sao10k-l3-8b-stheno-v3-2

developer_uid: azuruce

submission_id: sao10k-l3-8b-stheno-v3-2_v5

model_name: sao10k-l3-8b-stheno-v3-2_v5

model_group: Sao10K/L3-8B-Stheno-v3.2

status: torndown

timestamp: 2024-09-26T02:10:17+00:00

num_battles: 8084

num_wins: 3990

celo_rating: 1255.78

family_friendly_score: 0.5615928148810284

family_friendly_standard_error: 0.005586083324469752

submission_type: basic

model_repo: Sao10K/L3-8B-Stheno-v3.2

model_architecture: LlamaForCausalLM

model_num_parameters: 8030261248.0

best_of: 8

max_input_tokens: 1024

max_output_tokens: 64

latencies: [{'batch_size': 1, 'throughput': 0.8784030422995321, 'latency_mean': 1.1383330142498016, 'latency_p50': 1.1289907693862915, 'latency_p90': 1.2551552534103394}, {'batch_size': 4, 'throughput': 1.8633272428109908, 'latency_mean': 2.136485642194748, 'latency_p50': 2.1350563764572144, 'latency_p90': 2.4180601358413694}, {'batch_size': 5, 'throughput': 2.0139312262084403, 'latency_mean': 2.4702379727363586, 'latency_p50': 2.473196506500244, 'latency_p90': 2.7517942667007445}, {'batch_size': 8, 'throughput': 2.2374157380504025, 'latency_mean': 3.5450237095355988, 'latency_p50': 3.5504664182662964, 'latency_p90': 3.979949712753296}, {'batch_size': 10, 'throughput': 2.2786116668076524, 'latency_mean': 4.345209246873855, 'latency_p50': 4.319067120552063, 'latency_p90': 4.906959080696106}, {'batch_size': 12, 'throughput': 2.357042784213855, 'latency_mean': 5.032307345867157, 'latency_p50': 4.969245076179504, 'latency_p90': 5.714993333816528}, {'batch_size': 15, 'throughput': 2.349569723375835, 'latency_mean': 6.282395701408387, 'latency_p50': 6.269867300987244, 'latency_p90': 7.097652173042297}]

gpu_counts: {'NVIDIA RTX A5000': 1}

display_name: sao10k-l3-8b-stheno-v3-2_v5

is_internal_developer: True

language_model: Sao10K/L3-8B-Stheno-v3.2

model_size: 8B

ranking_group: single

throughput_3p7s: 2.27

us_pacific_date: 2024-09-25

win_ratio: 0.49356754082137555

generation_params: {'temperature': 1.0, 'top_p': 1.0, 'min_p': 0.0, 'top_k': 40, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n', '<|eot_id|>', '<|end_of_text|>'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}

formatter: {'memory_template': '', 'prompt_template': '', 'bot_template': '{bot_name}: {message}\n', 'user_template': '{user_name}: {message}\n', 'response_template': '### Response:\n{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 sao10k-l3-8b-stheno-v3-2-v5-mkmlizer
Waiting for job on sao10k-l3-8b-stheno-v3-2-v5-mkmlizer to finish
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║     _____            __           __                                ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║       /___/                                                         ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║                                                                     ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  Version: 0.11.12                                                   ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  https://mk1.ai                                                     ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║                                                                     ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  The license key for the current software has been verified as      ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  belonging to:                                                      ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║                                                                     ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  Chai Research Corp.                                                ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ║                                                                     ║
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: Downloaded to shared memory in 21.206s
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpn4zjmv17, device:0
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: Saving flywheel model at /dev/shm/model_cache
intervitens-mini-magnum-5180-v5-mkmlizer: Downloaded to shared memory in 35.657s
intervitens-mini-magnum-5180-v5-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmppk7chcgk, device:0
intervitens-mini-magnum-5180-v5-mkmlizer: Saving flywheel model at /dev/shm/model_cache
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: quantized model in 26.719s
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: Processed model Sao10K/L3-8B-Stheno-v3.2 in 47.925s
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: creating bucket guanaco-mkml-models
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/sao10k-l3-8b-stheno-v3-2-v5
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/sao10k-l3-8b-stheno-v3-2-v5/config.json
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/sao10k-l3-8b-stheno-v3-2-v5/special_tokens_map.json
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/sao10k-l3-8b-stheno-v3-2-v5/tokenizer_config.json
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/sao10k-l3-8b-stheno-v3-2-v5/tokenizer.json
intervitens-mini-magnum-5180-v5-mkmlizer: quantized model in 35.662s
intervitens-mini-magnum-5180-v5-mkmlizer: Processed model intervitens/mini-magnum-12b-v1.1 in 71.319s
intervitens-mini-magnum-5180-v5-mkmlizer: creating bucket guanaco-mkml-models
intervitens-mini-magnum-5180-v5-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
intervitens-mini-magnum-5180-v5-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/intervitens-mini-magnum-5180-v5
intervitens-mini-magnum-5180-v5-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/intervitens-mini-magnum-5180-v5/config.json
intervitens-mini-magnum-5180-v5-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/intervitens-mini-magnum-5180-v5/special_tokens_map.json
intervitens-mini-magnum-5180-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/intervitens-mini-magnum-5180-v5/tokenizer_config.json
intervitens-mini-magnum-5180-v5-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/intervitens-mini-magnum-5180-v5/tokenizer.json
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/sao10k-l3-8b-stheno-v3-2-v5/flywheel_model.0.safetensors
sao10k-l3-8b-stheno-v3-2-v5-mkmlizer: 
Loading 0:   0%|          | 0/291 [00:00<?, ?it/s]
Loading 0:   1%|          | 2/291 [00:04<11:34,  2.40s/it]
Loading 0:   2%|▏         | 6/291 [00:04<03:05,  1.54it/s]
Loading 0:   4%|▍         | 11/291 [00:05<01:21,  3.42it/s]
Loading 0:   5%|▌         | 15/291 [00:05<00:52,  5.29it/s]
Loading 0:   8%|▊         | 22/291 [00:05<00:27,  9.77it/s]
Loading 0:   9%|▉         | 27/291 [00:05<00:19, 13.31it/s]
Loading 0:  11%|█         | 32/291 [00:05<00:14, 17.38it/s]
Loading 0:  13%|█▎        | 38/291 [00:05<00:11, 22.02it/s]
Loading 0:  15%|█▍        | 43/291 [00:05<00:09, 26.27it/s]
Loading 0:  17%|█▋        | 50/291 [00:05<00:07, 33.72it/s]
Loading 0:  19%|█▉        | 56/291 [00:05<00:06, 35.85it/s]
Loading 0:  21%|██        | 61/291 [00:06<00:08, 27.64it/s]
Loading 0:  23%|██▎       | 68/291 [00:06<00:06, 34.94it/s]
Loading 0:  25%|██▌       | 74/291 [00:06<00:05, 36.96it/s]
Loading 0:  27%|██▋       | 79/291 [00:06<00:05, 38.95it/s]
Loading 0:  30%|██▉       | 86/291 [00:06<00:04, 45.64it/s]
Loading 0:  32%|███▏      | 92/291 [00:06<00:04, 44.76it/s]
Loading 0:  33%|███▎      | 97/291 [00:07<00:04, 43.68it/s]
Loading 0:  36%|███▌      | 104/291 [00:07<00:03, 48.38it/s]
Loading 0:  38%|███▊      | 110/291 [00:07<00:04, 43.05it/s]
Loading 0:  40%|███▉      | 115/291 [00:07<00:04, 43.03it/s]
Loading 0:  42%|████▏     | 121/291 [00:07<00:03, 46.74it/s]
Loading 0:  43%|████▎     | 126/291 [00:07<00:03, 45.21it/s]
Loading 0:  45%|████▌     | 131/291 [00:07<00:03, 45.17it/s]
Loading 0:  47%|████▋     | 136/291 [00:07<00:03, 46.25it/s]
Loading 0:  48%|████▊     | 141/291 [00:08<00:04, 36.80it/s]
Loading 0:  51%|█████     | 148/291 [00:08<00:03, 44.19it/s]
Loading 0:  53%|█████▎    | 153/291 [00:08<00:03, 43.82it/s]
Loading 0:  54%|█████▍    | 158/291 [00:08<00:03, 43.26it/s]
Loading 0:  56%|█████▌    | 163/291 [00:08<00:02, 44.25it/s]
Loading 0:  58%|█████▊    | 168/291 [00:08<00:04, 25.75it/s]
Loading 0:  60%|██████    | 175/291 [00:08<00:03, 33.51it/s]
Loading 0:  62%|██████▏   | 180/291 [00:09<00:03, 36.09it/s]
Loading 0:  64%|██████▎   | 185/291 [00:09<00:02, 37.96it/s]
Loading 0:  66%|██████▌   | 191/291 [00:09<00:02, 37.77it/s]
Loading 0:  67%|██████▋   | 196/291 [00:09<00:02, 38.77it/s]
Loading 0:  69%|██████▉   | 202/291 [00:09<00:02, 43.42it/s]
Loading 0:  71%|███████   | 207/291 [00:09<00:01, 42.76it/s]
Loading 0:  73%|███████▎  | 212/291 [00:09<00:01, 43.01it/s]
Loading 0:  75%|███████▍  | 217/291 [00:09<00:01, 44.19it/s]
Loading 0:  76%|███████▋  | 222/291 [00:10<00:01, 36.08it/s]
Loading 0:  79%|███████▊  | 229/291 [00:10<00:01, 43.64it/s]
Loading 0:  80%|████████  | 234/291 [00:10<00:01, 44.41it/s]
Loading 0:  82%|████████▏ | 240/291 [00:10<00:01, 40.98it/s]
Loading 0:  85%|████████▌ | 248/291 [00:10<00:00, 48.68it/s]
Loading 0:  87%|████████▋ | 254/291 [00:10<00:00, 45.47it/s]
Loading 0:  89%|████████▉ | 259/291 [00:10<00:00, 44.74it/s]
Loading 0:  91%|█████████ | 265/291 [00:11<00:00, 48.30it/s]
Loading 0:  93%|█████████▎| 271/291 [00:11<00:00, 33.43it/s]
Loading 0:  95%|█████████▍| 276/291 [00:11<00:00, 32.81it/s]
Loading 0:  98%|█████████▊| 284/291 [00:11<00:00, 40.75it/s]
Loading 0: 100%|█████████▉| 290/291 [00:11<00:00, 40.19it/s]
                                                            
intervitens-mini-magnum-5180-v5-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/intervitens-mini-magnum-5180-v5/flywheel_model.0.safetensors
intervitens-mini-magnum-5180-v5-mkmlizer: 
Loading 0:   0%|          | 0/363 [00:00<?, ?it/s]
Loading 0:   1%|▏         | 5/363 [00:00<00:11, 32.50it/s]
Loading 0:   4%|▎         | 13/363 [00:00<00:06, 53.27it/s]
Loading 0:   5%|▌         | 19/363 [00:00<00:07, 48.65it/s]
Loading 0:   7%|▋         | 25/363 [00:00<00:06, 49.46it/s]
Loading 0:   9%|▊         | 31/363 [00:00<00:06, 52.10it/s]
Loading 0:  10%|█         | 37/363 [00:00<00:06, 49.42it/s]
Loading 0:  12%|█▏        | 43/363 [00:00<00:06, 49.91it/s]
Loading 0:  13%|█▎        | 49/363 [00:00<00:06, 51.96it/s]
Loading 0:  15%|█▌        | 55/363 [00:01<00:06, 49.21it/s]
Loading 0:  17%|█▋        | 61/363 [00:01<00:08, 37.24it/s]
Loading 0:  18%|█▊        | 66/363 [00:01<00:07, 37.17it/s]
Loading 0:  20%|█▉        | 72/363 [00:01<00:07, 41.02it/s]
Loading 0:  21%|██▏       | 78/363 [00:01<00:07, 40.66it/s]
Loading 0:  23%|██▎       | 83/363 [00:01<00:06, 41.13it/s]
Loading 0:  25%|██▍       | 90/363 [00:01<00:05, 46.31it/s]
Loading 0:  26%|██▋       | 96/363 [00:02<00:06, 44.39it/s]
Loading 0:  28%|██▊       | 101/363 [00:02<00:06, 42.39it/s]
Loading 0:  29%|██▉       | 106/363 [00:02<00:06, 42.69it/s]
Loading 0:  31%|███       | 112/363 [00:02<00:05, 45.83it/s]
Loading 0:  32%|███▏      | 117/363 [00:02<00:05, 43.07it/s]
Loading 0:  34%|███▍      | 123/363 [00:02<00:05, 41.90it/s]
Loading 0:  35%|███▌      | 128/363 [00:02<00:05, 41.26it/s]
Loading 0:  37%|███▋      | 134/363 [00:03<00:05, 45.52it/s]
Loading 0:  38%|███▊      | 139/363 [00:03<00:05, 44.47it/s]
Loading 0:  40%|███▉      | 144/363 [00:03<00:07, 28.62it/s]
Loading 0:  41%|████      | 149/363 [00:03<00:07, 29.84it/s]
Loading 0:  43%|████▎     | 156/363 [00:03<00:05, 35.99it/s]
Loading 0:  44%|████▍     | 161/363 [00:03<00:05, 37.12it/s]
Loading 0:  46%|████▌     | 166/363 [00:03<00:05, 38.11it/s]
Loading 0:  47%|████▋     | 171/363 [00:04<00:04, 40.20it/s]
Loading 0:  48%|████▊     | 176/363 [00:04<00:05, 35.59it/s]
Loading 0:  51%|█████     | 184/363 [00:04<00:04, 43.89it/s]
Loading 0:  52%|█████▏    | 190/363 [00:04<00:04, 42.91it/s]
Loading 0:  54%|█████▎    | 195/363 [00:04<00:04, 41.90it/s]
Loading 0:  56%|█████▌    | 202/363 [00:04<00:03, 46.85it/s]
Loading 0:  57%|█████▋    | 207/363 [00:04<00:03, 47.46it/s]
Loading 0:  58%|█████▊    | 212/363 [00:05<00:03, 39.16it/s]
Loading 0:  60%|██████    | 218/363 [00:05<00:03, 42.89it/s]
Loading 0:  61%|██████▏   | 223/363 [00:05<00:04, 31.15it/s]
Loading 0:  63%|██████▎   | 227/363 [00:05<00:04, 32.26it/s]
Loading 0:  64%|██████▎   | 231/363 [00:05<00:04, 31.33it/s]
Loading 0:  65%|██████▌   | 237/363 [00:05<00:03, 37.52it/s]
Loading 0:  67%|██████▋   | 242/363 [00:05<00:03, 38.81it/s]
Loading 0:  68%|██████▊   | 247/363 [00:06<00:02, 41.50it/s]
Loading 0:  70%|██████▉   | 253/363 [00:06<00:02, 42.31it/s]
Loading 0:  71%|███████   | 258/363 [00:06<00:02, 41.83it/s]
Loading 0:  73%|███████▎  | 265/363 [00:06<00:02, 47.92it/s]
Loading 0:  75%|███████▍  | 271/363 [00:06<00:02, 45.78it/s]
Loading 0:  76%|███████▌  | 276/363 [00:06<00:01, 45.31it/s]
Loading 0:  78%|███████▊  | 282/363 [00:06<00:01, 48.18it/s]
Loading 0:  79%|███████▉  | 287/363 [00:06<00:01, 45.22it/s]
Loading 0:  80%|████████  | 292/363 [00:07<00:01, 43.66it/s]
Loading 0:  82%|████████▏ | 297/363 [00:07<00:01, 43.67it/s]
Loading 0:  83%|████████▎ | 302/363 [00:07<00:01, 43.95it/s]
Loading 0:  85%|████████▍ | 307/363 [00:14<00:23,  2.36it/s]
Loading 0:  86%|████████▌ | 311/363 [00:14<00:16,  3.09it/s]
Loading 0:  87%|████████▋ | 315/363 [00:14<00:11,  4.08it/s]
Loading 0:  88%|████████▊ | 320/363 [00:14<00:07,  5.76it/s]
Loading 0:  90%|████████▉ | 326/363 [00:14<00:04,  8.28it/s]
Loading 0:  91%|█████████ | 330/363 [00:14<00:03, 10.27it/s]
Loading 0:  93%|█████████▎| 338/363 [00:14<00:01, 15.97it/s]
Loading 0:  95%|█████████▍| 344/363 [00:15<00:00, 20.00it/s]
Loading 0:  96%|█████████▌| 349/363 [00:15<00:00, 23.45it/s]
Loading 0:  98%|█████████▊| 356/363 [00:15<00:00, 29.81it/s]
Loading 0: 100%|█████████▉| 362/363 [00:15<00:00, 31.75it/s]
                                                            
Job intervitens-mini-magnum-5180-v5-mkmlizer completed after 92.78s with status: succeeded
Stopping job with name intervitens-mini-magnum-5180-v5-mkmlizer
Pipeline stage MKMLizer completed in 93.70s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.13s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service intervitens-mini-magnum-5180-v5
Waiting for inference service intervitens-mini-magnum-5180-v5 to be ready
Job sao10k-l3-8b-stheno-v3-2-v5-mkmlizer completed after 82.88s with status: succeeded
Stopping job with name sao10k-l3-8b-stheno-v3-2-v5-mkmlizer
Pipeline stage MKMLizer completed in 83.24s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.11s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service sao10k-l3-8b-stheno-v3-2-v5
Waiting for inference service sao10k-l3-8b-stheno-v3-2-v5 to be ready
Inference service intervitens-mini-magnum-5180-v5 ready after 210.46234560012817s
Pipeline stage MKMLDeployer completed in 210.99s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 2.0063576698303223s
Received healthy response to inference request in 2.007730722427368s
Received healthy response to inference request in 1.7218413352966309s
Received healthy response to inference request in 1.763218879699707s
Received healthy response to inference request in 1.5497138500213623s
5 requests
0 failed requests
5th percentile: 1.584139347076416
10th percentile: 1.6185648441314697
20th percentile: 1.6874158382415771
30th percentile: 1.730116844177246
40th percentile: 1.7466678619384766
50th percentile: 1.763218879699707
60th percentile: 1.8604743957519532
70th percentile: 1.957729911804199
80th percentile: 2.0066322803497316
90th percentile: 2.00718150138855
95th percentile: 2.007456111907959
99th percentile: 2.0076758003234865
mean time: 1.8097724914550781
Pipeline stage StressChecker completed in 11.01s
run pipeline stage %s
Running pipeline stage TriggerMKMLProfilingPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
Pipeline stage TriggerMKMLProfilingPipeline completed in 4.33s
Shutdown handler de-registered
intervitens-mini-magnum-_5180_v5 status is now deployed due to DeploymentManager action
Inference service sao10k-l3-8b-stheno-v3-2-v5 ready after 220.49805998802185s
Pipeline stage MKMLDeployer completed in 220.88s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 1.8211777210235596s
Received healthy response to inference request in 1.6525094509124756s
Received healthy response to inference request in 1.1939432621002197s
Received healthy response to inference request in 1.2626774311065674s
Received healthy response to inference request in 1.4868927001953125s
5 requests
0 failed requests
5th percentile: 1.2076900959014893
10th percentile: 1.2214369297027587
20th percentile: 1.2489305973052978
30th percentile: 1.3075204849243165
40th percentile: 1.3972065925598145
50th percentile: 1.4868927001953125
60th percentile: 1.5531394004821777
70th percentile: 1.6193861007690429
80th percentile: 1.6862431049346924
90th percentile: 1.7537104129791259
95th percentile: 1.7874440670013427
99th percentile: 1.8144309902191162
mean time: 1.483440113067627
Pipeline stage StressChecker completed in 8.16s
run pipeline stage %s
Running pipeline stage TriggerMKMLProfilingPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
Pipeline stage TriggerMKMLProfilingPipeline completed in 1.73s
Shutdown handler de-registered
sao10k-l3-8b-stheno-v3-2_v5 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 0.14s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.13s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service sao10k-l3-8b-stheno-v3-2-v5-profiler
Waiting for inference service sao10k-l3-8b-stheno-v3-2-v5-profiler to be ready
Inference service sao10k-l3-8b-stheno-v3-2-v5-profiler ready after 220.5638735294342s
Pipeline stage MKMLProfilerDeployer completed in 220.95s
run pipeline stage %s
Running pipeline stage MKMLProfilerRunner
kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/sao10k-l3-8b-stheno-v3-2-v5-profiler-predictor-00001-deploldjqm:/code/chaiverse_profiler_1727317195 --namespace tenant-chaiml-guanaco
kubectl exec -it sao10k-l3-8b-stheno-v3-2-v5-profiler-predictor-00001-deploldjqm --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1727317195 && python profiles.py profile --best_of_n 8 --auto_batch 5 --batches 1,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,105,110,115,120,125,130,135,140,145,150,155,160,165,170,175,180,185,190,195 --samples 200 --input_tokens 1024 --output_tokens 64 --summary /code/chaiverse_profiler_1727317195/summary.json'
kubectl exec -it sao10k-l3-8b-stheno-v3-2-v5-profiler-predictor-00001-deploldjqm --namespace tenant-chaiml-guanaco -- bash -c 'cat /code/chaiverse_profiler_1727317195/summary.json'
Pipeline stage MKMLProfilerRunner completed in 786.82s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Checking if service sao10k-l3-8b-stheno-v3-2-v5-profiler is running
Tearing down inference service sao10k-l3-8b-stheno-v3-2-v5-profiler
Service sao10k-l3-8b-stheno-v3-2-v5-profiler has been torndown
Pipeline stage MKMLProfilerDeleter completed in 2.18s
Shutdown handler de-registered
sao10k-l3-8b-stheno-v3-2_v5 status is now inactive due to auto deactivation removed underperforming models
sao10k-l3-8b-stheno-v3-2_v5 status is now torndown due to DeploymentManager action