resume from the Model_zoo error

I'm following this  tutorial, and train the simCLR net based on my own dataset, it worked.
the config file i based was: /simclr_resnet50_8xb32-coslr-200e_in1k.py
I change:
optimizer.lr = 0.3 / 8,
cfg.data.samples_per_gpu = 32
cfg.data.workers_per_gpu = 2

Then I download the pre-trained data from Model_zoo
bash {dist_train_script} {new_config_file_path} 2 --work_dir {selfsup_output_dir} --resume_from {pretrained_model_path}

Error message
I check think the main issue is KeyError: 'meta'.
/home/yy/Software/anaconda3/envs/openmmlab/bin/python /home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/src/Meningioma_And_Angiopericytoma/segmentation/test/test_mmselfsup.py
/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/distributed/launch.py:178: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions

  warnings.warn(
WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/mmselfsup/utils/setup_env.py:42: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  warnings.warn(
/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/mmselfsup/utils/setup_env.py:42: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
  warnings.warn(
2022-10-09 18:30:56,802 - mmselfsup - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:18) [GCC 10.3.0]
CUDA available: True
GPU 0,1: NVIDIA GeForce GTX 1080 Ti
CUDA_HOME: /usr
NVCC: Cuda compilation tools, release 9.1, V9.1.8
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.12.1
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.6
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.3.2  (built against CUDA 11.5)
  - Magma 2.6.1
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.13.1
OpenCV: 4.6.0
MMCV: 1.6.1
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.6
MMSelfSup: 0.10.0+2838656
------------------------------------------------------------

2022-10-09 18:30:57,618 - mmselfsup - INFO - Distributed training: True
2022-10-09 18:30:58,494 - mmselfsup - INFO - Config:
model = dict(
    type='SimCLR',
    backbone=dict(
        type='ResNet',
        depth=50,
        in_channels=3,
        out_indices=[4],
        norm_cfg=dict(type='SyncBN'),
        zero_init_residual=True),
    neck=dict(
        type='NonLinearNeck',
        in_channels=2048,
        hid_channels=2048,
        out_channels=128,
        num_layers=2,
        with_avg_pool=True),
    head=dict(type='ContrastiveHead', temperature=0.1))
data_source = 'ImageNet'
dataset_type = 'MultiViewDataset'
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
    dict(type='RandomResizedCrop', size=224),
    dict(type='RandomHorizontalFlip'),
    dict(
        type='RandomAppliedTrans',
        transforms=[
            dict(
                type='ColorJitter',
                brightness=0.8,
                contrast=0.8,
                saturation=0.8,
                hue=0.2)
        ],
        p=0.8),
    dict(type='RandomGrayscale', p=0.2),
    dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5),
    dict(type='ToTensor'),
    dict(
        type='Normalize',
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225])
]
prefetch = False
data = dict(
    samples_per_gpu=32,
    workers_per_gpu=2,
    train=dict(
        type='MultiViewDataset',
        data_source=dict(
            type='ImageNet',
            data_prefix=
            '/home/yy//Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/result/HPC/coco/test/sliced_image',
            ann_file=
            '/home/yy//Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/result/HPC/coco/test/selfsup_anno.txt'
        ),
        num_views=[2],
        pipelines=[[{
            'type': 'RandomResizedCrop',
            'size': 224
        }, {
            'type': 'RandomHorizontalFlip'
        }, {
            'type':
            'RandomAppliedTrans',
            'transforms': [{
                'type': 'ColorJitter',
                'brightness': 0.8,
                'contrast': 0.8,
                'saturation': 0.8,
                'hue': 0.2
            }],
            'p':
            0.8
        }, {
            'type': 'RandomGrayscale',
            'p': 0.2
        }, {
            'type': 'GaussianBlur',
            'sigma_min': 0.1,
            'sigma_max': 2.0,
            'p': 0.5
        }, {
            'type': 'ToTensor'
        }, {
            'type': 'Normalize',
            'mean': [0.485, 0.456, 0.406],
            'std': [0.229, 0.224, 0.225]
        }]],
        prefetch=False))
optimizer = dict(
    type='LARS',
    lr=0.0375,
    weight_decay=1e-06,
    momentum=0.9,
    paramwise_options=dict({
        '(bn|gn)(\d+)?.(weight|bias)':
        dict(weight_decay=0.0, lars_exclude=True),
        'bias':
        dict(weight_decay=0.0, lars_exclude=True)
    }))
optimizer_config = dict()
lr_config = dict(
    policy='CosineAnnealing',
    min_lr=0.0,
    warmup='linear',
    warmup_iters=10,
    warmup_ratio=0.0001,
    warmup_by_epoch=True)
runner = dict(type='EpochBasedRunner', max_epochs=200)
checkpoint_config = dict(interval=10, max_keep_ckpts=3)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
cudnn_benchmark = True
log_level = 'INFO'
load_from = None
resume_from = '/home/yy//Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/result/HPC/coco/test/checkpoints/simclr_resnet50_8xb32-coslr-200e_in1k_20220428-46ef6bb9.pth'
workflow = [('train', 1)]
persistent_workers = True
opencv_num_threads = 0
mp_start_method = 'fork'
seed = 0
work_dir = '/home/yy//Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/result/HPC/coco/test/selfsup//simclr_resnet50_1xb32_coslr-1e_test'
auto_resume = False
gpu_ids = range(0, 2)

2022-10-09 18:30:58,494 - mmselfsup - INFO - Set random seed to 0, deterministic: False
2022-10-09 18:30:58,671 - mmselfsup - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
2022-10-09 18:30:58,804 - mmselfsup - INFO - initialize NonLinearNeck with init_cfg [{'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]
paramwise_options --                                     backbone.bn1.weight: weight_decay=0.0
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2022-10-09 18:31:03,184 - mmselfsup - INFO - load checkpoint from local path: /home/yy//Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/result/HPC/coco/test/checkpoints/simclr_resnet50_8xb32-coslr-200e_in1k_20220428-46ef6bb9.pth
2022-10-09 18:31:03,352 - mmselfsup - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: conv1.weight, bn1.weight, bn1.bias, bn1.running_mean, bn1.running_var, bn1.num_batches_tracked, layer1.0.conv1.weight, layer1.0.bn1.weight, layer1.0.bn1.bias, layer1.0.bn1.running_mean, layer1.0.bn1.running_var, layer1.0.bn1.num_batches_tracked, layer1.0.conv2.weight, layer1.0.bn2.weight, layer1.0.bn2.bias, layer1.0.bn2.running_mean, layer1.0.bn2.running_var, layer1.0.bn2.num_batches_tracked, layer1.0.conv3.weight, layer1.0.bn3.weight, layer1.0.bn3.bias, layer1.0.bn3.running_mean, layer1.0.bn3.running_var, layer1.0.bn3.num_batches_tracked, layer1.0.downsample.0.weight, layer1.0.downsample.1.weight, layer1.0.downsample.1.bias, layer1.0.downsample.1.running_mean, layer1.0.downsample.1.running_var, layer1.0.downsample.1.num_batches_tracked, layer1.1.conv1.weight, layer1.1.bn1.weight, layer1.1.bn1.bias, layer1.1.bn1.running_mean, layer1.1.bn1.running_var, layer1.1.bn1.num_batches_tracked, layer1.1.conv2.weight, layer1.1.bn2.weight, layer1.1.bn2.bias, 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layer4.0.bn3.weight, layer4.0.bn3.bias, layer4.0.bn3.running_mean, layer4.0.bn3.running_var, layer4.0.bn3.num_batches_tracked, layer4.0.downsample.0.weight, layer4.0.downsample.1.weight, layer4.0.downsample.1.bias, layer4.0.downsample.1.running_mean, layer4.0.downsample.1.running_var, layer4.0.downsample.1.num_batches_tracked, layer4.1.conv1.weight, layer4.1.bn1.weight, layer4.1.bn1.bias, layer4.1.bn1.running_mean, layer4.1.bn1.running_var, layer4.1.bn1.num_batches_tracked, layer4.1.conv2.weight, layer4.1.bn2.weight, layer4.1.bn2.bias, layer4.1.bn2.running_mean, layer4.1.bn2.running_var, layer4.1.bn2.num_batches_tracked, layer4.1.conv3.weight, layer4.1.bn3.weight, layer4.1.bn3.bias, layer4.1.bn3.running_mean, layer4.1.bn3.running_var, layer4.1.bn3.num_batches_tracked, layer4.2.conv1.weight, layer4.2.bn1.weight, layer4.2.bn1.bias, layer4.2.bn1.running_mean, layer4.2.bn1.running_var, layer4.2.bn1.num_batches_tracked, layer4.2.conv2.weight, layer4.2.bn2.weight, layer4.2.bn2.bias, 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missing keys in source state_dict: backbone.conv1.weight, backbone.bn1.weight, backbone.bn1.bias, backbone.bn1.running_mean, backbone.bn1.running_var, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, 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Traceback (most recent call last):
  File "/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/tools/train.py", line 200, in <module>
    main()
  File "/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/tools/train.py", line 190, in main
    train_model(
  File "/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/mmselfsup/apis/train.py", line 213, in train_model
    runner.resume(cfg.resume_from)
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 374, in resume
    self._epoch = checkpoint['meta']['epoch']
KeyError: 'meta'
Traceback (most recent call last):
  File "/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/tools/train.py", line 200, in <module>
    main()
  File "/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/tools/train.py", line 190, in main
    train_model(
  File "/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/mmselfsup/apis/train.py", line 213, in train_model
    runner.resume(cfg.resume_from)
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 374, in resume
    self._epoch = checkpoint['meta']['epoch']
KeyError: 'meta'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 487) of binary: /home/yy/Software/anaconda3/envs/openmmlab/bin/python
Traceback (most recent call last):
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/distributed/run.py", line 752, in run
    elastic_launch(
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/yy/Software/anaconda3/envs/openmmlab/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
/home/yy/Documents/XY/ToDoList/Medical-Image-Precrocessing_Toolkit/ref/git-repo/mmselfsup/tools/train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2022-10-09_18:31:08
  host      : yy
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 488)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2022-10-09_18:31:08
  host      : yy
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 487)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

Process finished with exit code 0

Thanks to . and songyang zhang; i found that to load the pretrained model we have to change the load_from in config file.
Here is the code:
from mmcv import Config
cfg = Config(RAW_CONFIG_FILE_PATH)
cfg.load_from = PRETRAINED_MODEL_PATH
cfg.dump(UPDATED_CONFIG_FILE_PATH)

Thanks
nye


fix(spanner): use ExecuteStreamingSqlfor PDML operations

The streaming call allows us to properly resume the operation via
PartialResultSet::resume_token (already handled lower in the stack).
part of #4714 (it almost fixes it, I just need to tweak some timeouts).

This change is

https://codecov.io/gh/googleapis/google-cloud-cpp/pull/5197 says
Missing base report. Unable to compare commits because the base of the pull request did not upload a coverage report.
according to the log the base is https://codecov.io/gh/googleapis/google-cloud-cpp/commit/4b12aea940d4abb1d502fb7cd41419d3c7d1feac which has a report... so, I guess I can rebase.


Updates step by step nav header

What

Changed the nav header to use a heading element instead of a div element.
Why

This failed WCAG 2.1 SC 1.3.1 as the component acted as a heading, but wasn't marked up as a heading.
Visual Changes

None.

Changelog?


Service operator docs for RDS: Use PGPASSWORD rather than cat and manual copy+paste

It occurred to me this could be improved while talking to Daniel Besbrode,
based off an environment variable I saw used in some of their code.

We should probably not "mount" the secret in the example at all ... it's not very idiomatic ... the way dcs are binding environment variables from secrets is probably what we should be encouraging.... https://github.com/alphagov/doc-checking/blob/master/configuration/dcs-helm/templates/audit-consumer-deployment.yaml


[vite-typescript] how to use 'import.meta'

Pre-flight checklist

[X] I have read the contribution documentation for this project.
[X] I agree to follow the code of conduct that this project uses.
[X] I have searched the issue tracker for a bug that matches the one I want to file, without success.

Electron Forge version
6.4.1
Electron version
26.0.0
Operating system
macOS 12.4
Last known working Electron Forge version
none
Expected behavior
when I use 'import.meta' in renderer.ts, prompt error :'The "import.meta" meta property is only allowed when the "--module" option is "es2020", "es2022", "esnext", "system", "node16", or "nodenext"'
tsconfig.json
{   "compilerOptions": {     "target": "ESNext",     "module": "commonjs",     "allowJs": true,     "skipLibCheck": true,     "esModuleInterop": true,     "noImplicitAny": true,     "sourceMap": true,     "baseUrl": ".",     "outDir": "dist",     "moduleResolution": "node",     "resolveJsonModule": true   },   "include": ["src"] }
if I change 'commonjs' to 'ESNext', then run  command 'npm run start', got errors:
An unhandled rejection has occurred inside Forge: SyntaxError: Cannot use import statement outside a module
Is there a way to fix this?
Actual behavior
can use 'import.meta' in [vite-typescript]
Steps to reproduce
   use 'import.meta' in renderer.ts

Additional information
No response

You can try this!
{
  "compilerOptions": {
-   "module": "commonjs",
+   "module": "ESNext",
  }
}

🤔 However this seems to conflict here 👉 https://github.com/electron/forge/pull/3178#issuecomment-1592454485

You can try this!
{
  "compilerOptions": {
-   "module": "commonjs",
+   "module": "ESNext",
  }
}

🤔 However this seems to conflict here 👉 #3178 (comment)

Then when compiling with vite,i cannot use 'import.meta'.Is that so?

@w3cways How did you finally solve this problem?

@w3cways How did you finally solve this problem?

{   "ts-node": {     // these options are overrides used only by ts-node     // same as our --compilerOptions flag and our TS_NODE_COMPILER_OPTIONS environment variable     "compilerOptions": {       "module": "commonjs"     }   },   "compilerOptions": {     "target": "esnext",     "module": "esnext",     "lib": ["dom", "dom.iterable", "esnext"],     "isolatedModules": true,     "allowJs": true,     "skipLibCheck": true,     "esModuleInterop": true,     "noImplicitAny": true,     "sourceMap": true,     "baseUrl": ".",     "outDir": "dist",     "moduleResolution": "Node",     "resolveJsonModule": true,     "typeRoots": ["./types"],     "types": ["vite/client", "@types/node"]   },   "include": ["src/*", "./types"],   "exclude": ["node_modules"] }

What is the expected way to do this now?
I followed the setup using npm init electron-app@latest my-app -- --template=vite-typescript and still am unable to use import.meta


Re: Help on generating Ada bindings for C/C++

Good day to you all.
I am interested in generate Ada binding for C/C++ so I will be happy if you can provide short steps/description how on this done from your expertise and your lead. Many thanks!
I have seen the use of  e.g ==> gcc  -fdump-ada-spec  ./test.h     which out create bindings for .ads
but want I want to know is if I have both test.h  and test.c  how can I auto generate both test.ads (spec) and test.adb (package body).
Many thanks!
God blesses!!!
Best regards,
Sanyaade

Hello sanyaade,
You seem to already have the command you need. GCC is able to dump Ada specification files when given C header files. You typically don't need the C implementation files. For example here I am running this on the gdnative folder to dump the Ada specs. These files generally aren't perfect, and require a bit of massaging by hand to work well, but it's a great way to get started as it does most of the dull grunt work of porting the types and functions.
Here is an example of me using gcc to dump the specs for the gdnative c headers I used in this project. I had to manually clean them up quite a bit to get the final version I have in gdnative-thin.ads
C:\Users\user\Desktop\godot_headers\gdnative
λ dir
 Volume in drive C has no label.

C:\Users\user\Desktop\godot_headers\gdnative
λ gcc -fdump-ada-spec gdnative.h -I..

Hello sanyaade,
You seem to already have the command you need. GCC is able to dump Ada specification files when given C header files. You typically don't need the C implementation files. For example here I am running this on the gdnative folder to dump the Ada specs. These files generally aren't perfect, and require a bit of massaging by hand to work well, but it's a great way to get started as it does most of the dull grunt work of porting the types and functions.
Here is an example of me using gcc to dump the specs for the gdnative c headers I used in this project. I had to manually clean them up quite a bit to get the final version I have in gdnative-thin.ads
C:\Users\user\Desktop\godot_headers\gdnative
λ dir
 Volume in drive C has no label.

C:\Users\user\Desktop\godot_headers\gdnative
λ gcc -fdump-ada-spec gdnative.h -I..

@MichaelAllenHardeman :
Many thanks for your reply. Much appreciated!
I have been looking at other means of translating C source files to Ada. Surprisingly the -fdump-ada-spec work but I have to manually add keyword => "body" to those translated files so I need to get that file save it as -fdump-ada-spec-body, modify that version to work the way I want.  This is a must for those 100s of source files.
Other options: merge into single file before -fdump-ada-spec. I need to tryout this as well plus all possible tricks!
Once again many thanks for your reply.
God blesses!!!
Best regards,
Sanyaade

@MichaelAllenHardeman :
Many thanks for your reply. Much appreciated!
I have been looking at other means of translating C source files to Ada. Surprisingly the -fdump-ada-spec work but I have to manually add keyword => "body" to those translated files so I need to get that file save it as -fdump-ada-spec-body, modify that version to work the way I want.  This is a must for those 100s of source files.
Other options: merge into single file before -fdump-ada-spec. I need to tryout this as well plus all possible tricks!
Once again many thanks for your reply.
God blesses!!!
Best regards,
Sanyaade

@sanyaade-teachings
-fdump-ada-spec is a tool for translating C header files so you can use built C binaries.
-fdump-ada-spec is not for translating C source code. While I'm sure there are tools that might attempt that I've never used them before. The translated code would probably not be desirable anyway as Ada gives you so much more power to express the meaning of your code to the compiler and to the reader.

@sanyaade-teachings
-fdump-ada-spec is a tool for translating C header files so you can use built C binaries.
-fdump-ada-spec is not for translating C source code. While I'm sure there are tools that might attempt that I've never used them before. The translated code would probably not be desirable anyway as Ada gives you so much more power to express the meaning of your code to the compiler and to the reader.


custom.scss file not found

When rendering a .ipynb or .qmd file in VS Code with header that includes
format:
    revealjs:
        theme: [default, custom.scss]

I get the error ERROR: NotFound: No such file or directory (os error 2).
If I remove the custom.scss file from the header, then my slides render just fine, albeit without my custom rules.
custom.scss is in the same folder as the .ipynb or .qmd file I am rendering.
What do I need to do to get quarto to find my custom.scss file?

MacOS 12.4
VS Code 1.71.0
quarto 1.1.189
VS Code Quarto Extension 1.38.0

I'm not able to reproduce this locally. Could you create a small git repo that reproduces the problem?
Once the failure occurs, try defining QUARTO_PRINT_STACK=true in the Terminal and then running quarto render on the file (that would allow us to know what line of code is not finding an expected file).


Ouput CSV files with the csv file extension

[X] Closes #9044
[ ] Tests added / passed
[ ] Passes pre-commit run --all-files

This PR causes Dask to output files like part-1.csv and part-2.csv by default instead of 0.part and 1.part when ddf.to_csv("some/folder") is run.  This PR should not change the behavior when the user specifies a globstring like ddf.to_csv("some/folder/whatever-*.csv").
Users that still want the .part file extension can run ddf.to_csv("some/folder/*.part").  They'll need to update their code, but this can be made backwards compatible for legacy systems.
The implementation in this PR was suggested by @martindurant in this issue: "the quick fix for dask is to add its own "*" into the path".
Feel free to leave thoughts / suggestions.  The high level motivation for this PR is that I'm writing blog posts / a Dask book and the default behavior is hard to explain.  Users are confused that their CSV files aren't being output with the right file extension.  Hopefully this new default file extension is more intuitive.

@rjzamora - thanks for reviewing and agree with your user-friendly messaging.  Let's see what James thinks.
@martindurant - feel free to provide feedback too if you have any thoughts!

All good from me

@jcrist - thanks for taking a look.  Updated the code to output a filename that's consistent with what to_parquet outputs - good catch!

@MrPowers Thanks for this PR! What do you think about @ian-r-rose's comments?


Move security and login related Behat tests back into framework

Affected Version
4.x
Description
Behat tests were moved from framework to admin in #8112, but we're now seeing test failures in silverstripe/admin that are caused by and testing changes in framework. I vote for moving the login and security related tests back into framework from admin.

I don't think this is a question of concurrency and Travis slowness, it's where the functionality lives which Behat tests. The login from logic is contained in silverstripe/framework, not silverstripe/admin, so tests related to it should also live there.

Yeah exactly. Also testing features (like login/logout) that you get in a project that doesn't have the admin module installed.

Yeah that makes sense. The whole authentication workflow is a feature of framework, not admin. As long as "security tests" excludes SecurityAdmin, I'm fine with this. The original migration of the framework behat tests was probably a bit too greedy.


Corrected the autocomplete feature of hue for sparksql hiveserver2 interface.

What changes were proposed in this pull request?

(Please fill in changes proposed in this fix)

How was this patch tested?

(Please explain how this patch was tested. Ex: unit tests, manual tests)
(If this patch involves UI changes, please attach a screen-shot; otherwise, remove this)
According the issue https://github.com/cloudera/hue/issues/2973. The autocomplete feature does not work for tables in Spark sql Hiveserver2 .So corrected the code accordingly. The rca for the error is mentioned by me in the issue.The following change wont break any existing code.

#2973

After this code the sparksql table prompt works .
Please review Hue Contributing Guide before opening a pull request.

Hi @divincode, thanks for the PR! I'll review it shortly.

will update the pr and address the comments.

Hi @Harshg999 , apologies i will address the comments by Wednesday. Was busy with my work .


Additional header examples with other commenting methods

Is this a BUG REPORT or FEATURE REQUEST?:
feature
What happened:
Currently only one specific comment style is provided as example license header.
What you expected to happen:
It should give various headers with different commenting styles for different languages.
How to reproduce it (as minimally and precisely as possible):
Try using the current example headers with JS.

Using /* and */ should cover most languages. The rest is up to the maintainer to fix.


✨ Make a EVENTS page

An events page with all the upcoming and past events in the form of cards.

Done need some UI changes there


Ready Whim for CPAN-style distribution/installation/configuration

This one does a lot of work towards making Whim "installable", rather than something to run out of its own directory. Fixes #12, fixes #13, fixes #24, fixes #28.
This is another pre-alpha big mess of changes all bound up together. Hopefully this summary makes it more clear:

Rolls in a Minilla superstructure.

Has a lightly edited minil.toml Minilla-config file.

Manually converted README.md into README.pod, so that Minilla can automatically translate it back into README.md. Both of these files are checked in here. (I have long since learned not to try to talk Minilla out of stuff like this, and to just roll with it.)

Takes Mojolicious's own advice for making an app installable

Moves public and templates under the lib directory

See https://mojolicious.org/perldoc/Mojolicious/Guides/Cookbook#Making-your-application-installable

We opt to use Minilla rather than Build.PL + the mojo cpanify command, though.

Moves the app's "home", in the Mojolicious sense, to $HOME/.whim

You can put template and docroot directories there, and files there will override the installed ones

The author photo directory (which Whim needs write-access to) lives here

A config file can live here; if not, Whim uses sensible defaults

If you create a directory called log here, it fills with logs (Mojo default magic)

You can override the home location with $WHIM_HOME environment variable

None of this is documented!!! We'll get to that!!!

This project is young enough that I understand silence as tacit approval! In it goes.


This material drop-down are not supporting autocomplete ?

I want to add autocomplete functionality, how can I use that?

Find a plug-in

Can you please provide any link of plug-in?

Nope sorry. This library isn't maintained anymore. Find a different hurry plugin to achieve what you need


Getting started section for learning resources

The learning resources page should have a getting started section that provides a starting place for those looking to get started. Perhaps the getting started section could even be on the first page?

Topics to cover

How things are connected and the fact that there is probably an order to learning that makes things easier
field agnostic skills: the unix filesystem, commandline, installation, looking up errors
topic specific resources: omics resources
starting point for those new to biology?

If this is laid out as a "track", then we can cover topics in a basic order that also would allow people to start at different points.
Thinking through this a bit more to see if there is a way to outline this (would need to find appropriate content for each of these)

basics of genetics, cellular and molecular biology -- learn the basic biological concepts underlying biology such as DNA structure, function, protein synthesis, cellular processes, etc. (this might actually be out of the scope of this area?)
introduction to bioinformatics/computational biology -- learn what these are and their role in modern biological research
foundational computational skills -- interacting with the commandline, understanding file system structure, installing programs, using IDEs, basic programming and scripting, making sense of errors, working with tidy data and data structures, manipulating data (format conversions, etc.)
working reproducibly -- creating, using, and sharing workflows, capturing metadata
topic specific resources -- common analyses and common file types and stuctures that are dependent  upon the task/field (these will be what leads into the specific categories -- so stop here?)


fix: prevent looping on error during render of error page

Follow up of: #4257
Resolves: #4140
Types of changes

[x] Bug fix (a non-breaking change which fixes an issue)
[ ] New feature (a non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Description

The $forceUpdate cannot be called async with eg $nextTick as then the NuxtError component would still be rendered and trigger the infinite reloading.
Checklist:

[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly. (PR: #)
[x] I have added tests to cover my changes (if not applicable, please state why)
[x] All new and existing tests are passing.

Codecov Report

Merging #6217 into dev will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##              dev    #6217   +/-   ##
=======================================
  Coverage   95.75%   95.75%
=======================================
  Files          80       80
  Lines        2660     2660
  Branches      684      684
=======================================
  Hits         2547     2547
  Misses         97       97
  Partials       16       16

Flag
Coverage Δ

#e2e
100% <ø> (ø)
:arrow_up:

#fixtures
50.75% <ø> (ø)
:arrow_up:

#unit
92.44% <ø> (ø)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update f65c72d...f1542b3. Read the comment docs.

Codecov Report

Merging #6217 into dev will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##              dev    #6217   +/-   ##
=======================================
  Coverage   95.75%   95.75%
=======================================
  Files          80       80
  Lines        2660     2660
  Branches      684      684
=======================================
  Hits         2547     2547
  Misses         97       97
  Partials       16       16

Flag
Coverage Δ

#e2e
100% <ø> (ø)
:arrow_up:

#fixtures
50.75% <ø> (ø)
:arrow_up:

#unit
92.44% <ø> (ø)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update f65c72d...f1542b3. Read the comment docs.

Just to make sure: could someone please confirm it is correct that navigation to /info renders nothing in the e2e test? When I test this manually with nuxt then in dev mode it returns an error page but with build/start it indeed returns nothing. If you then refresh the page it says server error.


Windows 10 defender virus detection

Virus Total does show a infected file for the amd windows build. Same goes for windows defender I guess. I initially found it via a Joplin plugin and listed the issue there https://github.com/laurent22/joplin/issues/9055
I did dug into the code for the unofficial wakatime Joplin plugin code just to find out that it does download the cli if required.
I sis upload the wakatime cli file to virus total and it got the same hash as the issue I wrote on the Joplin board. So I'm pretty sure the zip is the route cause.
Talking about the newest release https://github.com/wakatime/wakatime-cli/releases/tag/v1.85.2
I will ask windows defender to scan the file tomorrow and write a comment if it will throw an warning.
Environment:

OS: Windows 10
Platform: amd64

Related to past similar issues:
#775 #660 #690 #693 #782 #689 #654

I'm seeing no detections when scanning just now with virustotal.com:
v1.85.2/wakatime-cli-windows-amd64.zip
v1.85.2/wakatime-cli-windows-arm64.zip
v1.85.2/wakatime-cli-windows-386.zip

That's interessing, it does show something for me

But it does link it to a file I scanned yesterday related to a Plugin for the "Joplin" app. Which does download the newest cli. That file was scanned since Windows showed me a warning which is does for the windows-amd64.exe as well.

Now I'm also getting one detection Trojan.Khalesi.bice by Jiangmin when using the same file but uploading the unzipped exe from the zip:

v1.85.2/wakatime-cli-windows-amd64.exe

I did check for system updates and it looks like there is a an update for the windows defender database. I will scan the file again after the update is installed. Version of the update is v5.118 (KB890830).
Hopefully this will solve the false positive, I will report back.

I've uploaded wakatime-cli-windows-amd64.exe v1.85.2 with sha256sum 81283b5f3d667be97927661c1a075e1ffd96d01c9000c2fe3fe24939a670abb9 to Windows as a false positive:

Is that the same sha256sum you get for your wakatime-cli-windows-amd64.exe?

Not sure how I could verify this, could you guide me where to get the hash from?

Uploading the exe here gives the sha256sum:
https://emn178.github.io/online-tools/sha256_checksum.html

Saldy WIndows does not allow me to unzip the file since the defender does block any interaction immediately. I can only check the zip, which does logically show another sha256 value.
Since the cache folder from joplin is also already deleted I cannot verify if the file indeed where identically. I could boot up my linux machine, download the zip from github and upload the exe there but this would be pointless, right?

Since I used the same Joplin plugin on another machine, I could indeed find a file which does contain the "wakatim-cli.exe". Scanning it via the provided tool does return the same sha256
81283b5f3d667be97927661c1a075e1ffd96d01c9000c2fe3fe24939a670abb9
This at least tells me that the plugin wasn't the initial cause of the issue.

Saldy WIndows does not allow me to unzip the file since the defender does block any interaction immediately. I can only check the zip, which does logically show another sha256 value.

Which value do you get for the zip? Or you can upload the zip here and I'll check it's shasum.

After submitting to Defender the final determination is now Not malware. That means it shouldn't be flagged in a future Defender database update.


Revert "Allow setting project ID via environment variable (#2574)"

Summary
This reverts commit 106e46b058569783128dd5a9845842748634b7cf.
I have no idea how it works but it seems it already worked before. So let's revert my PR
which actually does nothing but add complexity.
Change Type
Mark the type of change your PR introduces:

[ ] Bug fix (resolves an issue without affecting existing features)
[ ] Feature (adds new functionality without breaking changes)
[ ] Breaking change (may impact existing functionalities or require documentation updates)
[ ] Documentation (updates or additions to documentation)
[x] Refactoring or test improvements (no bug fixes or new functionality)

Testing
Outline how the changes were tested, including steps to reproduce and any relevant configurations.
Attach screenshots if helpful.
Review Checklist:

[x] Reviewed my own code for quality and clarity.
[ ] Added comments to complex or tricky code sections.
[ ] Updated any affected documentation.
[ ] Included tests that validate the fix or feature.
[ ] Checked that related changes are merged.

cc: @eleftherias

coverage: 39.81% (-0.02%) from 39.826%
when pulling 906d6037c23c540fb21690cd5dc6e051517787d4 on project-id-env-revert
into 106e46b058569783128dd5a9845842748634b7cf on main.


missing -Z --syslog-prefix stanza in man page

Hi, looks like the latest commit  https://github.com/jedisct1/dnscrypt-proxy/commit/41c93799ae3b41ff5e60eb8c7a3433e1428e8989 is still missing the newly introduced -Z --syslog-prefix stanza in the man page. It does show in the --help command. Just another FYI. Don't want to be too pedantic, but there's also a slight difference in the order in which items appear between the cli help & the man page. Many thanks for all work!
$ dnscrypt-proxy --help
dnscrypt-proxy 1.6.1
Options:
-a	--local-address=...
-d	--daemonize
-e	--edns-payload-size=...
-E	--ephemeral-keys
-K	--client-key=...
-h	--help
-L	--resolvers-list=...
-R	--resolver-name=...
-l	--logfile=...
-m	--loglevel=...
-n	--max-active-requests=...
-p	--pidfile=...
-X	--plugin=...
-N	--provider-name=...
-k	--provider-key=...
-r	--resolver-address=...
-S	--syslog
-Z	--syslog-prefix=...
-u	--user=...
-t	--test=...
-T	--tcp-only
-V	--version
Please consult the dnscrypt-proxy(8) man page for details.
$ man dnscrypt-proxy
DNSCRYPT-PROXY(8)                                            DNSCRYPT-PROXY(8)
NAME
dnscrypt-proxy - A DNSCrypt forwarder
SYNOPSIS
dnscrypt-proxy [options]
DESCRIPTION
dnscrypt-proxy  accepts  DNS  requests, authenticates and encrypts them
using dnscrypt and forwards them to a remote dnscrypt-enabled resolver.
   Replies from the resolver are expected to be authenticated or else they
   will be discarded.

   The  proxy  verifies the replies, decrypts them, and transparently for‐
   wards them to the local stub resolver.

   dnscrypt-proxy listens to <IP_ADDRESS> / port 53 by default.

WARNING
dnscrypt-proxy is not a DNS cache. Unless your operating system already
provides  a decent built-in cache (and by default, most systems don't),
clients shouldn't directly send requests to dnscrypt-proxy.
   Intead, run  a  DNS  cache  like  Unbound,  and  configure  it  to  use
   dnscrypt-proxy  as a forwarder. Both can safely run on the same machine
   as long as they use different IP addresses and/or different ports.

OPTIONS
·   -a, --local-address=[:port]: what local IP the daemon will lis‐
ten to, with an optional port. The default port is 53.
   ·   -d,  --daemonize:  detach  from  the  current  terminal and run the
       server in background.

   ·   -E, --ephemeral-keys: By default, queries are always sent with  the
       same  public key, allowing providers to link this public key to the
       different IP addresses you are using. This  option  requires  extra
       CPU  cycles,  but mitigates this by computing an ephemeral key pair
       for every query.

   ·   -e, --edns-payload-size=<bytes>: transparently add an OPT pseudo-RR
       to  outgoing  queries in order to enable the EDNS0 extension mecha‐
       nism. The payload size is the  size  of  the  largest  response  we
       accept  from the resolver before retrying over TCP. This feature is
       enabled by default, with a payload size of 1252  bytes.  Any  value
       below 512 disables it.

   ·   -h, --help: show usage.

   ·   -k,  --provider-key=<key>:  specify  the  provider  public key (see
       below).

   ·   -K, --client-key=<file>: use a static client secret key  stored  in
       <file>.

   ·   -L,  --resolvers-list=<file>:  path  to the CSV file containing the
       list of available resolvers, and the parameters to use them.

   ·   -l, --logfile=<file>: log events to this file instead of the  stan‐
       dard output.

   ·   -m,  --loglevel=<level>:  don't log events with priority above this
       level after the service has been started  up.  Default  is  6,  the
       value  for  LOG_INFO.  Valid  values  are 0 (system is unusable), 1
       (action must be taken  immediately),  2  (critical  conditions),  3
       (error  conditions), 4 (warning conditions), 5 (normal but signifi‐
       cant condition), 6 (informational) and 7 (debug-level messages).

   ·   -n, --max-active-requests=<count>: set the maximum number of simul‐
       taneous active requests. The default value is 250.

   ·   -p, --pidfile=<file>: write the PID number to a file.

   ·   -R,  --resolver-name=<name>:  name of the resolver to use, from the
       list of available resolvers (see -L).

   ·   -r, --resolver-address=<ip>[:port]: a DNSCrypt-capable resolver  IP
       address with an optional port. The default port is 443.

   ·   -S,  --syslog:  if  a log file hasn't been set, log diagnostic mes‐
       sages to syslog  instead  of  printing  them.  --daemonize  implies
       --syslog.

   ·   -t, --test=<margin>: don't actually start the proxy, but check that
       a valid certificate can be retrieved from the server  and  that  it
       will  remain  valid for the next margin minutes. The exit code is 0
       if a valid certificate can be used, 2 if no valid certificates  can
       be  used,  3 if a timeout occurred, and 4 if a currently valid cer‐
       tificate is going to expire before margin.  The  margin  is  always
       specificied in minutes.

   ·   -u, --user=<user name>: chroot(2) to this user's home directory and
       drop privileges.

   ·   -N,  --provider-name=<FQDN>:  the  fully-qualified  name   of   the
       dnscrypt certificate provider.

   ·   -T,  --tcp-only:  always  use TCP. A connection made using UDP will
       get a truncated response, so that the (stub) resolver retries using
       TCP.

   ·   -V, --version: show version number.

   A public key is 256-bit long, and it has to be specified as a hexadeci‐
   mal string, with optional columns.

COMMON USAGE EXAMPLE
$ dnscrypt-proxy --daemonize --resolver-name=...
   The resolver name is the first column (Name) in the CSV file.

USAGE EXAMPLE WITH A PRIVATE SERVER
$ dnscrypt-proxy --daemonize --provider-key=... --provider-name=... --resolver-address=...
BUGS AND SUPPORT
Please     report     issues     with      DNSCrypt      itself      to
https://dnscrypt.org/issues
SEE ALSO
hostip(8)
                               May 2016                  DNSCRYPT-PROXY(8)

---
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The man page is automatically generated by the ronn tool if you have it installed.
So, the pre-built page is not always updated except before a release.
The order should indeed be the same in the man page and in the options list.
This has been fixed, thanks!


CRASHING

I've updated but again it crashers my server

Attach server_log.txt.

Nothing, can it be pawnraknet crashing now


Timestamp

Hi, would be good if we can have function to set timestamp

Of course, I had never needed it, but it is true that on certain occasions it can be very useful.
I'm working in it. I hope to have the new release ready soon.


Run the prediction and export to GeoTIFF images failure

before is the MAC in the local environment, MAC successfully run your case, I have successfully changed the our own data,

But now I'm going to install the project to the new environment of ubuntu,is an example of an official, ，I didn't change anything, but the project stopped in this place

System:ubuntu18  gpu:nvidia1080ti

I've been waiting for 6 hours

Hi @zhan520,
we started to notice similar things happening with EOExecutor. Unfortunately we still didn't quite pinpoint what the problem is, but it has something to do with paralellising the workflow.
For now, can you confirm that it works if you do the following on a single CPU:
for args in tqdm(execution_args):
    workflow.execute(args)

Sorry for the issues.

Hi @zhan520,
we started to notice similar things happening with EOExecutor. Unfortunately we still didn't quite pinpoint what the problem is, but it has something to do with paralellising the workflow.
For now, can you confirm that it works if you do the following on a single CPU:
for args in tqdm(execution_args):
    workflow.execute(args)

Sorry for the issues.

Okay, I now off work, tomorrow I will try, thank you.

Hi @zhan520,
we started to notice similar things happening with EOExecutor. Unfortunately we still didn't quite pinpoint what the problem is, but it has something to do with paralellising the workflow.
For now, can you confirm that it works if you do the following on a single CPU:
for args in tqdm(execution_args):
    workflow.execute(args)

Sorry for the issues.

Thank you, I have solved, and successfully completed their own data，
But now I want to use my server GPU training, I made the following modification, but did not succeed

，I need to do ？

Hi @zhan520,
unfortunately, I'm not so familiar with running on GPU. Did you try to find some tutorials online? Perhaps: https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html

Hi @zhan520,
unfortunately, I'm not so familiar with running on GPU. Did you try to find some tutorials online? Perhaps: https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html

Thank you, I has been solved，

But now I want to use your own tif image data to train and predict, how should I go to convert EOPatch

Hi @zhan520,
we started to notice similar things happening with EOExecutor. Unfortunately we still didn't quite pinpoint what the problem is, but it has something to do with paralellising the workflow.
For now, can you confirm that it works if you do the following on a single CPU:
for args in tqdm(execution_args):
    workflow.execute(args)

Sorry for the issues.

Hi there,
Is there an update on the parallel processing? Single CPU doesn't help much if running at a national scale.
Thanks in advance!

This has been improved, and handles running workflows in parallel.
The option to run either multi-process or multi-thread is provided, which helps when running algorithms that already perform parallelization.


Autoplay attribute has no effect in web component

Animations autoplay by default, but setting autoplay to either false or 0 has no effect.
Demo: https://jsfiddle.net/te843bkf/

Thank you for the report @tobiaslockley, and I'm sorry for this omission. A fix was just released as part of v1.1.2 🎉


Modbus Flex FC - Loading Codes Issue

Which node-red-contrib-modbus version are you using?
5.30.0
What happened?
Trying to use the Modbus Flex FC node. I want to define my own map file and load it into the UI. I am not having success. The drop down never populates.
Server
None/This is related to a node that doesn't connect to a server
How can this be reproduced?
Ensure a codes.json file exists in the search path defined in the node:

A valid file exists but the function code selection does not populate.
What did you expect to happen?
No response
Other Information
No response

@grewek can you check that for us?

Sorry for the late reply, will take a look at it early next week.
Only thing you could try is to use a absolute path instead of a relative maybe that will help for now :)

Hi,
here is a little workaround for the issue, i will of course fix the issue as soon as i can.
Put your codes.json inside node-modules/node-red-contrib-modbus/extras/customMaps/ then inside
NODE-Red change the path from ./node_modules/node-red-contrib-modbus/extras/argumentMaps/defaults/
to extras/argumentMaps/customMaps/

Thanks for the above. I did try this with no success. Permissions are correct. Perhaps some UI feedback when you are able to push a fix so we can see what errors are raised if any.