text:
No library set for this platform

Hi I am using windows 10, godot 3.2.3 stable and I have installed local notification from nativelib, (localnotification was added automatically to autoload) and then on export setting I checked use custom build and checked local notification on tab plugins.
When te app starts I put this: localnotification.show("Hi","hi x2",5,1)
and is not working, on logcat I got this:
12-30 23:16:36.779 19477 19627 E godot   : **ERROR**: No library set for this platform
12-30 23:16:36.779 19477 19627 E godot   :    At: modules/gdnative/gdnative.cpp:290:initialize() - No library set for this platform
12-30 23:16:37.346 19477 19627 E godot   : **ERROR**: Condition "err" is true. Returned: err
12-30 23:16:37.346 19477 19627 E godot   :    At: modules/gdscript/gdscript.cpp:829:load_source_code() - Condition "err" is true. Returned: err
12-30 23:16:37.346 19477 19627 E godot   : **ERROR**: Cannot load source code from file 'res://scripts/localnotification.gd'.
12-30 23:16:37.346 19477 19627 E godot   :    At: modules/gdscript/gdscript.cpp:2286:load() - Condition "err != OK" is true. Returned: RES()
12-30 23:16:37.346 19477 19627 E godot   : **ERROR**: Failed loading resource: res://scripts/localnotification.gd. Make sure resources have been imported by opening the project in the editor at least once.
12-30 23:16:37.346 19477 19627 E godot   :    At: core/io/resource_loader.cpp:279:_load() - Condition "found" is true. Returned: RES()
12-30 23:16:37.346 19477 19627 E godot   : **ERROR**: Can't autoload: res://scripts/localnotification.gd
12-30 23:16:37.346 19477 19627 E godot   :    At: main/main.cpp:1757:start() - Condition "res.is_null()" is true. Continuing.

Also I would like to know if with this type of notification I can send a notification at a certain time?

@martinpelli Check your export settings. You should export all resources of project or you should check if localnotification.gd enabled for export.

@DrMoriarty yes is checked, maybe is the one click deploy I wil try with the apk

@DrMoriarty yes getting the same, and the app crash with an exported apk

@DrMoriarty on iOS I get stuck on bootsplash
and get this

@martinpelli Hi! The message "No library set for this platform" on android is not a fatal error. It shows because gdnative lib only used for iOS and for Android it used another plugin mechanism.
For iOS check if localnotification.xcframework set as Embed & Sign (on general page of settings in your xcode project).
Also the root of problem is that your gd wrapper doesn't loaded at runtime. I don't know why.
There is my minimal project for testing. You can check what the differs with your own project:
https://www.dropbox.com/s/ua9ea59cuzmbez6/nativelib-test.zip?dl=0

@martinpelli Hi! The message "No library set for this platform" on android is not a fatal error. It shows because gdnative lib only used for iOS and for Android it used another plugin mechanism.
For iOS check if localnotification.xcframework set as Embed & Sign (on general page of settings in your xcode project).
Also the root of problem is that your gd wrapper doesn't loaded at runtime. I don't know why.
There is my minimal project for testing. You can check what the differs with your own project:
https://www.dropbox.com/s/ua9ea59cuzmbez6/nativelib-test.zip?dl=0

@DrMoriarty so I found my problem. First of all the localnotification.xcframework was set as do not embed, so I changed it and the app was still crashing. I checked everything with the minimal project and I found the difference: the plugin was setting the autoload at scripts/localnotifications.gd and the file was put on Scripts/localnotifications.gd so change my folder name to scripts not Scripts and worked. When the app started It asked me if I allow notifications and I put yes. The log on xcode showed: Localnotifiaction plugin inited, showLocalNotification:Hi,2,1 but the notification never appear. So It is a problem or the notifications are managed with other tools like google cloud mes?

@DrMoriarty so I found my problem. First of all the localnotification.xcframework was set as do not embed, so I changed it and the app was still crashing. I checked everything with the minimal project and I found the difference: the plugin was setting the autoload at scripts/localnotifications.gd and the file was put on Scripts/localnotifications.gd so change my folder name to scripts not Scripts and worked. When the app started It asked me if I allow notifications and I put yes. The log on xcode showed: Localnotifiaction plugin inited, showLocalNotification:Hi,2,1 but the notification never appear. So It is a problem or the notifications are managed with other tools like google cloud mes?

@martinpelli Glad to hear that you solved the problem.
The local notifications doesn't depend on any third party service. The timer you set is seconds from the function call. So if you set 2 seconds and your app is still active at this time, notification will not be shown.
Usually I setup notifications when the app goes to background.

@martinpelli Glad to hear that you solved the problem.
The local notifications doesn't depend on any third party service. The timer you set is seconds from the function call. So if you set 2 seconds and your app is still active at this time, notification will not be shown.
Usually I setup notifications when the app goes to background.

@DrMoriarty I tested it and worked 2 times I dont know why is not showing every time that enters on the show function. You can close the issue I guess. Just a last question. Is not possible to make a notification at a certain hour? And for what I can get the token of a device?

@DrMoriarty I tested it and worked 2 times I dont know why is not showing every time that enters on the show function. You can close the issue I guess. Just a last question. Is not possible to make a notification at a certain hour? And for what I can get the token of a device?

Check your tag value. Call show function with the same tag will overwrite previous notification.
To make notification at specific time you can use this code:
var tt = OS.get_datetime()
tt.hour = 10
tt.minute = 30
if OS.get_unix_time_from_datetime(tt) < OS.get_unix_time():
    tt.day += 1
localnotification.show('message', 'title', OS.get_unix_time_from_datetime(tt) - OS.get_unix_time(), 1)

Device token is used for remote (push) notifications. Local notifications you can use without token.

Check your tag value. Call show function with the same tag will overwrite previous notification.
To make notification at specific time you can use this code:
var tt = OS.get_datetime()
tt.hour = 10
tt.minute = 30
if OS.get_unix_time_from_datetime(tt) < OS.get_unix_time():
    tt.day += 1
localnotification.show('message', 'title', OS.get_unix_time_from_datetime(tt) - OS.get_unix_time(), 1)

Device token is used for remote (push) notifications. Local notifications you can use without token.

@DrMoriarty you are right , all working perfect!  And is without sound or  my sound is not working?

@DrMoriarty you are right , all working perfect!  And is without sound or  my sound is not working?

Now it use system notification sound for Android and don't use sound for iOS. You can open new issue about sound configuration.

Now it use system notification sound for Android and don't use sound for iOS. You can open new issue about sound configuration.

authors: DrMoriarty martinpelli

repo: DrMoriarty/godot-local-notification


text:
Added sound output terminal functions

The sound output functions allow the user to control whether the internal TV speakers are used or whether an external destination like an AV receiver is the target.

Thamks, @696GrocuttT 👍

source: gharchive/pull-request

repo: Drafteed/pybravia

license: MIT

license_type: permissive

license_source: github-api


text:
Color

How to customize the progress bar color

Duplicate of #15

android:tint does not work for me, I'm making a circular progress bar if it makes any difference.

OK, app:mpb_indeterminateTint working for me.

source: gharchive/issue

authors: DreaminginCodeZH WilsonKifua rabw

repo: DreaminginCodeZH/MaterialProgressBar


text:
[iOS] Add Timetable List UI

Idea Description

Please create Timetable List UI

only preview is okey. (Toggle with sheet UI will be done by another PR!)
Implement this at TimetableListView.swift file and create TimetableListItemView.swift if needed.

Reference images and links

https://www.figma.com/file/NcSMs6dMsD88d4wOY0g3rK/DroidKaigi-2022-Conference-App?node-id=448%3A2009

🙋♂

@touyou Thanks 😄 We assigned!

authors: mhidaka ry-itto touyou


text:
:wrench: Fix Fixed a bug in session details

Issue

Nothing.

Overview (Required)

The start time was displayed in the end time, which was corrected.

Links

Nothing.

Screenshot

Before
After

👀


text:
Wird der Adapter nicht mehr gepflegt?

Der Adapter wird seit etlichen Monaten nicht mehr upgedated und ist momentan leider völlig nutzlos weil ich keine aktuellen Stati sehe. Vielleicht baut mal jemand einen Adapter den man auch sinnvoll nutzen kann.

Der Adapter wird seit etlichen Monaten nicht mehr upgedated und ist momentan leider völlig nutzlos weil ich keine aktuellen Stati sehe. Vielleicht baut mal jemand einen Adapter den man auch sinnvoll nutzen kann.

es tut mir leid die gesundheit hat mir sehr zu schaffen gemacht wodurch einiges Stil gelegen hat.
Ich werde mein bestes tun die issue in den nässten doch ab zu arbeiten möchte aber auch nichts versprechen, aber tja er wird gepflegt leider nicht so frequent wie ich selber gerne moechte

authors: DutchmanNL Moormerlander


text:
keyword changed

What does this PR do?
What packages have been affected by this PR?
Types of changes
What types of changes does your code introduce to this project?
Put an x in the boxes that apply

[ ] Bugfix (non-breaking change which fixes an issue)

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

[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)

Package version increase in cli package?
Checklist before merging
Put an x in the boxes that apply

[ ] These changes have been thoroughly tested.

[ ] Changes need to be immediately published on npm.

changes made

The importance of this language is to use with bhai. If you want to have a didi lang, please use your fork. @aniketsingh0104 @ankitchouhan1020 @ristri can we close this?

authors: Sonooo tbhaxor

repo: DulLabs/bhai-lang


text:
Shader not working in build, works in editor

When i do a build of my project the objects on which I have used the stylized surface w/ outline shaders are colored pink. In the log file I see the following error:
ERROR: Shader FlatKit/Stylized Surface shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
ERROR: Shader FlatKit/Stylized Surface With Outline shader is not supported on this GPU (none of subshaders/fallbacks are suitable)
I find this unlikely however, as I am running this on a brand new RTX 3090

If you're experiencing this issue in the builds only, and in the editor it looks correct, we've just sent an update that targets this issue to Unity for the review. If you would like to try it before it is released, please send me an email to info@dustyroom.com, and I'll send you the pack directly.
If that's not the case, please include the details like Unity version, Flat Kit version and rendering pipeline. Preferable, with screenshots of your Package Manager with Flat Kit selected, Graphics settings, Quality settings tabs — anything you think may help me reproduce your issue.

If you're experiencing this issue in the builds only, and in the editor it looks correct, we've just sent an update that targets this issue to Unity for the review. If you would like to try it before it is released, please send me an email to info@dustyroom.com, and I'll send you the pack directly.
If that's not the case, please include the details like Unity version, Flat Kit version and rendering pipeline. Preferable, with screenshots of your Package Manager with Flat Kit selected, Graphics settings, Quality settings tabs — anything you think may help me reproduce your issue.

If its already submitted for review I can wait. I will try it out when you
publish the update
On Sun, Jan 31, 2021 at 3:00 PM dustyroom-studio notifications@github.com
wrote:

If you're experiencing this issue in the builds only, and in the editor it
looks correct, we've just sent an update that targets this issue to Unity
for the review. If you would like to try it before it is released, please
send me an email to info@dustyroom.com, and I'll send you the pack
directly.
If that's not the case, please include the details like Unity version,
Flat Kit version and rendering pipeline. Preferable, with screenshots of
your Package Manager with Flat Kit selected, Graphics settings, Quality
settings tabs — anything you think may help me reproduce your issue.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/Dustyroom/flat-kit-doc/issues/16#issuecomment-770441282,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AAM7XA7C6CQCWNH5GZ4CMPLS4WZFPANCNFSM4W3VBOKQ
.

If its already submitted for review I can wait. I will try it out when you
publish the update
On Sun, Jan 31, 2021 at 3:00 PM dustyroom-studio notifications@github.com
wrote:

If you're experiencing this issue in the builds only, and in the editor it
looks correct, we've just sent an update that targets this issue to Unity
for the review. If you would like to try it before it is released, please
send me an email to info@dustyroom.com, and I'll send you the pack
directly.
If that's not the case, please include the details like Unity version,
Flat Kit version and rendering pipeline. Preferable, with screenshots of
your Package Manager with Flat Kit selected, Graphics settings, Quality
settings tabs — anything you think may help me reproduce your issue.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
https://github.com/Dustyroom/flat-kit-doc/issues/16#issuecomment-770441282,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AAM7XA7C6CQCWNH5GZ4CMPLS4WZFPANCNFSM4W3VBOKQ
.

The update is live! Thank you for your patience.
Please re-open the issue if for some reason this still occurs.

authors: dustyroom-studio mattrochon

repo: Dustyroom/flat-kit-doc


text:
iOS Memory Leak

Describe the bug
Well as the title said I get a Memory leak, but only when using WebP images.
Expected behavior
The Memory should be as high as for PNG (400MB for 200 Images) but well it is (1,9Gb for 200 Images).
Dependency versions

React Native version: >0.60.0
React version: 0.60.5
RNFastImage (7.0.2)

What should i do? the PNG Workaround does not suit us

I see the same thing happening, large memory consumption and high spikes when WebP images are getting loaded.

same me

Any update on it?

repo: DylanVann/react-native-fast-image

license: mit

license_source: bigquery


text:
regional masks not supported?

I've been working on adding a regional mask for a Gulf of Mexico RRM to do some statistical analysis. My goal is to plot the masked output with mosaic as a QC check, which fails. See below a successful example with ax.scatter using normalized vorticity compared to mosaic. Does this capability need to be added to the code, or is this user error? One reason this capability would be useful is that ax.scatter is much slower and the user has to carefully prescribe the dot size and dpi, which often takes a few iterations to get right. As a result, the boundaries of the masked data aren't always clear with a scatter plot.
Preliminaries:
#Packages
import numpy as np
import xarray as xr
import glob
import mosaic
import cartopy
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.feature as cfeature
import cmocean.cm as cmo
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib import ticker
from matplotlib.dates import DateFormatter
from matplotlib.ticker import AutoMinorLocator, MultipleLocator

land_10m = cfeature.NaturalEarthFeature('physical', 'land', '10m',
                                edgecolor='face',
                                facecolor=cfeature.COLORS['land'])

#HF output
path = '/lustre/scratch5/dschlichting/E3SM/scratch/chicoma-cpu/Tl319_GoM1pt5_GMPAS-IAF_norivers/run/Tl319_GoM1pt5_GMPAS-IAF.mpaso.hist.am.highFrequencyOutput.1997-0*-01_00.00.00.nc'
ds = xr.open_mfdataset(glob.glob(path), concat_dim="Time", combine = 'nested')
ds = ds.sortby(ds.xtime)
ds
#Initial condition
pathi = '/lustre/scratch5/dschlichting/runs/gom1pt5r2/ocean/global_ocean/GoM5/WOA23/init/initial_state/initial_state.nc'
dsi = xr.open_dataset(pathi)

lat_c = np.degrees(dsi.variables['latCell'][:])
lon_c = np.degrees(dsi.variables['lonCell'][:])
lat_rad = 1.0/np.cos(np.radians(lat_c.mean().values))

crs = ccrs.PlateCarree(central_longitude=0)
projection = crs
transform = ccrs.PlateCarree()

rv = ds.relativeVorticityAtSurface/dsi.fCell

#GoM regional mask
pathm = '/usr/projects/climate/dschlichting/repos/analysis_notebooks/gom1pt5/gom1pt5_gom_mask.nc'
mask = xr.open_dataset(pathm)
iGoM = np.where(mask.regionCellMasks[:,0] == 1)

The contents of the mask file look like:
xarray.Dataset
    Dimensions:
        nCells: 1258039 nRegions: 1 nVertices: 2536448 nRegionGroups: 2 maxRegionsInGroup: 1
    Coordinates: (0)
    Data variables:
        regionCellMasks
        (nCells, nRegions)
        int32
        ...
        nCellsInRegion
        (nRegions)
        int32
        ...
        regionVertexMasks
        (nVertices, nRegions)
        int32
        ...
        nVerticesInRegion
        (nRegions)
        int32
        ...
        regionNames
        (nRegions)
        |S64
        ...
        nRegionsInGroup
        (nRegionGroups)
        int32
        ...
        regionsInGroup
        (nRegionGroups, maxRegionsInGroup)
        int32
        ...
        regionGroupNames
        (nRegionGroups)
        |S64
        ...
    Indexes: (0)
    Attributes:

    parent_id :
        4u9cvgtcjd
        qgux71w9vn
        1zdkm06zwk
        4rdyc30dwg
    history :
        MpasMaskCreator.x /lustre/scratch5/dschlichting/runs/gom1pt5r2/ocean/global_ocean/GoM5/WOA23/init/initial_state/initial_state.nc gom1pt5_gom_mask.nc
        Mon Oct 28 11:58:02 2024: ncks --glb_att_add MPAS_Mesh_Short_Name=WC1.5to60E3r1 --glb_att_add MPAS_Mesh_Long_Name=WC1.5to60kmL80E3SMv3r1 --glb_att_add MPAS_Mesh_Prefix=WC --glb_att_add MPAS_Mesh_E3SM_Version=3 --glb_att_add MPAS_Mesh_Pull_Request=https://github.com/MPAS-Dev/MPAS-Model/pull/628 --glb_att_add MPAS_Mesh_WC_Revision=1 --glb_att_add MPAS_Mesh_WC_Version_Author=dylanschlichting --glb_att_add MPAS_Mesh_WC_Version_Author_Email=dylanschlichting --glb_att_add MPAS_Mesh_WC_Version_Creation_Date=20241028 --glb_att_add MPAS_Mesh_WC_Minimum_Resolution_km=1.5 --glb_att_add MPAS_Mesh_WC_Maximum_Resolution_km=60 --glb_att_add MPAS_Mesh_WC_Maximum_Depth_m=5500.0 --glb_att_add MPAS_Mesh_WC_Number_of_Levels=80 --glb_att_add MPAS_Mesh_Description=MPAS Gulf of Mexico submesoscale permitting mesh 3, with a focused 1.5-km resolution around North America and 80 vertical levels --glb_att_add MPAS_Mesh_Bathymetry=Bathymetry is from GEBCO 2023, combined with BedMachine Antarctica v3 around Antarctica. --glb_att_add MPAS_Initial_Condition=World Ocean Atlas 2023 climatology 1991-2020 --glb_att_add MPAS_Mesh_compass_Version=1.4.0a6 --glb_att_add MPAS_Mesh_JIGSAW_Version=not found --glb_att_add MPAS_Mesh_JIGSAW_Python_Version=1.0.0 --glb_att_add MPAS_Mesh_MPAS_Tools_Version=0.34.1 --glb_att_add MPAS_Mesh_NCO_Version=5.2.6 --glb_att_add MPAS_Mesh_ESMF_Version=8.6.1 --glb_att_add MPAS_Mesh_geometric_features_Version=1.4.0 --glb_att_add MPAS_Mesh_Metis_Version=5.1.0 --glb_att_add MPAS_Mesh_pyremap_Version=1.3.0 /lustre/scratch5/dschlichting/runs/gom1pt5r2/ocean/global_ocean/GoM5/WOA23/init/initial_state/initial_state.nc /lustre/scratch5/dschlichting/runs/gom1pt5r2/ocean/global_ocean/GoM5/WOA23/init/initial_state/initial_state_with_metadata.nc
        mpirun -n 512 ./ocean_model
    mesh_spec :
        1.0
    Conventions :
        MPAS
    source :
        MpasMeshConverter.x
    file_id :
        zgvntg2vxs

Working example with scatter:
t = 110;nrows=1;ncols=1
fig, ax = plt.subplots(nrows,ncols, figsize = (8,4),
                       subplot_kw={'projection': crs},
                       constrained_layout = True, dpi = 300)

m1 = ax.scatter(lon_c[iGoM[0]],lat_c[iGoM[0]],c=rv[t,iGoM[0]], s=1,
                cmap = cmo.balance, vmin = -1, vmax = 1,transform=transform)

fig.colorbar(m1, ax = ax, label = r'$(\partial_x v - \partial_y u)f^{-1}$', extend = 'both')

ax.set_extent([-98, -70, 13, 35], ccrs.PlateCarree())
ax.add_feature(land_10m, facecolor='0.8')
ax.set_aspect(lat_rad)
ax.coastlines(resolution='10m', linewidth = 0.4)

gl = ax.gridlines(linewidth=0.75, color='black', alpha=0.5, linestyle='dotted', draw_labels=True)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.right_labels = False
gl.left_labels = True
gl.top_labels = False
gl.bottom_labels = True

ax.set_title(r'Surface $\zeta f^{-1}$: '+ str(ds.xtime[t].values)[12:-2])
fig.canvas.draw()

Plot with mosaic
t = 110;nrows=1;ncols=1
fig, ax = plt.subplots(nrows,ncols, figsize = (8,4),
                       subplot_kw={'projection': crs},
                       constrained_layout = True, dpi = 300)

descriptor = mosaic.Descriptor(dsi, projection, transform)
m1 = mosaic.polypcolor(ax, descriptor, rv[t,iGoM[0]],
                       antialiaseds=False, cmap = cmo.balance, vmin = -1, vmax = 1)

fig.colorbar(m1, ax = ax, label = r'$(\partial_x v - \partial_y u)f^{-1}$', extend = 'both')

ax.set_extent([-98, -70, 13, 35], ccrs.PlateCarree())
ax.add_feature(land_10m, facecolor='0.8')
ax.set_aspect(lat_rad)
ax.coastlines(resolution='10m', linewidth = 0.4)

gl = ax.gridlines(linewidth=0.75, color='black', alpha=0.5, linestyle='dotted', draw_labels=True)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.right_labels = False
gl.left_labels = True
gl.top_labels = False
gl.bottom_labels = True

ax.set_title(r'Surface $\zeta f^{-1}$: '+ str(ds.xtime[t].values)[12:-2])
fig.canvas.draw()

Thanks for bringing this up! While mosaic does support this functionality, it's not entirely clear how to go about doing it from the existing documentation. I think you're question hints towards two easy but important improvements we should make:

Raise an error when the data array based to mosaic.polypcolor does not match the dimension of the dataset used to create the Descriptor. This should catch the regional sub-setting confusion you ran into above, and more generally catch other possible mismatched dims errors.
If I can hunt down an existing region mask on the LCRC database, I think it'd be useful to explicitly advise users on how to go about regional plotting in the documentation.

Here are some existing region masks:
https://web.lcrc.anl.gov/public/e3sm/diagnostics/mpas_analysis/region_masks/
Let me know if I can help you find one that matches a mesh you want to test with.

authors: andrewdnolan dylanschlichting xylar


text:
Change cosine bell to be single layer

Checklist

[x] Testing comment in the PR documents testing used to verify the changes

@cbegeman, this is a placeholder until we figure out how to have shared yaml options

Testing
We ran cosine bell with this branch on anvil and it ran successfully

@lconlon, it seems like @cbegeman's right. I looks like the changes to cosine_bell.cfg didn't get committed and pushed to this branch yet.  We'll do that tomorrow.
@cbegeman, we're waiting on #57, regardless so no need to review yet.  These changes will be updated once that goes in.

We re-tested with the recent changes (and rebase) and the tests still pass.

@lconlon and @xylar I just wanted to make you aware that this commit doesn't allow users to run single layer with debug tracers https://github.com/E3SM-Project/E3SM/pull/5590/commits/73b90f7a392587840855cc6a05945a23e0209196. Here's the relevant comment: https://github.com/E3SM-Project/E3SM/pull/5590#issuecomment-1522526731.
I'm not quite sure why this case works with debug tracers but dam_break did not. I guess we should change MPAS_LOG_CRIT to MPAS_LOG_WARN.

@cbegeman, thanks for the heads up.  I guess the current submodule must be from before this change or we wouldn't have been able to run cosine bell.  Since there aren't any dynamics other than advection, it could be that this test just doesn't trigger whatever problem you saw in https://github.com/E3SM-Project/E3SM/pull/5590#issuecomment-1522526731.
It does seem like the change in https://github.com/E3SM-Project/E3SM/commit/73b90f7a392587840855cc6a05945a23e0209196 would make it impossible to run the cosine bell in single layer.  I don't know how much time we want to spend investigating debugTracers in RK4 but maybe we could allow debugTracers when config_disable_tr_all_tend == .false. but disallow them when config_disable_tr_all_tend == .true..  We aren't disabling tracer tendencies here (because tracer advection is the whole point of the test case).
Maybe this is a combination you've tried already and caused crashes.  Let's chat more about this tomorrow.

@xylar I've already tried that combination. I think the best path forward is just to change MPAS_LOG_CRIT to MPAS_LOG_WARN and continue disabling debug and active tracers for the wetting and drying tests.

Thanks @cbegeman.  I can make the PR to E3SM if you like.

@lconlon I think the documentation for this test case does refer to 3 layers in the vertical_grid section. If so, that should be updated.

@cbegeman, thanks for catching that. I believe I have it updated now, but let us know if I missed any multilayer references that you had seen.

That's the one I had in mind!

I'll update the E3SM submodule and re-test, then I hope we're good to go on this.

Testing
I ran both cosine_bell test cases and they passed with this branch:
Test Runtimes:
06:26 PASS ocean/global_convergence/qu/cosine_bell
04:54 PASS ocean/global_convergence/icos/cosine_bell

authors: cbegeman lconlon xylar


text:
Handle dependencies with smaller time increments

Handle dependencies with smaller time increments. Resolves #599

In the latest implementation:
$ cd /lcrc/group/e3sm/ac.forsyth2/zppy_test_complete_run_output/test_599-post-600v3/v2.LR.historical_0201/post/scripts
e3sm_diags_atm_monthly_180x360_aave_model_vs_obs_1850-1851.status:WAITING 548775
e3sm_diags_atm_monthly_180x360_aave_model_vs_obs_1850-1853.status:WAITING 548777
e3sm_diags_atm_monthly_180x360_aave_model_vs_obs_1852-1853.status:WAITING 548776
e3sm_diags_atm_monthly_180x360_aave_mvm_model_vs_model_1852-1853_vs_1850-1851.status:WAITING 548782
e3sm_diags_lnd_monthly_mvm_lnd_model_vs_model_1850-1851_vs_1850-1851.status:WAITING 548783
global_time_series_1850-1860.status:WAITING 548786
ilamb_1850-1853.status:WAITING 548787
mpas_analysis_ts_1850-1854_climo_1850-1854.status:WAITING 548784
mpas_analysis_ts_1850-1860_climo_1855-1860.status:WAITING 548785

This pull request doesn't even touch mpas_analysis.py so something must be wrong in the dependency addition function, not in how it's called.

Test of min_case_add_dependencies shows 2 errors:
1st, global time series is looking for the wrong dependency:
global_time_series_1985-1994
...skipping because of dependency status file missing
   /lcrc/group/e3sm/ac.forsyth2/zppy_min_case_add_dependencies_output/unique_id/v3.LR.historical_0051/post/scripts/ts_atm_monthly_glb_1985-1994-0010.status

We want two 5-year increment ts files...
2nd, e3sm_diags_atm_monthly_180x360_aave_model_vs_obs_1985-1994 is failing: ref_start_yr = doesn't get defined in e3sm.py.

Tested with min_case_add_dependencies


text:
Update zstash error reporting

https://github.com/E3SM-Project/zstash/blob/master/zstash/utils.py#L42 just raises an exception without saying why the exception occurred. Update this and other exceptions as necessary to give more specifics.

It might also be good to check if the command includes hsi and if so specifically tell the user to be sure they logged into HSI at some point.


text:
Update input_output.py

Added a backup source for the SAM in a GH repository
This adds a fallback source for the SAM if the WIDER site is down. Solves #72
cc: @gubenda

Thanks for this update! I also like the new repo to cache the SAM files in.

authors: SeaCelo jdebacker

repo: EAPD-DRB/OG-ZAF


text:
[BREAKING] Command 명명 방식 변경

What's in this pull request

WebDynproCommand들의 명명 방식을 변경했습니다(참고: #69).

@coderabbitai review

repo: EATSTEAK/rusaint


text:
Wildcard pattern search + highlighting on Experimental Panel

[x] Regex construction pattern search from wildcards
[ ] Highlight on result table row
[ ] Highlight the pattern on Verovio rendered chant

Currently breaking contour search and location-based search for Aquitanian chants, prioritizing Square chants wildcard regex search and enable highlighting on each ResultTableRow

repo: ECHOES-from-the-Past/mei-analyser


text:
Problem when using g++ with a Kokkos version compiled with CUDA

When configuring Cajita with a Kokkos installation, that was compiled with CUDA support,
and using g++ as C++ compiler, there seems to be a confusion about compilers:
[ 60%] Building CXX object src/CMakeFiles/Cajita.dir/Cajita_Block.cpp.o
g++: error: unrecognized command line option ‘-Xcudafe’
g++: error: unrecognized command line option ‘--diag_suppress=esa_on_defaulted_function_ignored’
g++: error: unrecognized command line option ‘-arch=sm_70’
g++: error: unrecognized command line option ‘-Xcompiler’; did you mean ‘--compile’?

Probably in this case the nvcc_wrapper from the Kokkos installation needs to be used?

Yes! Here is how I configure with CUDA:

Also make sure that the NVCC_WRAPPER_DEFAULT_COMPILER environment variable is set to your c++ compiler

Thanks, that works for me in principle, if I pass "-w" as an CXX flag (removing all warnings). If I do not pass this flag, the following warning/error ends my compilation early:
[ 20%] Building CXX object googletest-build/googlemock/CMakeFiles/gmock.dir/src/gmock-all.cc.o
/home/halver/install/Cajita/build_cuda/googletest-src/googlemock/include/gmock/gmock-matchers.h:1775:17: error: unnecessary parentheses in declaration of ‘field_’ [-Werror=parentheses]
   const FieldType Class::*field_;
                 ^
cc1plus: all warnings being treated as errors

Is that some, that is too cautious due to my Kokkos installation, or can you reproduce that? I know, the origin of the warning / error is googletest, not the code itself.

@rhalver Did you configure Kokkos to build with -Werror?

Not that I could remember, I checked it and did not find -Werror, which is, what is confusing me about it. I basically used the Cabana building advice for building Kokkos with CUDA.

Does -Wall add -Werror?

Does -Wall add -Werror?

No.

Ok, I had a closer look at it and noticed, that -Werror is added from somewhere to the list of the compiler flags. The problem seems to be, that nvcc_wrapper throws for some reasons other warnings than g++. The case where the compilation stops with nvcc_wrapper throws no warning (and therefore no error) when using g++:
[ 20%] Building CXX object googletest-build/googlemock/CMakeFiles/gmock.dir/src/gmock-all.cc.o
cd /home/halver/install/Cajita/build_cuda/googletest-build/googlemock && /home/halver/kokkos/build/install/bin/nvcc_wrapper   -I/home/halver/install/Cajita/build_cuda/googletest-src/googlemock/include -I/home/halver/install/Cajita/build_cuda/googletest-src/googlemock -isystem /home/halver/install/Cajita/build_cuda/googletest-src/googletest/include -isystem /home/halver/install/Cajita/build_cuda/googletest-src/googletest  -Wall -Wextra -pedantic   -Wall -Wshadow -Werror -Wno-error=dangling-else -DGTEST_HAS_PTHREAD=1 -fexceptions -Wextra -Wno-unused-parameter -Wno-missing-field-initializers -std=c++11 -o CMakeFiles/gmock.dir/src/gmock-all.cc.o -c /home/halver/install/Cajita/build_cuda/googletest-src/googlemock/src/gmock-all.cc
/home/halver/install/Cajita/build_cuda/googletest-src/googlemock/include/gmock/gmock-matchers.h:1775:17: error: unnecessary parentheses in declaration of ‘field_’ [-Werror=parentheses]
   const FieldType Class::*field_;

compared to:
[  5%] Building CXX object googletest-build/googlemock/CMakeFiles/gmock.dir/src/gmock-all.cc.o
cd /home/halver/install/Cajita/build/googletest-build/googlemock && /projects/opt/centos7/gcc/8.2.0/bin/g++   -I/home/halver/install/Cajita/build/googletest-src/googlemock/include -I/home/halver/install/Cajita/build/googletest-src/googlemock -isystem /home/halver/install/Cajita/build/googletest-src/googletest/include -isystem /home/halver/install/Cajita/build/googletest-src/googletest  -Wall -Wextra -pedantic   -Wall -Wshadow -Werror -Wno-error=dangling-else -DGTEST_HAS_PTHREAD=1 -fexceptions -Wextra -Wno-unused-parameter -Wno-missing-field-initializers -std=c++11 -o CMakeFiles/gmock.dir/src/gmock-all.cc.o -c /home/halver/install/Cajita/build/googletest-src/googlemock/src/gmock-all.cc
[  7%] Linking CXX static library ../../lib/libgmock.a

A quick search indicated, that the -Wparanthesis warning might be overzealous in some gcc versions and I have no idea, with which gcc version the CUDA compiler was created, as it is not indicated in the module. So probably we can close this issue now, as the workaround for me is to pass "-w" to ignore the warning that leads to the error message, when using nvcc_wrapper.

OK makes sense - and not surpassing that nvcc would emit other warnings that a new g++ does not. @rhalver if you are comfortable closing go ahead and we can reopen as needed

authors: dalg24 rhalver sslattery

repo: ECP-copa/Cajita


text:
Automatic configuration of sites

Summary

Discovers VOs automagically from the properties of the projects in OpenStack. It will then create the appropriate cloud-info-provider configuration so it will just work. This could be eventually merged into the cloud-info-provider in the long run.

Related issue :

Building containers fails because of https://ggus.eu/index.php?mode=ticket_info&ticket_id=162082 😢


text:
feat: test filtering command line

fix #64

meaningless today

repo: ELS-RD/kernl


text:
brew install eosio

macOS: 10.13.6-x86_64
$ brew install eosio
==> Installing eosio from eosio/eosio
==> Downloading https://github.com/eosio/eos/archive/v1.8.0.tar.gz
==> Downloading from https://codeload.github.com/EOSIO/eos/tar.gz/v1.8.0
######################################################################## 100.0%
Warning: Cannot verify integrity of fcc4996828aa5cc0c832f7604b1fc6850923e428475e8f6709959f66bec12ef9--eos-1.8.0.tar.gz
A checksum was not provided for this resource
For your reference the SHA-256 is: 173122c922bd13bb837620bd27d25755efec294dd54a050fc1838e8546b640f7
Error: An exception occurred within a child process:
RuntimeError: Error, only supporting binary packages at this time

eosio only supports 10.14 now. That's probably the reason for your error but I have no way of testing 10.13 at the moment

the bottle has been updated to report a more intelligent error when not running 10.14+, closing issue as resolved

macOS Catalina: 10.15.6
➜  ~ brew install eosio
Updating Homebrew...
==> Auto-updated Homebrew!
Updated 1 tap (homebrew/core).
==> Updated Formulae
Updated 5 formulae.
==> Installing eosio from eosio/eosio
==> Downloading https://github.com/eosio/eos/archive/v2.0.7.tar.gz
Already downloaded: /Users/amaurycalleja/Library/Caches/Homebrew/downloads/835fef79b625d321e2f7b609c9e8f03822dcda89317b439a8a5c8835fbc244f6--eos-2.0.7.tar.gz
Warning: Cannot verify integrity of 835fef79b625d321e2f7b609c9e8f03822dcda89317b439a8a5c8835fbc244f6--eos-2.0.7.tar.gz
A checksum was not provided for this resource.
For your reference the SHA-256 is: 9a9ecd36242d620e0165766f41f5f942ff6d1a9edc12dac418a0ebcaf2dc4d8a
Error: An exception occurred within a child process:
RuntimeError: Error, only supporting binary packages at this time

authors: acalleja42 spoonincode ytuaeb

repo: EOSIO/eos


text:
net_plugin: shutdown segfault fix

Change Description

Avoid accessing connection data during operation_aborted since it can be called during shutdown when connection is being destroyed

Change Type
Select ONE:

[ ] Documentation

[x] Stability bug fix

[ ] Other

[ ] Other - special case

Testing Changes
Select ANY that apply:

[ ] New Tests

[x] Existing Tests

[ ] Test Framework

[ ] CI System

[ ] Other

Consensus Changes

[ ] Consensus Changes

API Changes

[ ] API Changes

Documentation Additions

[ ] Documentation Additions

please mention in description this is backport


text:
Adding Rain Guage NoteBook

Looking at CocoRahs data plotting rainfall data.

Looks good! Merging.

authors: bbridges-ASME zssherman

repo: EVS-ATMOS/chicago-data-warehouse


text:
time dimension crashes kernel

If running most_common regridder with the same dataset but with a time dimension the kernel crashes.

It seems that the origin of this bug was somewhere else. Having time dims in the most_common regridder seems to work fine when testing locally.

authors: BSchilperoort ClaireDons


text:
Add "Add Credit Card" examples for each language

New "Add Credit Card" example for each CL

The credit card number still doesn't match the responses last4

All credit card API calls are using a Visa card, expiration 01/2025, number 0123456789101234. The only thing that does not use this is a Mastercard in the billing response that is that secondary card, and therefore a different number.

Ok, then let's update the response to match this new number.
TLDR: I don't really care, as long as everything lines up.

The credit card number still doesn't match the responses last4

All credit card API calls are using a Visa card, expiration 01/2025, number 0123456789101234. The only thing that does not use this is a Mastercard in the billing response that is that secondary card, and therefore a different number.

Ok, then let's update the response to match this new number.
TLDR: I don't really care, as long as everything lines up.

It is.

repo: EasyPost/examples


text:
torch.compile agents

Hi everyone,
in this branch one can use torch.compile to compile the  Dreamer-V3 agent. In particular:

in the sheeprl/configs/algo/dreamer_v3.yaml one can decide what to compile and which arguments to the compile funtion to pass
For now one can compile the dynamic_learning, behaviour_learning and compute_lambda_values functions as well as every models in the build_agent
It's strictly necessary to have the nightly build of pytorch, i.e. torch>=2.3 because the torch.nn.functional.one_hot function has been fixed in the nightly build

Those are the results I've obtained:

The green one is with the compiled modules and functions from this commit
The gray one is with the main branch without compile

The command I run is:
python sheeprl.py exp=dreamer_v3 \
env=gym env.id=CartPole-v1 \
env.num_envs=4 \
fabric.accelerator=gpu \
fabric.precision=bf16-mixed \
algo=dreamer_v3_S \
algo.learning_starts=1024 \
algo.cnn_keys.encoder=\[\] \
algo.mlp_keys.encoder=\["vector"\] \
algo.cnn_keys.decoder=\[\] \
algo.mlp_keys.decoder=\["vector"\] \
algo.per_rank_sequence_length=64 \
algo.replay_ratio=0.5 \
algo.world_model.decoupled_rssm=False \
algo.world_model.learnable_initial_recurrent_state=False

My env is:
python -m torch.utils.collect_env
<frozen runpy>:128: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour
Collecting environment information...
PyTorch version: 2.3.0.dev20240314+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Pop!_OS 22.04 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-76060800daily20240311-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4060 Laptop GPU
Nvidia driver version: 550.67
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             16
On-line CPU(s) list:                0-15
Vendor ID:                          GenuineIntel
Model name:                         13th Gen Intel(R) Core(TM) i5-13500H
CPU family:                         6
Model:                              186
Thread(s) per core:                 2
Core(s) per socket:                 12
Socket(s):                          1
Stepping:                           2
CPU max MHz:                        4700.0000
CPU min MHz:                        400.0000
BogoMIPS:                           6374.40
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          448 KiB (12 instances)
L1i cache:                          640 KiB (12 instances)
L2 cache:                           9 MiB (6 instances)
L3 cache:                           18 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-15
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] mypy==1.2.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.2.1
[pip3] pytorch-triton==3.0.0+989adb9a29
[pip3] torch==2.3.0.dev20240314+cu121
[pip3] torchmetrics==1.3.2
[pip3] torchvision==0.18.0.dev20240314+cu121
[pip3] triton==2.2.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch-lightning         2.2.1                    pypi_0    pypi
[conda] pytorch-triton            3.0.0+989adb9a29          pypi_0    pypi
[conda] torch                     2.3.0.dev20240314+cu121          pypi_0    pypi
[conda] torchmetrics              1.3.2                    pypi_0    pypi
[conda] torchvision               0.18.0.dev20240314+cu121          pypi_0    pypi
[conda] triton                    2.2.0                    pypi_0    pypi

Everyone is welcome to test it out and run some experiments with Dreamer-V3 or any other algorithm (taking inspiration from the Dreamer-V3 agent).
We can keep this issue as a reference.
Thank you all!

I've run an experiment on a Lightning Studio with an A10G GPU with the following command:
python sheeprl.py exp=dreamer_v3_100k_ms_pacman fabric.devices=1 fabric.precision=32 fabric.accelerator=gpu

It has run in less than 6 hours and I've obtained the following results:

The test on different seeds are:
seed 5: 1970
seed 1024: 1910
seed 42: 1760
seed 1337: 1680
seed 8: 1270
seed 2: 1310

with a 1650 +- 271.72 average result

I have run a walker walk training with the commit: d640a41cc9a959e28e8f052376fc5e808ee0da1d (ReLU as activation function).
These are the configs that have been used for training, with the only difference being that you use decoupled RSSM.
The command used is the following:
python sheeprl.py exp=dreamer_v3_dmc_walker_walk algo.world_model.decoupled_rssm=True

Below the obtained results are reported:

The green line is the experiment run with the compile (~26h 34min). The red and blue lines were run without compile (~38h 40min).
My env is:
$ python -m torch.utils.collect_env

Collecting environment information...
PyTorch version: 2.3.0.dev20240314+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0
Clang version: Could not collect
CMake version: version 3.27.7
Libc version: glibc-2.35

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.0-46-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 535.54.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   39 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          12
On-line CPU(s) list:             0-11
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Core(TM) i7-8700T CPU @ 2.40GHz
CPU family:                      6
Model:                           158
Thread(s) per core:              2
Core(s) per socket:              6
Socket(s):                       1
Stepping:                        10
CPU max MHz:                     2400,0000
CPU min MHz:                     800,0000
BogoMIPS:                        4800.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       192 KiB (6 instances)
L1i cache:                       192 KiB (6 instances)
L2 cache:                        1,5 MiB (6 instances)
L3 cache:                        12 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-11
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; IBRS
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Mitigation; Microcode
Vulnerability Tsx async abort:   Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] mypy==1.2.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.3
[pip3] pytorch-lightning==2.1.3
[pip3] pytorch-triton==3.0.0+989adb9a29
[pip3] torch==2.3.0.dev20240314+cu121
[pip3] torch-tb-profiler==0.4.3
[pip3] torchmetrics==1.3.0
[pip3] torchvision==0.18.0.dev20240314+cu121
[pip3] triton==2.2.0
[conda] numpy                     1.26.3                   pypi_0    pypi
[conda] pytorch-lightning         2.1.3                    pypi_0    pypi
[conda] pytorch-triton            3.0.0+989adb9a29          pypi_0    pypi
[conda] torch                     2.3.0.dev20240314+cu121          pypi_0    pypi
[conda] torch-tb-profiler         0.4.3                    pypi_0    pypi
[conda] torchmetrics              1.3.0                    pypi_0    pypi
[conda] torchvision               0.18.0.dev20240314+cu121          pypi_0    pypi
[conda] triton                    2.2.0                    pypi_0    pypi

I've run an experiment on 4 A10G on a Lightning Studio with the following command:
python sheeprl.py exp=dreamer_v3_100k_ms_pacman fabric.devices=4 fabric.precision=32 fabric.accelerator=gpu

where I've manually changed the following:

Every model has been compiled with fullgraph=False and mode=max-autotune
The dynamic_learning, behaviour_learning and compute_lambda_values functions have been compiled with the default arguments
I've multiplied all learning rate by 4 (the number of gpus trained on)

It has run in less than 4 hours and those are the results:

The test on different seeds are:
seed 5: 1630
seed 1024: 1160
seed 42: 1330
seed 1337: 1650
seed 8: 1170
seed 2: 930

with a 1311.67 +- 259.77 average reward.

I have run a walker walk training with the commit: fab9f4858dd14993ded7055773eff670ab363de2 (SiLU as activation function).
These are the configs that have been used for training, I have run without the decoupled RSSM.
The command used is the following:
python sheeprl.py exp=dreamer_v3_dmc_walker_walk

The green line is the compiled experiment described above (with ReLU, decoupled RSSM, and the d640a41cc9a959e28e8f052376fc5e808ee0da1d commit). The grey line is the new experiment.
My env is:
$ python -m torch.utils.collect_env

Collecting environment information...
PyTorch version: 2.3.0.dev20240314+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0
Clang version: Could not collect
CMake version: version 3.27.7
Libc version: glibc-2.35

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.0-46-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 535.54.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   39 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          12
On-line CPU(s) list:             0-11
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Core(TM) i7-8700T CPU @ 2.40GHz
CPU family:                      6
Model:                           158
Thread(s) per core:              2
Core(s) per socket:              6
Socket(s):                       1
Stepping:                        10
CPU max MHz:                     2400,0000
CPU min MHz:                     800,0000
BogoMIPS:                        4800.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       192 KiB (6 instances)
L1i cache:                       192 KiB (6 instances)
L2 cache:                        1,5 MiB (6 instances)
L3 cache:                        12 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-11
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; IBRS
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Mitigation; Microcode
Vulnerability Tsx async abort:   Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] mypy==1.2.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.3
[pip3] pytorch-lightning==2.1.3
[pip3] pytorch-triton==3.0.0+989adb9a29
[pip3] torch==2.3.0.dev20240314+cu121
[pip3] torch-tb-profiler==0.4.3
[pip3] torchmetrics==1.3.0
[pip3] torchvision==0.18.0.dev20240314+cu121
[pip3] triton==2.2.0
[conda] numpy                     1.26.3                   pypi_0    pypi
[conda] pytorch-lightning         2.1.3                    pypi_0    pypi
[conda] pytorch-triton            3.0.0+989adb9a29          pypi_0    pypi
[conda] torch                     2.3.0.dev20240314+cu121          pypi_0    pypi
[conda] torch-tb-profiler         0.4.3                    pypi_0    pypi
[conda] torchmetrics              1.3.0                    pypi_0    pypi
[conda] torchvision               0.18.0.dev20240314+cu121          pypi_0    pypi
[conda] triton                    2.2.0                    pypi_0    pypi

Hi,
Thanks for this. Was testing this out, disable: False works fine, but with disable: True for all compile configs, getting this error:
Error executing job with overrides: ['exp=dreamer_v3', 'env=gym', 'env.id=CartPole-v1', 'env.num_envs=4', 'fabric.accelerator=gpu', 'fabric.precision=32-true', 'algo=dreamer_v3_S', 'algo.learning_starts=1024', 'algo.cnn_keys.encoder=[]', 'algo.mlp_keys.encoder=[vector]', 'algo.cnn_keys.decoder=[]', 'algo.mlp_keys.decoder=[vector]', 'algo.per_rank_sequence_length=64', 'algo.replay_ratio=0.5', 'algo.world_model.decoupled_rssm=False', 'algo.world_model.learnable_initial_recurrent_state=False']
Traceback (most recent call last):
  File "/home/sam/dev/sheeprl/sheeprl/cli.py", line 352, in run
    run_algorithm(cfg)
  File "/home/sam/dev/sheeprl/sheeprl/cli.py", line 190, in run_algorithm
    fabric.launch(reproducible(command), cfg, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/lightning/fabric/fabric.py", line 859, in launch
    return self._wrap_and_launch(function, self, *args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/lightning/fabric/fabric.py", line 945, in _wrap_and_launch
    return to_run(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/lightning/fabric/fabric.py", line 950, in _wrap_with_setup
    return to_run(*args, **kwargs)
  File "/home/sam/dev/sheeprl/sheeprl/cli.py", line 186, in wrapper
    return func(fabric, cfg, *args, **kwargs)
  File "/home/sam/dev/sheeprl/sheeprl/algos/dreamer_v3/dreamer_v3.py", line 758, in main
    train(
  File "/home/sam/dev/sheeprl/sheeprl/algos/dreamer_v3/dreamer_v3.py", line 341, in train
    policies: Sequence[Distribution] = actor(imagined_trajectories.detach())[1]
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/lightning/fabric/wrappers.py", line 139, in forward
    output = self._forward_module(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 387, in _fn
    return fn(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 977, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state, skip=1)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 411, in _convert_frame_assert
    return _compile(
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_utils_internal.py", line 70, in wrapper_function
    return function(*args, **kwargs)
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 700, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/utils.py", line 266, in time_wrapper
    r = func(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 568, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1116, in transform_code_object
    transformations(instructions, code_options)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 173, in _fn
    return fn(*args, **kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 515, in transform
    tracer.run()
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2237, in run
    super().run()
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 875, in run
    while self.step():
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 790, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 492, in wrapper
    return inner_fn(self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 730, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 339, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 293, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 736, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2418, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2534, in inline_call_
    tracer.run()
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 875, in run
    while self.step():
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 790, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 492, in wrapper
    return inner_fn(self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 730, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 339, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 293, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 736, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2418, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2534, in inline_call_
    tracer.run()
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 875, in run
    while self.step():
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 790, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 492, in wrapper
    return inner_fn(self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 730, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 339, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 293, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 736, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2418, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2534, in inline_call_
    tracer.run()
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 875, in run
    while self.step():
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 790, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 492, in wrapper
    return inner_fn(self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 730, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 339, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 293, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/functions.py", line 90, in call_function
    return tx.inline_user_function_return(self, [*self.self_args(), *args], kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 736, in inline_user_function_return
    return InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2418, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 2534, in inline_call_
    tracer.run()
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 875, in run
    while self.step():
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 790, in step
    self.dispatch_table[inst.opcode](self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 492, in wrapper
    return inner_fn(self, inst)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1260, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 730, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/user_defined.py", line 440, in call_function
    return super().call_function(tx, args, kwargs)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/variables/base.py", line 294, in call_function
    unimplemented(f"call_function {self} {args} {kwargs}")
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/_dynamo/exc.py", line 212, in unimplemented
    raise Unsupported(msg)
torch._dynamo.exc.Unsupported: call_function UserDefinedClassVariable(<class 'torch.Size'>) [SizeVariable()] {}

from user code:
   File "/home/sam/dev/sheeprl/sheeprl/algos/dreamer_v3/agent.py", line 838, in forward
    actions[i] = actions_dist[i].rsample()
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/distributions/one_hot_categorical.py", line 127, in rsample
    samples = self.sample(sample_shape)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/distributions/one_hot_categorical.py", line 95, in sample
    indices = self._categorical.sample(sample_shape)
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/distributions/categorical.py", line 133, in sample
    return samples_2d.reshape(self._extended_shape(sample_shape))
  File "/home/sam/dev/sheeprl/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py", line 268, in _extended_shape
    return torch.Size(sample_shape + self._batch_shape + self._event_shape)

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

Hi @geranim0, you need to update PyTorch to the nightly build (torch>=2.3) where they have fixed the one_hot.
Could you update the pytorch version and tell us your results?
Thank you

Hi @belerico ,
Yes I did replace the stock torch with the nightly, yielding this config
(.venv) sam@oldub:~/dev/sheeprl$ python -m torch.utils.collect_env
/usr/lib/python3.10/runpy.py:126: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour
  warn(RuntimeWarning(msg))
Collecting environment information...
PyTorch version: 2.4.0.dev20240418+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-27-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2070
Nvidia driver version: 545.29.06
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i5-4690K CPU @ 3.50GHz
CPU family:                         6
Model:                              60
Thread(s) per core:                 1
Core(s) per socket:                 4
Socket(s):                          1
Stepping:                           3
CPU max MHz:                        3900.0000
CPU min MHz:                        800.0000
BogoMIPS:                           7000.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm cpuid_fault invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt dtherm ida arat pln pts vnmi md_clear flush_l1d
Virtualization:                     VT-x
L1d cache:                          128 KiB (4 instances)
L1i cache:                          128 KiB (4 instances)
L2 cache:                           1 MiB (4 instances)
L3 cache:                           6 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT disabled
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Unknown: No mitigations
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Mitigation; Microcode
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-lightning==2.2.2
[pip3] pytorch-triton==3.0.0+989adb9a29
[pip3] torch==2.4.0.dev20240418+cu121
[pip3] torchaudio==2.2.0.dev20240418+cu121
[pip3] torchmetrics==1.3.2
[pip3] torchvision==0.19.0.dev20240418+cu121
[pip3] triton==2.2.0
[conda] Could not collect

I've just checked out the branch and tested around a bit using my own gym env. DreamerV3_S.
For a replay_ratio of 0.1 I am seeing a 3-4x speedup (dark blue):

For a replay_ratio of 5.0 it is around 2x (light blue vs. dark red):

In both cases, GPU utilization hovers around 60%:
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.171.04             Driver Version: 535.171.04   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 4090        Off | 00000000:01:00.0  On |                  Off |
| 30%   53C    P2             174W / 450W |   4525MiB / 24564MiB |     61%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A      1889      G   /usr/lib/xorg/Xorg                          183MiB |
|    0   N/A  N/A      2030      G   /usr/bin/gnome-shell                         44MiB |
|    0   N/A  N/A      5490      G   ...irefox/4451/usr/lib/firefox/firefox      291MiB |
|    0   N/A  N/A     35460      G   ...erProcess --variations-seed-version       61MiB |
|    0   N/A  N/A     72175      C   .../miniconda3/envs/sheeprl/bin/python     3844MiB |
+---------------------------------------------------------------------------------------+

My python env is:

PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35
Python version: 3.10.12 (main, Jul  5 2023, 18:54:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-35-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 535.171.04
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             24
On-line CPU(s) list:                0-23
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen 9 7900X 12-Core Processor
CPU family:                         25
Model:                              97
Thread(s) per core:                 2
Core(s) per socket:                 12
Socket(s):                          1
Stepping:                           2
CPU max MHz:                        5733,0000
CPU min MHz:                        400,0000
BogoMIPS:                           9400.12
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d
Virtualization:                     AMD-V
L1d cache:                          384 KiB (12 instances)
L1i cache:                          384 KiB (12 instances)
L2 cache:                           12 MiB (12 instances)
L3 cache:                           64 MiB (2 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-23
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] onnx==1.12.0
[pip3] pytorch-lightning==2.2.1
[pip3] torch==2.3.1
[pip3] torchmetrics==1.3.2
[pip3] torchvision==0.18.1
[pip3] triton==2.3.1
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch-lightning         2.2.1                    pypi_0    pypi
[conda] torch                     2.3.1                    pypi_0    pypi
[conda] torchmetrics              1.3.2                    pypi_0    pypi
[conda] torchvision               0.18.1                   pypi_0    pypi
[conda] triton                    2.3.1                    pypi_0    pypi

I cannot sheeprl-eval my trained model, since the keys in the world model's state_dict have different names:
Stacktrace

Error executing job with overrides: ['checkpoint_path=/home/drt/Desktop/sheeprl/sheeprl/logs/runs/dreamer_v3/PyFlyt/2024-06-23_19-34-31_dreamer_v3_PyFlyt_42/version_0/checkpoint/ckpt_730000_0.ckpt', 'fabric.accelerator=gpu', 'env.capture_video=True', 'seed=52']
Traceback (most recent call last):
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/sheeprl/cli.py", line 404, in evaluation
eval_algorithm(ckpt_cfg)
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/sheeprl/cli.py", line 267, in eval_algorithm
fabric.launch(command, cfg, state)
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/lightning/fabric/fabric.py", line 839, in launch
return self._wrap_and_launch(function, self, *args, **kwargs)
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/lightning/fabric/fabric.py", line 925, in _wrap_and_launch
return to_run(*args, **kwargs)
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/lightning/fabric/fabric.py", line 930, in _wrap_with_setup
return to_run(*args, **kwargs)
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/sheeprl/cli.py", line 262, in wrapper
return func(*args, **kwargs)
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/sheeprl/algos/dreamer_v3/evaluate.py", line 47, in evaluate
_, _, _, _, player = build_agent(
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/sheeprl/algos/dreamer_v3/agent.py", line 1186, in build_agent
world_model.load_state_dict(world_model_state)
File "/home/drt/miniconda3/envs/sheeprl/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2189, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for WorldModel:
Missing key(s) in state_dict: "encoder.mlp_encoder.model._model.0.weight", "encoder.mlp_encoder.model._model.1.weight", "encoder.mlp_encoder.model._model.1.bias", "encoder.mlp_encoder.model._model.3.weight", "encoder.mlp_encoder.model._model.4.weight", "encoder.mlp_encoder.model._model.4.bias", "encoder.mlp_encoder.model._model.6.weight", "encoder.mlp_encoder.model._model.7.weight", "encoder.mlp_encoder.model._model.7.bias", "rssm.recurrent_model.mlp._model.0.weight", "rssm.recurrent_model.mlp._model.1.weight", "rssm.recurrent_model.mlp._model.1.bias", "rssm.recurrent_model.rnn.linear.weight", "rssm.recurrent_model.rnn.layer_norm.weight", "rssm.recurrent_model.rnn.layer_norm.bias", "rssm.representation_model._model.0.weight", "rssm.representation_model._model.1.weight", "rssm.representation_model._model.1.bias", "rssm.representation_model._model.3.weight", "rssm.representation_model._model.3.bias", "rssm.transition_model._model.0.weight", "rssm.transition_model._model.1.weight", "rssm.transition_model._model.1.bias", "rssm.transition_model._model.3.weight", "rssm.transition_model._model.3.bias", "observation_model.mlp_decoder.model._model.0.weight", "observation_model.mlp_decoder.model._model.1.weight", "observation_model.mlp_decoder.model._model.1.bias", "observation_model.mlp_decoder.model._model.3.weight", "observation_model.mlp_decoder.model._model.4.weight", "observation_model.mlp_decoder.model._model.4.bias", "observation_model.mlp_decoder.model._model.6.weight", "observation_model.mlp_decoder.model._model.7.weight", "observation_model.mlp_decoder.model._model.7.bias", "observation_model.mlp_decoder.heads.0.weight", "observation_model.mlp_decoder.heads.0.bias", "reward_model._model.0.weight", "reward_model._model.1.weight", "reward_model._model.1.bias", "reward_model._model.3.weight", "reward_model._model.4.weight", "reward_model._model.4.bias", "reward_model._model.6.weight", "reward_model._model.7.weight", "reward_model._model.7.bias", "reward_model._model.9.weight", "reward_model._model.9.bias".
Unexpected key(s) in state_dict: "encoder._orig_mod.mlp_encoder.model._model.0.weight", "encoder._orig_mod.mlp_encoder.model._model.1.weight", "encoder._orig_mod.mlp_encoder.model._model.1.bias", "encoder._orig_mod.mlp_encoder.model._model.3.weight", "encoder._orig_mod.mlp_encoder.model._model.4.weight", "encoder._orig_mod.mlp_encoder.model._model.4.bias", "encoder._orig_mod.mlp_encoder.model._model.6.weight", "encoder._orig_mod.mlp_encoder.model._model.7.weight", "encoder._orig_mod.mlp_encoder.model._model.7.bias", "rssm.recurrent_model._orig_mod.mlp._model.0.weight", "rssm.recurrent_model._orig_mod.mlp._model.1.weight", "rssm.recurrent_model._orig_mod.mlp._model.1.bias", "rssm.recurrent_model._orig_mod.rnn.linear.weight", "rssm.recurrent_model._orig_mod.rnn.layer_norm.weight", "rssm.recurrent_model._orig_mod.rnn.layer_norm.bias", "rssm.representation_model._orig_mod._model.0.weight", "rssm.representation_model._orig_mod._model.1.weight", "rssm.representation_model._orig_mod._model.1.bias", "rssm.representation_model._orig_mod._model.3.weight", "rssm.representation_model._orig_mod._model.3.bias", "rssm.transition_model._orig_mod._model.0.weight", "rssm.transition_model._orig_mod._model.1.weight", "rssm.transition_model._orig_mod._model.1.bias", "rssm.transition_model._orig_mod._model.3.weight", "rssm.transition_model._orig_mod._model.3.bias", "observation_model._orig_mod.mlp_decoder.model._model.0.weight", "observation_model._orig_mod.mlp_decoder.model._model.1.weight", "observation_model._orig_mod.mlp_decoder.model._model.1.bias", "observation_model._orig_mod.mlp_decoder.model._model.3.weight", "observation_model._orig_mod.mlp_decoder.model._model.4.weight", "observation_model._orig_mod.mlp_decoder.model._model.4.bias", "observation_model._orig_mod.mlp_decoder.model._model.6.weight", "observation_model._orig_mod.mlp_decoder.model._model.7.weight", "observation_model._orig_mod.mlp_decoder.model._model.7.bias", "observation_model._orig_mod.mlp_decoder.heads.0.weight", "observation_model._orig_mod.mlp_decoder.heads.0.bias", "reward_model._orig_mod._model.0.weight", "reward_model._orig_mod._model.1.weight", "reward_model._orig_mod._model.1.bias", "reward_model._orig_mod._model.3.weight", "reward_model._orig_mod._model.4.weight", "reward_model._orig_mod._model.4.bias", "reward_model._orig_mod._model.6.weight", "reward_model._orig_mod._model.7.weight", "reward_model._orig_mod._model.7.bias", "reward_model._orig_mod._model.9.weight", "reward_model._orig_mod._model.9.bias".

authors: belerico defrag-bambino geranim0 michele-milesi

repo: Eclectic-Sheep/sheeprl


text:
[Enhancement] Public/Private Key Authentication

Hi there, I think a useful feature would be the allowing of public/private key authentication through something like pycrypto. This would allow service accounts to automatically poll data while still authenticating with the server without the need to store a username/password, which is unethical.

I'm a bit reluctant to add this logic into OpenTAXII itself. It seems to me that web server, that is required in order to run OpenTAXII in production, should take care of SSL-based access control and possibly authentication

authors: arcsector traut

repo: EclecticIQ/OpenTAXII


text:
Events cards should be more dinamic and provide more information

Intended behaviour of cards components at event tab is to:
Once click
Expand and provide more information
I would prefer to be able to redirect to a full-fledge landing page for that event. The event landing page, or expanded card component, should be generated from a JSON.

Hi Brayen,
It's a great idea. I'd like to share with you two prototypes for this:

Event landing page: with register button (main CTA), tags, event info, organizers and speakers
Community landing page:  with register button (main CTA), about info, tags, gallery, team and social media

You can click here for full experience (from home page to event/community landing page): https://www.figma.com/proto/x7GTNPvqJwtyzjfKX5NqH9/ECinTech?node-id=88-148&node-type=frame&t=YVhIU8QGS8Od4zIZ-0&scaling=min-zoom&content-scaling=fixed&page-id=0%3A1
(If you'd like to skip the full experience) Here's the link for community landing page (you can access the event landing page from here): https://www.figma.com/proto/x7GTNPvqJwtyzjfKX5NqH9/ECinTech?node-id=269-2110&node-type=frame&t=YVhIU8QGS8Od4zIZ-0&scaling=min-zoom&content-scaling=fixed&page-id=0%3A1

authors: bjgavilanes nsandoya

repo: Ecuador-In-Tech/web


text:
Update TEMPLATE_LIST.md

I forgot the name from my group here lol srry

I forgot the name from my group here lol srry

Not a problem!

Thank you! <3

authors: EddieDover pmenezesdev

repo: EddieDover/Theater-of-the-Mind


text:
Fixed the Typographical error on the gitpod.mdx component

What does this PR does
This PR Correct the typographical error on pages/docs/environments/gitpod.mdx component
Fixes Issue #9540

@Taki7980 This issue is in triage status and not yet assigned nor ready for dev.  This PR will be closed.
Please read our contributing guide before completing any work on issues. How To Contribute

repo: EddieHubCommunity/BioDrop


text:
[FEATURE] Add Component Footer to NextJs Project

Description
#1944
components/footer.js
Please assign it to me.
Screenshots

Additional information
No response

I'd like to work on this

can i work on this issue

Footer component has been added to Storybook
Please create specific issues for any future work

authors: DeeptanshuDas VedanthB eddiejaoude ranjankuldeep

repo: EddieHubCommunity/LinkFree


text:
thebarshablog profile can't be found in the data directory

Kindly share the reason behind truncating 1000 files and where do I recover my file from?
Additional information
No response

@thebarshablog There is a limit to files that GitHub can show on the UI
Your profile is still present in the database you can check all the files by cloning the fork into your local environment

@thebarshablog as your file name is your username you can find
it by appending your GitHub profile Name to the https://github.com/EddieHubCommunity/LinkFree/tree/main/data/
Here's the Link
https://github.com/EddieHubCommunity/LinkFree/tree/main/data/thebarshablog

let me know if that helps :)

@ManishBisht777 Thanks for pointing out. However, the above ones contain my events and testimonials. Where do I find my original file apart from events and testimonials?

@thebarshablog  you can append .json afterward to access your JSON file as well
https://github.com/EddieHubCommunity/LinkFree/blob/main/data/thebarshablog.json

@thebarshablog you can append .json afterward to access your JSON file as well https://github.com/EddieHubCommunity/LinkFree/blob/main/data/thebarshablog.json

I sincerely appreciate your help.


text:
[BUG FIX] milestones tab ui is not consistent with others

Fixes Issue
Closes #7945
BEFORE FIX

AFTER FIX

Thank you for the collaboration 👍 I will merge to a temporary branch and make some tweaks

Thanks Eddie

authors: ZeeMonk-pixel eddiejaoude


text:
chore: add vishalcoder139

Related issue

What changes does this PR do?
Screenshots (If any)
Questions

Please add yourself in alphabetical order also with the second and third letter and so on.
hello schmelto i understand panquestio 7  requested changes but i did not understand your requested changes can
you tell me in a easy visual way just like panquestio 7 told me in a picture way.

Hello Panquesito7 can you help me what i do next to  merge my changes in this repository. because i am very new to this
github enviourment. and also a new coding learner.

Hello Panquesito7 can you help me what i do next to merge my changes in this repository. because i am very new to this github enviourment. and also a new coding learner.

Hey there! I'll merge the PR now that it has been approved. You just need to wait until the maintainers merge it. 🙂

how can do solve my schmelto change requested ?


text:
[REVIEW] Anass Lebkhaiti

GitHub Profile Link:
https://github.com/Anaslbkh/
You can also join the Discord community here
Feel free to check out other cool repositories of the EddieHub Community here

Great profile! I just made a PR in your repository to add Twitter and website badges. Thank you for sharing your profile with us! :wink: Anaslbkh/Anaslbkh#1

Cool profile. I agree with the badges for the connect section

Thank you for sharing, we will review on a live stream soon 👍

@Anaslbkh The profile looks good, possibly add a banner/stats to your page.  Also maybe remove the / from the link in the connect with me section, I think it will look better this way.

Looks good, some thoughts...

Maybe get more active in open source
Add more information about yourself in the customised profile
Join EddieHub GitHub Org 😜

authors: Anaslbkh KendallDoesCoding Panquesito7 eddiejaoude schmelto

repo: EddieHubCommunity/support