Can not reproduce SAGN results on ogbn-papers100M

Thanks again for sharing this Pytorch implementation of SAGN paper.
I am trying to reprdouce SAGN results on ogbn-papers100M, by following the README.md.
In particular, I use the following command line
bash scripts/ogbn-papers100M/train_ogbn_papers100M_sagn_use_label.sh 0

But the test accuracy is only 50.64% for run-1 (also similar for run-2 and 3), which doesn't seem right.
Could you kindly point out where might go wrong? (I am able to reproduce the ogbn-products result though...)
The final result I got
Stage: 0, Val accuracy: 0.5312±0.0000
Stage: 0, Test accuracy: 0.5064±0.0000
Stage: 0, Preprocessing time: 214.5684±0.0000
Stage: 0, Training time: 8.8525±0.2004
Stage: 0, Inference time: 6.1529±0.1841
Stage: 1, Val accuracy: 0.5319±0.0000
Stage: 1, Test accuracy: 0.5069±0.0000
Stage: 1, Preprocessing time: 214.3332±0.0000
Stage: 1, Training time: 9.5329±0.2155
Stage: 1, Inference time: 6.2790±0.2097
Stage: 2, Val accuracy: 0.5301±0.0000
Stage: 2, Test accuracy: 0.5052±0.0000
Stage: 2, Preprocessing time: 211.9302±0.0000
Stage: 2, Training time: 9.5377±0.2354
Stage: 2, Inference time: 5.9175±0.2514

My enviroment dependencies are
torch.__version__ = '1.9.0+cu102'
dgl.__version__ = '0.7.1'
ogb.__version__ = '1.3.1'

and a snippet of the stage-1 training log:
gpu: 0
Using backend: pytorch
Namespace(K=3, acc_loss='acc', aggr_gpu=-1, attn_drop=0.0, avoid_features=False, batch_size=5000, dataset='ogbn-papers100M', dropout=0.4, epoch_setting=[100, 50, 50], eval_batch_size=100000, eval_every=1, gpu=0, inductive=False, input_drop=0.0, label_K=9, load_embs=False, load_label_emb=False, lr=0.001, mag_emb=False, mlp_layer=2, model='sagn', num_heads=1, num_hidden=1024, num_runs=1, probs_dir='../intermediate_outputs', seed=0, threshold=0
.7, use_labels=True, use_norm=False, warmup_stage=-1, weight_decay=0.0, weight_style='uniform')
----------------------------------------------------------------------------------------------------
Run 0 start training
----------------------------------------------------------------------------------------------------
Stage 0 start training
This will download 56.17GB. Will you proceed? (y/N) y
Downloading http://snap.stanford.edu/ogb/data/nodeproppred/papers100M-bin.zip
Downloaded 56.17 GB: 100%|██████████| 57519/57519 [5:16:39<00:00,  3.03it/s]
Extracting ../../dataset/papers100M-bin.zip
Loading necessary files...
This might take a while.
Processing graphs...
100%|██████████| 1/1 [00:00<00:00, 21620.12it/s]
Converting graphs into DGL objects...
100%|██████████| 1/1 [00:03<00:00,  3.43s/it]
Saving...
# Nodes: 111059956
# Edges:<PHONE_NUMBER>
# Train: 1207179
# Val: 125265
# Test: 214338
# Classes: 172
in_feats: 128
using label information
Compute neighbor-averaged feats last
Compute neighbor-averaged feats all
Preprocessing costs 214.5684 s
# Params: 8556888
Epoch 1, Time(s): 8.9580 6.4487, Best Val loss: 1.9340, Accs: Train: 0.5015, Val: 0.4731, Test: 0.4502, Best Val: 0.4731, Best Test: 0.4502
Epoch 2, Time(s): 8.9925 6.1916, Best Val loss: 1.8742, Accs: Train: 0.5153, Val: 0.4846, Test: 0.4625, Best Val: 0.4846, Best Test: 0.4625
Epoch 3, Time(s): 8.9684 6.2897, Best Val loss: 1.8330, Accs: Train: 0.5247, Val: 0.4860, Test: 0.4636, Best Val: 0.4860, Best Test: 0.4636
Epoch 4, Time(s): 8.9622 6.2030, Best Val loss: 1.8129, Accs: Train: 0.5321, Val: 0.4893, Test: 0.4679, Best Val: 0.4893, Best Test: 0.4679
Epoch 5, Time(s): 8.9568 6.2183, Best Val loss: 1.7954, Accs: Train: 0.5380, Val: 0.4929, Test: 0.4718, Best Val: 0.4929, Best Test: 0.4718
Epoch 6, Time(s): 8.9762 6.2083, Best Val loss: 1.7696, Accs: Train: 0.5423, Val: 0.5003, Test: 0.4802, Best Val: 0.5003, Best Test: 0.4802
Epoch 7, Time(s): 8.9878 6.2264, Best Val loss: 1.7507, Accs: Train: 0.5494, Val: 0.5062, Test: 0.4827, Best Val: 0.5062, Best Test: 0.4827
Epoch 8, Time(s): 9.0351 6.1784, Best Val loss: 1.7507, Accs: Train: 0.5530, Val: 0.5051, Test: 0.4808, Best Val: 0.5062, Best Test: 0.4827
Epoch 9, Time(s): 8.9691 6.1823, Best Val loss: 1.7349, Accs: Train: 0.5579, Val: 0.5076, Test: 0.4838, Best Val: 0.5076, Best Test: 0.4838
Epoch 10, Time(s): 8.8072 6.3027, Best Val loss: 1.7349, Accs: Train: 0.5611, Val: 0.5070, Test: 0.4842, Best Val: 0.5076, Best Test: 0.4838
...
Epoch 91, Time(s): 8.8762 6.5136, Best Val loss: 1.6488, Accs: Train: 0.6575, Val: 0.5274, Test: 0.5015, Best Val: 0.5312, Best Test: 0.5064
Epoch 92, Time(s): 8.5527 6.5071, Best Val loss: 1.6488, Accs: Train: 0.6565, Val: 0.5265, Test: 0.5010, Best Val: 0.5312, Best Test: 0.5064
Epoch 93, Time(s): 8.7992 6.3182, Best Val loss: 1.6488, Accs: Train: 0.6569, Val: 0.5237, Test: 0.4985, Best Val: 0.5312, Best Test: 0.5064
Epoch 94, Time(s): 8.9041 6.2423, Best Val loss: 1.6488, Accs: Train: 0.6586, Val: 0.5258, Test: 0.5017, Best Val: 0.5312, Best Test: 0.5064
Epoch 95, Time(s): 8.9713 6.1941, Best Val loss: 1.6488, Accs: Train: 0.6588, Val: 0.5247, Test: 0.5006, Best Val: 0.5312, Best Test: 0.5064
Epoch 96, Time(s): 8.8715 6.2098, Best Val loss: 1.6488, Accs: Train: 0.6593, Val: 0.5257, Test: 0.5001, Best Val: 0.5312, Best Test: 0.5064
Epoch 97, Time(s): 8.8921 6.2062, Best Val loss: 1.6488, Accs: Train: 0.6604, Val: 0.5277, Test: 0.5031, Best Val: 0.5312, Best Test: 0.5064
Epoch 98, Time(s): 8.9896 6.1606, Best Val loss: 1.6488, Accs: Train: 0.6597, Val: 0.5238, Test: 0.4995, Best Val: 0.5312, Best Test: 0.5064
Epoch 99, Time(s): 8.9732 6.2092, Best Val loss: 1.6488, Accs: Train: 0.6609, Val: 0.5246, Test: 0.5000, Best Val: 0.5312, Best Test: 0.5064
Epoch 100, Time(s): 8.9146 6.2544, Best Val loss: 1.6488, Accs: Train: 0.6607, Val: 0.5276, Test: 0.5026, Best Val: 0.5312, Best Test: 0.5064
Stage: 0, Best Epoch 62, Val 0.5312, Test 0.5064
----------------------------------------------------------------------------------------------------

It seems that your train_ogbn_papers100M_sagn_use_label.sh has not been updated. You update all necessary files by git pull.
The updated version should be:
cd "$(dirname $0)"
if [ ! -n "$1" ] ; then
    gpu="0"
else
    gpu="$1"
fi
echo "gpu: $gpu"
python -u ../../src/sagn.py \
    --dataset ogbn-papers100M \
    --gpu $gpu \
    --aggr-gpu -1 \
    --eval-every 1 \
    --model sagn \
    --zero-inits \
    --chunks 1 \
    --memory-efficient \
    --load-embs \
    --load-label-emb \
    --seed 0 \
    --num-runs 10 \
    --threshold 0.5 \
    --epoch-setting 100 100 100 100\
    --lr 0.001 \
    --batch-size 5000 \
    --num-hidden 1024 \
    --dropout 0.5 \
    --attn-drop 0. \
    --input-drop 0.0 \
    --label-drop 0.5 \
    --K 3 \
    --label-K 9 \
    --use-labels \
    --weight-decay 0 \
    --warmup-stage -1

I ran this script again to confirm the reproducibility:
gpu: 0
Using backend: pytorch
Namespace(K=3, acc_loss='acc', aggr_gpu=-1, alpha=0.5, attn_drop=0.0, avoid_features=False, batch_size=5000, chunks=1, data_dir='/mnt/ssd/ssd/dataset', dataset='ogbn-papers100M', dropout=0.5, emb_path='/home/scx/NARS/', epoch_setting=[100, 100, 100, 100], eval_batch_size=100000, eval_every=1, example_subsets_path='/home/scx/NARS/sample_relation_subsets/examples', fixed_subsets=False, gpu=0, inductive=False, input_drop=0.0, label_K=9, label_drop=0.5, load_embs=True, load_label_emb=True, lr=0.001, mag_emb=False, memory_efficient=True, mlp_layer=2, model='sagn', num_heads=1, num_hidden=1024, num_runs=10, probs_dir='../intermediate_outputs', sample_size=8, seed=0, threshold=[0.5], use_labels=True, use_norm=False, warmup_stage=-1, weight_decay=0.0, weight_style='attention', zero_inits=True)
----------------------------------------------------------------------------------------------------
Run 0 start training
----------------------------------------------------------------------------------------------------
Stage 0 start training
# Nodes: 111059956
# Edges:<PHONE_NUMBER>
# Train: 1207179
# Val: 125265
# Test: 214338
# Classes: 172
in_feats: 128
using label information
load saved embeddings
load saved label embedding
Preprocessing costs 30.0538 s
# Params: 8556888
Epoch 1, Time(s): 12.3461 6.8139, Best Val loss: 1.1323, Accs: Train: 0.6775, Val: 0.6730, Test: 0.6418, Best Val: 0.6730, Best Test: 0.6418
Epoch 2, Time(s): 11.7655 7.2737, Best Val loss: 1.0589, Accs: Train: 0.6848, Val: 0.6906, Test: 0.6604, Best Val: 0.6906, Best Test: 0.6604
Epoch 3, Time(s): 11.2602 7.0704, Best Val loss: 1.0318, Accs: Train: 0.6910, Val: 0.6946, Test: 0.6610, Best Val: 0.6946, Best Test: 0.6610
Epoch 4, Time(s): 11.3336 6.5859, Best Val loss: 1.0118, Accs: Train: 0.6959, Val: 0.6964, Test: 0.6653, Best Val: 0.6964, Best Test: 0.6653
Epoch 5, Time(s): 11.1030 6.6076, Best Val loss: 1.0118, Accs: Train: 0.7042, Val: 0.6928, Test: 0.6640, Best Val: 0.6964, Best Test: 0.6653
Epoch 6, Time(s): 11.3695 6.7636, Best Val loss: 1.0118, Accs: Train: 0.7048, Val: 0.6956, Test: 0.6645, Best Val: 0.6964, Best Test: 0.6653
Epoch 7, Time(s): 11.3102 6.6775, Best Val loss: 1.0118, Accs: Train: 0.7092, Val: 0.6941, Test: 0.6651, Best Val: 0.6964, Best Test: 0.6653
Epoch 8, Time(s): 11.4756 7.1545, Best Val loss: 0.9831, Accs: Train: 0.7087, Val: 0.6987, Test: 0.6690, Best Val: 0.6987, Best Test: 0.6690
Epoch 9, Time(s): 11.3708 6.9943, Best Val loss: 0.9831, Accs: Train: 0.7113, Val: 0.6903, Test: 0.6618, Best Val: 0.6987, Best Test: 0.6690
Epoch 10, Time(s): 11.4054 7.2193, Best Val loss: 0.9831, Accs: Train: 0.7131, Val: 0.6943, Test: 0.6675, Best Val: 0.6987, Best Test: 0.6690
Epoch 11, Time(s): 11.3513 6.8980, Best Val loss: 0.9831, Accs: Train: 0.7143, Val: 0.6963, Test: 0.6653, Best Val: 0.6987, Best Test: 0.6690
Epoch 12, Time(s): 11.2391 6.5296, Best Val loss: 0.9718, Accs: Train: 0.7174, Val: 0.6991, Test: 0.6691, Best Val: 0.6991, Best Test: 0.6691
Epoch 13, Time(s): 11.3723 6.4635, Best Val loss: 0.9633, Accs: Train: 0.7193, Val: 0.7026, Test: 0.6714, Best Val: 0.7026, Best Test: 0.6714
Epoch 14, Time(s): 11.1210 6.5076, Best Val loss: 0.9633, Accs: Train: 0.7206, Val: 0.7015, Test: 0.6730, Best Val: 0.7026, Best Test: 0.6714
Epoch 15, Time(s): 11.1393 6.4904, Best Val loss: 0.9498, Accs: Train: 0.7223, Val: 0.7030, Test: 0.6739, Best Val: 0.7030, Best Test: 0.6739
Epoch 16, Time(s): 10.9803 6.3941, Best Val loss: 0.9524, Accs: Train: 0.7178, Val: 0.7056, Test: 0.6753, Best Val: 0.7056, Best Test: 0.6753
Epoch 17, Time(s): 11.1285 6.4282, Best Val loss: 0.9524, Accs: Train: 0.7206, Val: 0.7033, Test: 0.6703, Best Val: 0.7056, Best Test: 0.6753
Epoch 18, Time(s): 11.2357 6.4760, Best Val loss: 0.9461, Accs: Train: 0.7232, Val: 0.7061, Test: 0.6730, Best Val: 0.7061, Best Test: 0.6730
Epoch 19, Time(s): 11.0705 6.4683, Best Val loss: 0.9461, Accs: Train: 0.7263, Val: 0.7007, Test: 0.6718, Best Val: 0.7061, Best Test: 0.6730
Epoch 20, Time(s): 11.3022 6.5206, Best Val loss: 0.9461, Accs: Train: 0.7289, Val: 0.6994, Test: 0.6673, Best Val: 0.7061, Best Test: 0.6730
Epoch 21, Time(s): 11.2209 6.4602, Best Val loss: 0.9342, Accs: Train: 0.7287, Val: 0.7076, Test: 0.6762, Best Val: 0.7076, Best Test: 0.6762
Epoch 22, Time(s): 11.2031 6.6754, Best Val loss: 0.9342, Accs: Train: 0.7278, Val: 0.7059, Test: 0.6714, Best Val: 0.7076, Best Test: 0.6762
Epoch 23, Time(s): 11.2321 6.4863, Best Val loss: 0.9342, Accs: Train: 0.7305, Val: 0.7034, Test: 0.6734, Best Val: 0.7076, Best Test: 0.6762
Epoch 24, Time(s): 11.0995 6.3956, Best Val loss: 0.9342, Accs: Train: 0.7328, Val: 0.7056, Test: 0.6733, Best Val: 0.7076, Best Test: 0.6762
Epoch 25, Time(s): 11.1217 6.3499, Best Val loss: 0.9342, Accs: Train: 0.7343, Val: 0.7049, Test: 0.6739, Best Val: 0.7076, Best Test: 0.6762
Epoch 26, Time(s): 11.3853 6.5175, Best Val loss: 0.9427, Accs: Train: 0.7351, Val: 0.7079, Test: 0.6753, Best Val: 0.7079, Best Test: 0.6753
Epoch 27, Time(s): 10.9898 6.3615, Best Val loss: 0.9380, Accs: Train: 0.7328, Val: 0.7079, Test: 0.6771, Best Val: 0.7079, Best Test: 0.6771
Epoch 28, Time(s): 11.2412 6.3631, Best Val loss: 0.9380, Accs: Train: 0.7378, Val: 0.7016, Test: 0.6732, Best Val: 0.7079, Best Test: 0.6771
Epoch 29, Time(s): 11.0929 6.4802, Best Val loss: 0.9217, Accs: Train: 0.7387, Val: 0.7085, Test: 0.6758, Best Val: 0.7085, Best Test: 0.6758
Epoch 30, Time(s): 11.0916 6.3476, Best Val loss: 0.9217, Accs: Train: 0.7393, Val: 0.7021, Test: 0.6720, Best Val: 0.7085, Best Test: 0.6758
Epoch 31, Time(s): 11.2482 6.3962, Best Val loss: 0.9217, Accs: Train: 0.7417, Val: 0.7061, Test: 0.6734, Best Val: 0.7085, Best Test: 0.6758
Epoch 32, Time(s): 11.0136 6.3797, Best Val loss: 0.9392, Accs: Train: 0.7405, Val: 0.7087, Test: 0.6757, Best Val: 0.7087, Best Test: 0.6757
Epoch 33, Time(s): 11.2031 6.3539, Best Val loss: 0.9392, Accs: Train: 0.7445, Val: 0.7061, Test: 0.6722, Best Val: 0.7087, Best Test: 0.6757
Epoch 34, Time(s): 11.0677 6.4507, Best Val loss: 0.9392, Accs: Train: 0.7431, Val: 0.7075, Test: 0.6737, Best Val: 0.7087, Best Test: 0.6757
Epoch 35, Time(s): 11.2001 6.4476, Best Val loss: 0.9392, Accs: Train: 0.7454, Val: 0.7081, Test: 0.6765, Best Val: 0.7087, Best Test: 0.6757
Epoch 36, Time(s): 10.9558 6.4229, Best Val loss: 0.9392, Accs: Train: 0.7471, Val: 0.7084, Test: 0.6767, Best Val: 0.7087, Best Test: 0.6757
Epoch 37, Time(s): 11.2129 6.3573, Best Val loss: 0.9392, Accs: Train: 0.7456, Val: 0.7065, Test: 0.6736, Best Val: 0.7087, Best Test: 0.6757
Epoch 38, Time(s): 11.0418 6.4160, Best Val loss: 0.9392, Accs: Train: 0.7496, Val: 0.7061, Test: 0.6745, Best Val: 0.7087, Best Test: 0.6757
Epoch 39, Time(s): 11.2088 6.3450, Best Val loss: 0.9392, Accs: Train: 0.7501, Val: 0.7079, Test: 0.6756, Best Val: 0.7087, Best Test: 0.6757
Epoch 40, Time(s): 10.9923 6.3743, Best Val loss: 0.9392, Accs: Train: 0.7505, Val: 0.7072, Test: 0.6752, Best Val: 0.7087, Best Test: 0.6757
Epoch 41, Time(s): 11.2196 6.5810, Best Val loss: 0.9392, Accs: Train: 0.7483, Val: 0.7016, Test: 0.6732, Best Val: 0.7087, Best Test: 0.6757
Epoch 42, Time(s): 11.2839 6.4900, Best Val loss: 0.9392, Accs: Train: 0.7535, Val: 0.7073, Test: 0.6725, Best Val: 0.7087, Best Test: 0.6757
Epoch 43, Time(s): 11.2403 6.7499, Best Val loss: 0.9392, Accs: Train: 0.7541, Val: 0.7039, Test: 0.6709, Best Val: 0.7087, Best Test: 0.6757
Epoch 44, Time(s): 11.1518 6.3897, Best Val loss: 0.9261, Accs: Train: 0.7558, Val: 0.7091, Test: 0.6769, Best Val: 0.7091, Best Test: 0.6769
Epoch 45, Time(s): 11.1544 7.0253, Best Val loss: 0.9262, Accs: Train: 0.7577, Val: 0.7092, Test: 0.6767, Best Val: 0.7092, Best Test: 0.6767
Epoch 46, Time(s): 11.1927 6.6576, Best Val loss: 0.9290, Accs: Train: 0.7575, Val: 0.7095, Test: 0.6777, Best Val: 0.7095, Best Test: 0.6777
Epoch 47, Time(s): 11.3045 6.5578, Best Val loss: 0.9290, Accs: Train: 0.7585, Val: 0.7090, Test: 0.6766, Best Val: 0.7095, Best Test: 0.6777
Epoch 48, Time(s): 11.1823 6.5747, Best Val loss: 0.9290, Accs: Train: 0.7596, Val: 0.7066, Test: 0.6757, Best Val: 0.7095, Best Test: 0.6777
Epoch 49, Time(s): 11.3159 6.4347, Best Val loss: 0.9290, Accs: Train: 0.7601, Val: 0.7094, Test: 0.6749, Best Val: 0.7095, Best Test: 0.6777
Epoch 50, Time(s): 11.1476 6.5520, Best Val loss: 0.9290, Accs: Train: 0.7610, Val: 0.7090, Test: 0.6752, Best Val: 0.7095, Best Test: 0.6777
Epoch 51, Time(s): 11.0583 6.4520, Best Val loss: 0.9290, Accs: Train: 0.7632, Val: 0.7070, Test: 0.6765, Best Val: 0.7095, Best Test: 0.6777
Epoch 52, Time(s): 11.0962 6.4093, Best Val loss: 0.9290, Accs: Train: 0.7636, Val: 0.7088, Test: 0.6766, Best Val: 0.7095, Best Test: 0.6777
Epoch 53, Time(s): 11.0632 6.5333, Best Val loss: 0.9290, Accs: Train: 0.7644, Val: 0.7093, Test: 0.6760, Best Val: 0.7095, Best Test: 0.6777
Epoch 54, Time(s): 11.1036 6.3928, Best Val loss: 0.9290, Accs: Train: 0.7641, Val: 0.7082, Test: 0.6736, Best Val: 0.7095, Best Test: 0.6777
Epoch 55, Time(s): 11.2058 6.5127, Best Val loss: 0.9210, Accs: Train: 0.7646, Val: 0.7102, Test: 0.6768, Best Val: 0.7102, Best Test: 0.6768
Epoch 56, Time(s): 11.0378 6.3666, Best Val loss: 0.9206, Accs: Train: 0.7657, Val: 0.7107, Test: 0.6766, Best Val: 0.7107, Best Test: 0.6766
Epoch 57, Time(s): 11.1935 6.3868, Best Val loss: 0.9206, Accs: Train: 0.7661, Val: 0.7056, Test: 0.6747, Best Val: 0.7107, Best Test: 0.6766
Epoch 58, Time(s): 10.9380 6.3294, Best Val loss: 0.9206, Accs: Train: 0.7665, Val: 0.7069, Test: 0.6754, Best Val: 0.7107, Best Test: 0.6766
Epoch 59, Time(s): 11.0536 6.4062, Best Val loss: 0.9206, Accs: Train: 0.7693, Val: 0.7081, Test: 0.6742, Best Val: 0.7107, Best Test: 0.6766
Epoch 60, Time(s): 11.0725 6.3433, Best Val loss: 0.9276, Accs: Train: 0.7687, Val: 0.7112, Test: 0.6778, Best Val: 0.7112, Best Test: 0.6778
Epoch 61, Time(s): 10.9945 6.4842, Best Val loss: 0.9276, Accs: Train: 0.7699, Val: 0.7095, Test: 0.6772, Best Val: 0.7112, Best Test: 0.6778
Epoch 62, Time(s): 11.1217 6.2837, Best Val loss: 0.9276, Accs: Train: 0.7705, Val: 0.7110, Test: 0.6778, Best Val: 0.7112, Best Test: 0.6778
Epoch 63, Time(s): 11.1217 6.4360, Best Val loss: 0.9276, Accs: Train: 0.7721, Val: 0.7088, Test: 0.6776, Best Val: 0.7112, Best Test: 0.6778
Epoch 64, Time(s): 11.0740 6.3278, Best Val loss: 0.9276, Accs: Train: 0.7751, Val: 0.7096, Test: 0.6765, Best Val: 0.7112, Best Test: 0.6778
Epoch 65, Time(s): 11.1608 6.3233, Best Val loss: 0.9276, Accs: Train: 0.7746, Val: 0.7095, Test: 0.6753, Best Val: 0.7112, Best Test: 0.6778
Epoch 66, Time(s): 11.2731 6.3717, Best Val loss: 0.9276, Accs: Train: 0.7737, Val: 0.7089, Test: 0.6752, Best Val: 0.7112, Best Test: 0.6778
Epoch 67, Time(s): 11.0740 6.3302, Best Val loss: 0.9276, Accs: Train: 0.7764, Val: 0.7078, Test: 0.6734, Best Val: 0.7112, Best Test: 0.6778
Epoch 68, Time(s): 11.1060 6.3396, Best Val loss: 0.9276, Accs: Train: 0.7761, Val: 0.7108, Test: 0.6762, Best Val: 0.7112, Best Test: 0.6778
Epoch 69, Time(s): 11.2765 6.3553, Best Val loss: 0.9276, Accs: Train: 0.7783, Val: 0.7102, Test: 0.6771, Best Val: 0.7112, Best Test: 0.6778
Epoch 70, Time(s): 11.1916 6.4201, Best Val loss: 0.9276, Accs: Train: 0.7787, Val: 0.7097, Test: 0.6752, Best Val: 0.7112, Best Test: 0.6778
Epoch 71, Time(s): 11.0243 6.4701, Best Val loss: 0.9276, Accs: Train: 0.7800, Val: 0.7083, Test: 0.6754, Best Val: 0.7112, Best Test: 0.6778
Epoch 72, Time(s): 11.0838 6.3190, Best Val loss: 0.9276, Accs: Train: 0.7814, Val: 0.7082, Test: 0.6735, Best Val: 0.7112, Best Test: 0.6778
Epoch 73, Time(s): 11.2189 6.3654, Best Val loss: 0.9276, Accs: Train: 0.7823, Val: 0.7077, Test: 0.6736, Best Val: 0.7112, Best Test: 0.6778
Epoch 74, Time(s): 11.1766 6.4053, Best Val loss: 0.9276, Accs: Train: 0.7818, Val: 0.7109, Test: 0.6752, Best Val: 0.7112, Best Test: 0.6778
Epoch 75, Time(s): 11.2290 6.4155, Best Val loss: 0.9276, Accs: Train: 0.7829, Val: 0.7084, Test: 0.6744, Best Val: 0.7112, Best Test: 0.6778
Epoch 76, Time(s): 10.9647 6.3714, Best Val loss: 0.9185, Accs: Train: 0.7837, Val: 0.7136, Test: 0.6778, Best Val: 0.7136, Best Test: 0.6778
Epoch 77, Time(s): 11.1712 6.4917, Best Val loss: 0.9185, Accs: Train: 0.7818, Val: 0.7125, Test: 0.6766, Best Val: 0.7136, Best Test: 0.6778
Epoch 78, Time(s): 11.1138 6.3683, Best Val loss: 0.9185, Accs: Train: 0.7848, Val: 0.7068, Test: 0.6731, Best Val: 0.7136, Best Test: 0.6778
Epoch 79, Time(s): 11.0365 6.5954, Best Val loss: 0.9185, Accs: Train: 0.7854, Val: 0.7100, Test: 0.6771, Best Val: 0.7136, Best Test: 0.6778
Epoch 80, Time(s): 11.0543 6.3692, Best Val loss: 0.9185, Accs: Train: 0.7869, Val: 0.7114, Test: 0.6779, Best Val: 0.7136, Best Test: 0.6778
Epoch 81, Time(s): 11.1618 6.3907, Best Val loss: 0.9185, Accs: Train: 0.7867, Val: 0.7077, Test: 0.6715, Best Val: 0.7136, Best Test: 0.6778
Epoch 82, Time(s): 11.1419 6.5275, Best Val loss: 0.9185, Accs: Train: 0.7878, Val: 0.7093, Test: 0.6759, Best Val: 0.7136, Best Test: 0.6778
Epoch 83, Time(s): 10.9723 6.3827, Best Val loss: 0.9185, Accs: Train: 0.7890, Val: 0.7069, Test: 0.6733, Best Val: 0.7136, Best Test: 0.6778
Epoch 84, Time(s): 11.1337 6.5113, Best Val loss: 0.9185, Accs: Train: 0.7907, Val: 0.7089, Test: 0.6744, Best Val: 0.7136, Best Test: 0.6778
Epoch 85, Time(s): 11.0837 6.4049, Best Val loss: 0.9185, Accs: Train: 0.7915, Val: 0.7114, Test: 0.6758, Best Val: 0.7136, Best Test: 0.6778
Epoch 86, Time(s): 11.1071 6.3904, Best Val loss: 0.9185, Accs: Train: 0.7915, Val: 0.7113, Test: 0.6756, Best Val: 0.7136, Best Test: 0.6778
Epoch 87, Time(s): 11.0380 6.4335, Best Val loss: 0.9185, Accs: Train: 0.7915, Val: 0.7093, Test: 0.6742, Best Val: 0.7136, Best Test: 0.6778
Epoch 88, Time(s): 11.0370 6.3834, Best Val loss: 0.9185, Accs: Train: 0.7902, Val: 0.7077, Test: 0.6747, Best Val: 0.7136, Best Test: 0.6778
Epoch 89, Time(s): 11.2002 6.3818, Best Val loss: 0.9185, Accs: Train: 0.7920, Val: 0.7098, Test: 0.6741, Best Val: 0.7136, Best Test: 0.6778
Epoch 90, Time(s): 11.0339 6.5572, Best Val loss: 0.9185, Accs: Train: 0.7942, Val: 0.7104, Test: 0.6755, Best Val: 0.7136, Best Test: 0.6778
Epoch 91, Time(s): 11.1006 6.4347, Best Val loss: 0.9185, Accs: Train: 0.7927, Val: 0.7127, Test: 0.6785, Best Val: 0.7136, Best Test: 0.6778
Epoch 92, Time(s): 10.9998 6.3598, Best Val loss: 0.9185, Accs: Train: 0.7947, Val: 0.7106, Test: 0.6764, Best Val: 0.7136, Best Test: 0.6778
Epoch 93, Time(s): 11.1375 6.4184, Best Val loss: 0.9185, Accs: Train: 0.7968, Val: 0.7103, Test: 0.6761, Best Val: 0.7136, Best Test: 0.6778
Epoch 94, Time(s): 11.1012 6.3701, Best Val loss: 0.9185, Accs: Train: 0.7970, Val: 0.7106, Test: 0.6771, Best Val: 0.7136, Best Test: 0.6778
Epoch 95, Time(s): 11.1586 6.3819, Best Val loss: 0.9185, Accs: Train: 0.7965, Val: 0.7101, Test: 0.6757, Best Val: 0.7136, Best Test: 0.6778
Epoch 96, Time(s): 11.1606 6.4722, Best Val loss: 0.9185, Accs: Train: 0.7979, Val: 0.7107, Test: 0.6757, Best Val: 0.7136, Best Test: 0.6778
Epoch 97, Time(s): 10.9804 6.3393, Best Val loss: 0.9185, Accs: Train: 0.7986, Val: 0.7099, Test: 0.6758, Best Val: 0.7136, Best Test: 0.6778
Epoch 98, Time(s): 11.1488 6.6311, Best Val loss: 0.9185, Accs: Train: 0.7971, Val: 0.7120, Test: 0.6771, Best Val: 0.7136, Best Test: 0.6778
Epoch 99, Time(s): 10.9412 6.3610, Best Val loss: 0.9185, Accs: Train: 0.8004, Val: 0.7099, Test: 0.6750, Best Val: 0.7136, Best Test: 0.6778
Epoch 100, Time(s): 11.1368 6.3668, Best Val loss: 0.9185, Accs: Train: 0.8029, Val: 0.7104, Test: 0.6752, Best Val: 0.7136, Best Test: 0.6778
Stage: 0, Best Epoch 76, Val 0.7136, Test 0.6778

In addition, our experiments are conducted on V100 (16G).

thanks again for your prompt reply! Just notice that your update the repo 1 hour ago with a new commit.
I will try again the script and see if I can reproduce your result. If yes, I will close this issue.
Thanks again!

Hi @skepsun ,
I am still not able to preproduce your results even using the latest commit.
The accuracies are very low at the beginning of epochs,
which is far smaller than the test accuracy of 0.66 as you shown above.
I checked the hyper-parameters Namspace, and most are the same as yours, except some data path related variables.
Not sure where goes wrong, could it be the difference of initial label embeddings or smoothed node features?
your advice is highly appreciated!
gpu: 0
Using backend: pytorch
Namespace(K=3, acc_loss='acc', aggr_gpu=-1, alpha=0.5, attn_drop=0.0, avoid_features=False, batch_size=5000, chunks=1, data_dir='/home/ec2-user/ws_1/gnn-exp', dataset='ogbn-paper
s100M', dropout=0.5, emb_path='/home/scx/NARS/', epoch_setting=[100, 100, 100, 100], eval_batch_size=100000, eval_every=1, example_subsets_path='/home/scx/NARS/sample_relation_su
bsets/examples', fixed_subsets=False, focal='first', gpu=0, inductive=False, input_drop=0.0, label_K=9, label_drop=0.5, label_mlp_layer=4, label_residual=False, load_embs=True, l
oad_label_emb=True, lr=0.001, mag_emb=False, memory_efficient=True, mlp_layer=2, model='sagn', num_heads=1, num_hidden=1024, num_runs=3, position_emb=False, pretrain_model='Compl
Ex', probs_dir='../intermediate_outputs', sample_size=8, seed=0, threshold=[0.5], use_labels=True, use_norm=False, warmup_stage=-1, weight_decay=0.0, weight_style='attention', ze
ro_inits=True)
----------------------------------------------------------------------------------------------------
Run 0 start training
----------------------------------------------------------------------------------------------------
Stage 0 start training
# Nodes: 111059956
# Edges:<PHONE_NUMBER>
# Train: 1207179
# Val: 125265
# Test: 214338
# Classes: 172
in_feats: 128
using label information
Compute neighbor-averaged feats last
saving initial label embeddings to /home/ec2-user/ws_1/gnn-exp/ogbn_papers100M/embedding/smoothed_label_emb_K_9.pt
Compute neighbor-averaged feats all
saving smoothed node features to /home/ec2-user/ws_1/gnn-exp/ogbn_papers100M/embedding/smoothed_embs_K_3.pt
load saved embeddings
load saved label embedding
Preprocessing costs 473.2077 s
# Params: 8556888
Epoch 1, Time(s): 10.8812 6.2818, Best Val loss: 4.7687, Accs: Train: 0.0714, Val: 0.0330, Test: 0.0280, Best Val: 0.0330, Best Test: 0.0280
Epoch 2, Time(s): 10.9308 6.3057, Best Val loss: 4.7687, Accs: Train: 0.0718, Val: 0.0319, Test: 0.0272, Best Val: 0.0330, Best Test: 0.0280
Epoch 3, Time(s): 10.9481 6.2545, Best Val loss: 4.7687, Accs: Train: 0.0715, Val: 0.0319, Test: 0.0272, Best Val: 0.0330, Best Test: 0.0280
Epoch 4, Time(s): 10.8378 6.2826, Best Val loss: 4.7638, Accs: Train: 0.0715, Val: 0.0330, Test: 0.0282, Best Val: 0.0330, Best Test: 0.0282
Epoch 5, Time(s): 10.9366 6.2538, Best Val loss: 4.7638, Accs: Train: 0.0717, Val: 0.0305, Test: 0.0259, Best Val: 0.0330, Best Test: 0.0282
Epoch 6, Time(s): 10.8459 6.2229, Best Val loss: 4.7638, Accs: Train: 0.0719, Val: 0.0328, Test: 0.0280, Best Val: 0.0330, Best Test: 0.0282
Epoch 7, Time(s): 10.9319 6.1961, Best Val loss: 4.7638, Accs: Train: 0.0718, Val: 0.0294, Test: 0.0250, Best Val: 0.0330, Best Test: 0.0282
Epoch 8, Time(s): 10.9075 6.1861, Best Val loss: 4.7725, Accs: Train: 0.0715, Val: 0.0330, Test: 0.0281, Best Val: 0.0330, Best Test: 0.0281
Epoch 9, Time(s): 10.8522 6.1936, Best Val loss: 4.7725, Accs: Train: 0.0715, Val: 0.0319, Test: 0.0271, Best Val: 0.0330, Best Test: 0.0281
Epoch 10, Time(s): 10.8956 6.1741, Best Val loss: 4.7740, Accs: Train: 0.0714, Val: 0.0330, Test: 0.0281, Best Val: 0.0330, Best Test: 0.0281
Epoch 11, Time(s): 10.9437 6.2341, Best Val loss: 4.7740, Accs: Train: 0.0719, Val: 0.0326, Test: 0.0279, Best Val: 0.0330, Best Test: 0.0281
Epoch 12, Time(s): 10.9307 6.2865, Best Val loss: 4.7740, Accs: Train: 0.0720, Val: 0.0323, Test: 0.0275, Best Val: 0.0330, Best Test: 0.0281
Epoch 13, Time(s): 10.9453 6.3235, Best Val loss: 4.7740, Accs: Train: 0.0718, Val: 0.0322, Test: 0.0273, Best Val: 0.0330, Best Test: 0.0281
Epoch 14, Time(s): 10.9284 6.1903, Best Val loss: 4.7740, Accs: Train: 0.0720, Val: 0.0286, Test: 0.0240, Best Val: 0.0330, Best Test: 0.0281
Epoch 15, Time(s): 10.9181 6.2205, Best Val loss: 4.7740, Accs: Train: 0.0719, Val: 0.0320, Test: 0.0272, Best Val: 0.0330, Best Test: 0.0281
Epoch 16, Time(s): 10.9167 6.2145, Best Val loss: 4.7740, Accs: Train: 0.0718, Val: 0.0324, Test: 0.0276, Best Val: 0.0330, Best Test: 0.0281
Epoch 17, Time(s): 10.9271 6.2003, Best Val loss: 4.7740, Accs: Train: 0.0719, Val: 0.0317, Test: 0.0269, Best Val: 0.0330, Best Test: 0.0281
Epoch 18, Time(s): 10.9196 6.2028, Best Val loss: 4.7740, Accs: Train: 0.0717, Val: 0.0323, Test: 0.0275, Best Val: 0.0330, Best Test: 0.0281
Epoch 19, Time(s): 10.8963 6.2247, Best Val loss: 4.7740, Accs: Train: 0.0721, Val: 0.0321, Test: 0.0274, Best Val: 0.0330, Best Test: 0.0281
Epoch 20, Time(s): 10.8915 6.1848, Best Val loss: 4.7740, Accs: Train: 0.0726, Val: 0.0305, Test: 0.0261, Best Val: 0.0330, Best Test: 0.0281
Epoch 21, Time(s): 10.9556 6.1958, Best Val loss: 4.7740, Accs: Train: 0.0721, Val: 0.0325, Test: 0.0278, Best Val: 0.0330, Best Test: 0.0281
Epoch 22, Time(s): 10.9264 6.2209, Best Val loss: 4.7740, Accs: Train: 0.0721, Val: 0.0325, Test: 0.0277, Best Val: 0.0330, Best Test: 0.0281
Epoch 23, Time(s): 10.9289 6.2305, Best Val loss: 4.7740, Accs: Train: 0.0721, Val: 0.0327, Test: 0.0279, Best Val: 0.0330, Best Test: 0.0281
Epoch 24, Time(s): 10.9238 6.2062, Best Val loss: 4.7740, Accs: Train: 0.0728, Val: 0.0303, Test: 0.0262, Best Val: 0.0330, Best Test: 0.0281
Epoch 25, Time(s): 10.9467 6.2121, Best Val loss: 4.7740, Accs: Train: 0.0720, Val: 0.0328, Test: 0.0280, Best Val: 0.0330, Best Test: 0.0281
Epoch 26, Time(s): 10.9134 6.1868, Best Val loss: 4.7740, Accs: Train: 0.0735, Val: 0.0251, Test: 0.0211, Best Val: 0.0330, Best Test: 0.0281
Epoch 27, Time(s): 10.9242 6.3630, Best Val loss: 4.7740, Accs: Train: 0.0722, Val: 0.0328, Test: 0.0280, Best Val: 0.0330, Best Test: 0.0281
Epoch 28, Time(s): 10.9921 6.3056, Best Val loss: 4.7740, Accs: Train: 0.0730, Val: 0.0306, Test: 0.0260, Best Val: 0.0330, Best Test: 0.0281
Epoch 29, Time(s): 10.9901 6.3066, Best Val loss: 4.7740, Accs: Train: 0.0729, Val: 0.0314, Test: 0.0269, Best Val: 0.0330, Best Test: 0.0281
Epoch 30, Time(s): 10.9566 6.3015, Best Val loss: 4.7740, Accs: Train: 0.0736, Val: 0.0285, Test: 0.0241, Best Val: 0.0330, Best Test: 0.0281
Epoch 31, Time(s): 10.9799 6.2402, Best Val loss: 4.7740, Accs: Train: 0.0733, Val: 0.0305, Test: 0.0261, Best Val: 0.0330, Best Test: 0.0281
Epoch 32, Time(s): 10.9788 6.2441, Best Val loss: 4.7740, Accs: Train: 0.0726, Val: 0.0321, Test: 0.0276, Best Val: 0.0330, Best Test: 0.0281
Epoch 33, Time(s): 10.9792 6.2465, Best Val loss: 4.7740, Accs: Train: 0.0733, Val: 0.0306, Test: 0.0261, Best Val: 0.0330, Best Test: 0.0281
Epoch 34, Time(s): 10.9442 6.2139, Best Val loss: 4.7740, Accs: Train: 0.0735, Val: 0.0306, Test: 0.0263, Best Val: 0.0330, Best Test: 0.0281
Epoch 35, Time(s): 10.9644 6.2200, Best Val loss: 4.7740, Accs: Train: 0.0731, Val: 0.0317, Test: 0.0272, Best Val: 0.0330, Best Test: 0.0281
Epoch 36, Time(s): 10.9193 6.2284, Best Val loss: 4.7740, Accs: Train: 0.0733, Val: 0.0317, Test: 0.0273, Best Val: 0.0330, Best Test: 0.0281
Epoch 37, Time(s): 10.9504 6.2573, Best Val loss: 4.7740, Accs: Train: 0.0740, Val: 0.0299, Test: 0.0256, Best Val: 0.0330, Best Test: 0.0281
Epoch 38, Time(s): 10.9300 6.2135, Best Val loss: 4.7740, Accs: Train: 0.0741, Val: 0.0309, Test: 0.0264, Best Val: 0.0330, Best Test: 0.0281
Epoch 39, Time(s): 10.9616 6.2492, Best Val loss: 4.7740, Accs: Train: 0.0736, Val: 0.0313, Test: 0.0269, Best Val: 0.0330, Best Test: 0.0281
Epoch 40, Time(s): 10.9380 6.2389, Best Val loss: 4.7740, Accs: Train: 0.0742, Val: 0.0305, Test: 0.0263, Best Val: 0.0330, Best Test: 0.0281
Epoch 41, Time(s): 10.9641 6.2373, Best Val loss: 4.7740, Accs: Train: 0.0742, Val: 0.0304, Test: 0.0262, Best Val: 0.0330, Best Test: 0.0281
Epoch 42, Time(s): 10.9541 6.2069, Best Val loss: 4.7740, Accs: Train: 0.0754, Val: 0.0274, Test: 0.0236, Best Val: 0.0330, Best Test: 0.0281
Epoch 43, Time(s): 10.9359 6.2408, Best Val loss: 4.7740, Accs: Train: 0.0752, Val: 0.0289, Test: 0.0249, Best Val: 0.0330, Best Test: 0.0281
Epoch 44, Time(s): 10.9516 6.2358, Best Val loss: 4.7740, Accs: Train: 0.0745, Val: 0.0303, Test: 0.0260, Best Val: 0.0330, Best Test: 0.0281
Epoch 45, Time(s): 10.9463 6.2324, Best Val loss: 4.7740, Accs: Train: 0.0755, Val: 0.0309, Test: 0.0263, Best Val: 0.0330, Best Test: 0.0281
Epoch 46, Time(s): 10.9420 6.2256, Best Val loss: 4.7740, Accs: Train: 0.0761, Val: 0.0290, Test: 0.0248, Best Val: 0.0330, Best Test: 0.0281
Epoch 47, Time(s): 10.9438 6.2540, Best Val loss: 4.7740, Accs: Train: 0.0765, Val: 0.0278, Test: 0.0240, Best Val: 0.0330, Best Test: 0.0281
Epoch 48, Time(s): 10.9248 6.2223, Best Val loss: 4.7740, Accs: Train: 0.0759, Val: 0.0298, Test: 0.0255, Best Val: 0.0330, Best Test: 0.0281
Epoch 49, Time(s): 10.9534 6.2339, Best Val loss: 4.7740, Accs: Train: 0.0775, Val: 0.0256, Test: 0.0220, Best Val: 0.0330, Best Test: 0.0281
Epoch 50, Time(s): 10.9424 6.2285, Best Val loss: 4.7740, Accs: Train: 0.0765, Val: 0.0287, Test: 0.0249, Best Val: 0.0330, Best Test: 0.0281
Epoch 51, Time(s): 10.9257 6.2413, Best Val loss: 4.7740, Accs: Train: 0.0763, Val: 0.0286, Test: 0.0250, Best Val: 0.0330, Best Test: 0.0281
Epoch 52, Time(s): 10.9437 6.2095, Best Val loss: 4.7740, Accs: Train: 0.0764, Val: 0.0293, Test: 0.0252, Best Val: 0.0330, Best Test: 0.0281
Epoch 53, Time(s): 10.9531 6.2355, Best Val loss: 4.7740, Accs: Train: 0.0778, Val: 0.0242, Test: 0.0208, Best Val: 0.0330, Best Test: 0.0281

Hi, @OctoberChang
We found some bugs in memory-efficient option, which saves temporary embeddings in a wrong way. The fix has been pushed. Please try it again, note that the previously saved smoothed_embs_K_3.pt and smoothed_label_emb_K_9.pt should be removed.

Hi @skepsun ,
I tried with the latest commit 08fe607,
and still not able to reproduce your results.
I git clone the repo on a new directory such that it downloaded the data,
and created the smoothed_embs_K_3.pt and smoothed_label_emb_K_9.pt from scratch.
It should not use any previously computed/cached results.
I compared your first epoch results with mine, and seems like the initialization is very different.
In your Epoch 1, the Best Val loss: 1.1323 is far smallar than mine Best Val loss: 1.9953...
See attached the training log:
Using backend: pytorch
Namespace(K=3, acc_loss='acc', aggr_gpu=-1, alpha=0.5, attn_drop=0.0, avoid_features=False, batch_size=5000, chunks=1, data_dir='/home/ec2-user/ws_1/gnn-exp/datasets', dataset='ogbn-papers100M', dropout=0.5, emb_path='/home/scx/NARS/', epoch_setting=[100, 100, 100, 100], eval_batch_size=100000, eval_every=1, example_subsets_path='/home/scx/NARS/sample_relation_subsets/examples', fixed_subsets=False, focal='first', gpu=0, inductive=False, input_drop=0.0, label_K=9, label_drop=0.5, label
_mlp_layer=4, label_residual=False, load_embs=True, load_label_emb=True, lr=0.001, mag_emb=False, memory_efficient=True, mlp_layer=2, model='sagn', num_heads=1, num_hidden=1024, num_runs=3, position_emb=False, pretrain_model='ComplEx', probs_dir='../intermediate_outputs', sample_size=8, seed=0, threshold=[0.5], use_labels=True, use_norm=False, warmup_stage=-1, weight_decay=0.0, weight_style='attention', zero_inits=True)
----------------------------------------------------------------------------------------------------
Run 0 start training
----------------------------------------------------------------------------------------------------
Stage 0 start training
# Nodes: 111059956
# Edges:<PHONE_NUMBER>
# Train: 1207179
# Val: 125265
# Test: 214338
# Classes: 172
in_feats: 128
using label information
Compute neighbor-averaged feats last
saving initial label embeddings to /home/ec2-user/ws_1/gnn-exp/datasets/ogbn_papers100M/embedding/smoothed_label_emb_K_9.pt
Compute neighbor-averaged feats all
saving smoothed node features to /home/ec2-user/ws_1/gnn-exp/datasets/ogbn_papers100M/embedding/smoothed_embs_K_3.pt
load saved embeddings
load saved label embedding
Preprocessing costs 244.2343 s
# Params: 8556888
Epoch 1, Time(s): 10.6819 6.0393, Best Val loss: 1.9953, Accs: Train: 0.4892, Val: 0.4660, Test: 0.4405, Best Val: 0.4660, Best Test: 0.4405
Epoch 2, Time(s): 10.6847 6.0234, Best Val loss: 1.9082, Accs: Train: 0.5069, Val: 0.4694, Test: 0.4463, Best Val: 0.4694, Best Test: 0.4463
Epoch 3, Time(s): 10.7338 5.8705, Best Val loss: 1.8521, Accs: Train: 0.5138, Val: 0.4896, Test: 0.4657, Best Val: 0.4896, Best Test: 0.4657
Epoch 4, Time(s): 10.7484 5.8229, Best Val loss: 1.8521, Accs: Train: 0.5211, Val: 0.4847, Test: 0.4626, Best Val: 0.4896, Best Test: 0.4657
Epoch 5, Time(s): 10.7756 5.8250, Best Val loss: 1.8074, Accs: Train: 0.5254, Val: 0.4965, Test: 0.4729, Best Val: 0.4965, Best Test: 0.4729
Epoch 6, Time(s): 10.7500 5.5752, Best Val loss: 1.7949, Accs: Train: 0.5310, Val: 0.4966, Test: 0.4754, Best Val: 0.4966, Best Test: 0.4754
Epoch 7, Time(s): 11.0643 6.0551, Best Val loss: 1.7815, Accs: Train: 0.5354, Val: 0.4967, Test: 0.4750, Best Val: 0.4967, Best Test: 0.4750
Epoch 8, Time(s): 11.0750 6.2598, Best Val loss: 1.7729, Accs: Train: 0.5386, Val: 0.4985, Test: 0.4772, Best Val: 0.4985, Best Test: 0.4772
Epoch 9, Time(s): 10.8949 6.3189, Best Val loss: 1.7644, Accs: Train: 0.5421, Val: 0.5049, Test: 0.4823, Best Val: 0.5049, Best Test: 0.4823
Epoch 10, Time(s): 10.9710 6.0760, Best Val loss: 1.7644, Accs: Train: 0.5451, Val: 0.4989, Test: 0.4773, Best Val: 0.5049, Best Test: 0.4823
Epoch 11, Time(s): 11.0681 6.1861, Best Val loss: 1.7644, Accs: Train: 0.5480, Val: 0.5021, Test: 0.4808, Best Val: 0.5049, Best Test: 0.4823
Epoch 12, Time(s): 11.0125 6.1222, Best Val loss: 1.7644, Accs: Train: 0.5506, Val: 0.5041, Test: 0.4818, Best Val: 0.5049, Best Test: 0.4823
Epoch 13, Time(s): 11.0448 6.1313, Best Val loss: 1.7644, Accs: Train: 0.5535, Val: 0.5024, Test: 0.4821, Best Val: 0.5049, Best Test: 0.4823
Epoch 14, Time(s): 10.8088 5.6406, Best Val loss: 1.7644, Accs: Train: 0.5558, Val: 0.5048, Test: 0.4836, Best Val: 0.5049, Best Test: 0.4823
Epoch 15, Time(s): 10.8093 5.6670, Best Val loss: 1.7279, Accs: Train: 0.5583, Val: 0.5088, Test: 0.4862, Best Val: 0.5088, Best Test: 0.4862
Epoch 16, Time(s): 10.8285 5.6628, Best Val loss: 1.7211, Accs: Train: 0.5595, Val: 0.5092, Test: 0.4882, Best Val: 0.5092, Best Test: 0.4882
Epoch 17, Time(s): 10.8236 5.5592, Best Val loss: 1.7122, Accs: Train: 0.5623, Val: 0.5111, Test: 0.4893, Best Val: 0.5111, Best Test: 0.4893
Epoch 18, Time(s): 10.8910 6.4607, Best Val loss: 1.7108, Accs: Train: 0.5637, Val: 0.5115, Test: 0.4911, Best Val: 0.5115, Best Test: 0.4911
Epoch 19, Time(s): 10.8827 5.6976, Best Val loss: 1.7108, Accs: Train: 0.5656, Val: 0.5088, Test: 0.4889, Best Val: 0.5115, Best Test: 0.4911
Epoch 20, Time(s): 10.8494 5.6706, Best Val loss: 1.7016, Accs: Train: 0.5677, Val: 0.5120, Test: 0.4908, Best Val: 0.5120, Best Test: 0.4908
...
Epoch 90, Time(s): 10.8694 5.5648, Best Val loss: 1.6647, Accs: Train: 0.6143, Val: 0.5230, Test: 0.5001, Best Val: 0.5246, Best Test: 0.5007
Epoch 91, Time(s): 10.7436 5.4603, Best Val loss: 1.6647, Accs: Train: 0.6145, Val: 0.5218, Test: 0.4987, Best Val: 0.5246, Best Test: 0.5007
Epoch 92, Time(s): 10.8035 5.4886, Best Val loss: 1.6647, Accs: Train: 0.6143, Val: 0.5214, Test: 0.4984, Best Val: 0.5246, Best Test: 0.5007
Epoch 93, Time(s): 10.7261 5.4684, Best Val loss: 1.6647, Accs: Train: 0.6141, Val: 0.5236, Test: 0.5000, Best Val: 0.5246, Best Test: 0.5007
Epoch 94, Time(s): 10.7327 5.4747, Best Val loss: 1.6647, Accs: Train: 0.6148, Val: 0.5204, Test: 0.4983, Best Val: 0.5246, Best Test: 0.5007
Epoch 95, Time(s): 10.8008 5.5063, Best Val loss: 1.6647, Accs: Train: 0.6152, Val: 0.5226, Test: 0.4995, Best Val: 0.5246, Best Test: 0.5007
Epoch 96, Time(s): 10.7883 5.4830, Best Val loss: 1.6647, Accs: Train: 0.6142, Val: 0.5204, Test: 0.4970, Best Val: 0.5246, Best Test: 0.5007
Epoch 97, Time(s): 10.7411 5.4639, Best Val loss: 1.6647, Accs: Train: 0.6158, Val: 0.5216, Test: 0.4989, Best Val: 0.5246, Best Test: 0.5007
Epoch 98, Time(s): 10.8070 5.5679, Best Val loss: 1.6647, Accs: Train: 0.6155, Val: 0.5226, Test: 0.4988, Best Val: 0.5246, Best Test: 0.5007
Epoch 99, Time(s): 10.7929 5.4953, Best Val loss: 1.6647, Accs: Train: 0.6164, Val: 0.5244, Test: 0.4994, Best Val: 0.5246, Best Test: 0.5007
Epoch 100, Time(s): 10.7253 5.5063, Best Val loss: 1.6647, Accs: Train: 0.6168, Val: 0.5225, Test: 0.4994, Best Val: 0.5246, Best Test: 0.5007
Stage: 0, Best Epoch 67, Val 0.5246, Test 0.5007

@OctoberChang Thanks for your feedback!
It's wired to see your far smaller results... I also created a new directory and git clone this repository. I renamed precomputed embedding files to ensure creating them from scratch. But I still got the same results as reported. Since you can reproduce the results of ogbn-products, I guess we could use different files for ogbn-papers100M. Here are md5sum and sha1sum values of associated files on my instance:

File name
MD5
SHA1

dgl_data_processed
84c18e311a444d8690b1968b71a4857e
1f537cc77e2120b2e8dece880dbdedf4c8ec421c

smoothed_embs_K_3.pt
a551d056f2c9e2c16643d5dddfc77354
08ab0c01b3bd8a7fa769bd022512b4dcff23a799

smoothed_label_emb_K_9.pt
2bbcc6f389e523b7ca4edfbd68c6aa93
d621321a409d6ec918d8912926e6865e4316b4b3

You can verify whether we are using the same dataset and embedding files.
You can also try on other devices, if possible, or modify the reset_parameters function to see the influences of the initialization codes.

Sorry for late reply, when recently I tried to perform label propagation on ogbn-papers100M, the similar things happened. After experiments and searching, I just find that it is a bug of dgl (>=0.7) compiled with DUSE_LIBXSMM=on. The results of message passing on very large graphs can sometimes be all-zeros. You can try earlier versions of dgl, or you can change precision of label_emb to double, which occupies more memory.


Inheriting BaseFOV on ClearControlFOV

Hi, @ziw-liu .
I also fixed a small bug (using channels when it should be channel_names) introduced during the PR review.

Can you also add some tests for the new properties/methods?

Sorry for flaky tests on my end. Trying to tidy these up in #147.


[Media] Added new LVA Edge SDK

Adding new lva edge sdk under the media directory.

lgtm :shipit:


npm run build in ./next-hardhat fails

Aim
Resolve the issue described in this thread: https://discord.com/channels/563037431604183070/1108319371832594442/1108476064742768781
Expected behavior
It should work right out of the box.
Bug
It should work right out of the box.
To reproduce
Described in the discord thread.
Versions
No response
Additional context
No response
Submission Checklist

[ ] Once I hit submit, I will assign this issue to the Project Board with the appropriate tags.

@signorecello wdyt?

Answering in the discord thread..


WIP Added Dataprep Base Hook, Dataprep Hook, Operator and docs

^ Add meaningful description above
Read the Pull Request Guidelines for more information.
In case of fundamental code change, Airflow Improvement Proposal (AIP) is needed.
In case of a new dependency, check compliance with the ASF 3rd Party License Policy.
In case of backwards incompatible changes please leave a note in UPDATING.md.

What is the difference between GoogleDataprepHook and DataprepBaseHook? I think one hook is enough.  Do you expect you to need to add more Hooks?

@mik-laj and @turbaszek I remember when I ask Kamil if I should create a base hook and he said yes. I thought that the Dataprep is different service than Google and requires its own connection like Google. But ok, I will remove Base Hook.


feat: Also replace abstract in template.

So visitors could preview the content if proper template is configured.

abstract usually show on the blog list page theme. The encrypted part only means to replace the origin blog content, we won't add a abstract to content and this should be done by theme not plugin.
You can fork a branch to fit your theme, but we won't merge this.
Thanks anyway.


dronekit and modes from pymavlink

I have received an increased number of requests to look into the PX4 integration in DroneKit, one of the problems that is bugging people is that we are facing a mode problem when connecting
`>>> Exception in message handler for HEARTBEAT

DroneKit is using the mode_mapping_bynumber helper from mavutil.py and we seem to be facing modes not defined there.
I don't have much much time to spend debugging this I'm looking for help from the PX4 community, there are plenty issues on our dronekit repo but this should be the official
https://github.com/dronekit/dronekit-python/issues/606

The PX4 custom modes mavlink values are defined here: https://github.com/PX4/Firmware/blob/master/src/modules/commander/px4_custom_mode.h#L46

@mrpollo Status?

Wasn't this resolved? @sanderux

I added and got merged a PR in both dronekit and pymavlink but mavutil needs to release a new version on pip before this will be operational. Not sure what the status is there

Any idea who takes care of that?

i think tridge


Updated k3po version

https://github.com/kaazing/tickets/issues/448 has been filed for the bug exposed here


If bucket does not exist, Node.One() should return ErrNotFound

If a bucket does not yet exist, Node.One() currently fails with:
fmt.Errorf("bucket %s doesn't exist", info.Name)
This happens if for example you try to look up an object by ID but no objects of that type have been saved yet. It is much more usable to simply return ErrNotFound. Alternatively, fail with an error that can be easily checked, rather than the generic fmt.Errorf(...)

Glad to see you agree and that it will be fixed. I'm currently checking the error msg on the error that I get back, which is far from ideal.
One suggestion if you want to return ErrNotFound but also indicate ErrBucketDoesntExist: you could make use of an error framework that provides causality (there are a few out there, or it's easy enough to create a custom error). Then you return ErrNotFound which would let you call Cause() on it which would return ErrBucketDoesntExist for those apps that really care why something was not found. But I suspect most apps just care that an item doesn't exist, the missing bucket is just an implementation detail.

I also don't think having ErrBucketDoesntExist  would be useful, It should be transparent and always return ErrNotFound when either the bucket doesn't exist or the resource is not found, just like MongoDB's collections.
I don't think Cause is necessary in this library (except if someone really thinks it is)

ErrNotFound should be just fine.

Interested in making a PR?

Of course.

PR #54
(@asdine you love emojis :) )

(i love them 👯 )


TypeError: 'float' object cannot be interpreted as an index

I'm trying to run the Seq2Seq model on the LibriSpeech corpus. I copied the config file for the TIMIT data and pointed it at Librispeech. Upon training...
(py27) [10:54 user@host:speech$] python train.py examples/librispeech/seq2seq_best.config
Traceback (most recent call last):
  File "train.py", line 145, in <module>
    run(config)
  File "train.py", line 80, in run
    start_and_end=data_cfg["start_and_end"])
  File "/people/user/speech/speech/loader.py", line 35, in __init__
    self.mean, self.std = compute_mean_std(audio_files[:max_samples])
  File "/people/user/speech/speech/loader.py", line 81, in compute_mean_std
    for af in audio_files]
  File "/people/user/speech/speech/loader.py", line 154, in log_specgram_from_file
    return log_specgram(audio, sr)
  File "/people/user/speech/speech/loader.py", line 165, in log_specgram
    detrend=False)
  File "/people/user/.conda/envs/py27/lib/python2.7/site-packages/scipy/signal/spectral.py", line 691, in spectrogram
    input_length=x.shape[axis])
  File "/people/user/.conda/envs/py27/lib/python2.7/site-packages/scipy/signal/spectral.py", line 1775, in _triage_segments
    win = get_window(window, nperseg)
  File "/people/user/.conda/envs/py27/lib/python2.7/site-packages/scipy/signal/windows/windows.py", line 2106, in get_window
    return winfunc(*params)
  File "/people/user/.conda/envs/py27/lib/python2.7/site-packages/scipy/signal/windows/windows.py", line 786, in hann
    return general_hamming(M, 0.5, sym)
  File "/people/user/.conda/envs/py27/lib/python2.7/site-packages/scipy/signal/windows/windows.py", line 1016, in general_hamming
    return general_cosine(M, [alpha, 1. - alpha], sym)
  File "/people/user/.conda/envs/py27/lib/python2.7/site-packages/scipy/signal/windows/windows.py", line 116, in general_cosine
    w = np.zeros(M)
TypeError: 'float' object cannot be interpreted as an index
(py27) [10:56 user@host:speech$]

Any ideas, @awni?

Two typ


State-of-the-art  Broad Phase CD Algorithms and Data Structures

Research and make a report.

https://github.com/Godlike/Arion/projects/1 Migration.


Fix level 1 observation comment not showing

Ticket: https://trello.com/c/Jcct1P59

These changes are superseded by https://github.com/ministryofjustice/moving-people-safely/pull/745 making this PR irrelevant.


amazon linux 2 - npm WARN deprecated<EMAIL_ADDRESS>Version no longer supported. Upgrade to @latest

I'm trying to install aws-azure-login on the most current amazon linux 2 (Karoo), and it won't.  I get several WARN statements and even after upgrading puppeteer to the @latest it still gives the WARN about 10.4.0.  So there must be something hard coded to use 10.4.0 right?  I found this in the package.json which lists dependancies "puppeteer": "^10.0.0".
But then I found node_modules/puppeteer/CHANGELOG.md and it has this line.
"## [10.4.0] https://github.com/puppeteer/puppeteer/compare/v10.2.0...v10.4.0 2021-09-21"
I'm not sure why it won't install or what I can do about it.
I'm using this as the installation guide
https://docs.aws.amazon.com/sdk-for-javascript/v2/developer-guide/setting-up-node-on-ec2-instance.html
After node install: Now using node v16.15.0 (npm v8.5.5)
And then:
[root@localhost ~]# npm install -g aws-azure-login
npm WARN deprecated<EMAIL_ADDRESS>The querystring API is considered Legacy. new code should use the URLSearchParams API instead.
npm WARN deprecated<EMAIL_ADDRESS>Version no longer supported. Upgrade to @latest
npm notice
npm notice New minor version of npm available! 8.5.5 -> 8.19.1
npm notice Changelog: https://github.com/npm/cli/releases/tag/v8.19.1
npm notice Run npm install -g<EMAIL_ADDRESS>to update!
npm notice
npm ERR! code 127
npm ERR! path /root/.nvm/versions/node/v16.15.0/lib/node_modules/aws-azure-login/node_modules/puppeteer
npm ERR! command failed
npm ERR! command sh -c node install.js
npm ERR! sh: node: command not found
npm ERR! A complete log of this run can be found in:
npm ERR!     /root/.npm/_logs/2022-09-03T02_52_43_769Z-debug-0.log
Last several lines of log file:
1645 info run<EMAIL_ADDRESS>install node_modules/aws-azure-login/node_modules/puppeteer node install.js
1646 info run<EMAIL_ADDRESS>install { code: 127, signal: null }
1647 timing reify:rollback:createSparse Completed in 364ms
1648 timing reify:rollback:retireShallow Completed in 0ms
1649 timing command:install Completed in 11598ms
1650 notice
1650 notice New ^[[33mminor^[[39m version of npm available! ^[[31m8.5.5^[[39m -> ^[[32m8.19.1^[[39m
1650 notice Changelog: ^[[36mhttps://github.com/npm/cli/releases/tag/v8.19.1^[[39m
1650 notice Run ^[[32mnpm install -g<EMAIL_ADDRESS>to update!
1650 notice
1651 verbose stack Error: command failed
1651 verbose stack     at ChildProcess. (/root/.nvm/versions/node/v16.15.0/lib/node_modules/npm/node_modules/@npmcli/promise-spawn/index.js:64:27)
1651 verbose stack     at ChildProcess.emit (node:events:527:28)
1651 verbose stack     at maybeClose (node:internal/child_process:1092:16)
1651 verbose stack     at Socket. (node:internal/child_process:451:11)
1651 verbose stack     at Socket.emit (node:events:527:28)
1651 verbose stack     at Pipe. (node:net:709:12)
1652 verbose pkgid<EMAIL_ADDRESS>1653 verbose cwd /root
1654 verbose Linux 4.14.290-217.505.amzn2.x86_64
1655 verbose argv "/root/.nvm/versions/node/v16.15.0/bin/node" "/root/.nvm/versions/node/v16.15.0/bin/npm" "install" "-g" "aws-azure-login"
1656 verbose node v16.15.0
1657 verbose npm  v8.5.5
1658 error code 127
1659 error path /root/.nvm/versions/node/v16.15.0/lib/node_modules/aws-azure-login/node_modules/puppeteer
1660 error command failed
1661 error command sh -c node install.js
1662 error sh: node: command not found
1663 verbose exit 127
1664 timing npm Completed in 11716ms
1665 verbose unfinished npm timer reify<PHONE_NUMBER>883
1666 verbose unfinished npm timer reify:build<PHONE_NUMBER>098
1667 verbose unfinished npm timer build<PHONE_NUMBER>098
1668 verbose unfinished npm timer build:deps<PHONE_NUMBER>099
1669 verbose unfinished npm timer build:run:install<PHONE_NUMBER>107
1670 verbose unfinished npm timer build:run:install:node_modules/aws-azure-login/node_modules/puppeteer<PHONE_NUMBER>107
1671 verbose code 127
Thank you

Is there a way to install this package in this situation ??

Is there a way to install this package in this situation ??

I had to copy it from a working server onto the newer server where it wouldn't install.


Option to yield to next middleware without setting response code

This is more of a question at this point, but why does ecstatic set the response code in some cases before it yields control to the next middleware in the chain.  So for example if estatic hits the 404 use case this status-handler#L37 sets the response code to 404 AND then calls next() if it exists.
I would prefer to pass in an option to not set the response code if another middleware will ultimately be invoked.  Philosophically I would make the case if estatic yields control to another middleware it shouldn't mutate anything on the response object since it "had it's chance" so to speak.

There's two things going on here. One is that this is a way for downstream middlewares to detect what happened inside ecstatic---without setting the status code there isn't really a good way to know why a request fell through. The other is an artifact of architecture---ecstatic has a few recursive calls, and I believe (it's been a while since I've looked at this) it branches based on req state.
Unfortunately, ecstatic is constrained by the fact that it's intended to be used as a middleware. That has unfortunate ramifications both pragmatic and philosophical beyond but including setting of status codes as a mechanism for custom 404/etc handling.


Added "Scroll Down Progress Bar" To The website

Hey @Suchitra-Sahoo  ,
issue closes #1086
I have added the Scroll down progress bar in All pages of the website
Type of PR

[X] Feature enhancement

Screenshots / videos (if applicable)

Checklist:

[X] I have performed a self-review of my code
[X] I have read and followed the Contribution Guidelines.
[X] I have tested the changes thoroughly before submitting this pull request.
[X] I have provided relevant issue numbers, screenshots, and videos after making the changes.

Please take a look and review it.

It is not present in buy products page

Complete it to get a level2 tag

Also keep script ans style file for scroll bar in separate folder don't mix it with html file.


Front-end App Insights Telemetry

Changes
User telemetry can be made available in App Insights if the user opts into Analytical Cookies.
I have set the AuthenticatedUserContext which means we can track individual user sessions throughout their journey. In addition, I have also included the click tracking plugin which enables us to have almost feature parity with Google Analytics and Google Tag Manager.
I am not sure if my Ruby code is correct for printing the currently authenticated user's name into the JS. Similarly, I'm not sure if there is anything more I need to do to print the connection string for App Insights.

Superseded by https://github.com/DFE-Digital/dfe-complete-conversions-transfers-and-changes/pull/1650


splatoon3schedule: support null boss object for Eggstra Work

Description
As it turns out, in Eggstra Work, the boss object is null. Support this by not displaying a boss string in case the corresponding object is null.
Copilot

copilot:all

Rendered:


Increase UIRenderFeature SortKey to always render UI on top of Sprites

PR Details
This change prevents UI elements from being rendered underneath Sprites.
Previous value is 128 - the default, same as SpriteRenderFeature, which meant that during sorting the UI could potentially end up rendered before the sprites (or not).
The value 192 was chosen to match the SortKey of BackgroundRenderFeature.
Motivation and Context
I was playing around with JumpyJet and noticed that for some combinations of entity order in the scene the UI was not rendered on top of everything else as it should be.

After I applied the change I played a bit with the order of entities as previously but now UI was always on top.
Types of changes

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

Checklist
I'm not sure about changes to the documentation or in-code comments for this change.

[ ] My change requires a change to the documentation.
[ ] I have added tests to cover my changes.
[ ] All new and existing tests passed.

If you want to see the difference, other than in game, during debugging you can break at sources\engine\Stride.Rendering\Rendering\RenderSystem.cs line 322 and view the renderViewStage.SortedRenderNodes. We want only the last items to have the UIRenderFeature.

How can I set a breakpoint on source code when debugging the game? All source code is exposed as meta data

@aunpyz you need to check out the source code and open the Stride.sln and attach the debugger to the running game.

Supposedly, even without checking out source code, it should download pdb from SourceLink and allow you to trace into Stride code out of the box.
Make sure SourceLink is enabled in VIsual Studio:

@xen2 Seems like it's enabled by default (at least on my end). But I can only trace up to source metadata just like the picture I posted above
@tebjan Thanks!! That's something I don't know up until now! Unfortunately, the breakpoint is not hit even I attach to a running game.
Here's what I got:

If you build Stride yourself, and use that version, you can open the specific file from Stride in the game's solution and break there. This way VS can auto attach to your game when you run it and it manages to match the source from nuget's pdb.

@manio143 I build all packages and switch nuget packages to the ones I built (I tried with JumpyJet solution), still getting the same result I mentioned
Would be nice if there's a tutorial for newcomers c# dev

@xen2 I see there's a failed check, but cannot view details. Is there something more I should do for this PR?

@manio143 I ran the test manually yesterday. It seems something didn't compile but it looks unrelated/spurious.
I am going to investigate.

OK, sorted out the tests, all good!


Build: Released 1.0.1 [skip ci]

1.0.1 (2023-07-13)
Fix

reporting if step defined (4b626e5)

:tada: This PR is included in version 1.0.2 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Leftover code from open-sourcing

https://github.com/facebookresearch/motion-search/blob/f255f7cfbbfb0865db684b3be505081f0c42013d/motion_search/fb_command_line_parser.h#L8
This should probably be removed (and the file renamed?)

Fixed by d2bc2b47a8


feature: &autochroot

Currently &autochdir changes the current working directory to that of the opened file.
At least equally useful would be a variant to change to that of its Git root (next parent directory containing a .gitfolder) instead (as it's often the project root directory).
There are many plug-ins to this end, but a built-in option can achieve this more robustly.
In fact, some plug-ins, say LSPs, implicitly assume that Vim is opened in the project root dir to work fine;
such an option would make them work more reliably (though of course final duty is on the plug-in author).
If that sounds sensible and need arise, then root markers of other version control systems such as Bazaar, Mercurial, ... could be taken into account.

Honestly, I am not sure this belongs into Vim. It sounds like a good use-case for a plugin.

This option might be useful, but like the autochdir option, it may conflict with the behavior of plugins, so it needs to be considered.

Fair enough, it's hard to make it reliable, though it's a problem being solved again and again.

It would be more convenient to prepare a function to calculate the root directory rather than automatically changing the current directory.

This sounds like a great milestone.
I guess we can agree on this being a common use case to operate on a file with respect to its project dir, often the git root dir.

like the autochdir option, it may conflict with the behavior of plugins, so it needs to be considered.

Plug-ins can only set them by autocmd's. Too late if other autocmd's rely on the current work dir being the project root, leading to involved workarounds like postponing them to BufWinEnter ++once events.
I deemed this early firing to be an advantage of autochdir.

a function to calculate the root directory

What about finddir('.git', getcwd(-1)..';') for starters?

Yes.  But it should be implemented by core?

Maybe a documentation entry about finddir('.git', expand('%')..';') will suffice to a great extent, as @chrisbra seems to prefer.
The implementation of the same functionality of fugitive.vim, which should be rather robust due to its larger user base,
https://github.com/tpope/vim-fugitive/blob/320b18fba2a4f2fe3c8225c778c687e0d2620384/plugin/fugitive.vim#L18
https://github.com/tpope/vim-fugitive/blob/320b18fba2a4f2fe3c8225c778c687e0d2620384/plugin/fugitive.vim#L18
plus helper functions, is more complicated though.

Yes. But it should be implemented by core?

Again I deemed the

early firing to be an advantage of &autochdir

in comparison to autocmd BufNew * ..., say.
Why does &autochdir exist if the same could be achieved by autocmds?

I don't know why autochdir exists...
I don't user the option, because it breaks other plugins behavior.

It would be more convenient to prepare a function to calculate the root directory rather than automatically changing the current directory. However, since detecting the root directory is inherently a difficult process, I'm not sure if it should be implemented in the core.

Although the vast majority today uses Git, I agree that this shouldn't be the only supported system. Under the premise that an 'autochroot' option is more robust than the current :autocmd-based plugin solutions (which I tend to agree with, although I'd like to see concrete proof of problems with the current approach), I would turn this into 'autochrootfunc'; i.e. a user-supplied function that is invoked and then returns the root directory (or empty string to disable). There could be a default $VIMRUNTIME/autoload/autochrootfunc/git.vim implementation, but users would be free to supply their own.

What about finddir('.git', expand('%')..';') for starters?

Better invoke git rev-parse --show-toplevel; it is possible to move the .git metadata away and there are subtle differences when Git submodules are used. (There's also git rev-parse --show-superproject-working-tree, and people may have different preferences as to what they consider their project root directory, reinforcing my point above that this needs to be configurable.)
I have written VcsRoot.vim for me (it's not yet a published plugin, but I've put it up on GitHub now); it supports lookup for Git, Mercurial, and Subversion so far.

I don't know why autochdir exists...

I mostly use a single instance of GVIM as a kind of IDE. I have multiple projects opened (usually in separate tab pages). The notion of addressing everything relative to the directory where Vim was launched doesn't make sense there, whereas opening files relative to the one that's currently active works really well. Most other GUI applications default to the previously used directory on File > Open (which is similar (though not identical) to 'autochdir'), so it's understandable that there's demand for such an option. I have set it, too.
So my Vim's native commands use file-relative addressing, which works very well for me; it's likely I'll open another Java file from the same package or another test, and there's no need to duplicate a possibly deep project hierarchy all the time. For those cases where I'll have to navigate to a completely different part of the project, I have :RootEdit, :RootSplit, etc., provided by my SpecialFileLocations.vim plugin (which relies on VcsRoot.vim for the root detection). The other client of VcsRoot.vim is my :RootTerminal command.

I don't use the option, because it breaks other plugins behavior.

Like the 'selection' option, this indeed has the potential to negatively affect plugins that are ignorant of it. I have submitted patches to all the plugins that I'm using. Once people are aware of it, it's not a big issue. I don't think this is an argument against a new 'autochroot', as 'autochdir' already exists, and if a plugin correctly handles the latter, the other should work, too.

I personally like 'autochdir' so much that I endured the few bits of plugin trouble that came with enabling it. I'd certainly give a :+1: to an 'autochroot' option (as long as it's extensible and not limited to Git), although I probably wouldn't enable it for myself. I prefer file-relative addressing with root-relative addressing supplied by my plugins.
If there's indeed demand for project-relative addressing, the discussed new option would be the best approach, as it's both more robust and also more discoverable than an external plugin. If that gets the related 'autochdir' out of its niche and increases awareness of plugin authors that they can't rely on a static current directory, I'd see this as a benefit overall.

I'd like to see concrete proof of problems with the current approach

For example, language servers (LS) use the current working directory on launch. This rather ought not to be $HOME (too many files to scan). Instead, mostly intended is a project root. Using autocmd's, one has to ensure they're fired in order. Of course one can solve this per plug-in, but since it's a common problem (for example, many LS plug-ins), how about a common solution?

git rev-parse --show-toplevel; it is possible to move the .git metadata away and there are subtle differences when Git submodules are used. (There's also git rev-parse --show-superproject-working-tree, and people may have different preferences as to what they consider their project root directory,

For starters, I thought of a simple criterion; usually submodules do not use .git folder markers. This is entering a rabbit hole. But to let the user decide, setting a .root marker should be possible.

whereas opening files relative to the one that's currently active works really well

Likely the files one opens are hierarchically close to the one opened, so that having the file's directory as a relative starting point is convenient.
I think the question was why the need for an option &autochdir was identified, when there are simple BufNew/Read autocmd's to, superficially, the same end.

I think the question was why the need for an option &autochdir was identified, when there are simple BufNew/Read autocmd's to, superficially, the same end.

Oh, I still remember that. Old versions of the help mentioned it:
'autochdir' 'acd'       boolean (default off)
                        {only available when compiled with the
                        |+netbeans_intg| or |+sun_workshop| feature}
        [...] This option is provided for
        backward compatibility with the Vim released with Sun ONE Studio 4
        Enterprise Edition.

So it was used for integration with a particular IDE, bound to special feature flags, not as a general-use feature. There's some more information in the history:

Sun Visual WorkShop has an "Editor of Choice" feature designed to let users
debug using their favorite editors.  For the 6.0 release we have added support
for gvim.  A workshop debug session will have a debugging window and an editor
window (possibly others as well).  The user can do many debugging operations
from the editor window, minimizing the need to switch from window to window.
The version of vim shipped with Sun Visual WorkShop 6 (also called Forte
Developer 6) is vim 5.3.  The features in this release are much more reliable
than the vim/gvim shipped with Visual WorkShop.  VWS users wishing to use vim
as their editor should compile these sources and install them in their
workshop release tree.

I think they could have solved this via autocmds back then; I'd guess it was simply easier for the core Vim developers of the time to do this directly in the C code (i.e. their area of expertise)?!
However, around the same time (Vim 5.7), related plugins (CD.vim) appeared and VimTip 101 documented a simple autocmd for doing this. I myself also relied on autocmds as a fallback, as many distribution-provided Vim versions didn't have 'autochdir' compiled in (and I only started compiling Vim with custom features much later). That changed over time (the binary size difference became less of an issue), and Vim 8.1.0763 dropped the Sun Workshop support and turned 'autochdir' into a normal option.

Okay, so these reads as if autocmds suffice; there is no inherent need of an option. As mentioned above, I deemed it crucial for ensuring the correct triggering order

Maybe it still could be so as :help runtimepath reads, for example on Linux or Macos,
					Unix:  "$HOME/.vim or
						$XDG_CONFIG_HOME/vim,
						$VIM/vimfiles,
						$VIMRUNTIME,
						$VIM/vimfiles/after,
						$HOME/.vim/after"

So these autocmd's must be set by the user (and not by a built-in $VIMRUNTIME file, say) as soon somewhere at the beginning of her vimrc to ensure others can rely on the current work dir suitably set.

Assuming there's demand for this feature, I would not use autocmds. With the proposed new option, plugins can check :if &autochroot (and then presumably do counteractions like saving and restoring the current directory if necessary). A solution based on conventions and :autocmd would be much more difficult to detect.
A home-grown customization (with autocmds) can adapt to or even monkey-patch misbehaving plugins. With a built-in feature, plugins should observe and behave. What to use should be based on how much demand there is for the feature.


use dat-encoding

@juliangruber and I came up with this, https://github.com/juliangruber/dat-encoding to reduce the link size a bit. It's fully backwards compat with what we have now but links will be 50 chars instead of 64 making them valid dns subdomains

it also does link validation for us and supports potential dat.land links with a key in them

should be easy to add this to this module, we just need to replace the buf.toString('hex') for the dat links with datEncoding.encode(buf), and the cli needs to use .decode(str) on input links


Automating SINGE for branching trajectories

We should investigate strategies to automate the workflow of splitting a branching trajectory into multiple cell-fate based subdirectories, followed by SINGE analysis on each subdirectory and obtaining GRNs based on these analyses. Some initial thoughts are provided in USAGE.md.

I think this is a very complex problem. A future work is to judge the bifurcation interval rather than a whole trajectory, with only a few but important genes considered.

It is definitely a complex problem.  @atuldeshpande is working on pull request #57 that will bring major improvements for running SINGE on branching trajectories.  Our approach is to add support for indicator variables for each cell so that only the cell corresponding to a particular branch or path can be selected.  SINGE will then run automatically on all user-defined branches.  We'll also add an example showing how to create the branch indicator data structure with the output from dynverse.
That will provide a way to run SINGE on the bifurcation interval instead of the whole trajectory.  SINGE does not do any gene selection.  A user should decide which genes are important using an appropriate statistical test or external criteria before running SINGE.


[FALSE-POSITIVE]

Domains or links
egniol.co.in
More Information
How did you discover your web site or domain was listed here?

Incorrectly marked as Phishing on Phishtank or OpenPhish?
Also showing on Virustotal.com

Have you requested removal from other sources?
Not able to find other way
Additional context
"Egniol.co.in" is a well-established and reputable financial consulting firm that provides a range of services to its clients. They have been operating for several years, and their website contains important information, resources, and contact details for their clients and potential customers. The false positive detection is not only affecting the company's reputation but also hindering its ability to conduct business effectively.
:exclamation:
We understand being listed on a Phishing Database like this can be frustrating and embarrassing for many web site owners. The first step is to remain calm. The second step is to rest assured one of our maintainers will address your issue as soon as possible. Please make sure you have provided as much information as possible to help speed up the process.
Send a Pull Request for faster removal
Users who understand github and creating Pull Requests can assist us with faster removals by sending a PR to mitchellkrogza/phishing repository, on the falsepositive.list file

https://github.com/mitchellkrogza/phishing/blob/main/falsepositive.list
Please include the same above information to help speed up the whitelisting process.

Not in the list. Closing.


Range enum

This PR improves handling of param :foo, enum: 1..1000. It also allows float enums.
This builds on #21; only the last commit is new.
Note: it's not clear to me why the API allows type: {a: 1}, enum: {a: 1} and enum: 1..42 but not type: 1..42. I find this inconsistent and would suggest to either allow it, or forbid type: {...}. This PR doesn't address this.

I don't plan on rebasing this PR in the near future.

I understand.
I'll take a look into a possible API for ranges myself. After the refactoring is complete, I need to think about further directions for the library. Really want to keep things tidy, it is already overthought at some points (for ex., response processing, as discussed here) and I feel like param(...) DSL API starts to get out of hand, becoming one more of thousands of "typed attr DSL for Ruby" already in presence.


SYS-877 Add post booking information to bookings

When you retrieve a booking the post booking information is sent with the response

:+1:


capabilities need renaming for better namespacing

LIBRATBAG_CAP_SWITCHABLE_RESOLUTION to LIBRATBAG_CAP_RESOLUTION_SWITCHABLE, and so on.
API break again, but we haven't released since the last one (iirc)

Will this really be an API break knowing that libratbag.so will not be exposed anymore?

nope, if we remove the .so anyway we don't care about the changes in the API.


Test data validation

As discord changes new usernames from username#discriminator to username from now so it cause issue when changing it to the new username as it requires discriminator too.
Please fix that so that we can add new usernames instead of that old one.

You can use #0000 as the discrim. I have no intention to change the validation for now, so that it is explicit whether a username has a discrim, or not, rather than it just being that someone may have forgotten to include it.


Add more color

Add a splash of color to my blog. Make the following elements more colorful:

webpage title
the "hi there" text on the homepage
tags (I want each tag to have its own "theme color", and anytime that tag shows up in a post it will have that color as its background)
the copyright disclaimer at the bottom
the "made with ..." text
maybe other things I haven't noticed yet

Hey! I am new to Github! I just saw your issue. I feel I can help with this. Being newbie! I would like to know how and where to start😅.

Hey! I am new to Github! I just saw your issue. I feel I can help with this. Being newbie! I would like to know how and where to start😅.

Hey there, welcome to GitHub and welcome to my repository! If you want to take a shot at this one, feel absolutely free to. I will let you know, though, that my weblog is unique in that it is "hosted" using weblog.lol, a service only available to omg.lol members, so testing your changes may end up being difficult if you aren't on omg.lol already.
The documentation for weblog.lol is also kind of all over the place, and isn't finalized (because weblog.lol is still a beta service), so you may have some issues with that as well. I know that some docs are available at https://weblog.lol and others at https://advent.omg.lol, and then some more documentation is available in the omg.lol GitHub repository at neatnik/omg.lol
But yeah, just wanted to give you a fair warning! If you still want to go ahead and try this out, I would greatly appreciate it! <3


signal/sig_dispatch: Revert changes that cause a regression

Summary
This reverts changes that cause a regression in signal handling, more info in #8740
The problem still remains where signal actions are not performed / the user signal handler is not called if:

The signal recipient is in syscall
The signal recipient is waiting for a signal, i.e. stcb->task_state == TSTATE_WAIT_SIG

Only the process wake up part works, but the signal action is never performed. I need more time to fix this.
Impact
Fix regression
Testing
CI, code goes back to something that passes https://github.com/linux-test-project/ltp/blob/master/testcases/open_posix_testsuite/conformance/interfaces/timer_settime/9-2.c

@xiaoxiang781216 I suggest we do the revert so I can take my time fixing the system call related issues


JS: fetchRecipe(cuisine) fuction is done.

File: script.js
function: fetchRecipe(cuisine)
status: Completed and tested
Parameters Type: String
What it do?: Take in the string incoming parameter to the function and use it as the Spoonacular API recipe call. Pass that into the call url as the cuisine parameter value to get a recipe result. Break down the response by picking out only the values that needed, then repackage it back into an object  and return it to the caller.
Note from PR owner: completed the function and push to remote repo eon.

all please review and approval for the merge as this a end of night push regardless whatever it is a task completed push.

Great! thanks!


[Jenkins]:

Inputs
Provide the following optional inputs:
folders
Available commands
The following commands can be executed by adding a comment to this issue:

/audit
/dry-run --source-url https://fdisk.cc:41479/job/:jenkins-job
/migrate --source-url https://fdisk.cc:41479/job/:jenkins-job --target-url https://github.com/zbazztian/:github-repository

/audit

/migrate --source-url https://fdisk.cc:41479/job/run-unit-tests-and-build-image
--target-url https://github.com/zbazztian/coffeeshop-jenkins

/migrate --source-url https://fdisk.cc:41479/job/run-unit-tests-and-build-image
--target-url https://github.com/zbazztian/coffeeshop


[system] css utility cannot parse the theme variable

The css unility cannot parse the theme variable
import { css, style } from "@material-ui/system";

const textColor = style({
  prop: "color",
  themeKey: "palette"
});

const palette = css(textColor);

const output = palette({
  theme: {
    palette: {
      primary: "blue"
    }
  },
  css: {
    color: "primary",
    padding: 10
  }
});

console.log(output); //{"color":"primary","padding":10}

CodeSandBox:
https://codesandbox.io/s/confident-rosalind-38tdk?file=/index.js
Mui version: v4.11.3 ✓

Will fix it later.

We don't plan to port bug fixes to v4. Could you provide a reproduction using v5? In v5 I'm no longer able to call the return value of css: https://codesandbox.io/s/peaceful-cartwright-pudr6

The css utility has been removed from v5.

The css utility has been removed from v5.

Thanks for the clarification. We can close them since the issue got resolved implicitly.


Added ILMT Content

Signed-off-by: veena-ux<EMAIL_ADDRESS>

Has this been superseded by #55?

Has this been superseded by #55?

Yes, closing this.

Superseded by #55


🌟Feature : Add "Terms of Service" and "Privacy Policy" Pages

Idea Contribution

[X] I have read all the feature request issues.
[X] I'm interested in working on this issue
[X] I'm part of GSSOC organization

Explain feature request
Hey @jfmartinz ,
i want to create and add two separate pages in the website's footer by adding links to two new pages:
Terms of Service and Privacy Policy.
These pages are essential for informing users about the legal aspects of using our site and ensuring compliance with privacy regulations.
What will i do

Create Terms of Service Page

Include comprehensive terms that cover the use of our website, user responsibilities, limitations of liability, and other relevant legal

Create Privacy Policy Page

Detail how we collect, use, and protect user data.

Update Footer

Add links to the newly created "Terms of Service" and "Privacy Policy" pages in the website's footer.

ScreenShots
This is where we can add the links to navigate to the respective pages.

Could you Please assign me this?
thankyou!

@jfmartinz

@jfmartinz

@jfmartinz

@jfmartinz , Please assign me this issue

@jfmartinz , Please assign me this issue

@jfmartinz , Please take a look


Run inspec check from Packer run

See http://www.fastrobot.com/blog/creating-tested-gce-images-using-cheftest-kitcheninspecpacker for example

This is definitely a way to go.
But that approach installs inspec on target machine which is not acceptable.
We should take a look at https://github.com/devinturner/packer-provisioner-inspec. They run Inspec locally but support SSH connection only. Maybe we could update that code (and make PR for Packer).
Also see http://annaken.github.io/testing-packer-builds-with-serverspec


Import: Add import indication for repository level

Add key in refstore to indicate that an import has occurred in repository level.
We'll use the following:
GET import endpoint with key = ""
When creating a new import - update the key in addition to the importID key.
@eladlachmi FYI

Will be completed in #6069


Update create-secure-guest-sharing-environment.md

Updated the section Set up web-only access for guests to reduce ambiguity
per issue#https://github.com/MicrosoftDocs/microsoft-365-docs/issues/10161

@JohanFreelancer9 Please copy review

Learn Build status updates of commit 5a9d946:
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Learn Build status updates of commit 0004426:
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@MikePlumleyMSFT Please merge this PR. Thanks

Learn Build status updates of commit fb3948b:
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Learn Build status updates of commit 2000249:
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python312Packages.opencv-python: init at 4.9.0

similar to https://github.com/NixOS/nixpkgs/pull/351712

Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
24.11 Release Notes (or backporting 23.11 and 24.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[x] Fits CONTRIBUTING.md.

Add a :+1: reaction to pull requests you find important.

Result of nixpkgs-review pr 353249 run on x86_64-linux 1

6 packages failed to build:

python311Packages.deepface
python311Packages.deepface.dist
python311Packages.ffcv
python311Packages.ffcv.dist
python311Packages.mtcnn
python311Packages.mtcnn.dist

72 packages built:

deface
deface.dist
immich-machine-learning
immich-machine-learning.dist
manim (python312Packages.manim)
manim-slides
manim-slides.dist
manim.dist (python312Packages.manim.dist)
mavproxy
mavproxy.dist
mokuro
mokuro.dist
open-webui
open-webui.dist
python311Packages.albucore
python311Packages.albucore.dist
python311Packages.albumentations
python311Packages.albumentations.dist
python311Packages.grad-cam
python311Packages.grad-cam.dist
python311Packages.imagecorruptions
python311Packages.imagecorruptions.dist
python311Packages.imantics
python311Packages.imantics.dist
python311Packages.insightface
python311Packages.insightface.dist
python311Packages.invisible-watermark
python311Packages.invisible-watermark.dist
python311Packages.layoutparser
python311Packages.layoutparser.dist
python311Packages.manim
python311Packages.manim-slides
python311Packages.manim-slides.dist
python311Packages.manim.dist
python311Packages.opencv-python
python311Packages.opencv-python.dist
python311Packages.pdf2docx
python311Packages.pdf2docx.dist
python311Packages.qudida
python311Packages.qudida.dist
python311Packages.rapidocr-onnxruntime
python311Packages.rapidocr-onnxruntime.dist
python311Packages.videocr
python311Packages.videocr.dist
python312Packages.albucore
python312Packages.albucore.dist
python312Packages.albumentations
python312Packages.albumentations.dist
python312Packages.grad-cam
python312Packages.grad-cam.dist
python312Packages.imagecorruptions
python312Packages.imagecorruptions.dist
python312Packages.imantics
python312Packages.imantics.dist
python312Packages.insightface
python312Packages.insightface.dist
python312Packages.invisible-watermark
python312Packages.invisible-watermark.dist
python312Packages.layoutparser
python312Packages.layoutparser.dist
python312Packages.manim-slides
python312Packages.manim-slides.dist
python312Packages.opencv-python
python312Packages.opencv-python.dist
python312Packages.pdf2docx
python312Packages.pdf2docx.dist
python312Packages.qudida
python312Packages.qudida.dist
python312Packages.rapidocr-onnxruntime
python312Packages.rapidocr-onnxruntime.dist
python312Packages.videocr
python312Packages.videocr.dist


Document imported exceptions

Adds some more documentation and examples to the exceptions module.

I'm going to merge this now and then add a PR which hopefully improves coverage again. Thanks!


Set default of encoding to encoding embedded in font file

Currently, the encoding is based off the encoding entry in the font dictionary. According to the spec, if there exists no entry, the encoding should be grabbed from the font file. The differences are then applied to this encoding.

Implemented in #802


feat(external-plugins) add guide on testing with js plugins

Review

@reviewer
Summary

Reason
Add guide on how to test JS plugins
Testing

@tlblessing Yes that will apply on both sides, I will copy paste the change to OSS.

Direct review links:
https://deploy-preview-2868--kongdocs.netlify.app/enterprise/2.4.x/external-plugins/#testing


[Bug]: can't start docker container in ubuntu

Description
After the recent changes, I tried restarting wildcard-nginx but its status is showing Exited only.
Steps to reproduce the behaviour:

I have removed the wildcard-nginx container first.
Then used ./start-https.sh https://release.app.appsmith.com command.
Then I used docker ps -a to find the status of container but it was showing Exited.

Important Details

Version: [20.04.1]
OS: [Ubuntu]
Browser [Chrome]

Please add logs here @devrk96

@ofpiyush Please find the logs here
/docker-entrypoint.sh: /docker-entrypoint.d/ is not empty, will attempt to perform configuration /docker-entrypoint.sh: Looking for shell scripts in /docker-entrypoint.d/ /docker-entrypoint.sh: Launching /docker-entrypoint.d/10-listen-on-ipv6-by-default.sh 10-listen-on-ipv6-by-default.sh: Getting the checksum of /etc/nginx/conf.d/default.conf 10-listen-on-ipv6-by-default.sh: Enabled listen on IPv6 in /etc/nginx/conf.d/default.conf /docker-entrypoint.sh: Launching /docker-entrypoint.d/20-envsubst-on-templates.sh /docker-entrypoint.sh: Configuration complete; ready for start up 2020/11/27 08:23:04 [emerg] 1#1: host not found in upstream "host.docker.internal" in /etc/nginx/conf.d/app.conf:29 nginx: [emerg] host not found in upstream "host.docker.internal" in /etc/nginx/conf.d/app.conf:29

For the simplest quickfix in app/client/start-https.sh change line 67 from
proc_version="$(cat /proc/version)"

to
proc_version="microsoft"

I'll test why it worked on windows the last time and push a proper fix in a few minutes.

@ofpiyush The user's problem is on Ubuntu. Are you sure proc_version="microsoft" will fix the issue?

@mohanarpit yes the fix in #1891 was wrong, it replaced a negative if with a positive one, essentially making the script do the exact opposite thing for both linux and WSL.
My nginx in WSL was running which kept working, made me believe that the new script was working.
In my rush to hotfix the script to work for mac users, I didn't notice that my docker had crashed too. 🙈


[Java] CPU Utilization 100%

Search before asking

[X] I had searched in the issues and found no similar issues.

Version
0.7.0
code
public class FurySerializer implements Serializer {

private final ThreadSafeFury fury;

public FurySerializer(){
    LoggerFactory.useSlf4jLogging(true);
    this.fury = Fury.builder().withLanguage(Language.JAVA)
            // Allow to deserialize objects unknown types, more flexible
            // but may be insecure if the classes contains malicious code.
            .withRefTracking(true)
            .requireClassRegistration(false)
            .buildThreadSafeFury();
}

@Override
public String name() {
    return "fury";
}

@Override
public byte[] serialize(Object obj) throws IOException {
    return fury.serialize(obj);
}

@Override
public Object deserialize(byte[] bytes) throws IOException {
    return fury.deserialize(bytes);
}

}

public class SerializationUtils {

private static Serializer g_serializer;

/**
 * 初始化序列化器
 * @param ser  serialization method
 * @param props serializer properties
 */
public static void init() {
    g_serializer = new FurySerializer();
}

 public static Object deserialize(byte[] bytes) throws IOException {
    if (bytes == null || bytes.length == 0)
        return null;
        return g_serializer.deserialize(bytes);
    }
}

出现大量线程CPU100%，异常：
at java.util.WeakHashMap.put(java.base@17.0.12/WeakHashMap.java:459)
at org.apache.fury.ThreadLocalFury.lambda$new$2(ThreadLocalFury.java:60)
at org.apache.fury.ThreadLocalFury$$Lambda$1294/0x00007f9fb0bcd7e8.get(Unknown Source)
at java.lang.ThreadLocal$SuppliedThreadLocal.initialValue(java.base@17.0.12/ThreadLocal.java:305)
at java.lang.ThreadLocal.setInitialValue(java.base@17.0.12/ThreadLocal.java:195)
at java.lang.ThreadLocal.get(java.base@17.0.12/ThreadLocal.java:172)
at org.apache.fury.ThreadLocalFury.deserialize(ThreadLocalFury.java:130)
at com.eryansky.j2cache.util.FurySerializer.deserialize(FurySerializer.java:40)
Anything Else?
No response
Are you willing to submit a PR?

[ ] I'm willing to submit a PR!

@eryanwcp Do you create too much Fury instance? As the document cleared:

Could you use ThreadPoolFury instead? Seems you created too much threads, and every thread will create a new Fury instance

@chaokunyang Only One Fury instance.
Spring Boot 3.3+dragonwell
https://github.com/eryanwcp/ec/blob/master/j2cache-spring-boot-starter/src/main/java/com/eryansky/j2cache/cache/support/util/J2CacheSerializer.java

private final ThreadSafeFury fury; should be private final static ThreadSafeFury fury; instead.

`private final WeakHashMap<LoaderBinding, Object> allFury = new WeakHashMap();
public ThreadLocalFury(Function<ClassLoader, Fury> furyFactory) {
    this.bindingThreadLocal = ThreadLocal.withInitial(() -> {
        LoaderBinding binding = new LoaderBinding(furyFactory);
        binding.setBindingCallback(this.factoryCallback);
        binding.setClassLoader(Thread.currentThread().getContextClassLoader());
        this.allFury.put(binding, (Object)null);
        return binding;
    });
    Fury fury = ((LoaderBinding)this.bindingThreadLocal.get()).get();
    ClassResolver._addGraalvmClassRegistry(fury.getConfig().getConfigHash(), fury.getClassResolver());
}`

It seems that allFury. put() has caused a concurrency conflict

Could you provide more details? ThreadLocalFury is created only once, I don't see concurrency conflict

This is threadLocal, and each thread will create one. When multiple threads are created simultaneously, they will be put into allFury at the same time

I see, thanks. This is indeed a bug, would you like to create a pr?

sorry，I am not familiar with using PR. using Collections.synchronizedMap(new WeakHashMap())  or ConcurrentHashMap can solve this problem.
sorry

@qinzhikui thanks for your suggestion, I fixed it in https://github.com/apache/fury/pull/1889


EPERM issue after using npm-windows-upgrade

Hey,
First of all thanks a lot for this great tool! I've managed to update my npm to newest version without any problem. Although after this process I can't install modules without running console as administrator. Currently I am using ConEmu with GitBash and after running for example npm install -g ember-cli I get an error:
npm ERR! Windows_NT 6.1.7601
npm ERR! argv "c:\\Program Files\\nodejs\\node.exe" "c:\\Program Files\\nodejs\\node_modules\\npm\\bin\\npm-cli.js" "install" "-g" "ember-cli"
npm ERR! node v4.0.0
npm ERR! npm  v3.3.2
npm ERR! path c:\Program Files\nodejs\node_modules\.staging
npm ERR! code EPERM
npm ERR! errno -4048
npm ERR! syscall mkdir

npm ERR! Error: EPERM: operation not permitted, mkdir 'c:\Program Files\nodejs\node_modules\.staging'
npm ERR!     at Error (native)
npm ERR!  { [Error: EPERM: operation not permitted, mkdir 'c:\Program Files\nodejs\node_modules\.staging']
npm ERR!   errno: -4048,
npm ERR!   code: 'EPERM',
npm ERR!   syscall: 'mkdir',
npm ERR!   path: 'c:\\Program Files\\nodejs\\node_modules\\.staging' }
npm ERR!
npm ERR! Please try running this command again as root/Administrator.

npm ERR! Please include the following file with any support request:
npm ERR!     C:\Users\marw\Workspace\npm-debug.log
/c/Program Files/nodejs/npm: line 27:  6072 Signal 112              (core dumped) "$NODE_EXE" "$NPM_CLI_JS" "$@"

Hey @zoamel, thanks for reporting! Do you always have to install modules as Administrator - or only global modules? For global modules, requiring admin rights makes sense.

@felixrieseberg only global modules. Although earlier I didn't have to do that, and neither on osx/ubuntu. From what I know, having to install global node modules as admin can cause some unexpected troubles at some point (at least on nix systems)

Hey @zoamel, the behavior is fine on Windows - but if you want to change the location of your global modules (and make them less system-wide), you can do that like so: https://stackoverflow.com/questions/7300132/how-to-use-npm-with-node-exe/9366416#9366416


ETH Logo is off centre

Hello @ricburton, thanks for the feedback. I did not design this icon initially, but I took the time to review it carefully.
I think this is intended. But the applied offset is however a little bit off.
I tried the example you have shown and it is off-centered as well (to the left this time). Given the alpha blending asymmetry, the icon needs to be offset to the right in order to correctly distribute its mass. That is easier to see when the icon is small: I'm always doing optical centering by reducing the size dramatically.
Applying an offset of .75 (instead of 1 currently) seems correct in this case (in short, if I had to put an integer I would have chosen 1 like the original).

Please take a look and tell me if that makes sense to you.
I can see in your example that you reduced the size of the icon as well. Maybe we can settle it this way: a smaller icon, with .75 — let me know what you think.

I have not given an example SVG yet :)
Want me to export and send over?
Glad to see the project accepts contributions.

You need to slightly scale the ETH logo up by a pixel.

I have not given an example SVG yet :)

I was talking about the image you have uploaded with on the left side the current state of the ETH icon, on the right side your version which is horizontally centered (and with a smaller icon size). Seems to be the "Scaled down" version from the second image. Isn't it?

Glad to see the project accepts contributions.

Well, as mentioned in the README, the previous contributors added the part saying pull requests are not accepted. Mainly because it takes more time to fix an icon than to make it. But I'm pleased to see suggestions like yours — those are always welcome.
I'm not sure I totally understand the image you have just uploaded here. There are three different topics.

First, what aspect ratio we choose. If the aspect ratio is not conform to the Ethereum branding one, then the one who made the icon did a mistake and we indeed need to change it.
Next, regarding the size in the circle I understand you would prefer the icon to be larger in the circle. I'm not opposed to it but it should look harmonious with the other icons (for example ADX has a comparable surface).
Regardless of the aspect ratio or icon size, the point of my first answer was that the icon should not be horizontally centered. One of the main value of this repository is optical centering and this is in most cases not pure horizontal or vertical centering.

Please give me answers on each of those three points so we can break down the problem.

Here you go

!
I can compress it too if that helps. But this is perfectly in the middle.

Explaining the thinking more.
ETH is the most popular asset

I think the the offset is intentional because of the pixel distribution of small sizes. Having even numbers (16px, 24px) you do not have a center pixel which would make an edge exactly on the center more blurry due to interpolation. So, by applying an offset the edge starts getting more pronounced on one of the sides and thus looks sharper even on really small sizes.