junhua024-chai-16-full-12_429

developer_uid: junhua024
submission_id: junhua024-chai-16-full-12_429_v4
model_name: junhua024-chai-16-full-12_429_v4
model_group: junhua024/chai_16_full_1
status: torndown
timestamp: 2025-07-19T21:14:23+00:00
num_battles: 7778
num_wins: 3831
celo_rating: 1275.37
family_friendly_score: 0.5660000000000001
family_friendly_standard_error: 0.007009193962218481
submission_type: basic
model_repo: junhua024/chai_16_full_12_o_ffn_1925
model_architecture: MistralForCausalLM
model_num_parameters: 12772070400.0
best_of: 8
max_input_tokens: 1024
max_output_tokens: 64
reward_model: default
latencies: [{'batch_size': 1, 'throughput': 0.5886658435967769, 'latency_mean': 1.6986369574069977, 'latency_p50': 1.693365454673767, 'latency_p90': 1.8719090700149537}, {'batch_size': 3, 'throughput': 1.0455789655229621, 'latency_mean': 2.857008607387543, 'latency_p50': 2.8436204195022583, 'latency_p90': 3.150275444984436}, {'batch_size': 5, 'throughput': 1.2459244473508033, 'latency_mean': 3.9963858830928802, 'latency_p50': 3.986496686935425, 'latency_p90': 4.478617238998413}, {'batch_size': 6, 'throughput': 1.307380902191801, 'latency_mean': 4.573181643486023, 'latency_p50': 4.573763728141785, 'latency_p90': 5.03920464515686}, {'batch_size': 8, 'throughput': 1.3629766437831004, 'latency_mean': 5.836787446737289, 'latency_p50': 5.885541915893555, 'latency_p90': 6.521351480484009}, {'batch_size': 10, 'throughput': 1.3903799002685888, 'latency_mean': 7.141915227174759, 'latency_p50': 7.098589301109314, 'latency_p90': 8.119324207305908}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: junhua024-chai-16-full-12_429_v4
is_internal_developer: False
language_model: junhua024/chai_16_full_12_o_ffn_1925
model_size: 13B
ranking_group: single
throughput_3p7s: 1.21
us_pacific_date: 2025-07-19
win_ratio: 0.49254307019799437
generation_params: {'temperature': 1.0, 'top_p': 0.88, 'min_p': 0.0, 'top_k': 10, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}
formatter: {'memory_template': "{bot_name}'s Persona: {memory}\n####\n", 'prompt_template': '{prompt}\n<START>\n', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '{bot_name}:', 'truncate_by_message': False}
Resubmit model
Shutdown handler not registered because Python interpreter is not running in the main thread
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLizer
Starting job with name junhua024-chai-16-full-12-429-v4-mkmlizer
Waiting for job on junhua024-chai-16-full-12-429-v4-mkmlizer to finish
junhua024-chai-16-full-12-429-v4-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
junhua024-chai-16-full-12-429-v4-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║   ██████   ██████  █████   ████  ████                               ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  ░░██████ ██████  ░░███   ███░  ░░███                               ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║   ░███░█████░███   ░███  ███     ░███                               ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║   ░███░░███ ░███   ░███████      ░███                               ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║   ░███ ░░░  ░███   ░███░░███     ░███                               ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║   ░███      ░███   ░███ ░░███    ░███                               ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║   █████     █████  █████ ░░████  █████                              ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  ░░░░░     ░░░░░  ░░░░░   ░░░░  ░░░░░                               ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  Version: 0.29.15                                                   ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  Features: FLYWHEEL, CUDA                                           ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  Copyright 2023-2025 MK ONE TECHNOLOGIES Inc.                       ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  https://mk1.ai                                                     ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  The license key for the current software has been verified as      ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  belonging to:                                                      ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  Chai Research Corp.                                                ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║  Expiration: 2028-03-31 23:59:59                                    ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ║                                                                     ║
junhua024-chai-16-full-12-429-v4-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
Failed to get response for submission chaiml-nis-qwen32b-sim_98336_v34: HTTPConnectionPool(host='chaiml-nis-qwen32b-sim-98336-v34-predictor.tenant-chaiml-guanaco.k.chaiverse.com', port=80): Read timed out. (read timeout=12.0)
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`
junhua024-chai-16-full-12-429-v4-mkmlizer: Downloaded to shared memory in 136.227s
junhua024-chai-16-full-12-429-v4-mkmlizer: Checking if junhua024/chai_16_full_12_o_ffn_1925 already exists in ChaiML
junhua024-chai-16-full-12-429-v4-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmp_spb8c2g, device:0
junhua024-chai-16-full-12-429-v4-mkmlizer: Saving flywheel model at /dev/shm/model_cache
junhua024-chai-16-full-12-429-v4-mkmlizer: quantized model in 31.302s
junhua024-chai-16-full-12-429-v4-mkmlizer: Processed model junhua024/chai_16_full_12_o_ffn_1925 in 167.615s
junhua024-chai-16-full-12-429-v4-mkmlizer: creating bucket guanaco-mkml-models
junhua024-chai-16-full-12-429-v4-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
junhua024-chai-16-full-12-429-v4-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/junhua024-chai-16-full-12-429-v4/nvidia
junhua024-chai-16-full-12-429-v4-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/junhua024-chai-16-full-12-429-v4/nvidia/config.json
junhua024-chai-16-full-12-429-v4-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/junhua024-chai-16-full-12-429-v4/nvidia/special_tokens_map.json
junhua024-chai-16-full-12-429-v4-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/junhua024-chai-16-full-12-429-v4/nvidia/tokenizer_config.json
junhua024-chai-16-full-12-429-v4-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/junhua024-chai-16-full-12-429-v4/nvidia/tokenizer.json
junhua024-chai-16-full-12-429-v4-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/junhua024-chai-16-full-12-429-v4/nvidia/flywheel_model.0.safetensors
junhua024-chai-16-full-12-429-v4-mkmlizer: 
Loading 0:   0%|          | 0/363 [00:00<?, ?it/s]
Loading 0:   1%|          | 2/363 [00:00<00:23, 15.25it/s]
Loading 0:   1%|▏         | 5/363 [00:00<00:20, 17.20it/s]
Loading 0:   3%|▎         | 12/363 [00:00<00:12, 28.46it/s]
Loading 0:   5%|▍         | 17/363 [00:00<00:11, 30.25it/s]
Loading 0:   6%|▋         | 23/363 [00:00<00:10, 32.07it/s]
Loading 0:   9%|▊         | 31/363 [00:00<00:07, 43.28it/s]
Loading 0:  10%|▉         | 36/363 [00:01<00:10, 32.69it/s]
Loading 0:  11%|█▏        | 41/363 [00:01<00:09, 34.12it/s]
Loading 0:  14%|█▍        | 50/363 [00:01<00:07, 43.08it/s]
Loading 0:  15%|█▌        | 55/363 [00:01<00:08, 35.62it/s]
Loading 0:  16%|█▋        | 59/363 [00:01<00:08, 34.59it/s]
Loading 0:  18%|█▊        | 65/363 [00:01<00:08, 35.75it/s]
Loading 0:  19%|█▉        | 69/363 [00:02<00:08, 34.86it/s]
Loading 0:  21%|██        | 75/363 [00:02<00:08, 35.48it/s]
Loading 0:  22%|██▏       | 80/363 [00:02<00:08, 33.78it/s]
Loading 0:  24%|██▎       | 86/363 [00:02<00:07, 39.08it/s]
Loading 0:  25%|██▌       | 91/363 [00:02<00:07, 37.77it/s]
Loading 0:  27%|██▋       | 97/363 [00:02<00:06, 38.68it/s]
Loading 0:  28%|██▊       | 101/363 [00:02<00:06, 37.58it/s]
Loading 0:  29%|██▉       | 105/363 [00:03<00:07, 34.87it/s]
Loading 0:  31%|███       | 113/363 [00:03<00:05, 42.26it/s]
Loading 0:  33%|███▎      | 118/363 [00:03<00:06, 35.07it/s]
Loading 0:  34%|███▎      | 122/363 [00:03<00:07, 34.07it/s]
Loading 0:  35%|███▌      | 128/363 [00:03<00:06, 35.82it/s]
Loading 0:  36%|███▋      | 132/363 [00:03<00:06, 34.95it/s]
Loading 0:  38%|███▊      | 138/363 [00:03<00:06, 35.48it/s]
Loading 0:  39%|███▉      | 143/363 [00:04<00:06, 35.27it/s]
Loading 0:  41%|████      | 149/363 [00:04<00:05, 36.37it/s]
Loading 0:  43%|████▎     | 157/363 [00:04<00:04, 45.49it/s]
Loading 0:  45%|████▍     | 162/363 [00:04<00:05, 34.53it/s]
Loading 0:  46%|████▌     | 167/363 [00:04<00:05, 33.97it/s]
Loading 0:  48%|████▊     | 176/363 [00:04<00:04, 42.33it/s]
Loading 0:  50%|████▉     | 181/363 [00:05<00:05, 35.48it/s]
Loading 0:  51%|█████     | 185/363 [00:05<00:05, 34.85it/s]
Loading 0:  53%|█████▎    | 191/363 [00:05<00:04, 36.69it/s]
Loading 0:  54%|█████▎    | 195/363 [00:05<00:04, 35.88it/s]
Loading 0:  55%|█████▌    | 201/363 [00:05<00:04, 35.61it/s]
Loading 0:  57%|█████▋    | 206/363 [00:05<00:04, 35.13it/s]
Loading 0:  58%|█████▊    | 212/363 [00:05<00:04, 36.06it/s]
Loading 0:  61%|██████    | 220/363 [00:06<00:03, 45.55it/s]
Loading 0:  62%|██████▏   | 225/363 [00:06<00:03, 35.34it/s]
Loading 0:  63%|██████▎   | 230/363 [00:06<00:03, 35.50it/s]
Loading 0:  66%|██████▌   | 239/363 [00:06<00:02, 43.48it/s]
Loading 0:  67%|██████▋   | 244/363 [00:06<00:03, 36.99it/s]
Loading 0:  69%|██████▊   | 249/363 [00:06<00:02, 38.03it/s]
Loading 0:  70%|██████▉   | 254/363 [00:07<00:02, 37.22it/s]
Loading 0:  71%|███████   | 258/363 [00:07<00:02, 36.20it/s]
Loading 0:  73%|███████▎  | 264/363 [00:07<00:02, 36.72it/s]
Loading 0:  74%|███████▍  | 269/363 [00:07<00:02, 36.32it/s]
Loading 0:  76%|███████▌  | 275/363 [00:07<00:02, 36.91it/s]
Loading 0:  77%|███████▋  | 281/363 [00:07<00:01, 41.10it/s]
Loading 0:  79%|███████▉  | 286/363 [00:07<00:01, 39.25it/s]
Loading 0:  80%|████████  | 291/363 [00:07<00:01, 38.25it/s]
Loading 0:  81%|████████▏ | 295/363 [00:08<00:01, 36.73it/s]
Loading 0:  83%|████████▎ | 302/363 [00:08<00:01, 40.75it/s]
Loading 0:  85%|████████▍ | 307/363 [00:08<00:01, 35.17it/s]
Loading 0:  86%|████████▌ | 311/363 [00:08<00:01, 33.74it/s]
Loading 0:  87%|████████▋ | 317/363 [00:08<00:01, 35.94it/s]
Loading 0:  88%|████████▊ | 321/363 [00:08<00:01, 34.65it/s]
Loading 0:  90%|█████████ | 327/363 [00:09<00:01, 34.16it/s]
Loading 0:  91%|█████████▏| 332/363 [00:09<00:00, 34.49it/s]
Loading 0:  93%|█████████▎| 338/363 [00:09<00:00, 35.58it/s]
Loading 0:  95%|█████████▌| 346/363 [00:09<00:00, 45.32it/s]
Loading 0:  97%|█████████▋| 351/363 [00:09<00:00, 25.53it/s]
Loading 0:  98%|█████████▊| 355/363 [00:09<00:00, 27.67it/s]
Loading 0:  99%|█████████▉| 360/363 [00:10<00:00, 31.57it/s]
                                                            
Job junhua024-chai-16-full-12-429-v4-mkmlizer completed after 201.16s with status: succeeded
Stopping job with name junhua024-chai-16-full-12-429-v4-mkmlizer
Pipeline stage MKMLizer completed in 201.69s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.16s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service junhua024-chai-16-full-12-429-v4
Waiting for inference service junhua024-chai-16-full-12-429-v4 to be ready
Failed to get response for submission chaiml-nis-qwen32b-sim_98336_v34: HTTPConnectionPool(host='chaiml-nis-qwen32b-sim-98336-v34-predictor.tenant-chaiml-guanaco.k.chaiverse.com', port=80): Read timed out. (read timeout=12.0)
Retrying (%r) after connection broken by '%r': %s
Failed to get response for submission chaiml-nis-qwen32b-sim_98336_v34: HTTPConnectionPool(host='chaiml-nis-qwen32b-sim-98336-v34-predictor.tenant-chaiml-guanaco.k.chaiverse.com', port=80): Read timed out. (read timeout=12.0)
Failed to get response for submission chaiml-nis-qwen32b-sim_98336_v34: HTTPConnectionPool(host='chaiml-nis-qwen32b-sim-98336-v34-predictor.tenant-chaiml-guanaco.k.chaiverse.com', port=80): Read timed out. (read timeout=12.0)
Inference service junhua024-chai-16-full-12-429-v4 ready after 341.5812575817108s
Pipeline stage MKMLDeployer completed in 342.87s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.5210039615631104s
Received healthy response to inference request in 1.5860285758972168s
Received healthy response to inference request in 1.8582782745361328s
Received healthy response to inference request in 1.538125991821289s
Received healthy response to inference request in 1.8577384948730469s
5 requests
0 failed requests
5th percentile: 1.5477065086364745
10th percentile: 1.5572870254516602
20th percentile: 1.5764480590820313
30th percentile: 1.6403705596923828
40th percentile: 1.749054527282715
50th percentile: 1.8577384948730469
60th percentile: 1.8579544067382812
70th percentile: 1.8581703186035157
80th percentile: 1.9908234119415285
90th percentile: 2.2559136867523195
95th percentile: 2.388458824157715
99th percentile: 2.4944949340820313
mean time: 1.8722350597381592
Pipeline stage StressChecker completed in 10.94s
run pipeline stage %s
Running pipeline stage OfflineFamilyFriendlyTriggerPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
triggered trigger_guanaco_pipeline args=%s
Pipeline stage OfflineFamilyFriendlyTriggerPipeline completed in 0.77s
run pipeline stage %s
Running pipeline stage TriggerMKMLProfilingPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
triggered trigger_guanaco_pipeline args=%s
Pipeline stage TriggerMKMLProfilingPipeline completed in 0.70s
Shutdown handler de-registered
junhua024-chai-16-full-12_429_v4 status is now deployed due to DeploymentManager action
Shutdown handler registered
run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Skipping teardown as no inference service was successfully deployed
Pipeline stage MKMLProfilerDeleter completed in 0.13s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.12s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service junhua024-chai-16-full-12-429-v4-profiler
Waiting for inference service junhua024-chai-16-full-12-429-v4-profiler to be ready
Shutdown handler registered
run pipeline %s
run pipeline stage %s
Running pipeline stage OfflineFamilyFriendlyScorer
Evaluating %s Family Friendly Score with %s threads
%s, retrying in %s seconds...
Evaluating %s Family Friendly Score with %s threads
Pipeline stage OfflineFamilyFriendlyScorer completed in 4742.55s
Shutdown handler de-registered
junhua024-chai-16-full-12_429_v4 status is now inactive due to auto deactivation removed underperforming models
junhua024-chai-16-full-12_429_v4 status is now torndown due to DeploymentManager action