policy name.  This means the reconciler code could be simplified a bit to avoid looping over all policies looking for a match on name.
I also refactored the CR status update code a bit to hopefully make it clearer.
Testing

Unit tests with a mocked out grpc client.
Still doing manual e2e testing against an installation on a local Kind cluster, including observing a policy CR successfully get applied to a Central instance.

Closed in favor of #12647


Dosnt work on polkit and gnome-screensaver

I tried all the known issues but still not work

Ok i fix it on #646


點選文字輸入區時，原有文字會被清空

What steps will reproduce the problem?
1.在行列輸入模式，會導致點選網址列時，網址直接被清空，
以致於無法剪下網址。也會導致按Ctrl+C(複製)時，變成執行Ctr
l+X(剪下)的行為，不易編輯文字。

What is the expected output? What do you see instead?

What version of the product are you using? On what operating system?

Please provide any additional information below.

Original issue reported on code.google.com by<EMAIL_ADDRESS>on 7 Jan 2015 at 3:21

0001-Fix-issue-21-text-cleared-accidentally-when-select-t.txt
I have a potential fix, but since I don't really understand the ime flow, just upload it for the authors review.

谢谢，
但是，
怎么用这个文本文件呢？

0001-Fix-issue-21-text-cleared-accidentally-when-select-t.txt
I have a potential fix, but since I don't really understand the ime flow, just upload it for the authors review.

谢谢，
但是，
怎么用这个文本文件呢？

Should be fixed by https://github.com/google/jscin/pull/29
Wait for next release (0.25) that should have this fix.


XRPCError: too many concurrent writes

I'll concede, this is an issue anyone running a simple bot with a typical feed from a blog or something that updates once in a while will never bump into, but for "news bots", that monitors dozens of feeds at the source, you can quickly end up with dozens of posts because the publishers schedule their articles to go out in the morning or during the day at specific time, hence creating suddenly a lot of posts to make.
So the error that bluesky throws is this :
/build/node_modules/@atproto/xrpc/src/client.ts:126         throw new XRPCError(resCode, res.body.error, res.body.message)               ^ XRPCError: too many concurrent writes     at ServiceClient.call (/build/node_modules/@atproto/xrpc/src/client.ts:126:15)     at processTicksAndRejections (node:internal/process/task_queues:95:5)     at async PostRecord.create (/build/node_modules/@atproto/api/src/client/index.ts:1506:17) {   status: 400,   error: 'ConcurrentWrites',   success: false } error Command failed with exit code 1. info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command. yarn run v1.22.19 $ ts-node ./app/index.ts
So this again is a feature request, either to try to handle this error in a better way, without interrupting the watch for new posts (apparently now, when this occurs, I have to re UP the containers, so the bot will stay silent until this happens)
OR
by adding a queuing system, by delaying posts for 5 or more minutes so that the "to many concurrent writes" are completely avoided.
Would that be possible or it's not something you have in mind ?
edit : I have noticed that this mostly happens the first time the bot is launched, so perhaps, when instantiating the bot, the simplet path would be to only post the first 5 last items for example ?

Sure, I can work on this. I can build a queue system (that is optionally enabled in the config) which can post the posts in the queue every x minutes.

Hey there! Just for my testing purposes, could you give me the RSS feed URL you're using that provides this error?

Yes of course, here is the link
You can change the number 1 at the end of the RSS feed to increase the number of articles published in the last X hours
so if you pick say last 2h it should be able to reproduce the error at the first shot

Can't seem to replicate the rate limit at the moment, interesting. I'll keep trying, don't know if they may have changed something internally?

It's possible because I haven't seen it on my side anymore but I still think a queuing system would be a nice feature (if possible), from the user perspective, to avoid having a dozen skeets in one shot?

Yeah, I'll still work on the queue - it's almost done.

I managed to get this error :
/build/node_modules/@atproto/xrpc/src/client.ts:126                                                                                │
│           throw new XRPCError(resCode, res.body.error, res.body.message)                                                                                │
│                 ^                                                                                │
│   XRPCError: too many concurrent writes                                                                                │
│       at ServiceClient.call (/build/node_modules/@atproto/xrpc/src/client.ts:126:15)                                                                                │
│       at processTicksAndRejections (node:internal/process/task_queues:95:5)                                                                                │
│       at async PostRecord.create (/build/node_modules/@atproto/api/src/client/index.ts:1519:17) {                                                                                │
│     status: 400,                                                                                │
│     error: 'ConcurrentWrites',                                                                                │
│     success: false                                                                                │
│   }                                                                                │
│   error Command failed with exit code 1.                                                                                │
│   info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.                                                                                │
│   yarn run v1.22.19                                                                                │
│   $ ts-node ./app/index.ts

Seems like their rate limiting is working again. The queue is closed to being finished, just have to finalize some things.

Yay that's great to know!
I went back to your branch with my bots to enjoy the new OpenGraph (had to disable description tho) it's working perfectly!
Thanks a lot for your code, it's really the best and most complete implementation to run bots on Bluesky!

Created a PR, if you could test this out and leave me some feedback on the PR, that would be greatly appreciated!


Merkle mountain range partial

Describe your changes
This adds the MmrPartial structure, useful to keep track of a single entry in the MMR and have infrequent updates.
Checklist before requesting a review

Repo forked and branch created from next according to naming convention.
Commit messages and codestyle follow conventions.
Relevant issues are linked in the PR description.
Tests added for new functionality.
Documentation/comments updated according to changes.

We'll probably come back to this later - so, don't close it yet. As for rebasing, if it is not too much effort then, yes - I'd keep it up to date.

superseded by #195


eCAL@Windows: cannot cmake against ecal due to not found eCAL::mon_plugin_lib

Describe the bug
Currently it is not possible to link against the eCAL Master, as CMake is missing the mon_plugin_lib target:
CMake Error at C:/eCAL/lib/cmake/eCAL/eCALTargets-release.cmake:89 (set_property):
  set_property could not find TARGET eCAL::mon_plugin_lib.  Perhaps it has
  not yet been created.
Call Stack (most recent call first):
  C:/eCAL/lib/cmake/eCAL/eCALTargets.cmake:161 (include)
  C:/eCAL/lib/cmake/eCAL/eCALConfig.cmake:62 (include)
  CMakeLists.txt:9 (find_package)

CMake Error at C:/eCAL/lib/cmake/eCAL/eCALTargets-release.cmake:90 (set_target_properties):
  set_target_properties Can not find target to add properties to:
  eCAL::mon_plugin_lib
Call Stack (most recent call first):
  C:/eCAL/lib/cmake/eCAL/eCALTargets.cmake:161 (include)
  C:/eCAL/lib/cmake/eCAL/eCALConfig.cmake:62 (include)
  CMakeLists.txt:9 (find_package)

To Reproduce
Steps to reproduce the behavior:

Download binary release for eCAL and install it on Windows
CMake against it

Desktop (please complete the following information):

OS: Windows
Version master @ g5f75e928

@FlorianReimold
Please be aware of the implication of these changes.
Td:lr You cannot use eCAL anymore without having Qt in your system!
The long answer
Mainly, now we have a strong dependency of ecAL SDK to QT, because eCAL::mon_plugin_lib also links QT libraries, if Qt is not available (or there is not find_package(QT) call, a find_package(eCAL) call will fail.
This is also the case if your not doing anything with eCAL::mon_plugin_lib, but just writing a pure eCAL communication example.
We need to avoid this at all cost!
We should strongly consider to use CMake find_package(eCAL COMPONENT core) or find_package(eCAL COMPONENT apps) components for this.
This means, that all components have to create their own ecal-core-targets.cmake, ecal-apps-targets.cmake file, and ecal-targets.cmake includes these different generated file.
Basically, this is what Boost and other larger libraries do.
This also means that you can also do find_package(eCAL-core REQUIRED) which is equivalent to find_package(eCAL REQUIRED COMPONENT core).
Also, and I know that all of you will disagree, I think that we should move away from prebuild sdks, and at the very least split sdk and tools installers.
Instead, rather spend some time on integration with package managers.


Deleting a column in a lightweight transaction raises a SyntaxException

Hi,
When using iff() and updating a column to None, the engine tries to execute an invalid query, ressembling this: UPDATE my_column_family SET  WHERE [...]. If other columns are updated, the update query is correctly executed, as well as the DELETE column FROM.... This also occurs when performing updates in a batch.
It does work as expected if iff() isn't used.
To re-create the bug (based on examples from the doc):
User.objects(id=1).iff(name='test').update(name=None)

I hadn't properly checked the executed queries. It looks like the IF statement is not included in the DELETE query.
Conditionally deleting columns is a feature implemented in cassandra 2.0.7.

cqlengine is being merged into the DataStax native Python driver.  I'm going to hold off on any new non-emergency issues, since it'll just make the merge more complicated.

We are migrating all Github issues to the public Jira. Please find this issue in Jira: https://datastax-oss.atlassian.net/browse/PYTHON-249


AttributeError: 'NoneType' object has no attribute 'rasterise'

Traceback (most recent call last):
File "tests/square_test.py", line 61, in
main()
File "tests/square_test.py", line 47, in main
dirt_pixels = get_dirt_pixels().eval()
File "tests/square_test.py", line 35, in get_dirt_pixels
height=canvas_height, width=canvas_width, channels=1
File "/home/ubuntu/dirt/dirt/rasterise_ops.py", line 49, in rasterise
return rasterise_batch(background[None], vertices[None], vertex_colors[None], faces[None], height, width, channels, name)[0]
File "/home/ubuntu/dirt/dirt/rasterise_ops.py", line 82, in rasterise_batch
return _rasterise_module.rasterise(
AttributeError: 'NoneType' object has no attribute 'rasterise'
@pmh47  please advise .
A bit urgent
I have tried reinstalling tf gpu packages , libopengl , export LD_LIBRARY_PATH but still getting same error

I met the same issue.

I met the same issue.

Hi there,
Thank you for sharing your great work to us. I am appreciated. Currently, I am facing the exact same problem as this and another thread claimed. I checked out the folder librasterise.so should locate at, but the file is not in there.
My CUDA version is 10.0.130, CMake 3.10.3, CuDNN 7.6.5, GCC 4.8.5, tensorflow-gpu 1.13.1. After claiming several variables, CUDA_HOME. LD_LIBRARY_PATH, PATH and CMAKE_LIBRARY_PATH, as suggested in previous issues, I was able to successfully install DIRT repo (at least it appears so :/). Also, my operation system is Linux 10-108-80-111 4.15.0-142-generic #146~16.04.1-Ubuntu SMP Tue Apr 13 09:27:15 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux.
'''
Processing /home/fangshukai/dirt-master
Requirement already satisfied: tensorflow-gpu>=1.6 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from dirt==0.3.0) (1.13.2)
Requirement already satisfied: numpy in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from dirt==0.3.0) (1.16.0)
Requirement already satisfied: protobuf>=3.6.1 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (3.17.3)
Requirement already satisfied: gast>=0.2.0 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.5.2)
Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.13.0)
Requirement already satisfied: astor>=0.6.0 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.8.1)
Requirement already satisfied: six>=1.10.0 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.16.0)
Requirement already satisfied: wheel>=0.26 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.36.2)
Requirement already satisfied: absl-py>=0.1.6 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (0.13.0)
Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.13.1)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.1.2)
Requirement already satisfied: grpcio>=1.8.6 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.40.0)
Requirement already satisfied: termcolor>=1.1.0 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.1.0)
Requirement already satisfied: keras-applications>=1.0.6 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-gpu>=1.6->dirt==0.3.0) (1.0.8)
Requirement already satisfied: mock>=2.0.0 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow-gpu>=1.6->dirt==0.3.0) (3.0.5)
Requirement already satisfied: markdown>=2.6.8 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu>=1.6->dirt==0.3.0) (3.2.2)
Requirement already satisfied: werkzeug>=0.11.15 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu>=1.6->dirt==0.3.0) (1.0.1)
Requirement already satisfied: h5py in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from keras-applications>=1.0.6->tensorflow-gpu>=1.6->dirt==0.3.0) (2.10.0)
Requirement already satisfied: importlib-metadata; python_version < "3.8" in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from markdown>=2.6.8->tensorboard<1.14.0,>=1.13.0->tensorflow-gpu>=1.6->dirt==0.3.0) (2.1.1)
Requirement already satisfied: zipp>=0.5 in /home/fangshukai/miniconda3/envs/MultiGarmentNet/lib/python3.5/site-packages (from importlib-metadata; python_version < "3.8"->markdown>=2.6.8->tensorboard<1.14.0,>=1.13.0->tensorflow-gpu>=1.6->dirt==0.3.0) (1.2.0)
Building wheels for collected packages: dirt
Running setup.py bdist_wheel for dirt ... done
Stored in directory: /home/fangshukai/.cache/pip/wheels/f7/b6/3a/f2a258821d145fac933046b4fe260c0a9bac8a98c4ad1bfe0c
Successfully built dirt
Installing collected packages: dirt
Found existing installation: dirt 0.3.0
Uninstalling dirt-0.3.0:
Successfully uninstalled dirt-0.3.0
Successfully installed dirt-0.3.0
'''
When I run test.py, the entire log is as follows
'''
(MultiGarmentNet) fangshukai@10-108-80-111:~/dirt-master$ python tests/square_test.py
WARNING: failed to load librasterise.so; rasterisation functions will be unavailable:
/home/fangshukai/dirt-master/dirt/librasterise.so: cannot open shared object file: No such file or directory
2021-09-13 15:05:03.768754: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2021-09-13 15:05:08.980765: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x4964910 executing computations on platform CUDA. Devices:
2021-09-13 15:05:08.981128: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:08.981271: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (1): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:08.981400: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (2): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:08.981493: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (3): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:08.981523: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (4): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:08.981621: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (5): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:08.981650: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (6): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:08.981674: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (7): TITAN X (Pascal), Compute Capability 6.1
2021-09-13 15:05:09.023536: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2021-09-13 15:05:09.026460: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x4aff2d0 executing computations on platform Host. Devices:
2021-09-13 15:05:09.026527: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): ,
2021-09-13 15:05:09.026859: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:04:00.0
totalMemory: 11.91GiB freeMemory: 11.77GiB
2021-09-13 15:05:09.027011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 1 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:06:00.0
totalMemory: 11.91GiB freeMemory: 11.77GiB
2021-09-13 15:05:09.027145: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 2 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:07:00.0
totalMemory: 11.91GiB freeMemory: 11.77GiB
2021-09-13 15:05:09.027273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 3 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:08:00.0
totalMemory: 11.91GiB freeMemory: 11.77GiB
2021-09-13 15:05:09.027406: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 4 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:0c:00.0
totalMemory: 11.91GiB freeMemory: 2.49GiB
2021-09-13 15:05:09.027535: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 5 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:0d:00.0
totalMemory: 11.91GiB freeMemory: 2.58GiB
2021-09-13 15:05:09.027659: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 6 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:0e:00.0
totalMemory: 11.91GiB freeMemory: 2.58GiB
2021-09-13 15:05:09.027783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 7 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:0f:00.0
totalMemory: 11.91GiB freeMemory: 2.58GiB
2021-09-13 15:05:09.056918: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 1 and 4, status: Internal: failed to enable peer access from 0x7f9604718e80 to 0x7f9614724dc0: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.098339: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 1 and 5, status: Internal: failed to enable peer access from 0x7f9604718e80 to 0x7f961c735370: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.098571: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 1 and 6, status: Internal: failed to enable peer access from 0x7f9604718e80 to 0x7f961073b020: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.098785: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 1 and 7, status: Internal: failed to enable peer access from 0x7f9604718e80 to 0x7f95f871db40: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.101460: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 2 and 4, status: Internal: failed to enable peer access from 0x7f960c6e00e0 to 0x7f9614724dc0: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.101676: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 2 and 5, status: Internal: failed to enable peer access from 0x7f960c6e00e0 to 0x7f961c735370: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.101887: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 2 and 6, status: Internal: failed to enable peer access from 0x7f960c6e00e0 to 0x7f961073b020: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.102097: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 2 and 7, status: Internal: failed to enable peer access from 0x7f960c6e00e0 to 0x7f95f871db40: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.102469: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 3 and 4, status: Internal: failed to enable peer access from 0x7f9608735570 to 0x7f9614724dc0: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.102679: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 3 and 5, status: Internal: failed to enable peer access from 0x7f9608735570 to 0x7f961c735370: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.102887: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 3 and 6, status: Internal: failed to enable peer access from 0x7f9608735570 to 0x7f961073b020: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.103095: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 3 and 7, status: Internal: failed to enable peer access from 0x7f9608735570 to 0x7f95f871db40: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.103373: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 4 and 1, status: Internal: failed to enable peer access from 0x7f9614724dc0 to 0x7f9604718e80: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.103584: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 4 and 2, status: Internal: failed to enable peer access from 0x7f9614724dc0 to 0x7f960c6e00e0: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.103791: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 4 and 3, status: Internal: failed to enable peer access from 0x7f9614724dc0 to 0x7f9608735570: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.104586: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 5 and 1, status: Internal: failed to enable peer access from 0x7f961c735370 to 0x7f9604718e80: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.104796: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 5 and 2, status: Internal: failed to enable peer access from 0x7f961c735370 to 0x7f960c6e00e0: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.105006: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 5 and 3, status: Internal: failed to enable peer access from 0x7f961c735370 to 0x7f9608735570: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.139776: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 6 and 1, status: Internal: failed to enable peer access from 0x7f961073b020 to 0x7f9604718e80: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.141791: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 6 and 2, status: Internal: failed to enable peer access from 0x7f961073b020 to 0x7f960c6e00e0: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.142763: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 6 and 3, status: Internal: failed to enable peer access from 0x7f961073b020 to 0x7f9608735570: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.144144: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 7 and 1, status: Internal: failed to enable peer access from 0x7f95f871db40 to 0x7f9604718e80: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.146194: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 7 and 2, status: Internal: failed to enable peer access from 0x7f95f871db40 to 0x7f960c6e00e0: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.147246: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1370] Unable to enable peer access between device ordinals 7 and 3, status: Internal: failed to enable peer access from 0x7f95f871db40 to 0x7f9608735570: CUDA_ERROR_TOO_MANY_PEERS: peer mapping resources exhausted
2021-09-13 15:05:09.148303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1, 2, 3, 4, 5, 6, 7
2021-09-13 15:05:09.204556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-09-13 15:05:09.219432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 1 2 3 4 5 6 7
2021-09-13 15:05:09.219652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N Y Y Y Y Y Y Y
2021-09-13 15:05:09.219774: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1:   Y N Y Y Y Y Y Y
2021-09-13 15:05:09.219895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 2:   Y Y N Y Y Y Y Y
2021-09-13 15:05:09.219989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 3:   Y Y Y N Y Y Y Y
2021-09-13 15:05:09.220112: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 4:   Y Y Y Y N Y Y Y
2021-09-13 15:05:09.220225: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 5:   Y Y Y Y Y N Y Y
2021-09-13 15:05:09.220318: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 6:   Y Y Y Y Y Y N Y
2021-09-13 15:05:09.220427: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 7:   Y Y Y Y Y Y Y N
2021-09-13 15:05:09.221087: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11446 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:04:00.0, compute capability: 6.1)
2021-09-13 15:05:09.225170: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 11446 MB memory) -> physical GPU (device: 1, name: TITAN X (Pascal), pci bus id: 0000:06:00.0, compute capability: 6.1)
2021-09-13 15:05:09.244745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 11446 MB memory) -> physical GPU (device: 2, name: TITAN X (Pascal), pci bus id: 0000:07:00.0, compute capability: 6.1)
2021-09-13 15:05:09.264280: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 11446 MB memory) -> physical GPU (device: 3, name: TITAN X (Pascal), pci bus id: 0000:08:00.0, compute capability: 6.1)
2021-09-13 15:05:09.279277: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:4 with 2246 MB memory) -> physical GPU (device: 4, name: TITAN X (Pascal), pci bus id: 0000:0c:00.0, compute capability: 6.1)
2021-09-13 15:05:09.294221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:5 with 2340 MB memory) -> physical GPU (device: 5, name: TITAN X (Pascal), pci bus id: 0000:0d:00.0, compute capability: 6.1)
2021-09-13 15:05:09.308995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:6 with 2340 MB memory) -> physical GPU (device: 6, name: TITAN X (Pascal), pci bus id: 0000:0e:00.0, compute capability: 6.1)
2021-09-13 15:05:09.321196: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:7 with 2340 MB memory) -> physical GPU (device: 7, name: TITAN X (Pascal), pci bus id: 0000:0f:00.0, compute capability: 6.1)
Traceback (most recent call last):
File "tests/square_test.py", line 61, in
main()
File "tests/square_test.py", line 47, in main
dirt_pixels = get_dirt_pixels().eval()
File "tests/square_test.py", line 35, in get_dirt_pixels
height=canvas_height, width=canvas_width, channels=1
File "/home/fangshukai/dirt-master/dirt/rasterise_ops.py", line 48, in rasterise
return rasterise_batch(background[None], vertices[None], vertex_colors[None], faces[None], height, width, channels, name)[0]
File "/home/fangshukai/dirt-master/dirt/rasterise_ops.py", line 81, in rasterise_batch
return _rasterise_module.rasterise(
AttributeError: 'NoneType' object has no attribute 'rasterise'
'''
If you need other information, please let me know. Thanks in advance!

Update:
I solved this issue by following
cd dirt mkdir build ; cd build cmake ../csrc make cd .. pip install -e .
instead of directly typing in pip install .

I use the " cd dirt mkdir build ; cd build cmake ../csrc make cd .. pip install -e ." ,
and tensorflow-1.15.0, python3.7, cuda10.0,
It have same error.  Need to set CUDA_HOME. LD_LIBRARY_PATH, PATH and CMAKE_LIBRARY_PATH ?


Add glow effect to text, match glow and text color to the original

With the alignment patch applied, this looks like this:

ohh that looks sweet :+1:


regarding #1026 pull request

Dear Thomas,
This is regarding my #1026 pull request, here is my understanding of  the reproducibility issue I was getting:

on line 451,  in the codes tokenizer is reloaded without setting do_lower_case, then if you use both do_train+do_eval, you will get different results than if you do do_eval only on the same directory since if you use only do_eval only, tokenizer is read from line 408 where do_lower_case is considered.

the second issue I see is that if you do both do_train and do_eval you read tokenizer from the output_dir, but if you do only do_eval you read tokenizer from args.model_name_or_path which can be different and could results in different results, so this is better to reload the tokenizer once from output_dir during the evaluation and remove it from training part.

thanks.
Best regards,
Rabeeh

Oh I see what you mean, indeed that a more general issue with saving and loading tokenizer.

Dear Thomas,
The pull request #1026 does not work unfortunately when using eval_all_check_points, and I was wondering if you could undo that merge, sorry for this, this new pull request here works for me.
thanks.

Ok let's do that for now and I'll think about a more general way to save tokenizer configurations.

awesome. thanks

Codecov Report

Merging #1158 into master will not change coverage.
The diff coverage is n/a.

@@          Coverage Diff           @@
##           master   #1158   +/-   ##
======================================
  Coverage    80.7%   80.7%
======================================
  Files          46      46
  Lines        7411    7411
======================================
  Hits         5981    5981
  Misses       1430    1430

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Codecov Report

Merging #1158 into master will not change coverage.
The diff coverage is n/a.

@@          Coverage Diff           @@
##           master   #1158   +/-   ##
======================================
  Coverage    80.7%   80.7%
======================================
  Files          46      46
  Lines        7411    7411
======================================
  Hits         5981    5981
  Misses       1430    1430

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 e0caab0...0a2fecd. Read the comment docs.

Addressing this up-stream with #1092


The authenticity of host 'github.com (<IP_ADDRESS>)' can't be established.

Cloning into 'Python-Flask-neo4j-Heroku-Example'...
The authenticity of host 'github.com (<IP_ADDRESS>)' can't be established.
RSA key fingerprint is SHA256:nThbg6kXUpJWGl7E1IGOCspRomTxdCARLviKw6E5SY8.
Are you sure you want to continue connecting (yes/no)? y
Please type 'yes' or 'no': yes
Warning: Permanently added 'github.com,<IP_ADDRESS>' (RSA) to the list of known hosts.
Permission denied (publickey).
fatal: Could not read from remote repository.
Please make sure you have the correct access rights
and the repository exists.

The authenticity of host 'github.com (<IP_ADDRESS>)' can't be established.
RSA key fingerprint is SHA256:nThbg6kXUpJWGl7E1IGOCspRomTxdCARLviKw6E5SY8.
Are you sure you want to continue connecting (yes/no)? y
Please type 'yes' or 'no': yes
Warning: Permanently added 'github.com,<IP_ADDRESS>' (RSA) to the list of known hosts.
Permission denied (publickey).
fatal: Could not read from remote repository.
Please make sure you have the correct access rights
and the repository exists.


ChallengeError is failing when parsing json due to incorrect 'status' type.

I'm seeing a cast exception when ChallengeErrorFromJson is being thrown due to a '400'.
The problem is that the code assumes the status is a string but it would appear that acme is returning an it.
Map (3 items)
"type" -> "urn:ietf:params:acme:error:connection"
"detail" -> "<IP_ADDRESS>: Fetching http://xxxxxx/.well-known/acme-challenge/XXXXX Timeout d…"
"status" -> 400

Note the status is an int.
The timeout is occurring as I'm behind a NAT which isn't configured correct but this still seems to be an issue that needs to be looked at.
What I'm not certain is whether the status is always an int.
I found this issue whilst trying to get shelf_letsencrypt running.
I've also reported an issue there:
https://github.com/gmpassos/shelf_letsencrypt/issues/9

The package was written by me to better understand the ACME protocol, so it may well be that there are still one or two bugs here. In the end, however, I was able to issue certificates via Let's Encrypt under real conditions. I am not sure if this is a special issue, cause i cannot find "urn:ietf:params:acme:error:connection" in the RFC. I would suggest to change it to an int and then try to manually issue a certificate via Let's encrypt by writing a small dart script to invoke the methods manually. If you can confirm that the package is still working we can change the string to int.

I've changed it to an int? and I can issue a certificate and the code works
if I force an error.
On Sun, 10 Dec 2023, 1:19 am Ephenodrom, @.***> wrote:

The package was written by me to better understand the ACME protocol, so
it may well be that there are still one or two bugs here. In the end,
however, I was able to issue certificates via Let's Encrypt under real
conditions. I am not sure if this is a special issue, cause i cannot find
"urn:ietf:params:acme:error:connection" in the RFC. I would suggest to
change it to an int and then try to manually issue a certificate via Let's
encrypt by writing a small dart script to invoke the methods manually. If
you can confirm that the package is still working we can change the string
to int.
—
Reply to this email directly, view it on GitHub
https://github.com/Ephenodrom/Dart-Acme-Client/issues/7#issuecomment-1848423358,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AAG32OBG4HTUNXORQIVEYBDYIRXO5AVCNFSM6AAAAABAMRWSG6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQNBYGQZDGMZVHA
.
You are receiving this because you authored the thread.Message ID:
@.***>

@bsutton Ok sounds good.


bug: How to tranform raw HTML to VElement

Describe the bug
I am developing a markdown renderer with vdom. As we all know, highlight.js is used to help us to highlight the source code. So I want to use it in the renderer, I try to use the following codes to meet my goal:
import { html } from "million/html"

export const fromStringToVNode = (content: string): VNode[] => {
    const h = html`${content}`
    if (Array.isArray(h)) {
        return h
    }

    return [h]
}

But I have not got the correct VNode but HTML string

Or,
import { fromStringToDomNode, fromDomNodeToVNode } from "million/ssr"

export const fromStringToVNode = (content: string): VNode => {
  return fromDomNodeToVNode(fromStringToDomNode(content))
}

But I have got the error Uncaught (in promise) TypeError: Cannot read properties of null (reading '__m_old_vnode')

So I want to know about how to transform raw HTML string (Maybe with \n) to VElement?
To Reproduce
Steps to reproduce the behavior:

import highlight.js
transform any TypeScript codes via highlight.js
input the codes before
Run

Expected behavior
Return correct VElement array
Device (please complete the following information):

OS: [e.g. iOS] Windows 11
Browser [e.g. chrome, safari] Edge Chromium
Version [e.g. 22] Latest

Additional context
Add any other context about the problem here.

I'm not able to repro the Uncaught (in promise) TypeError: Cannot read properties of null (reading '__m_old_vnode'). Could you try bumping to the latest version of Million to see if the issue persists?
Here is my repro code:
const code = hljs.highlight('
Hello World!
', { language: 'xml' }).value;
const vnode = fromDomNodeToVNode(fromStringToDomNode(`<div>${code}</div>`));

console.log(code);
console.log(vnode);

render(document.body, vnode);

Click to view console log

Hello World!

{
    "tag": "div",
    "props": {},
    "children": [
        {
            "tag": "span",
            "props": {
                "class": "hljs-tag"
            },
            "children": [
                "<",
                {
                    "tag": "span",
                    "props": {
                        "class": "hljs-name"
                    },
                    "children": [
                        "h1"
                    ],
                    "flag": 5
                },
                ">"
            ],
            "flag": 1
        },
        "Hello World!",
        {
            "tag": "span",
            "props": {
                "class": "hljs-tag"
            },
            "children": [
                ""
            ],
            "flag": 1
        }
    ],
    "flag": 1
}

Click to view document body

Hello World!

So htm is just a helper function to create vnodes. htm may support fragment because it is generalized for all Virtual DOM-based libraries (e.g. react, preact, etc.), but Million does not at the top-level node.
For instance, htm may generate:
html`<div></div><div></div>`
// [divVNode, divVNode]

And you can interpolate that within Million children, but not at top level:
const fragment =  [divVNode, divVNode];
render(document.body, fragment); // DOESN'T WORK

render(document.body, html`<div>${fragment}</div>`) // works, fragment is not at top level

Thanks for your explanation, I got it! I will try to fix this issue in my code~


chore: use google closure compiler to produce node.js & browser artef…

…acts

:tada: This PR is included in version 3.1.2 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Can I make the SQL editor the default double-click action when clicking on a connection?

I'm using the Mac version of dbeaver. Basically I have a bunch of SQL server connections and want the default action when I double click on them to be what's currently assigned to F3, open a SQL editor on the main database. Currently it expands the connection and shows me the database, users, administer, and system info folders.


DO NOT MERGE: SPIKE: Add new AWS root CA bundles

WHAT YOU DID
A brief description of the pull request:
How to test

How should it be reviewed?
What feedback do you need?
If manual testing is needed, give suggested testing steps.

Code review checklist
Logging

[ ] only emit log lines at ERROR level which require immediate attention from a support engineer. These will trigger a zendesk alert.

Documentation

[ ] Updated README.md for any of the following ?

Introduced any new environment variables / removed existing environment variable
Added new API / updated existing API definition

This spike is complete and a writeup has been included in the stories generated


Move prometheus annotation from service to pod level

In what area(s)?
/area sidecar-injector
What version of Dapr?
master
Expected Behavior

oms agent for azuremonitor scrape prometheus endpoint

Actual Behavior

oms agent for azuremonitor cannot scrape prometheus endpoint because azure monitor sees annotation in Pod level.

Steps to Reproduce the Problem

deploy dapr to aks where azure monitor is enabled.

We shouldn't move it, at minimum we should add to the pod level. but I wouldn't touch a user's resource on their behalf. Since Dapr managed the services, it's fine to do whatever we want with them.
I think that we should add in our docs an explicit saying that to use Azure Monitor + Prometheus, you need to add a pod level annotation.

We shouldn't move it, at minimum we should add to the pod level. but I wouldn't touch a user's resource on their behalf. Since Dapr manages the services, it's fine to do whatever we want with them.
I think that we should add in our docs an explicit saying that to use Azure Monitor + Prometheus, you need to add a pod level annotation.

that makes sense. we can document it.


Spyder 4: Is variable refactoring available?

So I'm using the latest build of Spyder 4 beta and would really like if some refactoring capability was available. Of course I can use VSC or PyCharm for this, but a really intelligent and advanced editor for Spyder 4 would make it the choice of scientific computing. is there anything similar that I can enable it (like some plugin)?

Sorry, refactoring is not in the plans for Spyder 4.


Fix image targets and permanent redirections

@destogl the image targets don't work with the link checker (they had typos in it, but the given paths isn't searchable by the linkchecker at all). Are they necessary? One can open the image with every browser from the context menu anyways.
Furthermore, I updated permanent redirections of some links.

@mergifyio backport humble


refs platform/1603: Task/secret managment

This PR enable secret manager storage for Postgres DB Password.
It's possible no more to pass a predefined password through a plain text string in this terraform module.
In case no password is defined, the module will create a secret manager istance filled with a random value.
In case you want to use a predefined password you have to create a GCP "secret manager" secret filled with the value you want and then initialize the correct variables in the module to correctly retrieve the value.

asking a review to @Monska85 too


Install Lefthook using a package manager in Windows

Is there a way to install lefthook on Windows using chocolatey package manager or winget? I want to write a bash script to automate this process of installing lefthook on my machine and other teammates.

@SAGARSURI Hi, you could add lefthook in any package manager you want, like here for python: https://github.com/life4/lefthook

Hey @Arkweid , Is it possible to add the .exe file in the asset of the release notes? I am planning to open up a PR for the winget package manager. They expect to mention the type of installer while submitting the PR.

Oke, I will add unzipped file in next release.

Thank you so much! @Arkweid

I have added this to my JIRA ticket and will resume work once this is done. 😄

On Windows 1803 or later, you can use Unix curl with curl (in CMD) or curl.exe (in PowerShell).
So when unzipped files are added to the release, you can install lefthook with the same commands in both Windows and Unix (although you have to change the download URL and set the PATH) 🎉

It does not appear exe ever started getting published. @Envek could this be added for next release?

This is counterintuitive. You should probably use a better package manager. For example, Chocolatey is able to unzip files without any issues.

Why should we use Chocolatey when Microsoft provide a default package manager i.e winget. It provides better security compared to other 3rd party package manager.

Why should we use Chocolatey when Microsoft provide a default package manager i.e winget. It provides better security compared to other 3rd party package manager.

I'm very sorry to see this attitude toward community-driven open-source projects. A 3rd party project is not insecure by default, and Microsoft projects are not any more secure. Instead, I suggest you decide based on the actual technical differences. For example, if a package manager lacks its basics such as installing zip files or if it allows decentralized packages.
P.S: since this is offtopic, I will not continue this discussion after this comment.

We want to support as many installations methods as possible (however, using as least effort as possible). So, let's add support to both (pull requests are welcome)

Chocolatey is the defacto (winget is newer), but for Go packages I'd suggest Scoop. Goreleaser has built in configuration to support Scoop. I've written chocolatey packages and it's not as straight forward.
Between Scoop, go install, and npm install you've covered a nice set of install options.
https://goreleaser.com/customization/scoop/

I would also love to have this installable via Scoop. I have some experience with GoReleaser so let me know if help is needed.

I would also love to have this installable via Scoop. I have some experience with GoReleaser so let me know if help is needed.

Now available in Scoop via the Main bucket:

https://github.com/ScoopInstaller/Main/pull/4967

GoReleaser is no longer needed as the bucket auto-updates the manifest itself.
As for Winget, I have opened a PR for it: https://github.com/microsoft/winget-pkgs/pull/112810. Once that's merged, the Winget Releaser action should be used to keep it up-to-date:

#526

@sitiom , thank you a lot! ❤🔥


upgrade kubeclipper platform or component

Signed-off-by: Xvv-v<EMAIL_ADDRESS>
What type of PR is this?
/kind feature

What this PR does / why we need it:
Which issue(s) this PR fixes:

Fixes #91
Special notes for reviewers:

Does this PR introduced a user-facing change?

Additional documentation, usage docs, etc.:

/cc @x893675

/cc @lixd
help review

Codecov Report

Merging #92 (a1957f8) into master (93c50dc) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master      #92   +/-   ##
=======================================
  Coverage   11.79%   11.79%
=======================================
  Files          98       98
  Lines       15096    15096
=======================================
  Hits         1780     1780
  Misses      13082    13082
  Partials      234      234

/cc @x893675

/cc @x893675

/approve


Installing CVAT: unable to find the IP address for the container \cvat

I am installing the v2.0.0-alpha version of cvat in my Ubuntu 16.04
The only changes I have made are:

I have set the CVAT_HOST to the IP address of my machine xxx.xxx.xxx.xxx
changed all 8080 ports to 8088 as my 8080 port is already in use.

After executing the command docker-compose -f docker-compose.yml up --build  , I am getting the following error.
traefik       | time="2022-01-09T23:51:29Z" level=error msg="service "cvat" error: unable to find the IP address for the container "/cvat": the server is ignored" providerName=docker container=cvat-cvat-fbcda30236d3385024e2d83db8b0ac06861095da56dfb001a79f338bcacd402f
when I run with -d, no errors shows up.

CVAT website has loaded, but after running docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser', nothing shows up to enter a username and password.

Try this alternative:
If you don’t have winpty installed or the above command does not work, you may also try the following:

# enter docker image first
docker exec -it cvat /bin/bash
# then run
python3 ~/manage.py createsuperuser

You only need to change the port here https://github.com/openvinotoolkit/cvat/blob/develop/docker-compose.yml#L87 like
      - 8088:8080

Does this solve the issue?

@bsekachev
it does not enter the docker image
Error response from daemon: Container e8a0fb8d1ddf82e1b6c65d6ec23f6713aa609a526b8897817c2c96798e94f56e is restarting, wait until the container is running

@bsekachev @azhavoro The problem is now solved. thanks!

Try this alternative:
If you don’t have winpty installed or the above command does not work, you may also try the following:

# enter docker image first
docker exec -it cvat /bin/bash
# then run
python3 ~/manage.py createsuperuser

this solved the problem, but all my tasks and annotations have been deleted for now. Wtf?

@yustiks , the command cannot delete any annotations and tasks. It just creates a new user.

i meet  this problem too ， the cvat docker is always  Restarting

cvat_ui       | 2022/03/29 09:09:38 [notice] 1#1: start worker process 50
cvat_ui       | 2022/03/29 09:09:38 [emerg] 50#50: initgroups(nginx, 101) failed (14: Bad address)
cvat exited with code 2
traefik       | time="2022-03-29T09:19:39Z" level=error msg="service "cvat" error: unable to find the IP address for the container "/cvat": the server is ignored" providerName=docker container=cvat-cvat-f57b7f1ed0173b56733f513bebc191ba15ce075ea3d0f9a967a6fa144b9ee826


Feature/subsequences

Add support for subsequences in broadbean.
Adding support for subsequences turned out to necessitate (or at least strongly encourage) a refactoring of some of the underlying data juggling. This PR will aim to do that.
Specifically, a standard format for a forged sequence has been agreed upon. Everything else (drivers that output specific formats, plotting) will be recast to use the forged sequences.
I will flag it here when the PR is ready for review:

[x] Review time!

Okay, I think this is finally in place. @QCoDeS/core


requirements.txt issue

Is line 19 correct in requirements.txt as it causes an error when i do pip install -r requirements.txt
Collecting pkg-resources==0.0.0 (from -r requirements.txt (line 19))   Could not find a version that satisfies the requirement pkg-resources==0.0.0 (from -r requirements.txt (line 19)) (from versions: ) No matching distribution found for pkg-resources==0.0.0 (from -r requirements.txt (line 19))

That is a bug that unfortunately hasn't seen a fix within pip. In order to fix this, remove the pkg-resources==0.0.0 line from the requirements.txt file, and run again.


update

update

This PR has no Slow CI triggered yet. Please trigger it and make sure all tests passing before merge.