PR: Fix a few typos in the FR locale file

Superseeds #4446, targetting the right branch this time :sheep:.
Some fixes for the French locale of Spyder. Mainly:

échèle <- échelle (“scale”)
barre d'outil <- barre d'outils (plural form on “tools”)
miscellaneous typos like missing ending "s" for some plural forms, weird phrasing, fixes to follow French typography rules, etc.
translation of a couple of missing messages

Disclaimer
I am a French native speaker from France: if some of the modified phrasings were correct Canadian (or elsewhere) French idioms, please let me know and I will be glad to revert the modifications.
Unfortunately, I do not currently have time to check the thousands of lines in the locale file. Hopefully I will be able to have a further look in the future if nobody else does it before.
Edit:
Ugh, definitively I am not an expert of the GitHub editor yet… I apologize for the super long commit message, I thought it was the PR message instead.

@afvincent, thanks a lot for moving your PR to 3.x!!
Could we ping you to help us with our French translation once new versions are about to be released?

Merging because the failures in Circle and Travis are unrelated to this work and they were fixed yesterday.

@ccordoba12 You're welcome.  About keeping up-to-date or improving the French translation, sure you can ping me when needed, and I'll see what I can do about it.

Ok, thanks a lot for your help!!

Thanks @afvincent for the help :-), we added you to https://github.com/spyder-ide/spyder/wiki/Dev:-Translations


Refactoring of pdt_locales

This is suggest refactoring version for issue #124 which intend to remove the redundant pdtLocale classes and init methods.

+1 to keeping the locales list very simple and explicit
+1 to this PR

Current coverage is 73.29%

Merging #130 into master will increase coverage by +4.13% as of 2771d36

@@            master    #130   diff @@
======================================
  Files           13      14     +1
  Stmts         1683    1539   -144
  Branches       283     292     +9
  Methods          0       0
======================================
- Hit           1164    1128    -36
- Partial         92      93     +1
+ Missed         427     318   -109

Review entire Coverage Diff as of 2771d36

Powered by Codecov. Updated on successful CI builds.

Just rebased.


Required validation not enforced at first page load

Subject of the issue
I'm opening this issue to revive #1975 which has been closed as stale, but it's still present as of v1.19.3.
Required error message not showing when:

Page initially loads with a field that's already filled in
User clears the field (backspace or del key)
User tabs to next field -> no error message
User tabs to previous field -> still no error message
User fills in something, leaves the field, goes back and empties the field -> now the required error message pops up

Your environment

version of jquery-validate: 1.19.3
which browser and its version: Chrome 91, Firefox 89

Steps to reproduce
http://jsfiddle.net/rvtj8zhL/
Expected behaviour
Error message should be displayed on first 'empty'
Actual behaviour
No error message is shown

i'm confirm the issue present as of v1.19.3. and the solution gave by https://github.com/jquery-validation/jquery-validation/issues/1975#issuecomment-360350493 fix my error. i dont know the consequences and ill go throught with care, my app is huge and for now i detect this issue at this point.
In my case the issue occurs when you have the field already validated and then focusout and focusin the input field. I figureout that not only focusout and focusin reproduce the error, also keybords shortcuts.
If this thread grows , ill still reporting


Build OAP Data Source

I am trying to build OAP Data Source， but fail to find
   <dependency>
      <groupId>org.apache.arrow</groupId>
      <artifactId>arrow-dataset</artifactId>
      <version>${arrow.version}</version>
  </dependency>

I also look into arrow repo, and don't find such artifact

Hi @baibaichen , I think you may want to build OAP optimized Arrow before doing so: https://github.com/Intel-bigdata/arrow/tree/oap-master
More information please refer to doc https://github.com/Intel-bigdata/OAP/tree/master/oap-data-source/arrow#build-and-install-intel-optimized-arrow-with-datasets-java-api


Control Pie Chart & Legend Area separately

Hi,
I am trying to make my Pie chart mobile-friendly, but using the dimension properties for the  component seems to resize the whole canvas, making the Pie chart unnecessarily small with a lot of unused white space (see image). Is it possible to make just the Pie chart bigger?

@dbrodie122 Thanks so much for the help!!! I really appreciate it.

@jerairrest thank YOU for making this library.  It saved my job, no joke!

@jerairrest I am trying to do the same using react-chartjs-2. However, for plotting Pie chart, i am getting undefined at the center of Pie chart. Can anyone help me how can we remove that?

@narenvaishnavi please open a new issue with code examples and a sanbox reproducing the bug you are describing. Closing this issue for now.


Probabilisitic set

Added the abstract type necessary to add conformal classifiers for general use (see related issue). Nothing done yet with respect to downstream tasks. Decided to use only ProbabilisticSet for now, as I'm not sure the other case is necessary. Also added the trait definition.

Codecov Report

Merging #167 (0704945) into dev (100c477) will decrease coverage by 0.55%.
The diff coverage is 0.00%.

@@            Coverage Diff             @@
##              dev     #167      +/-   ##
==========================================
- Coverage   88.70%   88.15%   -0.56%
==========================================
  Files           8        8
  Lines         354      346       -8
==========================================
- Hits          314      305       -9
- Misses         40       41       +1

Impacted Files
Coverage Δ

src/MLJModelInterface.jl
100.00% <ø> (ø)

src/model_traits.jl
74.07% <0.00%> (-2.29%)
:arrow_down:

src/metadata_utils.jl
98.33% <0.00%> (-0.03%)
:arrow_down:

src/equality.jl
100.00% <0.00%> (ø)

src/model_def.jl
100.00% <0.00%> (ø)

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

@pat-alt Perhaps you missed my suggestion above?

@pat-alt Perhaps you missed my suggestion above?

Indeed, I completely missed that, sorry! And thanks for doing, great to see this has now been merged. Thanks!


Can't this model be trained using multi nodes and multiple cards ?

My machine has a single node 4-card 16G graphics memory, and running the 16B model with multiple nodes will result in OOM regardless of how the number of nodes is set. Can this model be trained with multiple machines and cards
Here are some logs of the training：
2023-05-04 16:21:02,954] [INFO] [multinode_runner.py:65:get_cmd] Running on the following workers: c05r2n11,c05r2n16,c05r2n19,c05r3n02,c06r4n06,c06r4n09,c06r4n12,c06r4n17
[2023-05-04 16:21:02,955] [INFO] [runner.py:453:main] cmd = pdsh -f 1024 -w c05r2n11,c05r2n16,c05r2n19,c05r3n02,c06r4n06,c06r4n09,c06r4n12,c06r4n17 export NCCL_SOCKET_IFNAME=ib0; export UCX_MAX_EAGER_LANES=4; export UCX_MAX_RNDV_LANES=4; export UCX_ZCOPY_THRESH=auto; export UCX_RNDV_THRESH=auto; export UCX_DC_MLX5_NUM_DCI=16; export NCCL_IB_HCA=mlx5_0; export NCCL_DEBUG=info; export PYTHONPATH=/work/home/actvg1ue59/CodeGen-main;  cd /work/home/actvg1ue59/CodeGen-main; /work/home/actvg1ue59/miniconda3/envs/torch/bin/python -u -m deepspeed.launcher.launch --world_info=eyJjMDVyMm4xMSI6IFswLCAxLCAyLCAzXSwgImMwNXIybjE2IjogWzAsIDEsIDIsIDNdLCAiYzA1cjJuMTkiOiBbMCwgMSwgMiwgM10sICJjMDVyM24wMiI6IFswLCAxLCAyLCAzXSwgImMwNnI0bjA2IjogWzAsIDEsIDIsIDNdLCAiYzA2cjRuMDkiOiBbMCwgMSwgMiwgM10sICJjMDZyNG4xMiI6IFswLCAxLCAyLCAzXSwgImMwNnI0bjE3IjogWzAsIDEsIDIsIDNdfQ== --node_rank=%n --master_addr=c05r2n11 --master_port=9901 train.py
c05r3n02: Warning: Permanently added 'c05r3n02,<IP_ADDRESS>' (ECDSA) to the list of known hosts.^M
c05r2n16: Warning: Permanently added 'c05r2n16,<IP_ADDRESS>' (ECDSA) to the list of known hosts.^M
c05r2n19: Warning: Permanently added 'c05r2n19,<IP_ADDRESS>' (ECDSA) to the list of known hosts.^M
c06r4n06: Warning: Permanently added 'c06r4n06,<IP_ADDRESS>' (ECDSA) to the list of known hosts.^M
c06r4n09: Warning: Permanently added 'c06r4n09,<IP_ADDRESS>' (ECDSA) to the list of known hosts.^M
c06r4n12: Warning: Permanently added 'c06r4n12,<IP_ADDRESS>' (ECDSA) to the list of known hosts.^M
c06r4n17: Warning: Permanently added 'c06r4n17,<IP_ADDRESS>' (ECDSA) to the list of known hosts.^M
c05r2n11: Currently Loaded Modulefiles:
c05r2n11:   1) compiler/devtoolset/7.3.1   3) compiler/dtk/22.10.1
c05r2n11:   2) mpi/hpcx/gcc-7.3.1
c05r2n11: [2023-05-04 16:21:07,883] [INFO] [launch.py:96:main] 0 NCCL_SOCKET_IFNAME=ib0
c05r2n11: [2023-05-04 16:21:07,883] [INFO] [launch.py:96:main] 0 NCCL_IB_HCA=mlx5_0
c05r2n11: [2023-05-04 16:21:07,883] [INFO] [launch.py:96:main] 0 NCCL_DEBUG=info
c05r2n11: [2023-05-04 16:21:07,883] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c05r2n11: [2023-05-04 16:21:07,883] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=0
c05r2n11: [2023-05-04 16:21:07,884] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c05r2n11: [2023-05-04 16:21:07,884] [INFO] [launch.py:123:main] dist_world_size=32
c05r2n11: [2023-05-04 16:21:07,884] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=1
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:96:main] 7 NCCL_IB_HCA=mlx5_0
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 2 NCCL_IB_HCA=mlx5_0
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:96:main] 6 NCCL_DEBUG=info
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:96:main] 7 NCCL_DEBUG=info
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 2 NCCL_DEBUG=info
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=4
c06r4n09: [2023-05-04 16:21:36,546] [INFO] [launch.py:96:main] 5 NCCL_IB_HCA=mlx5_0
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 3 NCCL_IB_HCA=mlx5_0
c06r4n09: [2023-05-04 16:21:36,546] [INFO] [launch.py:96:main] 5 NCCL_DEBUG=info
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=6
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:123:main] dist_world_size=32
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:123:main] dist_world_size=32
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:123:main] dist_world_size=32
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 3 NCCL_DEBUG=info
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=2
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:123:main] dist_world_size=32
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=3
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=7
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:123:main] dist_world_size=32
c05r3n02: [2023-05-04 16:21:36,532] [INFO] [launch.py:123:main] dist_world_size=32
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c05r3n02: [2023-05-04 16:21:36,532] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c06r4n09: [2023-05-04 16:21:36,555] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c06r4n09: [2023-05-04 16:21:36,555] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=5
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=1
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:96:main] 7 NCCL_IB_HCA=mlx5_0
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 2 NCCL_IB_HCA=mlx5_0
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:96:main] 6 NCCL_DEBUG=info
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:96:main] 7 NCCL_DEBUG=info
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 2 NCCL_DEBUG=info
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=4
c06r4n09: [2023-05-04 16:21:36,546] [INFO] [launch.py:96:main] 5 NCCL_IB_HCA=mlx5_0
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 3 NCCL_IB_HCA=mlx5_0
c06r4n09: [2023-05-04 16:21:36,546] [INFO] [launch.py:96:main] 5 NCCL_DEBUG=info
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=6
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:123:main] dist_world_size=32
c05r2n16: [2023-05-04 16:21:36,542] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:123:main] dist_world_size=32
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:123:main] dist_world_size=32
c06r4n06: [2023-05-04 16:21:36,543] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c06r4n12: [2023-05-04 16:21:36,538] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:96:main] 3 NCCL_DEBUG=info
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=2
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:123:main] dist_world_size=32
c05r2n19: [2023-05-04 16:21:36,531] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=3
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=7
c05r3n02: [2023-05-04 16:21:36,531] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:122:main] global_rank_mapping=defaultdict(<class 'list'>, {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [4, 5, 6, 7], 'c05r2n19': [8, 9, 10, 11], 'c05r3n02': [12, 13, 14, 15], 'c06r4n06': [16, 17, 18, 19], 'c06r4n09': [20, 21, 22, 23], 'c06r4n12': [24, 25, 26, 27], 'c06r4n17': [28, 29, 30, 31]})
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:123:main] dist_world_size=32
c05r3n02: [2023-05-04 16:21:36,532] [INFO] [launch.py:123:main] dist_world_size=32
c06r4n17: [2023-05-04 16:21:36,534] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c05r3n02: [2023-05-04 16:21:36,532] [INFO] [launch.py:125:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
c06r4n09: [2023-05-04 16:21:36,555] [INFO] [launch.py:103:main] WORLD INFO DICT: {'c05r2n11': [0, 1, 2, 3], 'c05r2n16': [0, 1, 2, 3], 'c05r2n19': [0, 1, 2, 3], 'c05r3n02': [0, 1, 2, 3], 'c06r4n06': [0, 1, 2, 3], 'c06r4n09': [0, 1, 2, 3], 'c06r4n12': [0, 1, 2, 3], 'c06r4n17': [0, 1, 2, 3]}
c06r4n09: [2023-05-04 16:21:36,555] [INFO] [launch.py:109:main] nnodes=8, num_local_procs=4, node_rank=5
c05r2n11:   File "/work/home/actvg1ue59/miniconda3/envs/torch/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 946, in load_checkpoint_in_model
c05r2n11:     load_checkpoint_in_model(
c05r2n11:   File "/work/home/actvg1ue59/miniconda3/envs/torch/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 946, in load_checkpoint_in_model
c05r2n11:     load_checkpoint_in_model(
c05r2n11:   File "/work/home/actvg1ue59/miniconda3/envs/torch/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 946, in load_checkpoint_in_model
c05r2n11:     set_module_tensor_to_device(model, param_name, param_device, value=param, dtype=dtype)
c05r2n11:   File "/work/home/actvg1ue59/miniconda3/envs/torch/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 149, in set_module_tensor_to_device
c05r2n11:     set_module_tensor_to_device(model, param_name, param_device, value=param, dtype=dtype)
c05r2n11:   File "/work/home/actvg1ue59/miniconda3/envs/torch/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 149, in set_module_tensor_to_device
c05r2n11:         set_module_tensor_to_device(model, param_name, param_device, value=param, dtype=dtype)set_module_tensor_to_device(model, param_name, param_device, value=param, dtype=dtype)
c05r2n11:
c05r2n11:   File "/work/home/actvg1ue59/miniconda3/envs/torch/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 149, in set_module_tensor_to_device
c05r2n11:   File "/work/home/actvg1ue59/miniconda3/envs/torch/lib/python3.9/site-packages/accelerate/utils/modeling.py", line 149, in set_module_tensor_to_device
c05r2n11:         new_value = value.to(device)new_value = value.to(device)
c05r2n11:
c05r2n11:         new_value = value.to(device)
c05r2n11: new_value = value.to(device)
c05r2n11: RuntimeErrorRuntimeError: : HIP out of memory. Tried to allocate 576.00 MiB (GPU 3; 15.98 GiB total capacity; 15.83 GiB already allocated; 0 bytes free; 15.84 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_HIP_ALLOC_CONFHIP out of memory. Tried to allocate 576.00 MiB (GPU 1; 15.98 GiB total capacity; 15.83 GiB already allocated; 0 bytes free; 15.84 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_HIP_ALLOC_CONF
c05r2n11: RuntimeError
c05r2n11: RuntimeError: : HIP out of memory. Tried to allocate 576.00 MiB (GPU 2; 15.98 GiB total capacity; 15.83 GiB already allocated; 0 bytes free; 15.84 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_HIP_ALLOC_CONFHIP out of memory. Tried to allocate 576.00 MiB (GPU 0; 15.98 GiB total capacity; 15.83 GiB already allocated; 0 bytes free; 15.84 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_HIP_ALLOC_CONF

16GB graphics card may be somewhat limiting. Perhaps you could try CPU-offloading or offloading to NVMe to save more memory, as well as multi-node training.

What I mean is that I have used multiple nodes on my end, but still have OOM. Is it because the code itself does not support it? My multi card running mode is：
deepspeed  --num_nodes 16  --hostfile=./hostfiles/hostfile-dl-$SLURM_JOB_ID --num_gpus 4  --master_addr $dist_url  --master_port=9901  train.py


Remove time argument from all public APIs.

It's only used in the context of VirtualTimeScheduler, which only applies when running tests.
In that case, we instead use the process dictionary.

Coverage decreased (-0.06%) to 89.316% when pulling a059ae8336c54e20034891fe10e778d7817e7089 on hide-time into e6ebfe4b7fe77faef7e7af5f3011a25581b34d3e on master.

Coverage decreased (-0.06%) to 89.316% when pulling b8129d9fd9fe0ba634f1737c796f9bdc08afa5a0 on hide-time into e6ebfe4b7fe77faef7e7af5f3011a25581b34d3e on master.

Coverage decreased (-0.06%) to 89.316% when pulling d4291485f80377621cbb15cded34203aa6a90495 on hide-time into e6ebfe4b7fe77faef7e7af5f3011a25581b34d3e on master.

Coverage decreased (-0.06%) to 89.316% when pulling a8bb9f02839fa0a4b5c4fdc6fdaf001ddbabcd0b on hide-time into e6ebfe4b7fe77faef7e7af5f3011a25581b34d3e on master.


[e2e]: nodejs release main default slsa3

Repo: https://github.com/slsa-framework/example-package/tree/main
Run: https://github.com/slsa-framework/example-package/actions/runs/5769167172
Workflow file: https://github.com/slsa-framework/example-package/tree/main/.github/workflows/e2e.nodejs.release.main.default.slsa3.yml
Workflow runs: https://github.com/slsa-framework/example-package/actions/workflows/e2e.nodejs.release.main.default.slsa3.yml
Trigger: schedule
Branch: main
Date: Sat Aug  5 06:08:33 UTC 2023

Repo: https://github.com/slsa-framework/example-package/tree/v40.0.57
Run: https://github.com/slsa-framework/example-package/actions/runs/5775003151
Workflow file: https://github.com/slsa-framework/example-package/tree/main/.github/workflows/e2e.nodejs.release.main.default.slsa3.yml
Workflow runs: https://github.com/slsa-framework/example-package/actions/workflows/e2e.nodejs.release.main.default.slsa3.yml
Trigger: release
Branch: v40.0.57
Date: Sun Aug  6 06:16:27 UTC 2023
Tests are passing now. Closing this issue.


5451087D - UFC Undisputed 3

Validation

[X] I've read the FAQ.
[X] The Xenia build used is from a canary branch. (not MLBS/AlexVS/master/pull requests, etc)
[X] This issue isn't for help (tech support) with Xenia, or a game.
[X] I've checked if this game doesn't already have a compatibility report.

Xbox 360 Marketplace link
https://marketplace.xbox.com/en-US/Product/UFC-Undisputed-3/66acd000-77fe-1000-9115-d8025451087d#
Xenia version
https://github.com/xenia-canary/xenia-canary/commit/e90156719326935033e6578b94a363b59f576ddb/
Issues
The game runs perfectly without crashing (when running with invalid fetch constants set to true), however the tutorial (including the career tutorial) has blacked out, invisible, or corrupted trainers (80% of the time they corrupt to the point their models cover the entire screen, if you're lucky your tutorial red and blue corner fighters are just black, one is black and one is invisible, vice versa, etcetera); Create-a-fighters (CAF) are black, corrupted, or a mixture of both (doesn't seem that their model corrupts like the training characters though); the CAF editor itself runs fine with some minor graphical glitches on the character, as well as custom logos and banners not working; high-powered computer specs with V-sync disabled cause the game to run at 2-3x speed despite time scalar being set to default time scale due to extremely high frame rates (but that's not a game issue, just something to report).
Log
xenia.log.zip
Screenshots
CAF Editor Screenshots

Career Screenshots
(Tutorial Menu when first beginning career)

In-game Tutorial Screenshots

(What happens if you don't set invalid fetch constants to true)

In-game Practice Mode Screenshots
(This one is with roster fighters)

(This one is with my CAF)

Labels
state-gameplay, gpu-drawing-corrupt

Got lucky and had the training characters not completely corrupt just now.

I also had forgotten to add that the referee is invisible.
Other than that, crashing is rare and the only real issues are graphical.
(You can see the ref's shadow to the right of the fighters)

Appears that playing career mode with a roster fighter is causing crashing after the first fight won. Looking to find any kind of solution through in-game settings. Otherwise, the only way to progress in career mode is with a CAF. Attaching crash log if anybody wants to take a look.
xenia.zip

1cf9b16 broke the game, use earlier builds.
xenia.log

1cf9b16 broke the game, use earlier builds. xenia.log Update: needs protect_zero = false to launch.

is the career mode playable now?

Always have been. It just doesn't render CAFs properly.
One of the builds fixed DX12 rendering both CAFs, but I don't remember which one. Just get latest, and also apply d3d12_clear_memory_page_state = true to get rid of vertex explosions and missing referees. (and don't forget protect_zero = false if you do use recent builds)
If DX12 doesn't work for you, try Vulkan with FSI render target path. VK used to be better for a long while, but now DX12 is pretty much on par, so try DX12 first.
But no matter what you do, CAFs are full black/rainbowy - only silhouette is visible. You can make them yellow with scribble_heap = true. Works on both DX12 and VK, it's up to preference.
You can make CAFs mostly working only outside of career. You have to create or edit CAF with readback_resolve enabled, then you can disable it. Now your CAF will be ok-ish in exhibition, tournaments, title defense mode, etc. Everywhere but not in career for some reason.


Persistent menu problem

Hi there! I wrote a comment on the issue #24 thinking that my problem had some sort of connection with that bug but I was wrong.
Randomly whenever it wants, my app won't respond to any persistent menu button even for hours sometime, but will work on bot.on('message') callback.
My app is hosted on heroku and it seems that the problem doesn't respond to any logic, it happens really in a random way, there are no constants to consider (like the problem may appear at the resume of the heroku dyno from sleeping) and the bad news is that when this happen, my bot doesn't log anything, it's like Facebook is not calling the webhook in that situation.
So I don't even know how to debug this problem (probably it is caused by the library because other bots don't act like this).
I'll post the code ASAP and I must try to run it locally and tunnel it in order to consider or not heroku as a problem. Currently I'm running it with nodemon.
Sometimes refreshing the Facebook page on the browser and deleting the conversation on the android app solve the problems, sometimes not!
Ps: is it normal that the bot logs four or five POST requests for only one message sent?

Hello, I've been having the same issue for some days now and been struggling to find a legit reason. The only way to correct it is to delete the conversation on Messenger and start all over. It's okay for me, but I can't have my users do it every time. Maybe it's a Facebook problem?

Yeah I thought it too, but as I said other bots don't have this problem so I don't know...
You have the app deployed on heroku too?

@giacomocerquone, sorry for the late response. Yes, it's on Heroku.

I also had a strange issue where my persistent menu postbacks weren't being captured. I was running an ngrok local tunnel.
I couldn't even catch them on bot.on("postback", (payload, chat) => {
I finally fixed it by copy pasting a @Charca's sample menu code. This means that either:

I had a typo (but there were three keywords that could have been misspelled, all short and I looked at them 20 times)
There is a problem in the bootbot persistent menu creation code (very, very unlikely)
There is a random bug in the way incoming postbacks are handled (even less likely)
FB has a bug in the way they handle the creation of persistent menus and there was some kind of latency bug, who knows...

I am leaning on the last one because I have been using the platform for a bit and have had strange random bugs with things like Get Started which uses a similar API.
I know this is speculation and may not add to the discussion, and unfortunately I was participating in a bot hackathon and didn't have time to really get to the bottom of the issue.

In response to your ps., you may just be subscribed to a bunch of different callbacks.
The two most interesting ones for most bots would be message and message_postbacks. You set them up in your app settings under Messenger / Settings / Webhooks / Edit events.
https://developers.facebook.com/docs/messenger-platform/webhook-reference#setup

Correct for the subscription to different callbacks set up on developers.facebook.com.
Regarding the main issue here, thanks for your contribution! I actually forgot to post the code but I did subscribed to bot.on("postback", (payload, chat) => { too (and used a regex inside) but our problems are not actually the same.
If I understood correctly, in your situation no callback was being called while to me and @sotirelisc randomly sometimes it doesn't work, sometimes it works only on pc and not in messenger and sometimes it just works.
I'd say it's a facebook problem, but I don't get why other bots don't have it (verified on this one) or maybe it's just facebook that when the bot is not published insert it in some sort of a low priority queue (just saying random sh*t 😆).
The way I will test this situation is:

Tunneling the app and see what happens
If above doesn't work too, I'll roll out, through express, the shortest script possible that plays directly with the facebook api and set up a menu

@giacomocerquone, I will try to run some tests, too. I'm afraid Heroku has something to do with this. Free sleeping dyno, right?

@sotirelisc Right

@giacomocerquone, do you have nested persistent menu items? If so, can you verify that the issue exists only at the non-nested items?

My persistent menu is not nested!
Ps. sorry If I still haven't tested anything but I'm very very busy lately

@giacomocerquone, when you find time, try adding a nested item. Check if the problem doesn't exist there.

Are you folks sure you're following this?

Yes, I read it and I've always used the button like this:
bot.setGetStartedButton((payload, chat) => {   chat.say('Welcome to BootBot. What are you looking for?'); });

I am using a persistent menu in a different application in a different language and I rolled the response handlers without a framework and I am not receiving postbacks from the persistent menu. Not even to my local tunnel.
I don't know if your issue is a facebook bug, but my previous issue and current one definitely are facebook bugs. :/

Try to delete the conversation in your messenger client.
My persistent menu postbacks started working after I did that. Very unsatisfying fix.

@pferdefleisch, so you're saying this is more likely a Facebook issue?

Yes. I think it is most likely a Facebook issue. With the fixes being:

Make sure you add a get started button before your menu
Delete the conversation with the bot and reconnect with it
Do this from the page, not https://m.me or your app
Try to delete the menu with deletePersistentMenu and add it back again. Probably do this with get started too.

I don't remember if you can see what the responses are for these functions, but facebook will send you a 400 if your request is malformed. Make sure you're getting 200's back.
If all of this happens and you still have issues, report back here!

@pferdefleisch, I know that deleting the conversation fixes it, but in case of real users using the bot, you understand that this is not a solution.

I haven't run into this on something in the wild so, if it is a Facebook bug, I don't know how making it work on one conversation would affect other conversation with other users. This is all speculation. I have only run into this in a staging/dev environment luckily so that's as far as my experience goes.
Maybe I was overly optimistic when I wrote "fixes". What I meant was "This is what I have done in staging/dev to fix this problem when I was the only user interacting with the bot"

I don't know for sure, but it seems to me that this problem could be related to Convo. On some bots I created previously without any conversation (by using Convo I mean), the menu buttons are working perfectly fine. In the bot I'm working on atm, I used the conversations and I have the same problem that @giacomocerquone described. Are you in the same case or am I misled ?
Besides, I observed some wierd pattern to "workaround" this bug. As soon as I am changing anything in the code and updating the bot - I mean, a CTRL+S with Nodemon - the buttons will immediately stop working. If I refresh the facebook page or messenger (just refreshing it, not deleting the conversation) then the buttons are working again.
Feel free to tell me if I'm wrong somewhere but these are my observations until now. I'm working with ngrok.

@Jimlnth I've had issues, even without using conversations and even in another framework I'm working on in another language.
Maybe what you're referring to is related to https://github.com/Charca/bootbot/issues/24
I would much rather it be a bootbot bug though, because then we could fix it, so any more info you have, steps, etc, would be greatly appreciated.

Well, could be. I doubt it because in my case the buttons are not working even before I start a conversation (given that I changed the code without refreshing the page) and because I am not even trying to use the menu buttons during a conversation but rather before or after.
As I said, besides my feedback I don't have a lot more to say. My previous bots were built on the same environment and are still working cool. The only thing different here for me is the addition of Convo (that's why I am looking at him with suspicious eyes, but of course I could be wrong). I still doubt it's really a facebook bug because I tried some bots on messenger and the menu buttons are working, even with Convo-like features.

@Charca, have you not been around lately?

Sorry folks, I'm in the middle of moving to another country and I couldn't really follow up with the issues. I've been following this convo and it was my understanding that this was a problem with Facebook's Webhook API, right? It doesn't seem to be the same thing as #24 (which is an actual Bootbot bug).

@Charca, it's still not certain that it is 100% a Facebook issue. Personally, I haven't come across this problem in other bots. As I stated before, in my case, it doesn't happen on nested persistent menu items, only on the standalones.

An easy workaround I found: Make every item on your persistent menu nested. Tested and works!

@sotirelisc if this is true then it's likely a bootbot problem (and I hope so honestly, I don't know how much time it would take for facebook developers to fix it)

@giacomocerquone that's quite a jump in logic.
I'll be working on bots quite a bit more in the near future and I'll try to add something to at least make this easier to debug. I am still really leaning on this being a Facebook issue. Was just talking to a friend who built a bot for Pioneers fest that is having a ton of persistent menu problems. He was making the same arguments as @sotirelisc here.
Adding some kind of logging of request body here would be my first step.
I hope I get time and we can close this soon. I also hope @giacomocerquone is right and it's a bootbot bug because the alternative is pretty crap.

@pferdefleisch
It is a facebook issue, lots of bots affected.

https://developers.facebook.com/bugs/643632455833864
Hi all,
The postback issue should only happen right after updating your persistent menus.
After calling POST /messenger_profile, the menu is updated on our backend. However, it takes time for the changes to propagate to our client side (unless it you delete and enter the thread again). Therefore, when users tap on it, they are triggering mutation calls on stale data, which we throw exceptions.
This is the major reason for postback failures according to our logs. Please let me know if you're experiencing it in other cases.
Abby

Closing this issue and linking to it in the new Troubleshooting section (coming soon).


Feature: On favorite click

On click of an item under "Favorites" the page goes to the link to purchase tickets

If we store data in local storage as an object then we should be able to set and get out more than one piece of information per item.
something like this:
`var savedItem = {
band: "band name",
ticket: "websitetotickets.com"
}
localStorage.getItem(i.band) // to grab the band
localStorage.getItem(i.ticket) // to grab the ticket website
`


Let value_of and value_of_rec return expressions

Summary
value_of and value_of_rec now can now return Eigen expressions. So if their result is only used in a single expression it needs not be saved in memory. We can do that even before all functions are generalized as these two functions are not exposed to stan lang. value_of_rec now also uses perfect forwarding.
All functions using eigher of these two functions and acceptiong the result into a variable of type auto can only use such a variable once. Otherwise they would evaluate the expression multiple times. If they need to use it multiple times they can accept the result into Eigen::Ref. A helper fucntion to_ref has been added to simplify that. All existing cases that would evaluate result of one of these two functions have been fixed using to_ref.
Tests
Added tests for value_of and value_of_rec used on expressions.
Side Effects
See second paragraph of the Summary.
Release notes
value_of and value_of_rec now can now return Eigen expressions.
Checklist

[x] Math issue #1470

[x] Copyright holder: Tadej Ciglarič
The copyright holder is typically you or your assignee, such as a university or company. By submitting this pull request, the copyright holder is agreeing to the license the submitted work under the following licenses:
- Code: BSD 3-clause (https://opensource.org/licenses/BSD-3-Clause)
- Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

[x] the basic tests are passing

unit tests pass (to run, use: ./runTests.py test/unit)
header checks pass, (make test-headers)
dependencies checks pass, (make test-math-dependencies)
docs build, (make doxygen)
code passes the built in C++ standards checks (make cpplint)

[x] the code is written in idiomatic C++ and changes are documented in the doxygen

[x] the new changes are tested

Oh, it seems this need to wait on #1848 to be merged. Possibly also on #1847 and #1844.

@t4c1 Is there a design doc or anything on these expressions? Or if there isn't one, do you mind starting one?
I don't mind using these things, and I don't doubt that they can be useful, but I had a run-in with this pull request where I was banking on value_of giving me a regular Eigen type, and it gave me something else. This turned into a segfault in a weird place in the code so I had to revert the change in my branch (here).
The line in question is here. In that case I'm trying to save a copy of all the arguments in a tuple for the reverse pass. An expression won't work, cause the original memory will go out of scope.
I guess vaguely questions I have for this are:

If my function takes in something as input, when should I evaluate it? If I use the values more than once? More than twice?

If I get an input matrix, how much does an operator()(int i, int j) access cost?

When should I return an expression and when should I eval to return a non-expression?

If I have a variable a which is an expression, if f(a) evaluates a, would f(b) be able to take advantage of that evaluation or does it do its own?

Is there a way I can still get copies of variables when I need them? (if not I think I need a way -- see above).

I'll leave the rest of the question to tadej, but this should fix your code:
std::tuple<decltype(value_of(plain_type_t<T_Args>()))...> value_of_args_tuple_;

Addingplain_type_t will give the type of the final Eigen object that the expression would evaluate to

Oooo, right, and then the expression would copy. That makes sense thanks.

There is no design doc. Since Eigen was used by Stan Math for a long time I assumed people were somewhat familiar with it. I guess I was wrong and design doc really would be helpful. Anyway I will try to answer your questions here:

General rule of tumb is that you don't know how complex input expressions are so you should never evaluate them more than once.

This one is a bit more tricky. In general you are not losing much if you always evaluate before using elementwise access (also you should prefere .coeff and .coeffRef functions to [] and (), as they do not do bonds checks).

For now only the functions not exposed to stan language are allowed to return expressions. Exposed functions will be allowed to return expressions once all of them can take expressions as inputs.

You better evaluate an expression before calling two functions with it.

Assigning to plain_type_t<T> is a sure way to make a copy. In most cases Eigen::Ref is preferable. Or wait for #1899 to be merged - it introduces ref_type_t<T>, which avoids some weird edge cases of Eigen::Ref (its copy constructor does not copy anything).

I assumed people were somewhat familiar with it

Ha! Tricked you.

also you should prefere .coeff() and .coeffRef()

Yuck. Thanks for the explanation though. I saw some function calls like this the other day and wanted to change them to ().

For now only the functions, not exposed to stan language are allowed to return expressions.

Oh I see. Yes this would be good thing to talk about in a design doc, especially the rollout, since this leads to accidental breaks.

You better evaluate an expression before calling two functions with it.

Oooh, so I suppose part of this will include some stanc3 analysis to make sure this doesn't happen?

Oooh, so I suppose part of this will include some stanc3 analysis to make sure this doesn't happen?

I intended this to be transparent to Stan language and compiler - it all stays within Math. But if compiler can be somehow used to check that, I am alll for it.

Well since this could cause slowdowns if we accidentally use expressions more than once, we kinda have to push this up to the language level too to avoid getting silently bit by it.

I really do not understand how do you propose to do it. AFAIK stanc parses stan lang not C++. And this changes happen in C++.

Just to clarifiy:
matrix A[5,5] = D*F + 2*C*(D+4);
matrix B[5,5] = A*A + A*A;

This isn't problematic as a`` will be evaluated once assigned to a matrix. This is basically to reduce copies inside db + 2c*(d+4)`. Right?

Right. Doc PR is up here: https://github.com/stan-dev/design-docs/pull/23

Actually not to reduce copies. To reduce number of times matrices are loaded from RAM to CPU.

Yeah I meant the "copy" from RAM to the CPU. I didn't realize there was a doc already. Thanks.

This isn't problematic as A will be evaluated once assigned in the first line.

Ooooh, okay, I see. Yeah that was what I was worried about.
As long as matrix A[5,5] compiles to a non-expression type then that'll cause the copy to happen.

AFAIK stanc parses stan lang not C++

Well it uses the C++ and makes assumptions about the C++.
I vaguely remember at one point discussions about:
real a = 1.0;

Like should that be a var or should that be a double, and I thought there was discussion of leaning on auto to make that happen.
This would mean leaning on C++ auto for types is a no-go, but not sure if that problem still exists or whatever, but they are connected.


Issues with backup preparation times

Okay, so here's the deal :

In order to calculate which files to backup in an incremental / differential partial, we need to make a hash of each file.

This is... slow, because it effectively means reading the entire world from disk as if we were making a full backup, before we even start making one. We then have to re-read the files we want to backup from disk when the backup actually starts.

On larger worlds, this can lead to a long time sat on the Backup Starting message before any progress updates are sent, which can make a backup look stalled even when it wasn't.

It also extends the backup time pretty significantly, of course, because you're reading the entire world just to figure out what to backup.

Problem is... how can one solve this?

We kinda have to use hashes, because minecraft does not properly update file modification dates. We cannot use dates to tell if a file has changed. See #33 for more info.
We could remove the apparent stall by backing up a file as soon as we know how to back it up or not. However, this introduces another issue - the "smart chain reset" feature wouldn't work here - because we'd have finished a backup before we'd know if the chain should be reset or not.

This wouldn't actually speed up backups at all, but it'd remove the apparent stall.

We could, in theory, only hash parts of the file. This however has problems because we risk just skipping over the only part of a file that has changed.. thus not backing up a file that we should backup.

This wouldn't outright remove the apparent stall. However, it would significantly speed this stage up, and thus speed up backups as a whole.

Hide the stall.
Keep track of the time to hash the last few times take an average, and use that to display a progress while hashing. 😎

We kinda have to use hashes, because minecraft does not properly update file modification dates.
How about, we combine the two methods! If file modification date didn't change, check the file hash, if it did change, it means the file was for sure modified, so we can skip calculating hash and just back it up 😄

We kinda have to use hashes, because minecraft does not properly update file modification dates.

For MCA files, which I'd assume are the bulk of the processing load, we could probably use the chunk update timestamps in the header.
If that for some reason doesn't work we might be able to use the LastUpdate values of individual chunks. NBT can only be parsed linearly, but as soon as one LastUpdate has changed we can skip the rest of the file, and thanks to the MCA header each chunk could be parsed in parallel.
However, I'd strongly recommend adding a config option to disable 'smartness' like this, just in case a server admin is aware of anything that might screw with these indicators...

Keep track of the time to hash the last few times take an average, and use that to display a progress while hashing. 😎

As far as I can tell we wouldn't even need to estimate, since we know (1) how many files need to be hashed, (2) how big they are and (3) how many bytes of the current file have been processed so far.


Ver 1.0.13

Coverage decreased (-2.02%) to 97.505% when pulling 6374931e63fa84cdeece824a231f7bd5e284937c on dev into 59dd6577fd8b7d87448c1002fe131cf4bfd1386f on master.


Log the exception before swallowing it

Here the exception is swallowed without any logs. That makes debugging confusing. In my case, the test failed with the error "Docker must be present blah blah", but the actual issue was disk space problem.
Another solution could be to change the level of log in DockerClientFactory#checkDiskSpace to warning.
Let me know if you need the PR.

Thanks for raising the issue @akefirad.
Can you please share the whole of your logs? We have an actual check for sufficient disk space, that should report such unless you disabled pre-flight checks (which is fine to do for experienced users). The check should log Docker environment should have more than 2GB free disk space.


flatpak-builder: 1.2.0 -> 1.2.1

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://github.com/flatpak/flatpak-builder/releases.
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1 of 1 passed binary check by having a zero exit code.
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Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/hvxsxvgaxcsgq9k3xz6v0pzjpi4xs8bc-flatpak-builder-1.2.1 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A flatpak-builder https://github.com/r-ryantm/nixpkgs/archive/5446177e694ad38c622f8a423ea7a3ea1d1615b9.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/hvxsxvgaxcsgq9k3xz6v0pzjpi4xs8bc-flatpak-builder-1.2.1
ls -la /nix/store/hvxsxvgaxcsgq9k3xz6v0pzjpi4xs8bc-flatpak-builder-1.2.1/bin

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flatpak-builder

Maintainer pings
cc @jtojnar for testing.

Superseded by #157045

Not sure if the zstd support needs tar compiled with support for it or something.

Looks like it is built in GNU tar.


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Instructions for interacting with me using PR comments are available here.  If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository. I understand the commands that are listed here.


Status detection is only implemented for Custom Resources leading to improper RBACs for built-in resources

See https://github.com/operator-framework/java-operator-sdk/issues/1996

@metacosm Thanks for the quick fix. As soon as it is merged, I'll try again ;-)


[Potential bug] blockIdx.x mistakenly bound to irrelevant axis

While working on the recurrent nets, I ran into an issue that blockIdx.x is mistakenly bound to some irrelevant axis.
For example, when I bind ax1.outer in Line 81 in this gist to blockIdx.x, it affected the beginning of the lowered code with apparently wrong boundary checking, Line 27 in this gist, but what I did is only to bind blockIdx.x to Line 81...My code is provided here, in which Line 115 does the binding.
I suspect that rebinding blockIdx.x may cause some weird issues.
@wetliu @tqchen @ZihengJiang Thanks in advance!

@junrushao1994 can you take some time to look into this?


aws-sdk clients missing from dependencies

What happened?
I'm getting errors when trying to build a package using this module.
Error: node_modules/lambda-params-secrets/lib/responses.d.ts(1,32): error TS2307: Cannot find module '@aws-sdk/client-ssm' or its corresponding type declarations.
Error: node_modules/lambda-params-secrets/lib/responses.d.ts(2,50): error TS2307: Cannot find module '@aws-sdk/client-secrets-manager' or its corresponding type declarations.
Looking at the code I'm not entirely clear if these are required during runtime, or just in the typedefs. But building with tsc breaks because it's missing those modules.
Possible Solution
Add @aws-sdk/client-ssm and @aws-sdk/client-secrets-manager to dependencies in package.json
Additional Information
No response
Package Version
0.1.0
Lambda Node.js Version
16

I think those are declared in dev deps already, but should probably be either peerDependencies or dependencies?

Thanks for reporting the problem.  Let me take a look.

@fedonev I think this needs to be a peer dependency. Adding them direct to dependencies seems to have drastically increased the bundle size of this package.


Logo Proposal

hello...
Your project is amazing... I came across it and as a logo designer who also loves contributing to open source project, I decided to help create a LOGO for your project...
Currently working on some idea concepts that I feel you may like... I will update it here once I am done with the concepts so you can give me your feedback on any adjustment you may want...
I will appreciate your response...
Regards...

Hello,
I'm so sorry for answering late. Actually I never thought of a logo for this project but of course you can create a logo. I would be happy to use.
Best Regards.

Awesome...
I am working on it, and would update it here for your feedback

Hello @AnilFurkanOkcun
Here is my logo idea concept... you can share your feedback and the logo color of your choice.

@chimzycash Oh it looks unique and very cool. I really liked it. I am grateful for your professional work. Thank you.
About color, as you can see I use white(#FFF), black(#000 and #272727) and blue(#0192D2) for this project but I was thinking about changing blue one as #3981F9. I like blue :).
For logo, you can use white and blue like #3981F9(you don't have to use this one specifically) with whatever you want (black, gray etc.)
Thank you again for awesome work.

Ok... Thanks for your feedback @AnilFurkanOkcun ... I will make the color adjustments

Hello @AnilFurkanOkcun
Here is the updated design

Hi @chimzycash these are very beatiful works. Thank you very much, i think I like the second row first column rounded square one. If you want you can PR. I really like these, thank you again.

Ok.. Thanks for your response... I will prepare all the files and get them ready for your use.

@chimzycash Can you provide svg files too? By the way, I gave you credit in the readme file.

Thanks... Only Chimzy would be OK...
Would get all the vector files including SVG ready and send

Done. If you want you can send vector files to<EMAIL_ADDRESS>

Hi... Here is the Google Drive Link Containing the files including the vector files
https://drive.google.com/drive/folders/1sWVUtoxIhqqqd9IvTgIEtztH-PmXaTIn?usp=sharing

Hi @AnilFurkanOkcun ... Here is the Google Drive Link for the Files including the Vector and SVG Files

Thank you for everything @chimzycash


Implement resample

Have a lot of workflows that depends on resampling time series data
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html

Same here.

Close this due to the duplication with #1562.


i want to use this SDK for watch App is it anyway i can use it for apple watch

I am getting this error (watchOS 9.0) is not compatible with OpenAIKit (1.9.3), which does not support watchOS.Please help me for this.do not close it without any expalination

Hello there. We've tested out and all works fine. Our SDK supports minimum version WatchOS 6.0.
We've tested out in minimum availeble WatchOS version on Xcode 15.3 - WatchOS 7.0 and all works fine.
Try to install library with SPM, check you are imported our library and check your code is correct. Sometimes Xcode highlight wrong errors not related to code in error.
Also, if you creating not standalone app for WatchOS try out Watch Connectivity

i am getting this error from pods side [!] The platform of the target Thready AI Watch Watch App (watchOS 7.0) is not compatible with OpenAIKit (1.9.3), which does not support watchOS.

Try to install library with SPM.


[HACKTOBERFEST 2024] - Asynchronous JavaScript Quiz

[!IMPORTANT]
This issue is part of our 2024 Hacktoberfest initiative. If you are interested in working on this issue, refer to our top-level tasks to see if this issue is claimed, or to claim it if it is not.

This issue is to create the Asynchronous JavaScript quiz.
The file you should be working on is found in /curriculum/challenges/english/25-front-end-development/quiz-asynchronous-javascript.
All quiz questions need to relate to one of these topics:

What is asynchronous programming?
async attribute for script elements
fetch API
HTTP methods
res.json()
promises and promise chaining
.then(), .catch(), .finally()
async/await
Geolocation API and getCurrentPosition()

[!CAUTION]
Questions which do not fall under these topics will be rejected.

You are welcome to include code blocks in your questions, but please keep them short (four lines or less).
Please do NOT open a pull request until you have completed all questions. Your quiz should have exactly 20 questions.

Thank you for your interest in contributing. This issue will be opened for contribution when Hacktoberfest starts on October 1st.

[!CAUTION]
Pull requests which are created before October 1st will not be eligible for Hacktoberfest. We appreciate enthusiasm, but encourage you to hold off on contributing to this issue until the event has begun.

If you wish to be notified when this issue is opened for contribution, subscribe to notifications:

I would like to work on this

Hi. I would like to work on this. If possible do assign it to me. Thanks.

This issue has been assigned a contributor. To prevent extraneous notifications, the issue is now locked. If you have any questions, please reach out to me in our Discord community.

It has been seven days without activity. This issue is now open for reassignment.

I can take this up!

@naomi-lgbt , can you assign this issue to me

@khatri7 you are already assigned to #56204
@Abhi0049k this one's all yours!


feat(slides): add scroll attribute

Short description of what this resolves:
This adds a scroll attribute that can be applied to ion-slide when you want to be able to scroll the content inside a slide
Changes proposed in this pull request:

add css for scroll attribute on ion-slide

Ionic Version: 2.x
Fixes: #9839

Want to add some docs for this PR before getting it merged. Looking into best way to do that now.

Closing this for now in favor of this solution https://github.com/driftyco/ionic/issues/9839#issuecomment-272506225


Add CI tests and CompatHelper

In addition to this PR, one has to add a SSH deploy key (e.g., by following the instructions here: https://github.com/JuliaRegistries/CompatHelper.jl#122-instructions-for-setting-up-the-ssh-deploy-key) to allow CompatHelper to trigger the CI tests.

LGTM as far as I can judge, but I'm not really familiar with GitHub workflows.  I have added the deploy key, can you check if it works correctly?

Unfortunately, I don't think it's possible to check it - we have to wait until CompatHelper runs or trigger it manually once the action is available in the default branch.


Stack, stepper, and accordion - visual design

These 3 components need a visual design review, to understand their alignment.
Goals are:
Ensure consistency of global conventions re: color, sizes, spacing etc.
Explore compact sized versions
Explore indicative variations for differentiation once alignment is achieved

Some additional notes/context on the three components:
The stepper component is the accordion component but with the linear progression and forms requirement. The code/CSS for the accordion and stepper are the same components so visually they have the same spacing, padding, etc.
The stack view is sort of an accordion but is very limited to only key-value pairs of data whereas the accordion can support any generic content. The stackview is one of the oldest components in Clarity and is missing some features the new accordion/stepper component provides.
There was some discussion at one point to deprecate the stack view and only have the accordion since the accordion is much more flexible and supports more use cases. We can also port the paired data layout of the stackview to the accordion to make migrations easier and only have the two components, accordion/stepper in Clarity.

Design for visual alignment here:
https://www.figma.com/file/DSg29G9xgvIgZStrM950ltDr/stepper-stack-accordion?node-id=0%3A1
We need to validate the design and development implications of merging the accordion and stack view.


[issue-5946] upgrade to xstream 1.4.19

Description
This PR...

Fixes: #5946

Types of changes

[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] New feature (non-breaking change which adds functionality)
[X] Bug fix (non-breaking change which fixes an issue)
[ ] Enhancement (improves an existing feature and functionality)
[ ] Cleanup (Code refactoring and cleanup, that may add test cases)

Feature/Enhancement Scale or Bug Severity
Feature/Enhancement Scale

[ ] Major
[X] Minor

Bug Severity

[ ] BLOCKER
[X] Critical
[ ] Major
[ ] Minor
[ ] Trivial

Screenshots (if appropriate):
How Has This Been Tested?

@blueorangutan package

@rohityadavcloud a Jenkins job has been kicked to build packages. I'll keep you posted as I make progress.

Packaging result: :heavy_check_mark: el7 :heavy_check_mark: el8 :heavy_check_mark: debian :heavy_check_mark: suse15. SL-JID 2475

@blueorangutan test

@rohityadavcloud a Trillian-Jenkins test job (centos7 mgmt + kvm-centos7) has been kicked to run smoke tests

Trillian test result (tid-3188)
Environment: kvm-centos7 (x2), Advanced Networking with Mgmt server 7
Total time taken: 37114 seconds
Marvin logs: https://github.com/blueorangutan/acs-prs/releases/download/trillian/pr5947-t3188-kvm-centos7.zip
Smoke tests completed. 91 look OK, 0 have errors
Only failed tests results shown below:

Test
Result
Time (s)
Test File

Ready for merge @sureshanaparti


Hello. I got some problem when I run the Cuda_SRCNN for big size images.

Hello. I got some problem when I run the Cuda_SRCNN for big size images.
When I try running the Cuda_SRCNN for 654 * 664 size image, the neural network for Super_resolution works well.
But when I try running for big size images, 1120 * 1450, I got the fail message.
The message is
vl::impl::nnconv_forward_blas: getWorkspace [out of memory error] .
Do you know why I got the error messege for big size images?
In my case, the 654 * 664 size image is good to work in the Super resoultion.
Thank you for your attention
Best.

This mean your GPU is out of memory.
You may try net.conserveMemory=true; first.

Thank you for your kindness.
I got it.
As you know, the commend is already added  net.conserveMemory=true;
But it is not still worked.
Do you know how I can solve the problem?
My graphic card is Geforce GTX 980.
The graphic card should be upgraded?
Thank you for your help.

GPU Memory matters. If you have enough money, 1080TI(11GB) is the best card for the time being. Or you can try 1080(8GB).

thank you for your Kindness.
I got it. I will try using the 1080TI(11GB).


Implement ImageButton

bondolin

are there any low-hanging items a greenhorn might tackle

@Clancey

Sure!, an easy control would be the ImageButton!
You can look at how Image works

Not sure how to assign this to myself, but I'm looking into this.


-y flag for Haxelib upgrade - yes to all upgrades

To prevent severe Y finger repetitive strain injuries, it would be cool with an -y flag that would bypass all the "Upgrade library-x to 0.1.2 [y/n/a] ?" questions with "y" answer.

That's what "a" is for no ? (always)

The idea is to run it in non-interactive mode. Although I thought there was an option for that already...

Been using haxelib for more than a year. Just figured out what the "a" is for. :)

I'm pretty sure there are cases when selecting "a" doesn't work also.  When
I do a complex installation (many hxml files, each with many libs, most
with furhter dependencies) I find myself pressing "a" dozens of times.
I've never bothered to look into it but it would be nice to fix when we fix
this one also.
On Wed, Mar 4, 2015 at 11:42 AM, Fernando Serboncini <
<EMAIL_ADDRESS>wrote:

Been using haxelib for more than a year. Just figured out what the "a" is
for. :)
—
Reply to this email directly or view it on GitHub
https://github.com/HaxeFoundation/haxelib/issues/167#issuecomment-77092019
.


[1.20.4] [Forge] Exception when removing duplicates modules

Recently, a new error arose on Architectury transformer 5.2.87
The suspected commit is b79d342, this is the one that introduced the method to remove duplicate modules on SecureModuleFinder
Full stacktrace https://mclo.gs/jiHmNgx
Additional info:

Forge 49.1.4
Architectury Loom 1.7.412
Java 17
Windows 10

The issue is actually caused by the OS path separator
On linux the separator is / whereas windows has \


Issue overwriting existing file

The first time I execute the following command, it is successful.
osf --username myuser --project myproject upload myfile.dat myfile.dat

The second time I execute, I get the following error. Using the --force flag doesn't change the outcome.

Click to expand
Traceback (most recent call last):
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/urllib3/connectionpool.py", line 601, in urlopen
    chunked=chunked)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/urllib3/connectionpool.py", line 357, in _make_request
    conn.request(method, url, **httplib_request_kw)
  File "/usr/lib64/python3.6/http/client.py", line 1239, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "/usr/lib64/python3.6/http/client.py", line 1285, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "/usr/lib64/python3.6/http/client.py", line 1234, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/usr/lib64/python3.6/http/client.py", line 1065, in _send_output
    self.send(chunk)
  File "/usr/lib64/python3.6/http/client.py", line 986, in send
    self.sock.sendall(data)
  File "/usr/lib64/python3.6/ssl.py", line 965, in sendall
    v = self.send(data[count:])
  File "/usr/lib64/python3.6/ssl.py", line 935, in send
    return self._sslobj.write(data)
  File "/usr/lib64/python3.6/ssl.py", line 636, in write
    return self._sslobj.write(data)
ConnectionResetError: [Errno 104] Connection reset by peer

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/requests/adapters.py", line 440, in send
    timeout=timeout
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/urllib3/connectionpool.py", line 639, in urlopen
    _stacktrace=sys.exc_info()[2])
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/urllib3/util/retry.py", line 357, in increment
    raise six.reraise(type(error), error, _stacktrace)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/urllib3/packages/six.py", line 685, in reraise
    raise value.with_traceback(tb)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/urllib3/connectionpool.py", line 601, in urlopen
    chunked=chunked)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/urllib3/connectionpool.py", line 357, in _make_request
    conn.request(method, url, **httplib_request_kw)
  File "/usr/lib64/python3.6/http/client.py", line 1239, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "/usr/lib64/python3.6/http/client.py", line 1285, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "/usr/lib64/python3.6/http/client.py", line 1234, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/usr/lib64/python3.6/http/client.py", line 1065, in _send_output
    self.send(chunk)
  File "/usr/lib64/python3.6/http/client.py", line 986, in send
    self.sock.sendall(data)
  File "/usr/lib64/python3.6/ssl.py", line 965, in sendall
    v = self.send(data[count:])
  File "/usr/lib64/python3.6/ssl.py", line 935, in send
    return self._sslobj.write(data)
  File "/usr/lib64/python3.6/ssl.py", line 636, in write
    return self._sslobj.write(data)
urllib3.exceptions.ProtocolError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/scratch/standage/pico/env/bin/osf", line 11, in <module>
    sys.exit(main())
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/osfclient/__main__.py", line 104, in main
    exit_code = args.func(args)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/osfclient/cli.py", line 91, in wrapper
    return_value = f(cli_args)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/osfclient/cli.py", line 286, in upload
    store.create_file(remote_path, fp, update=args.force)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/osfclient/models/storage.py", line 82, in create_file
    response = self._put(url, params={'name': fname}, data=fp)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/osfclient/models/core.py", line 26, in _put
    return self.session.put(url, *args, **kwargs)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/osfclient/models/session.py", line 37, in put
    response = super(OSFSession, self).put(url, *args, **kwargs)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/requests/sessions.py", line 566, in put
    return self.request('PUT', url, data=data, **kwargs)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/requests/sessions.py", line 508, in request
    resp = self.send(prep, **send_kwargs)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/requests/sessions.py", line 618, in send
    r = adapter.send(request, **kwargs)
  File "/scratch/standage/pico/env/lib64/python3.6/site-packages/requests/adapters.py", line 490, in send
    raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))

Any ideas? Is this a problem with the client or the server? Thanks!

Are you sure you get exactly the same error? When you try to upload a file that already exists you should get an error telling you that. If you don't that is a bug in the client for sure.

Yes, it was precisely the same error message.
Oddly enough, this didn't happen yesterday on my Mac laptop. It was only a problem on a remote machine (Fedora). Both running osfclient 0.0.3 on Python 3.

FWIW, I am experience the same issue, but only for larger files.
On smaller files, the behavior is as expected: the client gives me a FileExistsError message if -f is not set, and succeeds is -f is set.
However, when I try this on a larger file I see the same stacktrace as @standage. This suggests there is something different in the server behavior.

OK, this shed some light on what's going on:
https://github.com/requests/requests/issues/2422
I believe that this code is never reached, since the server is closing the connection:
https://github.com/osfclient/osfclient/blob/cee05874c2596081f3a733fa010468305f862e12/osfclient/models/storage.py#L84
I'll try a patch...

I'm experiencing a very similar issue with ~25 MB files.


Use correct queue for ingress

Fixes #941

Codecov Report

Merging #942 into master will decrease coverage by <.01%.
The diff coverage is 0%.

@@            Coverage Diff            @@
##           master    #942      +/-   ##
=========================================
- Coverage    8.03%   8.03%   -0.01%
=========================================
  Files          50      50
  Lines        7724    7728       +4
=========================================
  Hits          621     621
- Misses       6977    6981       +4
  Partials      126     126

Impacted Files
Coverage Δ

pkg/operator/secret.go
0% <0%> (ø)
:arrow_up:

pkg/operator/services.go
0% <0%> (ø)
:arrow_up:

pkg/operator/servicemonitors.go
0% <0%> (ø)
:arrow_up:

pkg/operator/nodes.go
0% <0%> (ø)
:arrow_up:

pkg/operator/configmaps.go
0% <0%> (ø)
:arrow_up:

pkg/operator/deployments.go
0% <0%> (ø)
:arrow_up:

Continue to review full report at Codecov.

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