Warning tensor dont match shape when tried to finetune 2B tiny model

Hello i tried to finetune this model in my custom dataset  and get this warning also in when training loss some step is 0, i have 2 questions:

what actually warning tell, and how to fix it?
How i can know about 0.0 loss, where it come from, my data or training command

{'loss': 0.0, 'learning_rate': 3.9622788631247045e-05, 'epoch': 0.13}

 13%|█▎        | 43/334 [27:16<3:02:22, 37.60s/it]514
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:31:56,816] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1675.71 | bwd_microstep: 3747.74 | bwd_inner_microstep: 3747.72 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
1166
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:32:01,198] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1361.25 | bwd_microstep: 3001.34 | bwd_inner_microstep: 3001.33 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.25
361
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:32:06,646] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1676.71 | bwd_microstep: 3749.13 | bwd_inner_microstep: 3749.12 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
409
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:32:11,024] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1360.70 | bwd_microstep: 2998.22 | bwd_inner_microstep: 2998.21 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
753
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:32:15,404] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1363.06 | bwd_microstep: 2998.31 | bwd_inner_microstep: 2998.30 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
1823
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:32:20,845] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1672.43 | bwd_microstep: 3747.11 | bwd_inner_microstep: 3747.09 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
1973
[2024-05-20 15:32:24,656] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1190.24 | bwd_microstep: 2601.76 | bwd_inner_microstep: 2601.75 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
889
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:32:29,088] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | optimizer_allgather: 0.00 | optimizer_gradients: 2.23 | optimizer_step: 3.52
[2024-05-20 15:32:29,088] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1358.78 | bwd_microstep: 3004.31 | bwd_inner_microstep: 3001.98 | bwd_allreduce_microstep: 2.29 | step_microstep: 49.58
[2024-05-20 15:32:29,089] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 11658.82 | bwd: 25847.93 | bwd_inner: 25845.50 | bwd_allreduce: 2.33 | step: 50.78

 13%|█▎        | 44/334 [27:53<3:01:55, 37.64s/it]

{'loss': 0.0, 'learning_rate': 3.958425895241054e-05, 'epoch': 0.13}

 13%|█▎        | 44/334 [27:53<3:01:55, 37.64s/it]1794
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:32:34,540] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1679.51 | bwd_microstep: 3745.75 | bwd_inner_microstep: 3745.74 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
2039
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:32:39,989] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1676.08 | bwd_microstep: 3752.07 | bwd_inner_microstep: 3752.06 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.09
297
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:32:44,370] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1363.66 | bwd_microstep: 2997.98 | bwd_inner_microstep: 2997.96 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
870
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:32:49,821] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1679.09 | bwd_microstep: 3751.75 | bwd_inner_microstep: 3751.74 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
2660
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:32:55,254] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1671.85 | bwd_microstep: 3739.99 | bwd_inner_microstep: 3739.98 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.10
2603
warning: The size of tensor a (2017) must match the size of tensor b (3328) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([3328, 2048])
[2024-05-20 15:33:00,705] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1680.99 | bwd_microstep: 3749.10 | bwd_inner_microstep: 3749.09 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.18
2514
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:33:05,086] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1359.17 | bwd_microstep: 2998.35 | bwd_inner_microstep: 2998.34 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
1417
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:33:09,517] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | optimizer_allgather: 0.00 | optimizer_gradients: 2.33 | optimizer_step: 3.52
[2024-05-20 15:33:09,517] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1356.39 | bwd_microstep: 3005.79 | bwd_inner_microstep: 3003.42 | bwd_allreduce_microstep: 2.33 | step_microstep: 49.53
[2024-05-20 15:33:09,518] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 12466.69 | bwd: 27740.80 | bwd_inner: 27738.33 | bwd_allreduce: 2.37 | step: 50.60

 13%|█▎        | 45/334 [28:34<3:05:19, 38.48s/it]

{'loss': 0.0, 'learning_rate': 3.954387660207733e-05, 'epoch': 0.13}

 13%|█▎        | 45/334 [28:34<3:05:19, 38.48s/it]408
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:33:13,906] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1360.84 | bwd_microstep: 3002.02 | bwd_inner_microstep: 3002.01 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
1736
warning: The size of tensor a (2017) must match the size of tensor b (2304) at non-singleton dimension 0, input_embeds[selected].shape=torch.Size([2017, 2048]), vit_embeds.shape=torch.Size([2304, 2048])
[2024-05-20 15:33:18,287] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1362.05 | bwd_microstep: 2999.32 | bwd_inner_microstep: 2999.31 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.19
637
[2024-05-20 15:33:22,214] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1217.30 | bwd_microstep: 2690.86 | bwd_inner_microstep: 2690.85 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
1536
[2024-05-20 15:33:26,567] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1310.88 | bwd_microstep: 3023.22 | bwd_inner_microstep: 3023.20 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
1534
[2024-05-20 15:33:30,482] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1215.52 | bwd_microstep: 2680.89 | bwd_inner_microstep: 2680.87 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.11
1180
[2024-05-20 15:33:34,833] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1304.74 | bwd_microstep: 3028.13 | bwd_inner_microstep: 3028.12 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
1768
[2024-05-20 15:33:38,775] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1211.15 | bwd_microstep: 2712.15 | bwd_inner_microstep: 2712.14 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
1760
[2024-05-20 15:33:42,721] [INFO] [loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 2097152, reducing to 1048576
[2024-05-20 15:33:42,722] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1207.87 | bwd_microstep: 2690.65 | bwd_inner_microstep: 2688.30 | bwd_allreduce_microstep: 2.31 | step_microstep: 29.85
[2024-05-20 15:33:42,723] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 10190.26 | bwd: 22827.25 | bwd_inner: 22824.80 | bwd_allreduce: 2.35 | step: 30.99

 14%|█▍        | 46/334 [29:07<2:57:05, 36.89s/it]

{'loss': 0.7845, 'learning_rate': 3.954387660207733e-05, 'epoch': 0.14}

 14%|█▍        | 46/334 [29:07<2:57:05, 36.89s/it]417
[2024-05-20 15:33:47,043] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1292.38 | bwd_microstep: 3003.39 | bwd_inner_microstep: 3003.37 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.11
1482
[2024-05-20 15:33:50,968] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1188.69 | bwd_microstep: 2717.73 | bwd_inner_microstep: 2717.72 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
1649
[2024-05-20 15:33:54,909] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1194.25 | bwd_microstep: 2728.94 | bwd_inner_microstep: 2728.93 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
1581
[2024-05-20 15:33:58,891] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1205.65 | bwd_microstep: 2757.53 | bwd_inner_microstep: 2757.52 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
1438
[2024-05-20 15:34:02,874] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1206.80 | bwd_microstep: 2758.65 | bwd_inner_microstep: 2758.64 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.10
1756
[2024-05-20 15:34:07,172] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1290.06 | bwd_microstep: 2989.33 | bwd_inner_microstep: 2989.32 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
1528
[2024-05-20 15:34:11,447] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1283.86 | bwd_microstep: 2972.24 | bwd_inner_microstep: 2972.23 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
1539
[2024-05-20 15:34:15,744] [INFO] [loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 1048576, reducing to 524288
[2024-05-20 15:34:15,745] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1284.04 | bwd_microstep: 2966.13 | bwd_inner_microstep: 2963.76 | bwd_allreduce_microstep: 2.33 | step_microstep: 29.50
[2024-05-20 15:34:15,745] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 9945.62 | bwd: 22893.96 | bwd_inner: 22891.49 | bwd_allreduce: 2.37 | step: 30.54

 14%|█▍        | 47/334 [29:40<2:50:55, 35.73s/it]

{'loss': 0.8672, 'learning_rate': 3.954387660207733e-05, 'epoch': 0.14}

 14%|█▍        | 47/334 [29:40<2:50:55, 35.73s/it]481
[2024-05-20 15:34:19,654] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1187.01 | bwd_microstep: 2698.06 | bwd_inner_microstep: 2698.05 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.11
1718
[2024-05-20 15:34:23,597] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1198.16 | bwd_microstep: 2725.49 | bwd_inner_microstep: 2725.48 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
1584
[2024-05-20 15:34:27,856] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1281.38 | bwd_microstep: 2959.38 | bwd_inner_microstep: 2959.36 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
1608
[2024-05-20 15:34:31,806] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1195.85 | bwd_microstep: 2735.35 | bwd_inner_microstep: 2735.33 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
419
[2024-05-20 15:34:35,758] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1195.77 | bwd_microstep: 2737.19 | bwd_inner_microstep: 2737.18 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
602
[2024-05-20 15:34:39,689] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1191.89 | bwd_microstep: 2720.99 | bwd_inner_microstep: 2720.97 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
1425
[2024-05-20 15:34:43,920] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1271.59 | bwd_microstep: 2941.15 | bwd_inner_microstep: 2941.13 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.16
1471
[2024-05-20 15:34:47,784] [INFO] [loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 524288, reducing to 262144
[2024-05-20 15:34:47,785] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 1156.95 | bwd_microstep: 2657.62 | bwd_inner_microstep: 2655.15 | bwd_allreduce_microstep: 2.43 | step_microstep: 29.56
[2024-05-20 15:34:47,786] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 9678.49 | bwd: 22175.24 | bwd_inner: 22172.67 | bwd_allreduce: 2.47 | step: 30.69

 14%|█▍        | 48/334 [30:12<2:45:02, 34.63s/it]

{'loss': 0.9806, 'learning_rate': 3.954387660207733e-05, 'epoch': 0.14}

My data
{"id": 0, "image": "OCR_New_Template_KFC_Result/2.jpg", "conversations": [{"from": "human", "value": "Picture 1: <image>\nextract data from this receipt, return only json without any additional text or Markdown formatting with the following keys:`time`: timestamp indicating the time of this receipt's issuance,`type`: designation of the vendor. Return blank if you unsure about any information"}, {"from": "gpt", "value": "{'time': '12:02', 'type': 'kfc'}"}]}
{"id": 1, "image": "OCR_New_Template_KFC_Result/2.jpg", "conversations": [{"from": "human", "value": "Picture 1: <image>\nextract data from this receipt, return only json without any additional text or Markdown formatting with the following keys:`products`: A list of objects containing the following keys:,`product_total_money`: total amount payable for the product in this invoice,`product_vat`: VAT of the product purchased in this invoice,`product_amount`: quantity of the product purchased in this invoice,`product_code`: code of the product purchased in this invoice,`product_unit_price`: price per unit of the product purchased in this invoice,`product_original_price`: initial amount payable for the product in this invoice (before any discounts or taxes),`product_total_original_money`: total initial amount payable for the product in this invoice (before any discounts or taxes),`product_discount_money`: discount amount for the product in this invoice,`product_discount_wholesale_money`: wholesale discount amount for the product,`product_discount_retail_money`: retail discount amount for the product,`product_name`: designation of the product purchased in this invoice,`type`: designation of the vendor,`name`: title of the shop where the receipt is generated,`tax_number`: tax identifier printed on this receipt,`date`: date of this receipt's issuance,`total_quantity`: total count of products in this invoice,`staff`: designation or ID of the staff involved in generating this invoice, as printed on this invoice. Return blank if you unsure about any information"}, {"from": "gpt", "value": "{'products': [{'product_code': '', 'product_name': 'Ga Que Kem A', 'product_vat': '', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '86000', 'product_original_price': '', 'product_total_original_money': '', 'product_discount_money': '', 'product_discount_retail_money': '', 'product_discount_wholesale_money': ''}, {'product_code': '', 'product_name': 'Rice Skewer (2 @45,000.00)', 'product_vat': '', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '90000', 'product_original_price': '', 'product_total_original_money': '', 'product_discount_money': '', 'product_discount_retail_money': '', 'product_discount_wholesale_money': ''}, {'product_code': '', 'product_name': 'Popcorn R', 'product_vat': '', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '38000', 'product_original_price': '', 'product_total_original_money': '', 'product_discount_money': '', 'product_discount_retail_money': '', 'product_discount_wholesale_money': ''}, {'product_code': '', 'product_name': 'Add 3 Nuggets', 'product_vat': '', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '22000', 'product_original_price': '', 'product_total_original_money': '', 'product_discount_money': '', 'product_discount_retail_money': '', 'product_discount_wholesale_money': ''}], 'type': 'kfc', 'name': 'DUONG NGUYEN THAI HOC', 'tax_number': '', 'date': '07/10/2023', 'total_quantity': '', 'staff': 'Ngo Thu HUYNH Y NHI'}"}]}
{"id": 2, "image": "OCR_New_Template_KFC_Result/2.jpg", "conversations": [{"from": "human", "value": "Picture 1: <image>\nextract data from this receipt, return only json without any additional text or Markdown formatting with the following keys:`date`: date export this receipt,`total_quantity`: total quantity of the product in this invoice,`tax_number`: tax number printed on this receipt. Return blank if you unsure about any information"}, {"from": "gpt", "value": "{'date': '07/10/2023', 'total_quantity': '', 'tax_number': ''}"}]}
{"id": 3, "image": "OCR_New_Template_KFC_Result/4.jpg", "conversations": [{"from": "human", "value": "Picture 1: <image>\nextract data from this receipt, return only json without any additional text or Markdown formatting with the following keys:\n`name`: title of the shop where the receipt is generated\n`staff`: designation or ID of the staff involved in generating this invoice, as printed on this invoice\n`type`: designation of the vendor\n`total_quantity`: total count of products in this invoice\nReturn blank if you unsure about any information"}, {"from": "gpt", "value": "{'name': 'KFC AEON TAN PHU', 'staff': 'TRAN PHAM NGOC NHI', 'type': 'kfc', 'total_quantity': ''}"}]}
{"id": 4, "image": "OCR_New_Template_KFC_Result/4.jpg", "conversations": [{"from": "human", "value": "Picture 1: <image>\nextract data from this receipt, return only json without any additional text or Markdown formatting with the following keys:`total_quantity`: total quantity of products in this invoice,`staff`: name or ID of the staff exporting this invoice, printed in this invoice,`time`: time of exporting this receipt, in timestamp format,`products`: A list of objects containing the following keys:,`product_total_money`: total amount to be paid for the product in this invoice,`product_name`: name of the product purchased in this invoice,`product_original_price`: original amount to be paid for the product in this invoice (before any discounts or taxes),`product_amount`: amount of the product purchased in this invoice,`product_unit_price`: price per unit of the product purchased in this invoice,`product_total_original_money`: total original amount to be paid for the product in this invoice (before any discounts or taxes),`date`: date of exporting this receipt,`total_money`: total amount in this invoice. Return blank if you unsure about any information"}, {"from": "gpt", "value": "{'total_quantity': '', 'staff': 'TRAN PHAM NGOC NHI', 'time': '10:41', 'products': [{'product_name': 'Lunch-R.Flava', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '39000', 'product_original_price': '', 'product_total_original_money': ''}, {'product_name': 'Marker Lunch-R.Flava', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '5000', 'product_original_price': '', 'product_total_original_money': ''}, {'product_name': '3 Pumcheese', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '38000', 'product_original_price': '', 'product_total_original_money': ''}], 'date': '09/10/2023', 'total_money': '82000'}"}]}
{"id": 5, "image": "OCR_New_Template_KFC_Result/4.jpg", "conversations": [{"from": "human", "value": "Picture 1: <image>\nextract data from this receipt, return only json without any additional text or Markdown formatting with the following keys:`type`: designation of the vendor,`tax_number`: tax identifier printed on this receipt,`products`: A list of objects containing the following keys:,`product_unit_price`: price per unit of the product purchased in this invoice,`product_original_price`: initial amount payable for the product in this invoice (before any discounts or taxes),`product_amount`: quantity of the product purchased in this invoice,`product_discount_retail_money`: retail discount amount for the product,`product_total_original_money`: total initial amount payable for the product in this invoice (before any discounts or taxes),`product_code`: code of the product purchased in this invoice,`product_discount_price`: price after applying discount for the product,`product_name`: designation of the product purchased in this invoice,`product_total_money`: total amount payable for the product in this invoice,`product_vat`: VAT of the product purchased in this invoice,`product_discount_wholesale_money`: wholesale discount amount for the product,`product_discount_money`: discount amount for the product in this invoice,`total_money`: aggregate sum of money in this invoice,`date`: date of this receipt's issuance,`name`: title of the shop where the receipt is generated,`time`: timestamp indicating the time of this receipt's issuance,`staff`: designation or ID of the staff involved in generating this invoice, as printed on this invoice,`receipt_number`: unique identifier of this receipt. Return blank if you unsure about any information"}, {"from": "gpt", "value": "{'type': 'kfc', 'tax_number': '', 'products': [{'product_code': '', 'product_name': 'Lunch-R.Flava', 'product_vat': '', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '39000', 'product_original_price': '', 'product_total_original_money': '', 'product_discount_money': '', 'product_discount_price': '', 'product_discount_retail_money': '', 'product_discount_wholesale_money': ''}, {'product_code': '', 'product_name': 'Marker Lunch-R.Flava', 'product_vat': '', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '5000', 'product_original_price': '', 'product_total_original_money': '', 'product_discount_money': '', 'product_discount_price': '', 'product_discount_retail_money': '', 'product_discount_wholesale_money': ''}, {'product_code': '', 'product_name': '3 Pumcheese', 'product_vat': '', 'product_amount': '', 'product_unit_price': '', 'product_total_money': '38000', 'product_original_price': '', 'product_total_original_money': '', 'product_discount_money': '', 'product_discount_price': '', 'product_discount_retail_money': '', 'product_discount_wholesale_money': ''}], 'total_money': '82000', 'date': '09/10/2023', 'name': 'KFC AEON TAN PHU', 'time': '10:41', 'staff': 'TRAN PHAM NGOC NHI', 'receipt_number': ''}"}]}
.
.
.

My training command
deepspeed internvl/train/internvl_chat_finetune.py \
  --model_name_or_path "OpenGVLab/Mini-InternVL-Chat-2B-V1-5" \
  --conv_style "internlm2-chat" \
  --output_dir "output" \
  --meta_path "playground/train_data_internvl/meta_train.json" \
  --overwrite_output_dir True \
  --force_image_size 448 \
  --max_dynamic_patch 12 \
  --down_sample_ratio 0.5 \
  --drop_path_rate 0.1 \
  --pad2square False \
  --freeze_llm True \
  --freeze_mlp True \
  --freeze_backbone True \
  --use_backbone_lora 16 \
  --use_llm_lora 16 \
  --vision_select_layer -1 \
  --use_data_resampling False \
  --dataloader_num_workers 1 \
  --fp16 True \
  --num_train_epochs 1 \
  --per_device_train_batch_size 1 \
  --gradient_accumulation_steps 8 \
  --evaluation_strategy "no" \
  --save_strategy "steps" \
  --save_steps 200 \
  --save_total_limit 3 \
  --learning_rate 1e-5 \
  --weight_decay 0.01 \
  --warmup_ratio 0.03 \
  --lr_scheduler_type "cosine" \
  --logging_steps 1 \
  --max_seq_length 2048 \
  --do_train True \
  --grad_checkpoint True \
  --group_by_length True \
  --dynamic_image_size True \
  --use_thumbnail True \
  --ps_version 'v2' \
  --deepspeed "zero_stage1_config.json" \
  --report_to "tensorboard" \
  2>&1 | tee -a "${OUTPUT_DIR}/training_log.txt"

Thanks for concern my issuse

it's seem like my max_seq_length config dont fit with the token create from vision encoder and token from input from (token from vision encoder + token from input prompt > max_seq_length), i have reduced the max_dynamic_patch to 6 and increase max_seq_length to 3072 and the loss is normally calculate
[2024-05-22 10:13:18,169] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 501.41 | bwd_microstep: 1140.38 | bwd_inner_microstep: 1140.36 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.07
580
[2024-05-22 10:13:19,826] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 500.66 | bwd_microstep: 1138.88 | bwd_inner_microstep: 1138.87 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
3606
[2024-05-22 10:13:21,478] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 502.03 | bwd_microstep: 1133.16 | bwd_inner_microstep: 1133.14 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.06
5582
[2024-05-22 10:13:23,140] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 504.22 | bwd_microstep: 1140.17 | bwd_inner_microstep: 1140.16 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
375
[2024-05-22 10:13:24,748] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 490.07 | bwd_microstep: 1100.55 | bwd_inner_microstep: 1100.54 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
8520
[2024-05-22 10:13:26,361] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 491.61 | bwd_microstep: 1104.29 | bwd_inner_microstep: 1104.28 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
5102
[2024-05-22 10:13:28,039] [INFO] [loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 4096, reducing to 2048
[2024-05-22 10:13:28,040] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 500.44 | bwd_microstep: 1130.93 | bwd_inner_microstep: 1128.07 | bwd_allreduce_microstep: 2.81 | step_microstep: 30.67
[2024-05-22 10:13:28,041] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 3989.44 | bwd: 9025.53 | bwd_inner: 9022.58 | bwd_allreduce: 2.86 | step: 31.53
{'loss': 0.9967, 'learning_rate': 1.7364011282313732e-05, 'epoch': 0.79}
 26%|██▋       | 1240/4686 [8:26:19<12:54:36, 13.49s/it]12245
[2024-05-22 10:13:29,702] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 495.85 | bwd_microstep: 1138.39 | bwd_inner_microstep: 1138.37 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
3591
[2024-05-22 10:13:31,359] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 500.84 | bwd_microstep: 1138.85 | bwd_inner_microstep: 1138.84 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
2336
[2024-05-22 10:13:33,011] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 498.36 | bwd_microstep: 1136.27 | bwd_inner_microstep: 1136.26 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
6322
[2024-05-22 10:13:34,669] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 504.01 | bwd_microstep: 1135.85 | bwd_inner_microstep: 1135.84 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
4730
[2024-05-22 10:13:36,249] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 482.56 | bwd_microstep: 1080.04 | bwd_inner_microstep: 1080.03 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
4810
[2024-05-22 10:13:37,837] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 485.65 | bwd_microstep: 1084.45 | bwd_inner_microstep: 1084.44 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
6046
[2024-05-22 10:13:39,411] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 481.56 | bwd_microstep: 1075.33 | bwd_inner_microstep: 1075.32 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
12008
[2024-05-22 10:13:41,043] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | optimizer_allgather: 0.02 | optimizer_gradients: 3.30 | optimizer_step: 5.63
[2024-05-22 10:13:41,043] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 485.32 | bwd_microstep: 1075.22 | bwd_inner_microstep: 1072.26 | bwd_allreduce_microstep: 2.91 | step_microstep: 54.33
[2024-05-22 10:13:41,044] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 3934.02 | bwd: 8864.41 | bwd_inner: 8861.35 | bwd_allreduce: 2.96 | step: 55.35
{'loss': 0.1394, 'learning_rate': 1.7359333111243458e-05, 'epoch': 0.79}
 26%|██▋       | 1241/4686 [8:26:32<12:46:02, 13.34s/it]11392
[2024-05-22 10:13:42,629] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 479.43 | bwd_microstep: 1083.70 | bwd_inner_microstep: 1083.68 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
3426
[2024-05-22 10:13:44,217] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 484.28 | bwd_microstep: 1086.42 | bwd_inner_microstep: 1086.41 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
2626
[2024-05-22 10:13:45,785] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 480.38 | bwd_microstep: 1064.77 | bwd_inner_microstep: 1064.76 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
11991
[2024-05-22 10:13:47,370] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 484.58 | bwd_microstep: 1082.86 | bwd_inner_microstep: 1082.85 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
328
[2024-05-22 10:13:48,946] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 482.75 | bwd_microstep: 1076.80 | bwd_inner_microstep: 1076.79 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
11936
[2024-05-22 10:13:50,593] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 504.67 | bwd_microstep: 1124.15 | bwd_inner_microstep: 1124.14 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.10
4870
[2024-05-22 10:13:52,252] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 502.43 | bwd_microstep: 1140.04 | bwd_inner_microstep: 1140.03 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
4432
[2024-05-22 10:13:53,964] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | optimizer_allgather: 0.00 | optimizer_gradients: 3.29 | optimizer_step: 5.63
[2024-05-22 10:13:53,965] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 502.50 | bwd_microstep: 1138.18 | bwd_inner_microstep: 1135.14 | bwd_allreduce_microstep: 2.99 | step_microstep: 54.69
[2024-05-22 10:13:53,966] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 3920.90 | bwd: 8796.93 | bwd_inner: 8793.79 | bwd_allreduce: 3.03 | step: 55.69
{'loss': 0.1607, 'learning_rate': 1.735465142399873e-05, 'epoch': 0.79}
 27%|██▋       | 1242/4686 [8:26:45<12:38:35, 13.22s/it]12221
[2024-05-22 10:13:55,616] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 501.04 | bwd_microstep: 1126.24 | bwd_inner_microstep: 1126.23 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
5627
[2024-05-22 10:13:57,259] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 505.20 | bwd_microstep: 1121.28 | bwd_inner_microstep: 1121.26 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.15
4843
[2024-05-22 10:13:58,901] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 499.69 | bwd_microstep: 1124.76 | bwd_inner_microstep: 1124.74 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
6141
[2024-05-22 10:14:00,541] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 501.66 | bwd_microstep: 1120.81 | bwd_inner_microstep: 1120.80 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
6254
[2024-05-22 10:14:02,182] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 503.34 | bwd_microstep: 1119.73 | bwd_inner_microstep: 1119.72 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
4993
[2024-05-22 10:14:03,840] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 504.51 | bwd_microstep: 1136.40 | bwd_inner_microstep: 1136.38 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
6432
[2024-05-22 10:14:05,501] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 502.29 | bwd_microstep: 1140.95 | bwd_inner_microstep: 1140.94 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
6425
[2024-05-22 10:14:07,207] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | optimizer_allgather: 0.00 | optimizer_gradients: 3.26 | optimizer_step: 5.64
[2024-05-22 10:14:07,208] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 502.16 | bwd_microstep: 1133.68 | bwd_inner_microstep: 1130.76 | bwd_allreduce_microstep: 2.87 | step_microstep: 53.93
[2024-05-22 10:14:07,208] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 4019.79 | bwd: 9023.84 | bwd_inner: 9020.83 | bwd_allreduce: 2.91 | step: 54.99
{'loss': 0.0108, 'learning_rate': 1.734996622281639e-05, 'epoch': 0.8}
 27%|██▋       | 1243/4686 [8:26:58<12:38:50, 13.22s/it]5928
[2024-05-22 10:14:08,852] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 500.11 | bwd_microstep: 1121.37 | bwd_inner_microstep: 1121.36 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
1927
[2024-05-22 10:14:10,490] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 501.12 | bwd_microstep: 1119.33 | bwd_inner_microstep: 1119.32 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
12456
[2024-05-22 10:14:12,101] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 489.86 | bwd_microstep: 1104.08 | bwd_inner_microstep: 1104.07 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.08
12458
[2024-05-22 10:14:13,709] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 488.45 | bwd_microstep: 1102.53 | bwd_inner_microstep: 1102.51 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
3916
[2024-05-22 10:14:15,318] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 490.27 | bwd_microstep: 1101.87 | bwd_inner_microstep: 1101.85 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
5683
[2024-05-22 10:14:16,922] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 488.44 | bwd_microstep: 1098.80 | bwd_inner_microstep: 1098.79 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.10
6015
[2024-05-22 10:14:18,506] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 489.17 | bwd_microstep: 1077.52 | bwd_inner_microstep: 1077.51 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.09
3663
[2024-05-22 10:14:20,148] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | optimizer_allgather: 0.00 | optimizer_gradients: 3.28 | optimizer_step: 5.63
[2024-05-22 10:14:20,148] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 488.50 | bwd_microstep: 1082.86 | bwd_inner_microstep: 1079.92 | bwd_allreduce_microstep: 2.90 | step_microstep: 53.97
[2024-05-22 10:14:20,149] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd: 3935.80 | bwd: 8808.38 | bwd_inner: 8805.33 | bwd_allreduce: 2.94 | step: 54.87
{'loss': 0.9496, 'learning_rate': 1.734527750993495e-05, 'epoch': 0.8}
 27%|██▋       | 1244/4686 [8:27:11<12:33:44, 13.14s/it]4227
[2024-05-22 10:14:21,758] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 485.66 | bwd_microstep: 1095.62 | bwd_inner_microstep: 1095.61 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
4100
[2024-05-22 10:14:23,416] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 499.37 | bwd_microstep: 1141.14 | bwd_inner_microstep: 1141.12 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
3675
[2024-05-22 10:14:25,064] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 499.07 | bwd_microstep: 1132.15 | bwd_inner_microstep: 1132.13 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
5441
[2024-05-22 10:14:26,730] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 501.52 | bwd_microstep: 1142.54 | bwd_inner_microstep: 1142.53 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
3636
[2024-05-22 10:14:28,377] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 502.79 | bwd_microstep: 1127.50 | bwd_inner_microstep: 1127.48 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.13
5402
[2024-05-22 10:14:29,976] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 488.47 | bwd_microstep: 1093.74 | bwd_inner_microstep: 1093.72 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.12
3105
[2024-05-22 10:14:31,583] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 488.21 | bwd_microstep: 1101.28 | bwd_inner_microstep: 1101.27 | bwd_allreduce_microstep: 0.00 | step_microstep: 0.14
5599
[2024-05-22 10:14:33,248] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | optimizer_allgather: 0.00 | optimizer_gradients: 3.28 | optimizer_step: 5.63
[2024-05-22 10:14:33,248] [INFO] [logging.py:96:log_dist] [Rank 0] time (ms) | fwd_microstep: 488.39 | bwd_microstep: 1105.71 | bwd_inner_microstep: 1102.77 | bwd_allreduce_microstep: 2.90 | step_microstep: 54.21
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Hello got the same error with loss=0 did you resolve it ?

Hello got the same error with loss=0 did you resolve it ?

Yeah i have, this is solution for me

max_seq_length config dont fit with the token create from vision encoder and token from input prompt (token from vision encoder + token from input prompt > max_seq_length), i have reduced the max_dynamic_patch to 6 (to decrease token from vision encoder) and increase max_seq_length to 3072 and the loss is normal


Final content additions for Josh's subpages.

Adds text and references to the relevant places.

 Complete Josh page content

Merged as approved


Interactive Visualizations Tutorial Error

I'm trying to figure out how to implement linked interactivity in dashR. Since my app was unresponsive to clicks, I tested the first example in the Tutorial (starting from "Here's an simple example that prints these attributes to the screen") and discovered that this app does not execute properly when I copy-paste and run that exact code. While the tutorial page itself updates with the following when clicked:
{
  "points": [
    {
      "curveNumber": 0,
      "pointNumber": 0,
      "pointIndex": 0,
      "x": 1,
      "y": 4,
      "customdata": "c.a",
      "text": "a"
    }
  ]
}

... the app run locally does not update when clicked. Otherwise the app displays normally.
Would appreciate any assistance. Running Windows 10, R version 3.6.1, dash 1.0.0.

In case someone else finds this and you aren't able to run the code in the docs because the .csv file couldn't be found, here is the correct link: https://github.com/plotly/dash-docs/blob/dashr-squashed/dashr/chapters/callbacks/examples/indicators.csv.
Just download the file and adjust the path to access this file locally

Update on this: after checking the second example and finding it runs fine with access to the csv code provided with @firasm , I re-tested the first example, and observed in addition to the unresponsive app, the error:

error: could not find function "prettify"

printing to the powershell in which the app was launched. The addition of library(jsonlite) to the beginning of the app file results in a working dash app.
I am not certain whether this is the sole cause of the initial issue (I could have overlooked the error in my powershell Sunday evening) or whether you fixed something else (in which case, thanks!) but it looks like the only thing the documentation could use right now is adding additional library(jsonlite) call at the beginning of the example to make the dependency clear for anyone trying to run this code from their own machine.

Thanks again @ksedivyhaley for pointing this out; we've updated the documentation to reflect this suggestion. Closing as resolved.


[WIP]: Backport stm32, stm32f7, stm32h7 SPI DMA threshold

Standby, missing one little piece (https://github.com/PX4/NuttX/pull/88/commits/ee000cbf0ddba2b10f9fb5e644eba465b669cfd7).

Waiting on https://github.com/apache/incubator-nuttx/pull/424.

Tested in https://github.com/PX4/Firmware/pull/14238.


[backport-1.10] image-builder: disable reflink

Disable reflink when using DAX. Reflink is a xfs feature that cannot be
used together with DAX.
fixes #455
Signed-off-by: Julio Montes<EMAIL_ADDRESS>

/test-ubuntu


Integration with coinpayments.

possible of integrate with shapeshift? we can provide api info on it

@equitybase
Please, create new issue regarding this. We are always open for new integrations if the services are reliable and it can help future ICOs.
Please, provide as much details as you can in new issue. Reason, use-cases, API and any other information which you think can help us in development.

As of this integration when do you think it will be ready?
On Feb 20, 2018, at 3:09 PM, Andrey Degtyaruk <notifications<EMAIL_ADDRESS>wrote:
@equitybasehttps://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fequitybase&data=02|01||ea4dcfef3e3145553a9508d578b70602|84df9e7fe9f640afb435aaaaaaaaaaaa|1|0|636547649820093912&sdata=D9qwWiLL7N9I9z87Maf4V5hPdA5CboGGGhrACDSjAzA%3D&reserved=0
Please, create new issue regarding this. We are always open for new integrations if the services are reliable and it can help future ICOs.
Please, provide as much details as you can in new issue. Reason, use-cases, API and any other information which you think can help us in development.
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Reply to this email directly, view it on GitHubhttps://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FJincorTech%2Fbackend-ico-dashboard%2Fissues%2F86%23issuecomment-367153514&data=02|01||ea4dcfef3e3145553a9508d578b70602|84df9e7fe9f640afb435aaaaaaaaaaaa|1|0|636547649820093912&sdata=qpsMCQ4%2B7T9f9MPhxpKFRcX6Q61Qns%2F%2BdURq6rMGZGU%3D&reserved=0, or mute the threadhttps://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAiDnysnae_OG5TJIyvh-DwtiOncE_HZhks5tW1C0gaJpZM4SJOnE&data=02|01||ea4dcfef3e3145553a9508d578b70602|84df9e7fe9f640afb435aaaaaaaaaaaa|1|0|636547649820093912&sdata=U7%2BIZHVCCyYGPQHCybjs%2BVEzNr47XeG0bNG8XOleYgI%3D&reserved=0.

It depends on complexity, demand, free time of developers and many other factors so it's a bit hard to predict. I think we can do it by the end of March but I cannot give you any guarantee.
It would be much faster if one of our customers(current or future) will ask for this implementation so we get financial sources and will be able to do it not just like hobby in spare time but spend some business hours on this issue.
Currently you are the first person asking for this and it doesn't seems to be enough for rapid implementation. On the other hand as I said above your request looks reasonable and I think it will make this dashboard more attractive.

As of financial incentives what your guys looking for?
On Feb 20, 2018, at 3:32 PM, Andrey Degtyaruk <notifications<EMAIL_ADDRESS>wrote:
It depends on complexity, demand, free time of developers and many other factors so it's a bit hard to predict. I think we can do it by the end of March but I cannot give you any guarantee.
It would be much faster if one of our customers(current or future) will ask for this implementation so we get financial sources and will be able to do it not just like hobby in spare time but spend some business hours on this issue.
Currently you are the first person asking for this and it doesn't seems to be enough for rapid implementation. On the other hand as I said above your request looks reasonable and I think it will make this dashboard more attractive.
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@equitybase @mor888
We need some time for evaluation of the feature. You can email me to<EMAIL_ADDRESS>and we will discuss and find the best option.

https://github.com/JincorTech/backend-ico-dashboard/pull/103


zmin and zmax scaling in getBoundingBox

When the user requests a bounding box for an annotation at a resolution different from the default, the bounding box is obtained for the default channel resolution then scaled. However, I think the z-indices shouldn't be scaled because downsampling only happens in x and y.
(see: https://github.com/neurodata/spdb/blob/master/spatialdb.py#L881)
Removing the scaling from 881 and 884 fixed the bounds issues I was seeing. Let me know if this is actually a bug and I'll commit the change.
( @kunallillaney )

@alexbaden There is scaling in z for isotropic and near-isotropic data. The way we calculate scaling there is incorrect as it is generalized for x and y which is always 2. I think we need to have a separate scaling for z which is a factor of z-slices in the respective resolutions. @randalburns Does this sound right?

Following in the same vein, the cubedim setting in line 877 should be the cube dimension of the effectiveres, not the resolution: https://github.com/neurodata/spdb/blob/master/spatialdb.py#L877

@alexbaden I will talk to @randalburns tomorrow about this. Can you please post a sample query here for reference?

Sure. I think I got it working. I'll commit the script I'm using to the
examples dir later this afternoon.
On Thu, Jul 14, 2016 at 12:09 Kunal Lillaney<EMAIL_ADDRESS>wrote:

@alexbaden https://github.com/alexbaden I will talk to @randalburns
https://github.com/randalburns tomorrow about this. Can you please post
a sample query here for reference?
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Is done and committed into the allen branch of spdb.  Didn't want to commit to spdb.  Kunal want to merge?  I'm assigning to you.

I do not see any new commits to the spdb repo. There were some changes in the allen branch in April which have merged quite some time back. Was that it?


forc-crypto should be added to the nix flake

forc-crypto is missing from the nix flake, we should add it as it is an "officially" distributed tool now.

We should add forc-debug as well.


Possible cast error ?

Hi,
Still looking at the code, i think there could be a cast error in this piece of code :
https://github.com/spiral/framework/blob/master/src/Command/Router/ListCommand.php#L139
The "action" parameter in the class "Action" could be a string but also an array (as indicated in the docbloc : https://github.com/spiral/router/blob/master/src/Target/Action.php#L37).
You will surely have an cast array to string error. Perhaps adding a "join("|", $action)" could prevent this potential error.
Keep up the good work.

Thank you for the feedback. PR will be appreciated or it will have to wait for when we will have the capacity for this task.

I can push a PR.
I was thinking to add this piece of code to handle the case when the var is a string or an array :+1:
sprintf("%s->%s", 'controler', join('|', (array) $action))php
Is this ok with you ?
I also see there is no test for the command class, so i will only modify the ListCommand file.

This looks OK. This is debugging command so testing it wasn't in priority.

The PR is done, looking in the GithubAction, the build is green. Let me know if there is another thing to do.

PR Merged. Have a good day.


[Reference Room]  Threshold transition

When the character clicks a threshold object to leave the room i.e. a door,  a transition will take place and load the new room with the character spawning at a defined position in tiled.

@andrewrady  Do you have much work in progress on this?  Otherwise I'm going to bust it out today.  Don't worry there will be plenty more tasty issues coming to work on :)

@Jared-Sprague I haven't done much with this issue due to time, it's all you! 🙂

@Jared-Sprague did you have a chance to work on this? I was just wondering if you wanted help or been super busy and wanted help 🙂

@andrewrady   I'm so sorry for taking so long to get back to you I've been super busy with Open Jam
If you are still interested in working on this that would be great!  The object selection example in the reference room already has objects placed on the doors,  so that would be an easy example to start from.  You would need to do 2 things:

When a door is clicked the character walks to it
The the character position is moved and the camera bounds are changed to a new room outlined in Tiled.  A good example of how this works is how the Pipe transitions work in this mario example:  https://github.com/nkholski/phaser3-es6-webpack  It does basically the same thing and uses Tiled.

@Jared-Sprague sorry for the late response. Super cool, I hope Open Jam is a good time! I can look at it this weekend if haven't wrapped it up.

The pipes in this example are very similar:
https://github.com/nkholski/phaser3-es6-webpack

I'll pick this up and work on it this week.

@nwforrer   here is the pick of the whiteboard about how this should work.  Use the mario example between pipes and underworld rooms for inspiration.   We have two types of transitions,  "Position" and "Inspect"


Syntax highlighting broken for long wrapped lines in Typescript

Similar to https://github.com/Microsoft/vscode/issues/48858, long variable lines break syntax highlight in Typescript files.

VSCode Version: 1.24.1
Typescript: 2.9.1
OS Version: OSX 10.13.4

With this code:
this._addPositionOperations = promisify<
      PositionChangeSetResponse,
      AddPositionOperationsRequest,
      Metadata
    >(this.client.addPositionOperations.bind(this.client))

this._setPositionSummary = promisify<BooleanResponse, SetPositionSummaryRequest, Metadata>(
      this.client.setPositionSummary.bind(this.client)
    )

Compare:

Does this issue occur when all extensions are disabled?: Yes

https://github.com/Microsoft/vscode/issues/51157

Duplicate of https://github.com/Microsoft/TypeScript-TmLanguage/issues/479
This issue has been marked as wontfix upstream. See that thread for more details


FSDP support?

Hi, does mup support training with FSDP? I got a model training with DDP, but when switching to FSDP I get the following assertion.
assert hasattr(self.weight, 'infshape'), ( AssertionError: Please call set_base_shapes(...). If using torch.nn.DataParallel, switch to distributed training with torch.nn.parallel.DistributedDataParallel instead

We haven't added support yet. Happy to review changes if someone is interesting in adding support!

Actually this only solves one part of the issue.
Inside the FSDP-sharded MuReadout, you won't be able to access the original weight torch.nn.Parameter, and accordingly infshape will not be set again because FSDP replaces the parameters.
The following workaround worked for me. At the bottom, I attached a patch that changes MuReadout so that in addition to weight.infshape, it optionally looks for weight_infshape. Then it's only a matter of manually setting model.mu_readout_layer.weight_infshape = model.mu_readout_layer.weight.infshape, which can probably be nicely automatized using torch.nn.Module.apply.
diff --git a/mup/layer.py b/mup/layer.py
index 518a33b..8bb07af 100644
--- a/mup/layer.py
+++ b/mup/layer.py
@@ -25,12 +25,18 @@ class MuReadout(Linear):
             super().reset_parameters()

     def width_mult(self):
-        assert hasattr(self.weight, 'infshape'), (
-            'Please call set_base_shapes(...). If using torch.nn.DataParallel, '
-            'switch to distributed training with '
-            'torch.nn.parallel.DistributedDataParallel instead'
-        )
-        return self.weight.infshape.width_mult()
+        if not hasattr(self.weight, 'infshape'):
+            if not hasattr(self, 'weight_infshape'):
+                raise AssertionError(
+                    'Please call set_base_shapes(...). If using torch.nn.DataParallel, '
+                    'switch to distributed training with '
+                    'torch.nn.parallel.DistributedDataParallel instead'
+                )
+            else:
+                width_mult = self.weight_infshape.width_mult()
+        else:
+            width_mult = self.weight.infshape.width_mult()
+        return width_mult

     def _rescale_parameters(self):
         '''Rescale parameters to convert SP initialization to μP initialization.


In cpanel folder image uploaded fin but not show any image why?

Laravel version
8.2.2
PHP version
7.3
Voyager version
1.4.3
Database
8.0
Description
When i  install Voyager  on local host and try it on my local machine image upload + showing image perfectly
but when i install the script on cpanel image uploaded but not showing on list + also url showing 404 error why how to fixed this ?
Steps to reproduce

Expected behavior

Screenshots
No response
Additional context
No response

Just Add Symbol Link


Issue with 'n_excl'

Hi, many thanks for developing this. I am trying to run an FE regression on a large unabalanced panel. However, I keep getting the same error:
KeyError: 'n_excl'
Do you have any idea where this is coming from?

Hi! Thanks for reaching out and reporting - I think this is the first "real" bug issue I get, so that's very cool 😀
This is part of the multicollinearity checks that pyfixest does and during which it drops multicollinear variables.
Can you maybe provide me with a reproducible example? If you cannot / don't want to share your data publicly, maybe it would be ok to send it to me by email?
Best, Alex

Hi @manferdinig, just checking in - did you manage to fix this issue?

Ok, this happens whenFeols removes all variables because of multicollinearity. Maybe you misspecified your model? E.g. see this:
from pyfixest.estimation import feols
from pyfixest.utils import get_data
data = get_data()

fit = feols("Y ~ f1 | f1", data = data)

produces KeyError: 'n_excl'.
In the dev branch, I now return a proper error message in case this happens =)


Do I need to fill the source content of NamedSource by myself?

In the example in the repo README, the src of the NamedSource is a hardcoded source
let src = "source\n text\n here".to_string();

Does this mean that I need to hardcode some current error context codes for each error, and calculate the offset that needs to be labeled by myself?
I saw the SourceCode in the proc-macro, just got the ident, it seems that there is no step to get the source code file?

A SourceCode is anything that implements the SourceCode trait. If you want to work off a file more dynamically, you'll need to write an implementation for it. All you need to be able to do is tell miette how to read a specific span from it: https://github.com/zkat/miette/blob/main/src/protocol.rs#L211-L231

I always thought that miette was a crate like coloreyre. Later I figured out that miette is suitable for some lexical analysis or as a language interpreter. I'm not sure. I always want to let miette capture the context part of the current rs code to use in some fancy output can be displayed when panic, this is probably some misunderstanding of mine


Cannot add widget to dashboard after installing!

Hi Folks,
For the life of me I can't seem to find the widget to add to the dashboard from my on-premise hosted TFS.
Is it possible that I'm using a version of TFS that is too old to work with this widget?
I am using TFS 2015 and the widget appears to have been installed correctly.  I have installed it into my Collection, just can't see it in the list of widgets to add to the dashboard.
I have successfully installed other third-party widgets before.
Any pointers would be helpful!

Theoretically, it should work according to the VSTS Team. I don't have an instance I can test with. Can you attach a couple screenshots of what you do see? Looking to see your installed extensions as well as your list of widgets from a dashboard (alphabetically it should show up as Code Coverage Widgets). Alternatively, you can also send them to my private email shane_b_davis at outlook dot com if you'd rather not post them here.

Here is the extension manager showing it is installed in my project collection.

Here is the add widget dialog to select a widget from, and I can't find it available there at all!

Thoughts?

@normanhh3 I've connected with the VSTS Team to debug this. Which version of TFS2015 do you have installed? Update 1?

@normanhh3 Any response to this? the VSTS Team is awaiting our response so they can assist.

Hi man, sorry it has taken me so long to get back to you.
The version information I have from /tfs/_home/About shows version 14.95.25229.0

@normanhh3 After conferring with VSTS team it appears you have TFS2015 Update 2.1. For dashboard widget support, Update 3 must be installed. If you are willing to upgrade, I would like to know if it works for you. Otherwise, I will wait a few days before closing this issue. Thank you for reporting it.

Well now, that is kind of annoying!  I will confer with our folks who
manage the box to see if we can get an upgrade done.
On Feb 15, 2017 5:43 PM, "Shane Davis"<EMAIL_ADDRESS>wrote:

@normanhh3 https://github.com/normanhh3 After conferring with VSTS team
it appears you have TFS2015 Update 2.1. For dashboard widget support,
Update 3 must be installed. If you are willing to upgrade, I would like to
know if it works for you. Otherwise, I will wait a few days before closing
this issue. Thank you for reporting it.
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Closing this issue as dashboard widgets require TFS2015 Update 3 to be installed.


fix(ui): rewrite Accordion component

Webpack bundle size diff
Total diff: -7.4 KB

M
build/static/js/9.d3ef149d.chunk.js
37.7 KB
30.3 KB
-7.4 KB

M
[no source]
12.8 KB
12.8 KB
-33 B

-
react-collapsible
7.4 KB

-7.4 KB


Refact functions and structs

Improve Node struct
Create createItem() function
Change some functions signatures
Fix some functions code
Fix chestLib.h definition

Ok


Fixed OpenBLAS builds

Fixed OpenBLAS Builds failing issue by using Caffe Mobile #122 (Refer this for more details)
Added script to build OpenBLAS without failing
Added build-single.sh to build without OpenBLAS
Added build-all.sh to build libcaffe.so and libcaffe_jni.so for multiple android platforms

@sh1r0 Please review this
and let me know


Urls containing %5C (back slash) come through as a forward slash in in Nancy 2.0 ClintEastwood self hosting

Prerequisites

[ ] I have written a descriptive issue title
[ ] I have verified that I am running the latest version of Nancy
[ ] I have verified if the problem exist in both DEBUG and RELEASE mode
[ ] I have searched open and closed issues to ensure it has not already been reported

Description
When I have a url along the lines of http://localhost:8081/path/My\String the Nancy Request URL ends up being http://localhost:8081/path/My/String instead of  http://localhost:8081/path/My\String or the unescaped path.
System Configuration
Windows 7, .Net 4.5.2, VS2017 running URL requests through Chrome and Postman.

%5c is back-slash.
Just try decodeURI function in the browser console:
decodeURI(' http://localhost:8081/path/My%5CString ')
" http://localhost:8081/path/My\String "


Update README for legacy branch

Is this the text for the individual track 1 package README.md files? Or is this for the main README.md in the legacy branch?
Just some grammar to fix if it's for the main README.md to reference packages and not a package.

Is this the text for the individual track 1 package README.md files? Or is this for the main README.md in the legacy branch? Just some grammar to fix if it's for the main README.md to reference packages and not a package.

It's the README.md in the root, which you can see in the Files tab.


Add Tibshelf Community School

Official domain:
https://www.tibshelf.derbyshire.sch.uk/
Relevant IT course lasting longer than 1 year:
https://www.tibshelf.derbyshire.sch.uk/page/?title=Computer+Science&pid=78
Page with recognition of .sch.uk domain name in email addresses:
https://www.tibshelf.derbyshire.sch.uk/page/?title=How+to+get+in+touch&pid=99
Student email login portal:
https://outlook.com/students.tibshelf.derbyshire.sch.uk

@djam90 I have to close this request without merging, because you requested to add a non-existent domain to this repository: Host tibshelfcommunity.derbyshire.sch.uk not found: 3(NXDOMAIN) Please ensure that you are familiar with this repository rules located at the bottom of this page and submit a new request with the correct information. Thank you for your understanding!


Editing around codelens results in garbled codelens position

Extension: CodeMetrics

@alexandrudima I can't repro this, maybe it got fixed somehow in the past 7 months?

:+1: This got somehow fixed in the meantime.


new subtyping check for virtual methods

Static TypeScript doesn't correctly checking subtyping on virtual methods of subclasses.

This is not legal static typescript, but is accepted:
class F {
    i: number;
}

class G {
    i: number;
}

class A {
    foo(f: F) {

    }
}

class B {
    foo(f: G) {

    }
}

fixed


Heroku infinite deployment attempt even on failure

We have been getting infinite deployment issue with heroku on all of our websites

https://github.com/iterative/dvc.org/pull/4485
https://github.com/iterative/dvc.org/pull/4487
https://github.com/iterative/iterative.ai/pull/839

I opened a ticket on Heroku Support in addition to this to research more and the Heroku team replied it could be because we have Wait for CI to pass. So upon digging further, it turns out we have a loop because of Wait for CI to pass in Heroku and link check GHA workflow which gets triggered on deployment status. Since it's getting triggered even on failure causing the change in CI which in turn triggers the Heroku.
There is a few more test I am doing

https://github.com/iterative/dvc.org/pull/4525
https://github.com/iterative/dvc.org/pull/4526
Will also try to address https://github.com/iterative/iterative.ai/issues/810

Is it equivalent of waiting for linters and GH CI to pass?

Yes, we have been using it that way so that we can fail a few PRs(like renovate or branch with certain phrases) and it would halt the deployment. It was a hacky implementation as there is no simple way Heroku provides us to limit a few PRs from deployment.
I tried a few different ways to see if skipping GHA that gets triggered based on deployment_status and allowed it only when it's necessary. But, it still triggered the issue.
I will try to see with Heroku Support to see any way we can fix this. Otherwise, I think we should proceed by disabling the Wait for CI to pass and instead further limit the no of renovate PRs and maybe schedule monthly.


.Net Core support

I could not find information about netcore support.
Also dotnet template for it would be great.

https://github.com/nightroman/FarNet/issues/16

@denisvmedia Thank you for the link (and the answer, in fact).
@jinek I think I should add a note somewhere that netcore is not supported and not going to be supported for a while.

Sorry, I think this one can be closed as duplicate.

TODO Add some info to wiki, docs, etc. about not supported netcore.

I added two notes to the wiki:

https://github.com/nightroman/FarNet/wiki/FarNet
https://github.com/nightroman/FarNet/wiki/FarNet-module-template

This is all I can really do at the moment. Like I said, when C++/CLI is officially released, supported, and battle tested in .NET Core then I will start looking at it.

Hi @jinek Here is some hopefully good news - #24

FarNet 6 announcement -- https://github.com/nightroman/FarNet/pull/25


Attempting to play a stream crashes computer

When ever I play a stream, Jellyfin loads forever and then crashes the entire computer. This wasn't happening before the latest update. I've tested the steams I was trying to play in VLC and they play. When I try watching the same stream in Jellyfin, I watch the RAM in the Unraid Dashboard go towards 100%. The stream never starts and then the computer crashes.

Did you get it figured out? The playback side of the code didn't change at all

No I didn't, I thought I would try other things, but nothing is working. The log in EPlusTV shows the stream is being started, but in Jellyfin, it just spins like it is trying to start the stream and then the RAM continues to go up towards 100%

I've actually never gotten it to work in Jellyfin before - but I'm getting the m3u and xmltv from Channels. I wonder if I need to try playback directly from E+ instead

I did add some code a few weeks back (#111) to better support Emby, I wonder if that busted Jellyfin? I doubt it, but maybe if you hadn't updated since then?

EPlusTV Log: https://pastebin.com/GxgHCccU
Jellyfin Log: https://pastebin.com/KrqFWDMH

It was actually working for me up until this most recent update

It seems like the auto loop button in Live TV was the issue

Yes it did


'vueish' html attributes with html2htpy

'vueish' html attributes with html2htpy
html2htpy is not currently able to handle conversion of key word attributes prefixed with special characters such as @click or :show, which I stumped into while converting some templates containing attributes from alpine.js.
I added a test and a bit to the transformation algorithm to support converting these attributes to htpy's dictionary attr syntax.
def test_convert_vueish_attrs() -> None:
    input = """
      <button id="btn-id" @click="doSomething" type="submit">Submit</button>

    """

    actual = html2htpy(input, import_mode="no")
    assert actual == (
        "["
        'button("#btn-id",{"@click":"doSomething"},type="submit")["Submit"],'
        'img("#img-id",{":src":"imageSrc"})'
        "]"
    )

Not sure though if you want to support all sorts of strange non-standard attributes that might show up. I guess these are fairly common though, so I thought why not.

I think https://docs.python.org/3/library/stdtypes.html#str.isidentifier can be used to decide whether or not an attribute name should be a direct argument or be put into the dict:
>>> '@click'.isidentifier()
False
>>> ':src'.isidentifier()
False
>>> 'foo'.isidentifier()
True
>>> 'type'.isidentifier()
True

Also, keyword.iskeyword(x) can be used to add a trailing underscore consistently instead of hard coding special cases for for_ and class_.

I added checks for keyword/identifiers+dict attributes so it is hopefully robust with all kinds of attributes now. Published htpy 24.6.1 to pypi with this fix :)
 $ echo '' | html2htpy
from htpy import img

img("#img-id", {":src": "imageSrc"<EMAIL_ADDRESS>"foo=bar"})

Apologies for leaving that stale, I became quite busy at work. Was planning to come back to it, but here we are!

No worries! :)


Possible bug in latest iOS client version 1.2.6 - client does not connect

Since around 6 days the iOS app is not able to connect to the Outline server I have running. Previous to this the setup had been functional for several weeks. The error in the iOS UI:
Sorry, an unexpected error occurred.
Quiting and restarting may help. If this happens again, please submit feedback.

To Reproduce
Steps to reproduce the behavior:

Open Outline vpn client on iOS device
Tap the Connect button
See the error at the bottom of the screen

Expected behavior
Expected the vpn client to connect to the Outline server, and to be able to browse web over vpn connection.
Smartphone:
OS: iOS Version 12.1.1
Connection-type: Wifi
Additional context
I noticed that I have the latest version of the iOS client 1.2.6 installed so I guess that got updated automatically at some stage because I didn't knowingly update any apps on my device.
The only other thing that happened around the same time was the server changed it's public ip. That has since been reverted now, and vpn connections work fine from my laptop.
Does the iOS vpn client cache some network info in some way that could be causing the connection problem?
Is there a bug in the latest iOS 1.2.6 causing the connection issue and how should I troubleshoot?
[update: added connection type]

Hi @mjgs, I'm sorry that you're experiencing this issue. I have not been able to reproduce this issue, but have a few questions that may help pinpoint the problem.

You mentioned that the server IP changed. Are you able to connect to the server over the same WiFi network from your laptop? We only cache the connection configuration (i.e. server IP) when the device turns off while Outline is connected. Re-adding the invite may help.
Do you see the VPN icon flash at all when connecting?
Does the client succeed in connecting over mobile or a different network?

Lastly, if this happens again please submit feedback through the app and tag it with this issue number.

Thanks for the troubleshooting suggestions. Answers and a few questions inline:

You mentioned that the server IP changed. Are you able to connect to the server over the same WiFi network from your laptop? We only cache the connection configuration (i.e. server IP) when the device turns off while Outline is connected. Re-adding the invite may help.

Yes I can connect successfully using the client installed on my laptop using the same WIFI network. I tried generating a new key in the Outline Manager and opened it on the iOS device selecting to add a new server. Connecting to this server also errors in the same way.

Do you see the VPN icon flash at all when connecting? Does the client succeed in connecting over mobile or a different network?

No I didn't see any vpn icon when connecting using the connect button in the iOS app. I tried another WIFI network over the past few days, in a different country as it happens, and the error persisted. When using the toggle in the iOS device "Settings>vpn>vpn configuration" a spinning icon appeared where the vpn icon usually appears.
I tried again from the iOS app several times just now and on the 3rd attempt the connection succeeded. I haven't made any configuration changes since I created the issue. So it's been broken for about 10 days, then just as I was writing this reply the problem stopped happening.
I have a few questions:
Is it the case that if both devices are connected to the same WIFI, that no entities external to the WIFI network could differentiate between the laptop and ios client connections?
Is the networking for mobile devices different in some way? Do the iOS clients use ipv6 when on WIFI?
Could the error messages in the clients be more specific so as to be able to identify exactly if and where the connection is being blocked? Cleary posting this sort of information in a public forum like this github issue could be quite risky.
Do the servers still require the whole port range (1000-65000) to be openned in the firewall? (quite risky)

Lastly, if this happens again please submit feedback through the app and tag it with this issue number.

Why is this necesary?

Glad to hear the app is back working.

Is it the case that if both devices are connected to the same WIFI, that no entities external to the WIFI network could differentiate between the laptop and ios client connections?

That is correct, as long as they are using the same access key.

Is the networking for mobile devices different in some way? Do the iOS clients use ipv6 when on WIFI?

Outline's network stack is the same across platforms. How the OS handles routing is slightly different. Currently all clients are IPv4.

Could the error messages in the clients be more specific so as to be able to identify exactly if and where the connection is being blocked?

We try to be as descriptive as possible. I have seen errors like the one you describe on development devices; generally uninstalling and reinstalling the app helps.

Do the servers still require the whole port range (1024:65535) to be openned in the firewall?

Outline servers now support a single port for all traffic. We are working on making this configurable from the Outline Manager app.

Why is [feedback submission] necessary?

We receive technical logs (without PII) on feedback submissions that could help us troubleshoot or diagnose the problem.

Sorry, an unexpected error occurred.
Quiting and restarting may help. If this happens again, please submit feedback.

I encountered same error on my ipad (iOS 14.8, outline app 1.6.0). What helped me: I deleted outline server from the app, uninstalled outline app, rebooted the ipad, installed outline app again, after that connection works again.


Are you interested in stock markets?

Well, I’m partly interested as I want to understand how it works and how it affects and is affected by certain circumstances in the world. Therefore I have several stocks but not much. I try to get only stocks that I find ethically correct and ideally, environment-friendly.


resourceManager and external compiled css

Hi,
Is feather components supports resourceManager and compiled css ?
compiled css via mxmlc. (eg. mxmlc src\theme_dark.css -o bin\assets\theme_dark.swf).
My application needs multi-language support. In apache, I could change language locale by
"flex, resourceManager.localeChain=["en_US"]".
But I can't find resource manager in feather components...
Any recommended solution?

Feathers does not support CSS. It is not planned either.
There is no Feathers resource manager at this time. I would like to add one, though. However, is actually possible to use the Flex ResourceManager with the Feathers SDK. If you add a SWC with this class, you should be able to create and use resource bundles. I did it in one project.


Parameter bindings: parameter name support at left side added

Summary of the Pull Request
Support of System.Object members of every element
I added a additional Item in context menu which allows user to see methods of System.Objects class
Quality Checklist

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[ ] I have made corresponding changes to the documentation
[x] My changes generate no new warnings

Everything is good for fork repo

Everything is good for fork repo


Web Interface Configuration

Hi! I need to configure VPN server on web interface. Is it possible using this installer?

No.
This is the most popular web interface (but it is a commercial product):
https://openvpn.net/vpn-server/

No.
This is the most popular web interface (but it is a commercial product):
https://openvpn.net/vpn-server/

Thanks

Thanks


"Azd pipeline config" failed on Codespace

Describe the issue:
Failed configuring authentication when run azd pipeline config --provider github
The error message as follows:

Repro steps:

Run azd auth login
Run azd up
Run azd pipeline config --provider github

Environment:
OS: CodeSpace.
Template: https://github.com/Azure-Samples/ASA-Samples-Event-Driven-Application
Azd version: 0.8.0-beta.3-daily.2760917 (commit https://github.com/Azure/azure-dev/commit/2201b16963dc43ae045c52374c0dd395152cb309)
Expected behavior:
Run command azd pipeline config success on CodeSpace.
@rajeshkamal5050 for notification.

@yiliuTo you need to update your ASA template similar to this - https://github.com/Azure/azure-dev/pull/2126
@wbreza @ellismg this breaking change will be widespread in addition to changelog @savannahostrowski do you want to call this out in blog post too?

HI @rajeshkamal5050 and @vhvb1989 , after update the template by replacing secerts by vars like here, the test team still met the samilar error as below:

And still I cannot reproduce this issue with my own GitHub account, which maybe due to that I am the admin of the template GitHub repo. Would you like to share any suggestions about this error? Thanks

@vhvb1989 @yiliuTo worked offline and has fixed it.
Ideally, the repo should be a public template (like the other todo projects)
Right now is a regular github repo.
When the repo is a template, opening codespaces from it does not link a git remote to the repo,
A folder with a repo files copy is opened in the codespace, so running pipeline config will ask to create a new repo.
But if the repo is a not template and just regular repo, the codespace is opened git origin remote set to the repo. Pipeline config will not ask to creat a new repo and instead it will try to update the original

@v-xuto @blow-hey can you test this again?

@rajeshkamal5050  Got it. We will re-test it.

@rajeshkamal5050 We retested it in CodeSpace, and the following are the test results:

If we start from Code -> Create codespace on main, this issue can still be reproduced. And, this method is what we have been using.

If we start from the Use this template -> Open in a codespace, its test result is PASS.


Extension of the TargetDataset class.

Suggested Feature
A) The addition of a new TargetDataset class for supervised domain adaptation.
or
B) The extension of the TargetDataset class to return labels when passed a supervised flag.
I think option B could be cleaner?
Implementation
A)
Create a new class capable of returning target_labels named something like SupervisedTargetDataset.
or
B)
Update the init function of the TargetDataset to include a supervised flag.
    def __init__(self, dataset: Dataset, domain: int = 1, supervised=False):
        """
        Arguments:
            dataset: The dataset to wrap
            domain: An integer representing the domain.
        """
        super().__init__(dataset, domain, supervised)

Update the getitem method to behave differently under supervised domain adaptation.
    def __getitem__(self, idx: int) -> Dict[str, Any]:

        if supervised:
            img, target_labels = self.dataset[idx]
            return {
                "target_imgs": img,
                "target_domain": self.domain,
                "target_labels": target_labels,
                "target_sample_idx": idx,
            }
        else:
            img, _ = self.dataset[idx]
            return {
                "target_imgs": img,
                "target_domain": self.domain,
                "target_sample_idx": idx,
            }

Reasoning
To run supervised domain adaptation we need to have labels in the target domain but I think it would still be useful to distinguish between the two domains using different classes. Rather than using SourceDataset on a TargetDataset to achieve the same functionality.
With this change validators such as AccuracyValidator could be used on target_val in a supervised domain adaptation setting.

BTW: With these feature suggestions I am happy to do code PRs along with the docs as I previously mentioned!

My bad just saw this previous issue ... #13
Maybe the approach above might work?

I think a flag makes sense. There are 3 scenarios I can think of:

Real world unsupervised: the wrapped dataset has no labels so it's __getitem__ returns just img
Academic unsupervised: this is the way TargetDataset currently works. It assumes the wrapped dataset returns 2 arguments, imgs and labels
Real world / academic supervised: the scenario that you've described

So maybe a supervised flag like you've suggested, and a dataset_has_labels flag?
If dataset_has_labels = True then: img, target_labels = self.dataset[idx].
Otherwise it needs to be img = self.dataset[idx]

BTW: With these feature suggestions I am happy to do code PRs along with the docs as I previously mentioned!

Yes that would be great 👍

That looks good to me.
I'll put together a PR on this.
Thanks!

Just working on the PR now.
On this:

If dataset_has_labels = True then: img, target_labels = self.dataset[idx].
Otherwise it needs to be img = self.dataset[idx]

If the user calls TargetDataset with supervised can we assume that the dataset is capable of returning labels and try calling self.dataset with the img, label return?
Something like this.
# import cf into TargetDataset class
from ..utils import common_functions as c_f

if self.supervised:
    try:
        img, labels = self.dataset[idx]
    except ValueError as e:
        # not enough values to unpack error
        c_f.add_error_message(e, f"\nThe dataset does not contain labels supervised domain adaptation is not possible.")

To me it seems like  dataset_has_labels is nearly a duplication of supervised ?
We could throw an error to the user if they call TargetDataset with a supervised flag on a dataset which does not have the capability to return labels?
What are your thoughts on this?

I don't really like try/except though, because for example if the batch size is 2, then the above won't through a ValueError

Good point I didn't consider that.
Is the best approach to revert back to the two flag approach?

I agree having to set both supervised=True and dataset_has_labels=True could be annoying. So instead of try/except maybe check type:
img = self.dataset[idx]
if isinstance(img, (list, tuple)):
    img, labels = img

Thanks!