text:
Add pass qualifiers to StatsBomb deserializer

This pull request adds several pass qualifiers to the StatsBomb deserializer.
Context
The StatsBomb deserializer currently does not support pass qualifiers (#97). The StatsBomb deserializer currently also assigns set-piece qualifiers to shots that should only be assigned to passes.
Changes

The _get_event_qualifiers method in the StatsBomb deserializer has been decomposed into separate methods for extracting body part qualifiers (_get_body_part_qualifiers) and set-piece qualifiers (_get_set_piece_qualifiers). The latter method is only invoked for passes and no longer for shots.
The _get_pass_qualifiers method is added to the StatsBomb deserializer. This method supports the following eight qualifiers: Cross, Through Ball, Switch of Play, High Pass, Long Ball, Head Pass, Hand Pass and Assist.

This pull request assumes that multiple pass qualifiers can be assigned to a pass despite the get_qualifier_value method in the Event class only returning the first qualifier. However, this issue should be solved once #120 has been merged.

Great work here again Jan! Let me update @ThomasSeidl's PR #120 this evening and merge it. And also try to merge your PR directly after

authors: JanVanHaaren koenvo

repo: PySport/kloppy


text:
[Wyscout v3] Support for unknown player

In very rare cases Wyscout v3 event data contains an event that is executed by a player that is not listed out in the players of the executing team. The deserializes caused an error in that case. This fix sets the player as None if the player performing the event data is not listed as one of the players of the team performing the event.

What do you think about raising a DeserializationWarning when this happens? It might be helpful to notify the user that there's a potential issue with the data encountered during deserialization.

Does this work as an implementation @probberechts ?

source: gharchive/pull-request

authors: DriesDeprest probberechts

license_type: permissive

license_source: github-api


text:
bug: Translate not working in personal environment

Description
I am trying to run the below code:
from pythainlp.translate.en_th import EnThTranslator, ThEnTranslator
enth = EnThTranslator()
enth.translate("I Like dogs")
It works fine on Google coolabs
but it's only prints the below output in my python environment, I tried changing python versions tried 3.8.16 & 3.10.3 & 3.10.1
all the same output:
INFO | fairseq.file_utils | loading archive file C:\Users\LENOVO\pythainlp-data\scb_1m_th-en_spm_1.0\SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0\models
2023-08-29 17:39:35 | INFO | fairseq.file_utils | loading archive file C:\Users\LENOVO\pythainlp-data\scb_1m_th-en_spm_1.0\SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0\vocab
2023-08-29 17:39:38 | INFO | fairseq.tasks.translation | [th] dictionary: 29720 types
2023-08-29 17:39:38 | INFO | fairseq.tasks.translation | [en] dictionary: 29720 types
2023-08-29 17:39:40 | INFO | fairseq.models.fairseq_model | {'_name': None, 'common': {'_name': None, 'no_progress_bar': False, 'log_interval': 100, 'log_format': None, 'log_file': None, 'aim_repo': None, 'aim_run_hash': None, 'tensorboard_logdir': './checkpoints/1m-scb+mt-opus_27.5.2020/th-en/spm-spm/32000-joined/log', 'wandb_project': None, 'azureml_logging': False, 'seed': 1, 'cpu': False, 'tpu': False, 'bf16': False, 'memory_efficient_bf16': False, 'fp16': True, 'memory_efficient_fp16': False, 'fp16_no_flatten_grads': False, 'fp16_init_scale': 128, 'fp16_scale_window': None, 'fp16_scale_tolerance': 0.0, 'on_cpu_convert_precision': False, 'min_loss_scale': 0.0001, 'threshold_loss_scale': None, 'amp': False, 'amp_batch_retries': 2, 'amp_init_scale': 128, 'amp_scale_window': None, 'user_dir': None, 'empty_cache_freq': 0, 'all_gather_list_size': 16384, 'model_parallel_size': 1, 'quantization_config_path': None, 'profile': False, 'reset_logging': False, 'suppress_crashes': False, 'use_plasma_view': False, 'plasma_path': '/tmp/plasma'}, 'common_eval': {'_name': None, 'path': None, 'post_process': None, 'quiet': False, 'model_overrides': '{}', 'results_path': None}, 'distributed_training': {'_name': None, 'distributed_world_size': 1, 'distributed_num_procs': 1, 'distributed_rank': 0, 'distributed_backend': 'nccl', 'distributed_init_method': None, 'distributed_port': -1, 'device_id': 0, 'distributed_no_spawn': False, 'ddp_backend': 'c10d', 'ddp_comm_hook': 'none', 'bucket_cap_mb': 25, 'fix_batches_to_gpus': False, 'find_unused_parameters': False, 'gradient_as_bucket_view': False, 'fast_stat_sync': False, 'heartbeat_timeout': -1, 'broadcast_buffers': False, 'slowmo_momentum': None, 'slowmo_base_algorithm': 'localsgd', 'localsgd_frequency': 3, 'nprocs_per_node': 1, 'pipeline_model_parallel': False, 'pipeline_balance': None, 'pipeline_devices': None, 'pipeline_chunks': 0, 'pipeline_encoder_balance': None, 'pipeline_encoder_devices': None, 'pipeline_decoder_balance': None, 'pipeline_decoder_devices': None, 'pipeline_checkpoint': 'never', 'zero_sharding': 'none', 'fp16': True, 'memory_efficient_fp16': False, 'tpu': True, 'no_reshard_after_forward': False, 'fp32_reduce_scatter': False, 'cpu_offload': False, 'use_sharded_state': False, 'not_fsdp_flatten_parameters': False}, 'dataset': {'_name': None, 'num_workers': 1, 'skip_invalid_size_inputs_valid_test': True, 'max_tokens': 9750, 'batch_size': None, 'required_batch_size_multiple': 8, 'required_seq_len_multiple': 1, 'dataset_impl': None, 'data_buffer_size': 10, 'train_subset': 'train', 'valid_subset': 'valid', 'combine_valid_subsets': None, 'ignore_unused_valid_subsets': False, 'validate_interval': 1, 'validate_interval_updates': 0, 'validate_after_updates': 0, 'fixed_validation_seed': None, 'disable_validation': False, 'max_tokens_valid': 9750, 'batch_size_valid': None, 'max_valid_steps': None, 'curriculum': 0, 'gen_subset': 'test', 'num_shards': 1, 'shard_id': 0, 'grouped_shuffling': False, 'update_epoch_batch_itr': True, 'update_ordered_indices_seed': False}, 'optimization': {'_name': None, 'max_epoch': 150, 'max_update': 0, 'stop_time_hours': 0.0, 'clip_norm': 0.0, 'sentence_avg': False, 'update_freq': [16], 'lr': [0.0005], 'stop_min_lr': -1.0, 'use_bmuf': False, 'skip_remainder_batch': False}, 'checkpoint': {'_name': None, 'save_dir': './checkpoints/1m-scb+mt-opus_27.5.2020/th-en/spm-spm/32000-joined', 'restore_file': 'checkpoint_last.pt', 'continue_once': None, 'finetune_from_model': None, 'reset_dataloader': False, 'reset_lr_scheduler': False, 'reset_meters': False, 'reset_optimizer': False, 'optimizer_overrides': '{}', 'save_interval': 1, 'save_interval_updates': 0, 'keep_interval_updates': -1, 'keep_interval_updates_pattern': -1, 'keep_last_epochs': 25, 'keep_best_checkpoints': -1, 'no_save': False, 'no_epoch_checkpoints': False, 'no_last_checkpoints': False, 'no_save_optimizer_state': False, 'best_checkpoint_metric': 'loss', 'maximize_best_checkpoint_metric': False, 'patience': -1, 'checkpoint_suffix': '', 'checkpoint_shard_count': 1, 'load_checkpoint_on_all_dp_ranks': False, 'write_checkpoints_asynchronously': False, 'model_parallel_size': 1}, 'bmuf': {'_name': None, 'block_lr': 1.0, 'block_momentum': 0.875, 'global_sync_iter': 50, 'warmup_iterations': 500, 'use_nbm': False, 'average_sync': False, 'distributed_world_size': 1}, 'generation': {'_name': None, 'beam': 5, 'nbest': 1, 'max_len_a': 0.0, 'max_len_b': 200, 'min_len': 1, 'match_source_len': False, 'unnormalized': False, 'no_early_stop': False, 'no_beamable_mm': False, 'lenpen': 1.0, 'unkpen': 0.0, 'replace_unk': None, 'sacrebleu': False, 'score_reference': False, 'prefix_size': 0, 'no_repeat_ngram_size': 0, 'sampling': False, 'sampling_topk': -1, 'sampling_topp': -1.0, 'constraints': None, 'temperature': 1.0, 'diverse_beam_groups': -1, 'diverse_beam_strength': 0.5, 'diversity_rate': -1.0, 'print_alignment': None, 'print_step': False, 'lm_path': None, 'lm_weight': 0.0, 'iter_decode_eos_penalty': 0.0, 'iter_decode_max_iter': 10, 'iter_decode_force_max_iter': False, 'iter_decode_with_beam': 1, 'iter_decode_with_external_reranker': False, 'retain_iter_history': False, 'retain_dropout': False, 'retain_dropout_modules': None, 'decoding_format': None, 'no_seed_provided': False, 'eos_token': None}, 'eval_lm': {'_name': None, 'output_word_probs': False, 'output_word_stats': False, 'context_window': 0, 'softmax_batch': 9223372036854775807}, 'interactive': {'_name': None, 'buffer_size': 0, 'input': '-'}, 'model': Namespace(no_progress_bar=False, log_interval=100, log_format=None, tensorboard_logdir='./checkpoints/1m-scb+mt-opus_27.5.2020/th-en/spm-spm/32000-joined/log', seed=1, cpu=False, fp16=True, memory_efficient_fp16=False, fp16_no_flatten_grads=False, fp16_init_scale=128, fp16_scale_window=None, fp16_scale_tolerance=0.0, min_loss_scale=0.0001, threshold_loss_scale=None, user_dir=None, empty_cache_freq=0, all_gather_list_size=16384, model_parallel_size=1, checkpoint_suffix='', quantization_config_path=None, criterion='label_smoothed_cross_entropy', tokenizer=None, bpe='sentencepiece', optimizer='adam', lr_scheduler='inverse_sqrt', task='translation', num_workers=1, skip_invalid_size_inputs_valid_test=True, max_tokens=9750, max_sentences=None, required_batch_size_multiple=8, dataset_impl=None, train_subset='train', valid_subset='valid', validate_interval=1, fixed_validation_seed=None, disable_validation=False, max_tokens_valid=9750, max_sentences_valid=None, curriculum=0, distributed_world_size=1, distributed_rank=0, distributed_backend='nccl', distributed_init_method=None, distributed_port=-1, device_id=0, distributed_no_spawn=False, ddp_backend='c10d', bucket_cap_mb=25, fix_batches_to_gpus=False, find_unused_parameters=False, fast_stat_sync=False, broadcast_buffers=False, distributed_wrapper='DDP', slowmo_momentum=None, slowmo_algorithm='LocalSGD', localsgd_frequency=3, nprocs_per_node=1, arch='transformer', max_epoch=150, max_update=0, clip_norm=0.0, sentence_avg=False, update_freq=[16], lr=[0.0005], use_bmuf=False, save_dir='./checkpoints/1m-scb+mt-opus_27.5.2020/th-en/spm-spm/32000-joined', restore_file='checkpoint_last.pt', reset_dataloader=False, reset_lr_scheduler=False, reset_meters=False, reset_optimizer=False, optimizer_overrides='{}', save_interval=1, save_interval_updates=0, keep_interval_updates=-1, keep_last_epochs=25, keep_best_checkpoints=-1, no_save=False, no_epoch_checkpoints=False, no_last_checkpoints=False, no_save_optimizer_state=False, best_checkpoint_metric='loss', maximize_best_checkpoint_metric=False, patience=-1, no_token_positional_embeddings=False, no_cross_attention=False, cross_self_attention=False, layer_wise_attention=False, encoder_layerdrop=0, decoder_layerdrop=0, encoder_layers_to_keep=None, decoder_layers_to_keep=None, quant_noise_pq=0, quant_noise_pq_block_size=8, quant_noise_scalar=0, label_smoothing=0.1, adam_betas='(0.9, 0.98)', adam_eps=1e-08, weight_decay=0.0, use_old_adam=False, warmup_updates=4000, warmup_init_lr=1e-07, data='C:\Users\LENOVO\pythainlp-data\scb_1m_th-en_spm_1.0\SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0\vocab', source_lang='th', target_lang='en', load_alignments=False, left_pad_source=True, left_pad_target=False, max_source_positions=1024, max_target_positions=1024, upsample_primary=1, truncate_source=False, eval_bleu=False, eval_bleu_detok='space', eval_bleu_detok_args=None, eval_tokenized_bleu=False, eval_bleu_remove_bpe=None, eval_bleu_args=None, eval_bleu_print_samples=False, share_decoder_input_output_embed=True, dropout=0.3, encoder_embed_path=None, encoder_embed_dim=512, encoder_ffn_embed_dim=2048, encoder_layers=6, encoder_attention_heads=8, encoder_normalize_before=False, encoder_learned_pos=False, decoder_embed_path=None, decoder_embed_dim=512, decoder_ffn_embed_dim=2048, decoder_layers=6, decoder_attention_heads=8, decoder_normalize_before=False, decoder_learned_pos=False, attention_dropout=0.0, activation_dropout=0.0, activation_fn='relu', adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, share_all_embeddings=False, adaptive_input=False, decoder_output_dim=512, decoder_input_dim=512, no_scale_embedding=False, layernorm_embedding=False, sentencepiece_model='C:\Users\LENOVO\pythainlp-data\scb_1m_th-en_spm_1.0\SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0\bpe\spm.th.model', stop_min_lr=-1, _name='transformer', tie_adaptive_weights=False, checkpoint_activations=False, offload_activations=False, min_params_to_wrap=100000000), 'task': {'_name': 'translation', 'data': 'C:\Users\LENOVO\pythainlp-data\scb_1m_th-en_spm_1.0\SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0\vocab', 'source_lang': 'th', 'target_lang': 'en', 'load_alignments': False, 'left_pad_source': True, 'left_pad_target': False, 'max_source_positions': 1024, 'max_target_positions': 1024, 'upsample_primary': 1, 'truncate_source': False, 'num_batch_buckets': 0, 'train_subset': 'train', 'dataset_impl': None, 'required_seq_len_multiple': 1, 'eval_bleu': False, 'eval_bleu_args': None, 'eval_bleu_detok': 'space', 'eval_bleu_detok_args': None, 'eval_tokenized_bleu': False, 'eval_bleu_remove_bpe': None, 'eval_bleu_print_samples': False}, 'criterion': {'_name': 'label_smoothed_cross_entropy', 'label_smoothing': 0.1, 'report_accuracy': False, 'ignore_prefix_size': 0, 'sentence_avg': False}, 'optimizer': {'_name': 'adam', 'adam_betas': '(0.9, 0.98)', 'adam_eps': 1e-08, 'weight_decay': 0.0, 'use_old_adam': False, 'fp16_adam_stats': False, 'tpu': True, 'lr': [0.0005]}, 'lr_scheduler': {'_name': 'inverse_sqrt', 'warmup_updates': 4000, 'warmup_init_lr': 1e-07, 'lr': [0.0005]}, 'scoring': None, 'bpe': {'_name': 'sentencepiece', 'sentencepiece_model': 'C:\Users\LENOVO\pythainlp-data\scb_1m_th-en_spm_1.0\SCB_1M-MT_OPUS+TBASE_th-en_spm-spm_32000-joined_v1.0\bpe\spm.th.model', 'sentencepiece_enable_sampling': False, 'sentencepiece_alpha': None}, 'tokenizer': None, 'ema': {'_name': None, 'store_ema': False, 'ema_decay': 0.9999, 'ema_start_update': 0, 'ema_seed_model': None, 'ema_update_freq': 1, 'ema_fp32': False}}
C:\Users\LENOVO\AppData\Local\Programs\Python\Python310\lib\site-packages\fairseq\models\transformer\transformer_encoder.py:281: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ..\aten\src\ATen\NestedTensorImpl.cpp:179.)
x = torch._nested_tensor_from_mask(
Output ends here
Thanks in advance
Expected results
translated word in Thai
Current results
in description
Steps to reproduce
Code in description
PyThaiNLP version
4.0.2
Python version
3.8.16
Operating system and version
Windows11
More info
No response
Possible solution
No response
Files
No response

What is your size of memory? (RAM)

source: gharchive/issue

authors: midokhalaf wannaphong

repo: PyThaiNLP/pythainlp

license_source: bigquery


text:
logger.experiment.add_image() at end of epoch when using new simplified pl.Train/EvalResult objects

Question:
What are we going to consider best practice for visualizing images, embeddings, etc. to tensorboard when using pl.Train/EvalResult objects?
In light of #2651 and related PRs, what's the right way to do this?
Let's say we have a dataset of images, and we want to visualize a single batch of reconstructions once per epoch. I typically do this in validation_epoch_end(), using logger.experiment.add_image().
Code:
Let's say my code now looks like this:
 def validation_step(self, batch, batch_idx):
        step_dict = self._step(batch)
        result = pl.EvalResult(early_stop_on=step_dict['loss'],
                               checkpoint_on=step_dict['loss'],
                              )

        # logging
        result.log('avg_val_loss', step_dict['loss'],
                   on_epoch=True, reduce_fx=torch.mean)

        return result, step_dict

which works fine and is definitely much cleaner than the original method of returning multiple logging dicts. 😁
I now want to do something at the end of the validation loop so I specify:
def validation_epoch_end(self, outputs):
        img_batch = outputs[-1]['step_dict']['viz']['x_hat']
        img_batch = img_batch.view(img_batch.shape[0], *[1,32,32])
        grid = torchvision.utils.make_grid(img_batch)
        self.logger.experiment.add_image('x_hat', grid, self.current_epoch)

        avg_val_loss = torch.stack([x['avg_val_loss'] for x,y in outputs]).mean()
        return avg_val_loss

outputs in this case is a list of tuples where the first element is the EvalResult for each val step, and the second element contains step_dict which includes all losses and reconstructed x_hats for each val step.
Is there a better way? One potential downside to this is that outputs can eat up a significant chunk of memory if you're not careful.
What's your environment?

OS: [Ubuntu]
Packaging [pip]
Version [master]

I guess one other detail I will add is that it's unclear which of these has control over logging 'avg_val_loss', given that this used to be one role of validation_epoch_end and its magic keyword 'avg_val_loss' in the returned dict.
Using the new way outlined above, I'm still getting this error:
 RuntimeWarning: The metric you returned None must be a `torch.Tensor` instance, checkpoint not saved HINT: what is the value of loss in validation_epoch_end()?
  warnings.warn(*args, **kwargs)

Looks like using
return {'loss': avg_val_loss}
at the end of validation_epoch_end() fixes that warning, but when combined with EvalResult, I don't really understand why it should be necessary to return that at all for checkpointing.

It would be great an example possibly in Colab in which it is shown good practices logging the loss and other metrics as well as examples of samples (some images like in VAE,  for instance) over train/val/test.

I have this all set up for a VAE, but I want to make sure I'm doing best practices with the latest updates. Once we come to a consensus on this, I can provide a colab link. 😁 There's also the bolts repo, which we could update!

I'm trying to find a best practice for this. Any advice? (I work with 3D medical images so memory is definitely a concern!)

I'm trying to log some figures once per epoch in validation_epoch_end() but struggles to find a good practice.
In addition to model outputs, I also need to return some input data in (effectively your) step_dict for visualisation.
I don't want to accumulate a list in the validation loop as I only want one set of images per epoch. Any advice?
(I work with 3D medical images so memory is definitely a concern!)

I have this same question... what are the best practices for logging images? my usual wandb.log seems to no longer work, as it is now wandbLogger. I read the Train/EvalResults page but the documentation seems sparse here.

@joshclancy You should still be able to import wandb and log images separately.
Otherwise you also have access to wandb at self.logger.experiment within your trainer.

Maybe this should work in validation and testing:
https://github.com/PyTorchLightning/pytorch-lightning/blob/471ca375babad9093abf60683a8d0647ac33d4a8/pytorch_lightning/core/lightning.py#L347-L352

authors: Ceceu borisdayma joshclancy qiuhuaqi romesc-CMU

repo: PyTorchLightning/pytorch-lightning


text:
Lightning internal refactors

🐛 Bug

To Reproduce
Steps to reproduce the behavior:

Go to '...'
Run '....'
Scroll down to '....'
See error

Code sample

Expected behavior

Environment
Please copy and paste the output from our
environment collection script
(or fill out the checklist below manually).
You can get the script and run it with:
wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py
# For security purposes, please check the contents of collect_env_details.py before running it.
python collect_env_details.py

PyTorch Version (e.g., 1.0):
OS (e.g., Linux):
How you installed PyTorch (conda, pip, source):
Build command you used (if compiling from source):
Python version:
CUDA/cuDNN version:
GPU models and configuration:
Any other relevant information:

Additional context

Complete. A few straggler files here and there, but nothing we can't solve in these weeks

authors: edenlightning williamFalcon


text:
Resume training from a finished-training model will results in a new incorrect checkpoint

To verify a model if a model has finished training. I ran the training script again.
However, when I try to evaluate with the model, I got error that:
pytorch_lightning.utilities.exceptions.MisconfigurationException:
            you restored a checkpoint with current_epoch=11
            but the Trainer(max_epochs=10)

After digging a little bit, it seems that since the model has finished training, it will directly go to on_train_end, and save the model checkpoint with currect_epoch+1.
I am wondering if the design of saving checkpoints with "next epoch" is reasonable? Why not add the epoch number after resuming?

I moved +1 from https://github.com/PyTorchLightning/pytorch-lightning/blob/dec31b3e761115b0a938caf35649acd814f557c9/pytorch_lightning/trainer/connectors/checkpoint_connector.py#L254 to https://github.com/PyTorchLightning/pytorch-lightning/blob/dec31b3e761115b0a938caf35649acd814f557c9/pytorch_lightning/trainer/connectors/checkpoint_connector.py#L150

A simpler solution is adding an if before https://github.com/PyTorchLightning/pytorch-lightning/blob/b45b57cc587a11c3049fb5c605cf2d4b0689de2c/pytorch_lightning/trainer/trainer.py#L510

I add an if before https://github.com/PyTorchLightning/pytorch-lightning/blob/72f19768c828b734d8565ffef7b78fb9a57ba847/pytorch_lightning/trainer/training_loop.py#L178
         if self.trainer.current_epoch < self.trainer.max_epochs and not self.interrupted:
             self.check_checkpoint_callback(should_save=True, is_last=True)

duplicate https://github.com/PyTorchLightning/pytorch-lightning/issues/4176 ?
I think a simple solution would be to not do anything anything if current_epoch >= max_epoch

@rohitgr7 yes. It's the same.


text:
fix(wandb): allow use of sweeps

Before submitting

[X] Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
[X] Did you read the contributor guideline, Pull Request section?
[ ] Did you make sure to update the docs?
not applicable
[X] Did you write any new necessary tests?
[X] If you made a notable change (that affects users), did you update the CHANGELOG?

What does this PR do?
Fixes #1290 .
PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Did you have fun?
Make sure you had fun coding 🙃

The test has been updated but the way it's set up, we only see that the function has been called -> see here
This is due to the fact that wandb uses context and it does not work well with pytest (there was some errors with some platforms tested such as windows).
Similar behavior happens with other loggers which also use mocks and verify only that the correct function has been called.
Adding another test overwriting a previous value would only check the same function has been called (so no more than current test scope).


text:
🇸🇪 Included swedish strings

PR for Swedish strings.. :+1:

Thanks @jackbillstrom for your contribution 🎉

authors: Edujugon jackbillstrom

repo: PygmySlowLoris/vue-stepper

license: MIT


text:
[User Story] As a systems engineer, I would like to know the Wi-Fi specifications of a specific device

Is your user story related to a problem? Please describe.
As a systems engineer, I am frustrated by not knowing the Wi-Fi specifications of the device I am monitoring.
Describe the solution you'd like
I would like to see Wi-Fi specifications in my data and displayed on the application so I can easily associate Wi-Fi usage with the device I am monitoring.
Describe alternatives you've considered
Obtaining the information from a command line or shell-only method is tedious and not convenient.
Additional context
The information can be sent to either SQL-style tables or a MongoDB collection.
The wi-fi specifications should include:

Adapter name
SSID
Connection type
IPv4 address
IPv6 address

Adapter name is a variable and holds no standard across OS'.
Solution needs to have user state which network adapter to monitor.
Solution will have OS default adapter names if given name is not found.

repo: Pyxsys/ttg-healthcheck


text:
Some web index entries have no ResearchInfrastructure

This leads to an IndexError exception, eg:
https://lfw-ds001-i050.lifewatch.dev/webSearch/genericsearch?term=fish&page=1&searchtype=imagesearch

This comes from kb-indexer: https://github.com/QCDIS/kb-indexer/issues/17

repo: QCDIS/KMS-generic


text:
Strip attributes from channels when closing an instrument.

Contains the changes from https://github.com/QCoDeS/Qcodes/pull/3912 which should be merged before this.
instrument.close stripes the attributes of an instrument but not of the modules within the instrument. It is unclear to me if it really makes sense to strip the atters but if we do that we should also do it here

bors merge

bors merge

repo: QCoDeS/Qcodes


text:
Backport monitor

Backport Elm based monitor from QCoDeS DK
@jana-d

@jenshnielsen I just tested the PR from spyder. My system freezes after trying to start a monitor. I tried:
In [4]: m=qcodes.Monitor( gates.B0, gates.B1)

This results in the following error and my system being locked.
ERROR:asyncio:Exception in callback Monitor._create_task(<Future at 0x...state=pending>, <websockets.p...00211FE148AC8>)()
handle: <Handle Monitor._create_task(<Future at 0x...state=pending>, <websockets.p...00211FE148AC8>)()>
Traceback (most recent call last):
  File "C:\Users\eendebakpt\AppData\Local\Continuum\Anaconda3\envs\py35\lib\asyncio\events.py", line 125, in _run
    self._callback(*self._args)
  File "c:\svn\qcodes\qcodes\monitor\monitor.py", line 172, in _create_task
    task = self.loop.create_task(coro)
  File "C:\Users\eendebakpt\AppData\Local\Continuum\Anaconda3\envs\py35\lib\asyncio\base_events.py", line 261, in create_task
    task = tasks.Task(coro, loop=self)
  File "C:\Users\eendebakpt\AppData\Local\Continuum\Anaconda3\envs\py35\lib\asyncio\tasks.py", line 70, in __init__
    assert coroutines.iscoroutine(coro), repr(coro)
AssertionError: <websockets.py35.server.Serve object at 0x00000211FE148AC8>

I think this breaks with websockets 3.4 which is why setup.py limits it to 3.3. I initially missed that commit when backporting. Can you try with 3.3?

@jenshnielsen Thanks, using websockets 3.3 solved the issue. Will setting of parameters also be made an option? That would be important for us to make it more usefull.

It's an old monitor that we have used and found useful in Copenhagen for some time which others have been interested in using hence this pull request. It's not meant for setting. We are aware of that request and it will happen eventually

@WilliamHPNielsen  added a readme with a link to the ELM repo

authors: jenshnielsen peendebak


text:
spring-petclinic link from s3 bucket

https://qt-s3-new-testing.s3-us-west-2.amazonaws.com/spring-petclinic.jar

https://qt-s3-new-testing.s3-us-west-2.amazonaws.com/gameoflife.war

repo: QT-DevOps/DevOpsIssues


text:
Acquisition and parsing of GPS data

Describe the solution you'd like
I have uploaded the data we collected on 28/11/23 https://gist.github.com/alphaville/995a15ef74e8d5b3128c87667b5bfdaf. This is how the data looks like
$GPGLL,5434.81291,N,00556.15593,W,145737.00,A,A*70
$GPRMC,145738.00,A,5434.81285,N,00556.15627,W,0.351,,281123,,,A*63
$GPVTG,,T,,M,0.351,N,0.650,K,A*27
$GPGGA,145738.00,5434.81285,N,00556.15627,W,1,05,1.28,64.4,M,52.5,M,,*79
$GPGSA,A,3,12,32,24,15,19,,,,,,,,2.51,1.28,2.16*02
$GPGSV,3,1,10,02,02,344,37,12,56,202,22,15,26,166,27,17,,,35*49
$GPGSV,3,2,10,19,34,071,36,22,11,050,32,23,18,236,,24,72,108,35*72
$GPGSV,3,3,10,25,21,223,,32,26,309,41*75

Documentation
The data are structured according to the NMEA Standard Logs protocol. Examples:

GPGLL - Geographic Position
GPRMC - GPS specific information incl. latitude/longitude and speed
GPVTG - Track Made Good and Ground Speed
GPGGA - GPS fix data and undulation
GPGSA - GPS DOP and active satellites
GPGSV - GPS Satellites in View

First of all we need to decipher what all these mean and how the data are structured (read the docs).
Issues to address

We interface the GNSS over the serial at a baud rate of 500,000. We can write a simple interface that reads the data from serial (using pyserial) and parses it
We should follow what we did here for the anemometer: a thread runs in the background, collects data and stores it in memory, and then we can access is with a non-blocking function (or get a moving average, median, etc).
For some reason the data seems to be coming in at 1Hz, which is too slow. Can we speed this up?

Accuracy
In terms of absolute accuracy, it seems to be quite bad...

There seems to be negligible variance when the antenna is perfectly static.
We should see how sensitive it is: we should move it by 1m and see what measurements we get.

@pdavid747 @Runway27 Note that if you write your own code you won't have to use gpsd at all. Actually you shouldn't use it because it won't let you read the serial. Start by reading the documentation to understand what sort of data we get from the GPS and then build a simple DTO: a Python class whose attributes are the quantities the GNSS measures.

Accuracy of GPS measurements and conversion to degrees
⚠ The GPS coordinates are in degrees-minutes-seconds; after the conversion to a decimal representation, it turns out that the coordinates are fairly accurate.
@pdavid747 use the following function to convert the GPGLL data to degrees:
def deg_min_sec_to_decimal(dms, dir="N"):
    """
    Convert degrees-minutes-seconds from GPGLL to degrees (deciman)

    :param dms: degrees-minutes-seconds (must be positive)
    :param dir: direction (can be one of 'N', 'W', 'E', 'S')
    """
    dms_abs = np.abs(dms)
    degrees = np.floor(dms_abs/100)
    minutes = np.floor(dms_abs - 100 * np.floor(dms_abs/100))
    seconds = 10 * (dms_abs - np.floor(dms_abs))
    deg_decimal_abs = degrees + minutes / 60 + seconds / 3600
    dir_sign = -1 if (dir in ["S", "W"]) else 1
    return dir_sign * deg_decimal_abs

I installed PyGPSClient on Mac:

It allows to set the measurement update rate and many more settings.

Scatter plot of estimated positions while static:

while being connected to 3-8 satellites (the antenna is placed on metallic plate, but indoors having a decent but not perfect view of the sky; it has an elevation of around 75 degrees). We seem to be getting an accuracy of about ±10m at best, but we might get better results outdoors.

Some more results...

Figure 1. Satellites

Figure 2. SNR

Figure 3. Scatter plot (best one)

Figure 4. Scatter plot

Parameter
Value

Satellites in view
7 - 9

SIV
7-9

SIP
7

PDOP
3-3.5

HDOP
1.5-3

More data
From my other window with 7 satellites (10 in view), pdop: 1.97, and higher SNR

Done

repo: QUB-ASL/bzzz


text:
LeetHub doesn't work at GFG now

Describe the bug
LeetHub doesn't work at GFG, probably because GFG just updated its UI about Practice.
Expected behavior
LeetHub should upload the code to GitHub as before.
Screenshots

Screenshot of GFG Practice from my laptop a few minutes ago.

same here Leethub is not working

@garyellow absolutely right.
This issue is because the gfg site got an update.
gfg.js script requires an update.
here is a fix you can use locally.
I have opened a PR to fix it.
You can use it locally by the time the issue gets fixed.

@garyellow absolutely right. This issue is because the gfg site got an update. gfg.js script requires an update. here is a fix you can use locally. I have opened a PR to fix it. You can use it locally by the time the issue gets fixed.

It works.
Thx!!!

@garyellow absolutely right. This issue is because the gfg site got an update. gfg.js script requires an update. here is a fix you can use locally. I have opened a PR to fix it. You can use it locally by the time the issue gets fixed.

how to use it locally as you mentioned. can you please tell or share some article or video reference to understand how to use leethub locally till gfg bug resolved

@garyellow absolutely right. This issue is because the gfg site got an update. gfg.js script requires an update. here is a fix you can use locally. I have opened a PR to fix it. You can use it locally by the time the issue gets fixed.

It works. Thx!!!

brother can you help me to understand how i can use leethub locally in order to push gfg solution to GitHub repo successfully

@garyellow absolutely right. This issue is because the gfg site got an update. gfg.js script requires an update. here is a fix you can use locally. I have opened a PR to fix it. You can use it locally by the time the issue gets fixed.

It works. Thx!!!

brother can you help me to understand how i can use leethub locally in order to push gfg solution to GitHub repo successfully

@Dheeraj-Bhandari
u can follow the steps in README.md
the final part tells how to use it in local
u just needs to change the gfg.js file to the PR one after u clone to ur computer

@garyellow absolutely right. This issue is because the gfg site got an update. gfg.js script requires an update. here is a fix you can use locally. I have opened a PR to fix it. You can use it locally by the time the issue gets fixed.

It works. Thx!!!

brother can you help me to understand how i can use leethub locally in order to push gfg solution to GitHub repo successfully

Or you can simply clone from the patch-1 branch using the following command

git clone -b patch-1 https://github.com/ASahu16/LeetHub.git

Then follow the below steps:

pls upload a video of the issue solution on youtube. It will be more helpful.

repo: QasimWani/LeetHub


text:
OpenCV Version

Hello!
I was wondering if there is a particular reason why opencv is at version 4.6 instead of 4.8? I was able to successfully install version 4.8 with gstreamer and such, following qengineering tutorials, but was curious if maybe there's a performance difference.

@Klinefelters,
It seems you have downloaded the previous version.
The last one (7 -15) has OpenCV 4.8.0.

authors: Klinefelters Qengineering


text:
Fix groundenergy in EigenstateResult

Summary
Fix groundenergy in EigenstateResult to return a correct value.
Details and comments
Previously, it returns None, even if we have some energies. I changed not energies.size  to energies.size  in the 'if' sentence.
@property
def groundenergy(self) -> Optional[float]:
    """returns ground energy"""
    energies = self.eigenenergies
    if isinstance(energies, np.ndarray) and not energies.size:
        return energies[0].real
    return None

Pull Request Test Coverage Report for Build 2294279237

1 of 1 (100.0%)  changed or added relevant line in 1 file are covered.
1 unchanged line in 1 file lost coverage.
Overall coverage remained the same at 83.584%

Files with Coverage Reduction
New Missed Lines
%

qiskit_nature/results/eigenstate_result.py
1
93.85%

Totals

Change from base Build 2274591591:
0.0%

Covered Lines:
9185

Relevant Lines:
10989

💛  - Coveralls

authors: a-matsuo coveralls

repo: Qiskit/qiskit-nature


text:
RuntimeWarning: Could not import the Aer provider

Information

Qiskit Terra version: 0.9.0
Python version: 3.6.8
Operating system: IBM i

What is the current behavior?
python3 -c 'import qiskit'
/QOpenSys/pkgs/lib/python3.6/site-packages/qiskit/__init__.py:56: RuntimeWarning: Could not import the Aer provider from the qiskit-aer package. Install qiskit-aer or check your installation.
  RuntimeWarning)

Steps to reproduce the problem
python3 -c 'import qiskit'
What is the expected behavior?
successful import without warnings
Suggested solutions
__init.py__ is out of sync with the new Aer provider name and this needs to be corrected

Please see my comment on your PR: https://github.com/Qiskit/qiskit-terra/pull/3184#issuecomment-537708855 this was intended to be aer. For more background information you can also refer to the PR which added this here: https://github.com/Qiskit/qiskit-terra/pull/2842 (and the multiple issues that reference it later). The real fix for this warning is to abandon namespace packaging for qiskit. But that is larger in scope than just terra and effects all the qiskit packages so it's being tracked in the meta-repository here: https://github.com/Qiskit/qiskit/issues/559
I'm going to close this accordingly, but please feel free to reopen this issue if you think that I'm missing something or there is more to discuss on this.

authors: ThePrez mtreinish

repo: Qiskit/qiskit-terra


text:
Operator support for SciPy Sparse Matrices

Summary
Simple addition of sparse matrix support in quantum_info/ Operator.

This will break almost every method of Operator which require the data structure to be a Numpy array (eg add/sub/dot/compose on subsystems, tensor, expand, is_unitary, conversion to channels).
Rather than mix data structures in an adhoc way the correct way to do this would be to define a separate SparseOperator class.

I would also fix the operator to be of CSR type. Enforcing this will be fruitful in implementation.

authors: chriseclectic dongreenberg nonhermitian


text:
Enable ConsolidateBlocks pass to handle 1-length blocks.

Summary
Fixes #5203
ConsolidateBlocks replaces a block of consecutive gates with a single
unitary although it ignores blocks of length 1. This was causing
failures in the 'synthesis' translation mode where this pass is used
to calculate the unitaries used for the actual synthesis since
gates not in the basis but in 1-length blocks were added to the circuit
unchanged.
Details and comments
This commit identifies the cases where consolidation is not possible
and ignores consolidation for only those cases:

When the node is not a gate.
When the node is parameterized.

Notice the PR adds a continue to reduce a level of indentation inside the
main loop whenever any of the above conditions hold.
A further modification is also included in this commit to avoid calculating
the number of 2-qubit basis gates when the circuit has only 1-qubit.

Actually, this is only changing lines 5 lines but since I remove an indentation level, it seems like I'm changing a lot of things. You can set the comparison preferences to ignore the white space and see a better depiction of what I'm saying.

I'm going to close this PR as stale as I don't think it applies to the state of the ConsolidateBlocks pass anymore. But if I'm missing something or there is still more for this to do here, please feel free to reopen the PR and fix the merge conflicts and I'll review it.

authors: delapuente mtreinish


text:
Fix UnitarySynthesis for 3+ qubits when compiled for a backend

Summary
When encountering a 3+ qubit UnitaryGate, UnitarySynthesis currently replaces the whole dag with the definition of the block instead of appending it, see
https://github.com/Qiskit/qiskit/blob/079f69f7dd906299dfdf8c1262fa9bc1d75e478b/crates/accelerate/src/unitary_synthesis.rs#L375
Details and comments
This also leads to #13586 because the qubit/clbit interners are overwritten, but maybe more importantly this can lead to issues when multiple 3-qubit blocks are present in the circuit. E.g. running
import numpy as np
from qiskit.quantum_info import Operator
from qiskit.circuit.library import UnitaryGate
from qiskit import QuantumCircuit, transpile
from qiskit_ibm_runtime.fake_provider import FakeSherbrooke

backend = FakeSherbrooke()

x = QuantumCircuit(3)
x.x(2)
x_mat = Operator(x)

qc = QuantumCircuit(3, 1)
qc.unitary(x_mat, range(3))
qc.unitary(np.eye(2 ** 3), range(3))

tqc = transpile(qc, backend, optimization_level=0)
print(tqc.count_ops())

produces an empty circuit, since the last block implements the identity.
Some additional tests, such as the precise case of #13586 and a series of 3q UnitaryGates, should be added.

Pull Request Test Coverage Report for Build 12434705540
Details

2 of 2 (100.0%)  changed or added relevant lines in 1 file are covered.
23 unchanged lines in 2 files lost coverage.
Overall coverage decreased (-0.01%) to 88.941%

Files with Coverage Reduction
New Missed Lines
%

crates/qasm2/src/lex.rs
5
91.73%

crates/qasm2/src/parse.rs
18
96.23%

Totals

Change from base Build 12420636821:
-0.01%

Covered Lines:
79419

Relevant Lines:
89294

💛  - Coveralls

Thanks for the fix, @Cryoris!
Looks good! (modulo some minor spell-checking nitpicks about the release notes)

Some additional tests, such as the precise case of https://github.com/Qiskit/qiskit/issues/13586 and a series of 3q UnitaryGates, should be added.

Are you planning to add a test for that (like the one you have in the code snippet in the description)?

authors: Cryoris alexanderivrii coveralls

repo: Qiskit/qiskit


text:
Menu API & Listeners - Part 3 (task #809)

This PR is part of the work to be done for #809. Also, it depends on work completed by PR QoboLtd/cakephp-menu#103
The most important changes is that menus are being constructed using Menu Cell (along with the already implemented Event Listeners for the following templates:

src/Template/Element/Module/Menu/index_actions.ctp
src/Template/Element/Module/Menu/view_top.ctp

The following templates are being deprecated and will be removed in the next major version

src/Template/Element/menu-render.ctp
src/Template/Plugin/CsvMigrations/Element/Menu/related_actions.ctp
src/Template/Plugin/CsvMigrations/Element/Menu/view_top.ctp
src/Template/Plugin/Search/Element/Menu/dashboards-view-top.ctp
src/Template/Plugin/Search/Element/Menu/search-view-actions.ctp

Also,

Add event listener support for src/Template/Element/Module/Menu/related_actions.ctp

Codecov Report

Merging #627 into task-809-feature will decrease coverage by 0.02%.
The diff coverage is 0%.

@@                  Coverage Diff                   @@
##             task-809-feature     #627      +/-   ##
======================================================
- Coverage               31.29%   31.26%   -0.03%
  Complexity               1168     1168
======================================================
  Files                     105      105
  Lines                    4001     4004       +3
======================================================
  Hits                     1252     1252
- Misses                   2749     2752       +3

Impacted Files
Coverage Δ
Complexity Δ

src/Event/Plugin/Menu/View/SearchViewListener.php
0% <0%> (ø)
4 <0> (ø)
:arrow_down:

src/Event/Plugin/Menu/View/MenuEntityTrait.php
0% <0%> (ø)
6 <0> (ø)
:arrow_down:

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 3fbc0a4...1fe6be8. Read the comment docs.

authors: codecov-io softius

repo: QoboLtd/project-template-cakephp

license: mit


text:
Command line application for conda-store

Ping the rest api should be installable.
Need a similar environment to https://docs.coiled.io/user_guide/software_environment.html.

Closed by https://github.com/Quansight/conda-store/pull/29

repo: Quansight/conda-store


text:
chat bubble cannot be loaded

Bug description
Chat bubble icon in left side bar is missing

This was broken in #312. Previously it looked like

The problem is the relative link in https://github.com/Quansight/ragna/blob/0cb205f24ca9fb322ac32a2e1aaec6b34928cd2e/ragna/deploy/_ui/css/left_sidebar/button.css#L27
Since we are currently in /css/left_sidebar, it looks for /css/left_sidebar/imgs/chat_bubble.svg and fails.
How to reproduce the bug?
Open the web UI and look at the left sidebar
Versions and dependencies used
main after #312
Anything else?
This can be fixed by using url("../../imgs/chat_bubble.svg"), but I don't know if that is the best way to do it.

Using url("../../imgs/chat_bubble.svg") is a fair way to have it working. Another solution is to have the image base64-encoded in the CSS, but we don't want that.

authors: pierrotsmnrd pmeier

repo: Quansight/ragna


text:
Should we get rid of pip installs via bash commands?

Looks not too great - from chang_ramsey.rst

I don't mind them. We could use a :hide-output: if you like?

Hi @mmcky, I think we should keep the pip installs but it would be nice to have the hide-output class you mentioned above

authors: mmcky natashawatkins

repo: QuantEcon/lecture-source-py


text:
[search] feedback on 0.5.0

Here are some minor feedback points on 0.5.0 testing
It would good to tidy up the x when opening the box so that it doesn't clash with the search icon

@DrDrij has some comments around compatibility with mobile etc. re: css

Test deployment of lecture-python-programming is available here
https://6453038377900a224cc83f30--epic-agnesi-957267.netlify.app/search.html?q=python

Just writing the styles now :)

Thanks @DrDrij and @mmcky

@DrDrij no particular rush on this -- but let @AakashGfude if you need him to make any updates etc.

authors: AakashGfude DrDrij mmcky

repo: QuantEcon/quantecon-book-theme


text:
noncliff: enhanced error message for inapplicable Project! of GeneralizedStabilizer

I have incorporated the relevant details from the paper to create an informed error message. This PR is inspired from this comment: https://github.com/QuantumSavory/QuantumClifford.jl/pull/355#discussion_r1826569150
I think we only need one proj, so removed _proj₋ or _proj₊ in favor of proj.

[x] The code is properly formatted and commented.
[x] Substantial new functionality is documented within the docs.
[x] All new functionality is tested.
[x] All of the automated tests on github pass.
[x] We recently started enforcing formatting checks. If formatting issues are reported in the new code you have written, please correct them. There will be plenty of old code that is flagged as we are slowly transitioning to enforced formatting. Please do not worry about or address older formatting issues -- keep your PR just focused on your planned contribution.

The PR is ready for review. Please run it on the console to check if any further formatting changes are required.
Thank you!

marking as draft to make it easer for me to track what needs review

Thank you! I have incorporated your comments and checked the output on console.
Thank you for rewording as you see fit!

Thank you for polishing the PR!

authors: Fe-r-oz Krastanov


text:
State: Starting

Hello,
since a few days the state of my MIELE washing machine is set to „starting“.
But it is turned off…
many ideas or workarounds?

Since today same problem with my dryer. The characteristic is set to 1.
what can I do?

Can you post the Homebridge logging of the Miele@Home plugin?

Hi! Problem solved!
I‘ve lost the connection with the Miele App.
After a reconnect everything worked fine

Thanks. Somehow awkward the Miele app influences the results obtained by the Homebridge plugin though.

repo: QuickSander/homebridge-mieleathome


text:
Rescue rai from mappings nightmare

SystemToast.Type (unmapped) has been refactored to SystemToast.Id since Type is a more misleading name while Id more accurately states the intention as a sort of toast id that differentiates toasts from each other
MapIcon still needs to be completely refactored into MapDecoration (or the vice-versa..?)
accumulatedScrollX/Y has been decopypasted from Mojmap; it's now scrollDeltaX/Y, especially since there is no accumulation going on here (unlike cursorDeltaX/Y, although after some discussion, that name is alright)

my knight in shining armour

repo: QuiltMC/quilt-mappings


text:
🛑 Exports is down

In 056d947, Exports (https://services.autovance.com/exports/health) was down:

HTTP code: 503
Response time: 22 ms

Resolved: Exports is back up in bd8549d.

repo: QuorumDMS/av-uptime


text:
"num_sampled_nodes"报错

您好。
我在运行这个代码的时候报了这错，请问是哪里有问题吗？
求解答，谢谢。

Thank you for your interest in our work! 🤗
Based on the error message you encountered, it seems that your torch_geometric and pyg-lib versions differ from the tested environment of this code repository. Specifically, the class GlobalStorage in your torch_geometric version does not have a num_sampled_nodes member, which is causing the error.
To resolve this issue, I think it might be helpful if you ensure that your Python library versions align with our official settings. Specifically, use the following versions:

torch_geometric==2.2.0 or torch_geometric==2.3.1
pyg-lib==0.2.0+pt113cu117

Remember that while pyg-lib is optional for running this code, it significantly accelerates the sampling process. We highly recommend installing this library.
Feel free to ask any additional questions if you encounter further issues. We’re here to help! 🤝
You can also refer to our previous answer in Issue #1 since it may provide additional insights.

Besides, please be cautious with the global path variable ROOT_PATH defined in the file code/world.py. It would be advisable to update this path to match your local code folder path.

Thank you very much! The program finally ran after installing the pyg-lib!


text:
Fix #175

Also implement new rbind_results() function added w/ https://github.com/R-ArcGIS/arcgisutils/issues/38
Changes
Per the discussion in draft PR: https://github.com/R-ArcGIS/arcgislayers/pull/167 this PR fixes an issue w/ returning fields not specified in fields argument and
Issues that this closes
#175
Follow up tasks
Additional changes as described in draft PR.

This is super clear! Thank you!
We should add some tests here:

what happens when we return no fields? fields = ""
testing that when we have specified fields e.g fields = c("a", "b")
fields with objectid specified
using fields with a Table.

@elipousson if youre comfortable with me pushing to your branch I could write the tests

Caught one more issue - I forgot to remove the existing check for fields that was expecting a scalar input. All good now, I think.

authors: JosiahParry elipousson

repo: R-ArcGIS/arcgislayers


text:
Fixes for marvin drones

Sometimes in sp worker drones spawn as error.mdl
since errors don't have anims a check must be added

So that I better understand the change the PR does, it basically just exits early if Marvin model couldn't be loaded?

yeah
in sp ( with co-op ) worker drones sometimes spawn without a model.

not needed anymore

Out of the loop, what changed?

co-op is not getting merged soon because I want to completely rewrite it someday:tm: so might fiix it self or not :trollface:

authors: GeckoEidechse catornot


text:
伊泽瑞尔的白金学长会在9级还是11级之后失去特效。Will Ezreal's Platinum Senior lose its special effects after level 9 or 11?

伊泽瑞尔的白金学长在9级还是11级之前q的特效可以正常使用，但是9级还是11级之后，q的特效变成了其他皮肤的。切换其他皮肤，例如青花瓷、未来战士时，特效正常切换。希望大佬可以看看

#1

#1

authors: diyooor hotline1337 impossable


text:
s3, dataverse, owncloudアドオンにおけるRDM Addons Force to use非表示オプションの有効化

Purpose
RDM Addonsにおける UNSUPPORTED_FORCE_TO_USE_ADDONS オプションによる、設定画面のForce to use設定UIを非表示にするオプションが、s3, dataverse, owncloud アドオンにも適用されるよう修正しました。
Changes

admin/templates/rdm_addons/addons/{s3, dataverse, owncloud}_institution_settings.html ... UNSUPPORTED_FORCE_TO_USE_ADDONSオプションの適用

QA Notes
None
Side Effects
None
Ticket
GRDM-8521

merge先変更のため、closeします。


text:
🛑 R&D Lab Inc. is down

In d58cdbd, R&D Lab Inc. (http://rdlabinc.com) was down:

HTTP code: 0
Response time: 0 ms

Resolved: R&D Lab Inc. is back up in de26e47 after 12 minutes.

repo: RDLGBC/upptime


text:
refactor: Stateless region select

Make the region select stateless.
This allows for use of the select on multiple places.

/retest

repo: RHEnVision/provisioning-frontend


text:
tests, Increase tests timeout

Since cosa-ci introduced timeout with 10 minutes
default [0], and since on gitActions the tests
tend to run for ~10 minutes, Increasing test
timeout to 20 minutes.
[0] https://github.com/coreos/coreos-assembler/commit/621043498a68ee9eae9a44a795c2ccd891347bc5
Signed-off-by: Ram Lavi ralavi@redhat.com

https://github.com/coreos/coreos-assembler/pull/2576

repo: RHsyseng/rhcos-slb


text:
🛑 Ancient Pet Liver is down

In 77a85cf, Ancient Pet Liver (https://ancientpetliver.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Ancient Pet Liver is back up in a216c9d after 23 hours, 5 minutes.

repo: RJVCA/uptime


text:
🛑 CardioVance is down

In 60451ad, CardioVance (https://cardiovance.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CardioVance is back up in ab5a82d after 17 minutes.


text:
proto update

Seems like they had a blast switching around stuff again in the last update

Added a hotfix for requestPlayerinfo as well

So many changes

authors: Crazy-Duck jimmydorry


text:
🛑 Sensor Tracker login is down

In 84308b4, Sensor Tracker login (https://stg.ceotr.ca/sensor_tracker) was down:

HTTP code: 502
Response time: 129 ms

Resolved: Sensor tracker login is back up in bf78df8 after 44 days, 36 minutes.

repo: RKTowse/upptime


text:
🛑 CEOTR Home Page loads is down

In 342c143, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in 665ae99 after 2 minutes.


text:
🛑 CEOTR Home Page loads is down

In 6f075ba, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in 4f7ada9 after 2 minutes.


text:
🛑 CEOTR Home Page loads is down

In 750cffb, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in a382944 after 2 minutes.


text:
🛑 CEOTR Home Page loads is down

In 08566c2, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in 2605f2a after 2 minutes.


text:
🛑 Sensor Tracker login is down

In c3c73f1, Sensor Tracker login (https://stg.ceotr.ca/sensor_tracker) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Sensor tracker login is back up in d00db60 after 1 minute.


text:
🛑 Sensor Tracker login is down

In bf67659, Sensor Tracker login (https://stg.ceotr.ca/sensor_tracker) was down:

HTTP code: 502
Response time: 107 ms

Resolved: Sensor tracker login is back up in 9013473.


text:
🛑 CEOTR Home Page loads is down

In 4ef29db, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in 995f72a after 14 days, 5 hours, 32 minutes.


text:
🛑 CEOTR Home Page loads is down

In 4165429, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in 98c35d5.


text:
🛑 CEOTR Home Page loads is down

In a16fada, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in 1568b76.


text:
🛑 CEOTR Home Page loads is down

In 519e726, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in 4801000 after 2 minutes.


text:
🛑 Sensor Tracker login is down

In 3fc60ec, Sensor Tracker login (https://stg.ceotr.ca/sensor_tracker) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Sensor tracker login is back up in d08bc14 after .


text:
🛑 Sensor Tracker login is down

In cced6df, Sensor Tracker login (https://stg.ceotr.ca/sensor_tracker) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Sensor tracker login is back up in 0f7b62f after .


text:
🛑 CEOTR Home Page loads is down

In 78d11ce, CEOTR Home Page loads (https://stg.ceotr.ca CEOTR Home) was down:

HTTP code: 0
Response time: 0 ms

Resolved: CEOTR Home Page loads is back up in caedd8c.


text:
🛑 Sensor Tracker login is down

In 47787ca, Sensor Tracker login (https://stg.ceotr.ca/sensor_tracker) was down:

HTTP code: 502
Response time: 47 ms

Resolved: Sensor tracker login is back up in f0e30e0.


text:
🛑 Sensor Tracker login is down

In 7332a7e, Sensor Tracker login (https://stg.ceotr.ca/sensor_tracker) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Sensor tracker login is back up in 16a72fa after .


text:
🛑 RMUG Installations is down

In 392b254, RMUG Installations (https://installations.rmusergroup.net) was down:

HTTP code: 502
Response time: 322 ms

Resolved: RMUG Installations is back up in 9b34ef8 after 21 minutes.

repo: RM-User-Group/RMUG-Upptime


text:
weird error about blockIdx.x & gridDim.x

I am porting some cuda functions, which works well on nvidia gpu+cuda, to hip. I have a kernel with a small snippet of code as following. When i tested with a configuration of thread grid(1, 1) and thread block(16, 16), the right execution should be running into the code block of if 5 after if 1/2/3/4 testing (just testing the condition, not running into their blocks), but the real execution ran into code block of if 3. I debugged and retyped this code snippet to eliminate the effecting of text coding, then found that the problem was condition testting of if 2. If the condition test of if 2 is removed or a printf() is added in block of if 2 althrough code of if 2 won't be executed, then everything is all right. printf() shows that blockIdx.y is 0 and gridDim.y is 1, there is no signs of error. Can anyone give some clues to this weird problem?
code:
if (blockIdx.x < gridDim.x - 1) {  // if 1
...
}
if (blockIdx.y < gridDim.y - 1) {  // if 2
...
}
if (blockIdx.y < gridDim.y - 1) {  // if 3
...
}
else if (blockIdx.x < gridDim.x - 1) {  // if 4
...
}
else {  // if 5
...
}

Hello,
Can you post the full sample with launch config as well.

The launch configuration is  thread grid(1, 1) and thread block(16, 16), the full execution logic of my kernel is as following:
global void kernel() {
shared uchar data[16][17];
if (blockIdx.x < gridDim.x - 1 && blockIdx.y < gridDim.y - 1) {
...
}
else {
// load data to the shared memory
__syncthreads();
if (threadIdx.x < threadIdx_x && threadIdx.y < threadIdx_y) {
  if (blockIdx.x < gridDim.x - 1) {  // if 1
    ...
  }
  if (blockIdx.y < gridDim.y - 1) {  // if 2
    ...
  }

  if (blockIdx.y < gridDim.y - 1) {  // if 3
    ...
  }
  else if (blockIdx.x < gridDim.x - 1) {  // if 4
    ...
  }
  else {  // if 5
    ...
  }
}

}
}

@jimurk Is this still an issue for you? If resolved, please close ticket. Thanks!

authors: cjatin jimurk ppanchad-amd

repo: ROCm/HIP


text:
HIP SDK 5.7 installer installs HIP runtime 5.2

Problem Description
Download ROCm 5.7.1 (actually it is AMD Software PRO Edition 23.Q4) for win10/11 from https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html, then install, after finish, you can see the HIP runtime dll version is actually 5.2 in C:\Windows\System32\amdhip64.dll, why the version is not 5.7.1?

Operating System
Windows 10
CPU
5950x
GPU
AMD Radeon RX 7900 XT
ROCm Version
ROCm 5.7.1
ROCm Component
HIP
Steps to Reproduce
No response
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
No response
Additional Information
No response

I believe that file is installed by the graphics driver rather than the HIP SDK. Are you using a 23.x version of AMD Adrenaline Edition driver package?

Yes, it is installed by graphics driver(the pro edition not adrenaline edition), but the graphics driver is packed together with HIP SDK 5.7, when I select full install(including hip sdk + graphics driver), the HIP runtime and HIP SDK looks unmatched.

Hi @wenqingli, could you please advise if you are still seeing this issue. If so, what version of the HIP SDK do you see under "Installed apps"? Thanks!

No, the HIP version turns to 5.7 with latest amd software, hence close this issue, thank you guys.

authors: bdenhollander harkgill-amd wenqingli


text:
GROMACS regression in ROCm 2.3

The major GROMACS kernel flavors regressed by ~30% on both Fiji and Vega from 2.2 to 2.3.

Hi @pszi1ard , could you provide the samples/steps to reproduce the performance regression?

The wall-time of the top (time-wise) kernel should indicate where the issue is. I believe AMD has internal pref regression testing of GROMACS, so I'm hoping I don't need to write line-by-line instructions here as perf-testing GROMACS should be a known thing.

Hi @pszi1ard
Can you provide us the version of the Gromacs you are observing issue with?

I did the performance measurements with the 2019.2 code but I doubt it is very dependent on the GROMACS version.
I observe major regressions in multiple kernels; for the a 192k sized input (e.g. inputs inside this tarball 0192/ -- generate input tpr with gmx grompp -f pme.mdp)(http://ftp.gromacs.org/benchmarks/water_GMX50_bare.tar.gz):
nbnxn_kernel_ElecEw_VdwLJCombGeom_F_opencl: -32%
pmeGatherKernel: -350%
pmeSolveXYZKernel: -26%
clFFT kernels: ballpark 2x slower

pmeGatherKernel: -350%

This one issues a warning about failing to unroll a loop, so that is probably (part?) of the reason. Can I force unroll?

Wrestled through the pain of running the profiler, the most likely main reasons for the nbnxn_kernel_ElecEw_VdwLJCombGeom_F_opencl regression is VGPR/SGPR usage that went from 77/62 to 95/66 dropping occupancy to 0.2; already the original VGPR use is higher than what I believe it should be (I think this should be able to run at 0.5 occupancy).
I hope this helps. Please let me know if there is any progress and what the ETA for the fix to land in a release is -- the current 2.3 is just unusable even for development.

Still no fix after the 2.5 release.

FYI: The 2.8 release is still severely regressed (so was the 2.7).

FYI: no change in 2.9 either.

This bug is still not fixed in 3.8.
I've identified it to be related to a missed optimization of float3 register array allocation which was introduced sometime after ROCm 2.2 (at least based on my historic performance data).

This is all about the GROMACS OpenCL backend, I don't think this was ever addressed, but this backend is deprecated in favor of the SYCL backed.

Thanks for the reply!

repo: ROCm/ROCm


text:
[Issue]: amdsmi does not work on Windows due to missing dll

Problem Description
I ran amdsmi.exe on Windows, but it cannot load the dynamic lib because it's missing:
PS C:\Windows\System32> .\amdsmi.exe --version
Traceback (most recent call last):
  File "PyInstaller\loader\pyimod03_ctypes.py", line 53, in __init__
  File "ctypes\__init__.py", line 376, in __init__
FileNotFoundError: Could not find module 'libsmi_host.dll' (or one of its dependencies). Try using the full path with constructor syntax.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "main.py", line 25, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "exceptions.py", line 22, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\__init__.py", line 73, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\amdsmi_host\gpuvsmi_package\amdsmi\__init__.py", line 24, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\amdsmi_host\gpuvsmi_package\amdsmi\smi_interface.py", line 30, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\amdsmi_host\gpuvsmi_package\amdsmi\smi_wrapper.py", line 160, in <module>
  File "PyInstaller\loader\pyimod03_ctypes.py", line 55, in __init__
pyimod03_ctypes.install.<locals>.PyInstallerImportError: Failed to load dynlib/dll 'libsmi_host.dll'. Most likely this dynlib/dll was not found when the application was frozen.
[24972] Failed to execute script 'main' due to unhandled exception!

Instead, there are only libamdsmi_host.dll and libamdsmi_guest.dll:

If I copied and rename the libamdsmi_host.dll to libsmi_host.dll, it raises another error:
PS C:\Windows\System32> .\amdsmi.exe --version
Traceback (most recent call last):
  File "main.py", line 25, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "exceptions.py", line 22, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\__init__.py", line 73, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\amdsmi_host\gpuvsmi_package\amdsmi\__init__.py", line 24, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\amdsmi_host\gpuvsmi_package\amdsmi\smi_interface.py", line 30, in <module>
  File "<frozen importlib._bootstrap>", line 1178, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
  File "PyInstaller\loader\pyimod02_importers.py", line 352, in exec_module
  File "amdsmi_import\amdsmi_host\gpuvsmi_package\amdsmi\smi_wrapper.py", line 1686, in <module>
  File "ctypes\__init__.py", line 389, in __getattr__
  File "ctypes\__init__.py", line 394, in __getitem__
AttributeError: function 'gpuvsmi_init' not found
[27576] Failed to execute script 'main' due to unhandled exception!

Operating System
Windows 10.0.22631
CPU
AMD Ryzen 7 5800X 8-Core Processor
GPU
AMD Radeon Pro W6800
ROCm Version
ROCm 5.7.1
ROCm Component
amdsmi
Steps to Reproduce
No response
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
No response
Additional Information
My AMD GPU driver version info are:
Driver version
23.30.16-231128a-398955E-INTERNAL-AMD-Software-PRO-Edition
AMD Windows Driver version
31.0.23016.5

Hope this helps

There are some similar symbols:
strings /mnt/c/Windows/System32/libamdsmi_host.dll | grep "smi"
libamdsmi_host.dll
amdsmi_event_create
amdsmi_event_destroy
amdsmi_event_read
amdsmi_get_clock_info
amdsmi_get_dfc_fw_table
amdsmi_get_fb_layout
amdsmi_get_fw_error_records
amdsmi_get_fw_info
amdsmi_get_gpu_activity
amdsmi_get_gpu_asic_info
amdsmi_get_gpu_bad_page_info
amdsmi_get_gpu_board_info
amdsmi_get_gpu_device_bdf
amdsmi_get_gpu_device_uuid
amdsmi_get_gpu_driver_version
amdsmi_get_gpu_ras_feature_info
amdsmi_get_gpu_total_ecc_count
amdsmi_get_gpu_vbios_info
amdsmi_get_guest_data
amdsmi_get_num_vf
amdsmi_get_partition_profile_info
amdsmi_get_pcie_link_caps
amdsmi_get_pcie_link_status
amdsmi_get_power_cap_info
amdsmi_get_power_info
amdsmi_get_processor_handle_from_bdf
amdsmi_get_processor_handles
amdsmi_get_processor_type
amdsmi_get_socket_handles
amdsmi_get_socket_info
amdsmi_get_temp_metric
amdsmi_get_vf_bdf
amdsmi_get_vf_data
amdsmi_get_vf_fw_info
amdsmi_get_vf_handle_from_bdf
amdsmi_get_vf_handle_from_idx
amdsmi_get_vf_info
amdsmi_get_vf_partition_info
amdsmi_get_vf_uuid
amdsmi_get_xgmi_info
amdsmi_init
amdsmi_shut_down

Are the APIs renamed?

Had some internal discussions and confirmed that "AMD SMI for Windows is not supported officially on consumer cards".
We are planning testing and improved support in the future.

authors: Inokinoki dmitrii-galantsev

repo: ROCm/amdsmi


text:
Fix Last Batch Policy PR

This PR fixes the image unit tests

@swetha097 @rrawther - merge pending CI results

repo: ROCm/rocAL


text:
[Issue]: compilation failure with gcc-15: ‘uint64_t’ does not name a type

Problem Description
(somewhat similar to https://github.com/ROCm/ROCR-Runtime/issues/227)
Hi, as reported in https://bugs.gentoo.org/938112, gcc-15 was able to unscrable some headers, and now rocm_smi_lib build fails with:
In file included from /var/tmp/portage/dev-util/rocm-smi-6.1.1/work/rocm_smi_lib-rocm-6.1.1/include/rocm_smi/rocm_smi_gpu_metrics.h:47,
                 from /var/tmp/portage/dev-util/rocm-smi-6.1.1/work/rocm_smi_lib-rocm-6.1.1/src/rocm_smi_gpu_metrics.cc:44:
/var/tmp/portage/dev-util/rocm-smi-6.1.1/work/rocm_smi_lib-rocm-6.1.1/include/rocm_smi/rocm_smi_common.h:149:5: error: ‘uintptr_t’ does not name a type
  149 |     uintptr_t func_id_iter;
      |     ^~~~~~~~~
/var/tmp/portage/dev-util/rocm-smi-6.1.1/work/rocm_smi_lib-rocm-6.1.1/include/rocm_smi/rocm_smi_common.h:53:1: note: ‘uintptr_t’ is defined in header ‘<cstdint>’; this is probably fixable by adding ‘#include <cstdint>’
   52 | #include <unordered_set>
  +++ |+#include <cstdint>
   53 |

Could you add #include <cstdint> to https://github.com/ROCm/rocm_smi_lib/blob/rocm-6.2.0/include/rocm_smi/rocm_smi_common.h? That's the only change needed for gcc-15. Thanks!
Operating System
Gentoo
CPU

GPU
AMD Instinct MI300X
ROCm Version
ROCm 6.2.0
ROCm Component
rocm_smi_lib
Steps to Reproduce
No response
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
No response
Additional Information
No response

Hi @AngryLoki, how are you installing gcc-15 prior to building rocm_smi_lib?

@harkgill-amd , emerge -va sys-devel/gcc:15 --autounmask-continue --autounmask-write && eselect gcc set x86_64-pc-linux-gnu-15. But it OS/installation method makes no difference, GCC 15 will reach every distro eventually.
Here is a link with minimal reproduction: https://godbolt.org/z/dqGbnG8bY . This is not an issue of GCC (due to standard).
I'll add a PR, maybe it helps anybody.

After finally getting gcc-15 installed and working on Ubuntu 24.04, I was able to reproduce the issue and confirm your fix resolves it. Thanks for the PR, I'll confirm with the team if they would like to go forward and merge it or push the changes internally for simplicity.

authors: AngryLoki harkgill-amd


text:
docs: Add section on building rocprofsys for no GPU

Adds brief information in install.rst regarding

Provided build scripts
What to do when building rocprofiler-systems w/o ROCm
Recent changes to the configuration settings as a result of rocprofiler-sdk support.

Fixes minor formatting issues in install.rst resulting in Sphinx build warnings

preview: https://advanced-micro-devices-demo--57.com.readthedocs.build/projects/rocprofiler-systems/en/57/install/install.html#rocm-systems-profiler-without-rocm

@pranswarup could you take a look?

Added https://advanced-micro-devices-demo--57.com.readthedocs.build/projects/rocprofiler-systems/en/57/install/install.html#using-the-build-script and updated https://advanced-micro-devices-demo--57.com.readthedocs.build/projects/rocprofiler-systems/en/57/install/install.html#rocm-systems-profiler-without-rocm
Could you help make sure it's accurate and provide feedback? @dgaliffiAMD @pranswarup Thanks.

@ROCm/rocm-documentation

repo: ROCm/rocprofiler-systems


text:
Non std shape auto contiguous

This PR is to resolve a problem in parsing the ssd-10 model.
The problem is, after inserting contiguous in the auto_contiguous pass, standard output shape of some operators becomes non-standard. Then, if the next operator requires standard input shape, an exception is throw.
For example, if we pass the following model:
Input (standard shape) -> transpose (transposed) -> softmax (transposed) -> transpose (standard) -> gather.
It works fine, and no contiguous is required.
In the auto_contiguous pass, a contiguous is inserted after the first transpose. Then we need to replace the first transpose with the contiguous and recompute all shapes. When it comes to the gather operator, its input is a transposed shape, and an exception is thrown.
The solution is in the recompute_shape() function. If it is called by the auto_contiguous pass and shape of an instruction is changed, and the shape is non_standard, we do not recompute shape of its output. The reason is: since its output shape is non_standard, a contiguous op will be added after the instruction, which will recompute shape for later operators.

Could we fix this issue by moving backwards in auto_contiguous? This way later instructions that do need contiguous will already have it inserted.

Could we fix this issue by moving backwards in auto_contiguous? This way later instructions that do need contiguous will already have it inserted.

Looks like it does not work. For example, for the unit test I added, if go backward, when we check the second transpose, its output shape is standard, so no contiguous is added. Then when it comes to the first transpose, a contiguous is added, and recompute_shape is called, which will cause the problem that input shape of gather is not standard.

Why does gather require standard shape? It uses the tensor_view to index the elements so it should work for any shape.

repo: ROCmSoftwarePlatform/AMDMIGraphX


text:
Update test_mdgraph for gfx90a and gfx10

@Slimakanzer IIRC #809 enables 1x1 fused asm kernel for gfx90a. And #772 enables fused Winograd for gfx10. Please update tests as necessary and check locally. Thanks.
/cc @Kirpich30000
Originally posted by @atamazov in https://github.com/ROCmSoftwarePlatform/MIOpen/issues/813#issuecomment-808487766

via #813

repo: ROCmSoftwarePlatform/MIOpen


text:
Use pub pkgs for HIP/rocBLAS/MIOpen in TF CI

Now that we're defaulting to ROCm 2.0, let's test dropping all of our old workarounds for HIP/rocBLAS/MIOpen/etc.

1 (likely unrelated) failure on ROCm
//tensorflow/python/kernel_tests:self_adjoint_eig_op_test                FAILED in 18.2s
  /home/jenkins/workspace/tensorflow-upstream-unit-tests/bazel-ci_build-cache/.cache/bazel/_bazel_jenkins/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/k8-opt/testlogs/tensorflow/python/kernel_tests/self_adjoint_eig_op_test/test.log

With these types of error messages:
2019-01-03 01:10:48.107316: E tensorflow/stream_executor/rocm/rocm_blas.cc:2335] Unable to register rocBLAS factory: Attempting to register factory for plugin rocBLAS when one has already been registered
2019-01-03 01:10:48.107367: E tensorflow/stream_executor/rocm/rocm_dnn.cc:5125] Unable to register MIOpen factory: Attempting to register factory for plugin MIOpen when one has already been registered

At a minimum, we should try to get rid of the HIP workaround.  Recently, it started breaking our nightly dev builds due to some packaging errors.

@parallelo these are benign:
2019-01-03 01:10:48.107316: E tensorflow/stream_executor/rocm/rocm_blas.cc:2335] Unable to register rocBLAS factory: Attempting to register factory for plugin rocBLAS when one has already been registered
2019-01-03 01:10:48.107367: E tensorflow/stream_executor/rocm/rocm_dnn.cc:5125] Unable to register MIOpen factory: Attempting to register factory for plugin MIOpen when one has already been registered

authors: parallelo whchung


text:
[AstrophysicsDataSystemBridge] Add bridge

Closes #1064

Looks good. Clean. Why don't you use shorthand arrays?

I like the explicit syntax. Feel free to merge if you approve, thanks.

authors: dvikan yamanq

repo: RSS-Bridge/rss-bridge

license: unlicense


text:
ENH: MIP image filter for DRR image calculation

MIPForwardProjectionImageFilter is derived from
JosephForwardProjectionImageFilter and computes maximum intensity step along the ray casting on the projection
Example with parallel geometry beam.
DRR image with JosephForwardProjectionImageFilter:

DRR image with MIPForwardProjectionImageFilter:

Thank you for your contribution. Have you tried to avoid copy pasting the original code of Joseph's projector? It should not be necessary if one uses a functor.

I've tried, but couldn't make it. Mainly because the SumAlongRay functor is a tally, in case of MIP we need a step with the maximum intensity not the sum of the steps along a ray. IMHO.

I see. I guess including the sum as a reference parameter of SumAlongRay and doing the sum in the functor would solve this?
This code also needs to be changed using lambdas in my opinion but that's a more ambitious PR.

Or doing a new AccumulateAlongRay lambda?

I guess including the sum as a reference parameter of SumAlongRay and doing the sum in the functor would solve this?

I will try to use your suggestion, and use sum as a reference parameter in functor.

Functor is changed, so copy-pasted part is removed.

This code also needs to be changed using lambdas in my opinion but that's a more ambitious PR.

Do you suggest of using lambdas instead of functors?

Functor is changed, so copy-pasted part is removed.

Thanks! Looks better.

Do you suggest of using lambdas instead of functors?

Yes, something like what's been done here for ITK unary image filters. It would make the code look nicer but I'm not sure it would be as efficient. To be tested...

There are some style errors:
https://github.com/RTKConsortium/RTK/actions/runs/7742228424/job/21110871876?pr=579
I would also suggest to change name from MIPForwardProjectionImageFilter to MaximumIntensityProjectionImageFilter to follow ITK's guidelines to avoid abbreviations.

There are some style errors:
https://github.com/RTKConsortium/RTK/actions/runs/7742228424/job/21110871876?pr=579
I would also suggest to change name from MIPForwardProjectionImageFilter to MaximumIntensityProjectionImageFilter to follow ITK's guidelines to avoid abbreviations.

I will fix these errors.

Filter was renamed, and MIP filter was added to rtkforwardprojections application.

I think the code is ready to be merged. It would be good to add a test and the wrapping. If not, please rebase on master and squash all commits in one. I'll then merge and do them in another PR.

I can try. I'll let it know in case of problems.

Is there a instruction how to test python wrapping?

Is there a instruction how to test python wrapping?

Ideally you can add a test similar to rtkFirstReconstruction.py and enable it here. Then you need to build RTK with testing and python wrapping enabled to be able to run the test with ctest

Thanks @LucasGandel! I had forgotten about this.
@MichaelColonel If you just want to test that newly created wheels will contain the wrapping, wheels are automatically generated by the CI, you can download them as artifacts and test them locally. Currently, this PR does not compile the wheel https://github.com/RTKConsortium/RTK/pull/579/checks#step:5:2085

I have a question about writing a wrapper for MIP filter. I've took as a basis the JosephForwardProjectionAttenuated wrapper, since both class are derived from JosephForwardProjection.
My wrapper is
set(WRAPPER_AUTO_INCLUDE_HEADERS OFF)
itk_wrap_named_class("rtk::Functor::MaximumIntensityAlongRay" "rtkFunctorMaximumIntensityAlongRay")
  foreach(t ${WRAP_ITK_REAL})
    itk_wrap_template("${ITKM_${t}}${ITKM_${t}}" "${ITKT_${t}}, ${ITKT_${t}}")
  endforeach()
itk_end_wrap_class()
itk_wrap_named_class("rtk::Functor::MaximumIntensityProjectedValueAccumulation" "rtkFunctorMaximumIntensityProjectedValueAccumulation")
  foreach(t ${WRAP_ITK_REAL})
    itk_wrap_template("${ITKM_${t}}${ITKM_${t}}" "${ITKT_${t}}, ${ITKT_${t}}")
  endforeach()
itk_end_wrap_class()
set(WRAPPER_AUTO_INCLUDE_HEADERS ON)

itk_wrap_class("rtk::JosephForwardProjectionImageFilter" POINTER)
  foreach(t ${WRAP_ITK_REAL})
    itk_wrap_template("I${ITKM_${t}}3I${ITKM_${t}}3SWM${ITKM_${t}}D${ITKM_${t}}IPC"
      "itk::Image<${ITKT_${t}}, 3>, itk::Image< ${ITKT_${t}}, 3>, rtk::Functor::InterpolationWeightMultiplication<${ITKT_${t}}, ${ITKT_${t}}, ${ITKT_${t}}>, rtk::Functor::MaximumIntensityProjectedValueAccumulation<${ITKT_${t}}, ${ITKT_${t}}>, rtk::Functor::MaximumIntensityAlongRay<${ITKT_${t}}, ${ITKT_${t}}>")
  endforeach()
itk_end_wrap_class()

itk_wrap_class("rtk::MaximumIntensityProjectionImageFilter" POINTER)
  foreach(t ${WRAP_ITK_REAL})
    itk_wrap_template("I${ITKM_${t}}3I${ITKM_${t}}3SWM${ITKM_${t}}D${ITKM_${t}}IPC"
      "itk::Image<${ITKT_${t}}, 3>, itk::Image< ${ITKT_${t}}, 3>, rtk::Functor::InterpolationWeightMultiplication<${ITKT_${t}}, ${ITKT_${t}}, ${ITKT_${t}}>, rtk::Functor::MaximumIntensityProjectedValueAccumulation<${ITKT_${t}}, ${ITKT_${t}}>, rtk::Functor::MaximumIntensityAlongRay<${ITKT_${t}}, ${ITKT_${t}}>")
  endforeach()
itk_end_wrap_class()

itk_wrap_class("rtk::MaximumIntensityProjectionImageFilter" POINTER)
  foreach(t ${WRAP_ITK_REAL})
    itk_wrap_template("I${ITKM_${t}}3I${ITKM_${t}}3" "itk::Image<${ITKT_${t}}, 3>, itk::Image<${ITKT_${t}}, 3>")
  endforeach()
itk_end_wrap_class()

During the compilation there are multiple warning messages like below, as a result python packages can't be created.
rtkJosephForwardProjectionImageFilterIF3IF3SWMFDFIPC_Pointer in /work/ITK-cp39-cp39-manylinux_2_28_x64/Wrapping/Typedefs/rtkMaximumIntensityProjectionImageFilter.idx is already defined in /work/ITK-cp39-cp39-manylinux_2_28_x64/Wrapping/Typedefs/rtkJosephForwardAttenuatedProjectionImageFilter.idx.

Do you have a tutorial of some kind, how to write a wrapper for derived classes?

The available tutorial is the ITK software guide section 9.5. The warning indicate that you already have an instance with the same name so you need to change the first paramter of the itk_wrap_template macro.

It looks like compilation with python and tests are OK! Should i squash the commits into one?

Yes please squash! I tested the wrapping and it works perfectly. Thanks!

Squashed in one commit.

Thanks for your contribution!

authors: LucasGandel MichaelColonel SimonRit

repo: RTKConsortium/RTK


text:
Broken links in RTX-KG2 results

See this issue: https://github.com/NCATSTranslator/Feedback/issues/497
Basically, there is a trailing } in one of the urls

I don't know if this is due to what's stored in RTX-KG2, or if the UI is parsing the info incorrectly. I do know it's an ARAX/RTX-KG2 edge though, as I had selected the check box to only return results from those ARA/KPs

When I ran match (n)-[e]-(m) where n.name="Flutamide" and m.name="congenital adrenal hyperplasia" return n,e,m on kg2canonicalized2.rtx.ai, none of the results contain anything about clinical trials:
Edge 1:
{
  "predicate": "biolink:treats",
  "primary_knowledge_source": "infores:chembl",
  "domain_range_exclusion": "False",
  "publications_info": "{}",
  "kg2_ids": [
    "CHEMBL.COMPOUND:CHEMBL806---biolink:treats---None---None---None---MESH:D000312---identifiers_org_registry:chembl.compound"
  ],
  "subject": "PUBCHEM.COMPOUND:3397",
  "id": "30382301",
  "object": "MONDO:0018479"
}

Edge 2:
{
  "predicate": "biolink:treats",
  "primary_knowledge_source": "infores:semmeddb",
  "domain_range_exclusion": "False",
  "publications_info": "{'PMID:10720048': {'publication date': '2000 Mar', 'sentence': 'We conclude that the regimen of flutamide, testolactone, reduced hydrocortisone dose, and fludrocortisone provides effective control of congenital adrenal hyperplasia with reduced risk of glucocorticoid excess.', 'subject score': 1000, 'object score': 1000}, 'PMID:8855797': {'publication date': '1996 Oct', 'sentence': 'A preliminary study of flutamide, testolactone, and reduced hydrocortisone dose in the treatment of congenital adrenal hyperplasia.', 'subject score': 1000, 'object score': 1000}}",
  "kg2_ids": [
    "UMLS:C0016384---SEMMEDDB:treats---None---None---None---UMLS:C0001627---SEMMEDDB:"
  ],
  "subject": "PUBCHEM.COMPOUND:3397",
  "id": "8061436",
  "object": "MONDO:0018479",
  "publications": [
    "PMID:10720048",
    "PMID:8855797"
  ]
}

I don't believe KG2pre ingests any clinical trials data, so I'm not sure where this data could be coming from.

authors: dkoslicki ecwood


text:
Update run_standard_drc.py

This changes the created tcl file (run_magic_drc.tcl) to incorporate the name of the layout in its pathname.
This allows this script to be run on multiple layouts simultaneously.

Pulled and merged.  Update will be mirrored to github overnight.

authors: RTimothyEdwards donn


text:
How to get and use CUDA Debugger API

The in-kernel checkpoint/restart relies on CUDA Debugger API to work, so
I tried to read Debugger API :: CUDA Toolkit Documentation (nvidia.com) but found it was unclearly documented, especially how to initialize the api, and how to attach and detach a program. I made some attempts but never successfully initialized CUDA Debugger API. I also looked into the code of cuda-gdb, but I found it didn't use CUDA Debugger API, but implemented its own set of debugger api by communicating directly to the driver and filled its own functions into the CUDBGAPI_st struct.
I looked into the code of the project and found the code called gdb_init and then just called cudbgGetAPI. I'm not sure what is happening here. How do you intergrate a part of cuda-gdb into your project and get CUDA Debugger API? Could you please explain this (and anything about CUDA Debugger API) for me? Does the raw CUDA Debugger API the same as the cuda-gdb implementation or it just can't work without cuda-gdb?

Thank you for your reply. This is the best we can do now I think.

repo: RWTH-ACS/cricket


text:
Installing without AWS dependencies?

First, thanks for building the awesome library. It works great! 👏
I've built a library that subclasses pathlib.Path to work with bucket storage, and it uses smart_open to provide the streamable io interface.
When installing smart_open it pulls in the AWS dependencies even if I'm only using it for GCS. I see that you all have setup the package to allow installing only the dependencies for each service, but it seems incomplete?
https://github.com/RaRe-Technologies/smart_open/blob/b6cf769266b34ad2146e9ee67cc56b331ab8e91b/setup.py#L88-L91
I found this comment about the extra dependencies being temporary, and I wonder if you have a timeline for a release that removes this?

I don't think there's any timeline, but being more modular in installing extras is something we'd like to do. Are you able to open a PR?
Related to #463.

Sure, I opened a PR with a first pass: https://github.com/RaRe-Technologies/smart_open/pull/534

authors: justindujardin piskvorky


text:
Fixed types (scaling) when querying data.

Also fixed bug where timestamps disappeared if there was no tag for the first queried tagname. #49

I disabled the "trailing-whitespace" pattern, thats not important :) i think it will be effective from the next run. If you want a green build, you could remove the whitespace and commit again. I think we dont need to take the codacy check too serious with issues like this and can still merge if the check failed, but it also informs us on really bad things, if for example we accidentally check in a secret.
I will look through the list of patterns we want to check on, such that we dont have a red build for every little thing.

authors: maikberthelsen rafaelschlatter

repo: RaaLabs/TSIClient


text:
System.IO.FileNotFoundException: Could not load file or assembly 'Microsoft.WindowsAzure.Storage, Version=6.2.0.0,

Error: System.IO.FileNotFoundException: Could not load file or assembly 'Microsoft.WindowsAzure.Storage, Version=6.2.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35' or one of its dependencies. The system cannot find the file specified.
File name: 'Microsoft.WindowsAzure.Storage, Version=6.2.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35'
at Cake.AzureStorage.AzureStorage.DeleteBlobsByPrefix(AzureStorageSettings settings)
at Submission#0.DeleteBlobsByPrefix(AzureStorageSettings settings)
at Submission#0.<.ctor>b__16()
at Cake.Core.ActionTask.Execute(ICakeContext context)
at Cake.Core.DefaultExecutionStrategy.Execute(CakeTask task, ICakeContext context)
at Cake.Core.CakeEngine.ExecuteTask(ICakeContext context, IExecutionStrategy strategy, Stopwatch stopWatch, CakeTask task, CakeReport report)
at Cake.Core.CakeEngine.RunTarget(ICakeContext context, IExecutionStrategy strategy, String target)
at Cake.Scripting.BuildScriptHost.RunTarget(String target)
at Submission#0..ctor(Session session, Object& submissionResult)
at Submission#0.(Session session)
at Roslyn.Scripting.CommonScriptEngine.Execute[T](String code, String path, DiagnosticBag diagnostics, Session session, Boolean isInteractive)
at Roslyn.Scripting.Session.Execute(String code)
at Cake.Core.Scripting.ScriptRunner.Run(IScriptHost host, FilePath scriptPath, IDictionary`2 arguments)
at Cake.Commands.BuildCommand.Execute(CakeOptions options)
at Cake.CakeApplication.Run(CakeOptions options)
at Cake.Program.Main()
WRN: Assembly binding logging is turned OFF.
To enable assembly bind failure logging, set the registry value HKLM\Software\Microsoft\Fusion!EnableLog to 1.
Note: There is some performance penalty associated with assembly bind failure logging.
To turn this feature off, remove the registry value [HKLM\Software\Microsoft\Fusion!EnableLog].

@dzenand @JRKelso Have you included configured the Azure Storage Nuget package to install as part of your cake file?  Cake has changed a lot recently and we will be updating this and the examples to aid in configuration.

@phillipsj  I followed the example I have added #addin Cake.AzureStorage on top of the cake file.
Do i need to pull some additional nuget packages?

Did anyone ever manage to work this out? With the latest package I get a similar error but for Microsoft.WindowsAzure.Storage 7.2.1.0

Yes I believe I have.  I will make sure the fix gets pushed up.  I
apologize for the delay.
On Monday, November 7, 2016, Jonathan Stowell notifications@github.com
wrote:

Did anyone ever manage to work this out? With the latest package I get a
similar error but for Microsoft.WindowsAzure.Storage 7.2.1.0
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/RadioSystems/Cake.AzureStorage/issues/5#issuecomment-258965341,
or mute the thread
https://github.com/notifications/unsubscribe-auth/ABfthd4cqWsNKFW8lrFMc1ZyCsvoO1eLks5q75VrgaJpZM4JPwx5
.

Thanks,
Is there anything I can do to help or is there a workaround I can use to get this running? I find it odd this was last release on the 10th of October on nuget but doesn't work with the newer Cake packages. I'm using the latest Cake "0.16.2".
Apologizes for the lack of understanding around the problem, if needs be I can pull the source and start debugging myself, but if you've solved it, it makes life easier ;-)

You might try to do
#r "tools/Addins/WindowsAzure.Storage/lib/net40/Microsoft.WindowsAzure.Storage.dll"
as a workaround temporarily to get it functioning and see the file. Right now I'm trying to get the latest version tested against our stuff to see if we can get this working out the box like it should, although all my attempts have failed so far to get it to automatically reference the file. @phillipsj  may have to see if he can figure it out this evening if I don't get this resolved soon.

@jonathanstowell @JRKelso I have discovered the issue.  Some depedencies got out of sync with the Azure Storage dll.

@jonathanstowell @JRKelso That was the issue, the dependencies got out of sync with the Azure Storage library, strong typing got to like it.  Please start using version 0.9.45.

@phillipsj Ah cool, I actually managed to get this working by cloning the repo, building it locally and referencing the dll directly.
I have made a change locally which allows me to use the local emulator as the CloudStorageAccount has a static profile to connect to a local emulator.
When I get a chance I'll fork make these changes and submit a pull request.

authors: JRKelso dzenand jonathanstowell phillipsj