developer_uid: shiroe40
submission_id: cycy233-nemo-p-v1-c1_v1
model_name: auto
model_group: cycy233/nemo-p-v1-c1
status: torndown
timestamp: 2024-09-16T09:18:31+00:00
num_battles: 10424
num_wins: 4943
celo_rating: 1234.77
family_friendly_score: 0.0
submission_type: basic
model_repo: cycy233/nemo-p-v1-c1
model_architecture: MistralForCausalLM
model_num_parameters: 12772070400.0
best_of: 8
max_input_tokens: 512
max_output_tokens: 64
latencies: [{'batch_size': 1, 'throughput': 0.7011765416192783, 'latency_mean': 1.4260888695716858, 'latency_p50': 1.4236812591552734, 'latency_p90': 1.5979671955108643}, {'batch_size': 3, 'throughput': 1.3348734929856592, 'latency_mean': 2.2390236294269563, 'latency_p50': 2.2483190298080444, 'latency_p90': 2.48084979057312}, {'batch_size': 5, 'throughput': 1.590275083013812, 'latency_mean': 3.1284197771549227, 'latency_p50': 3.1521193981170654, 'latency_p90': 3.480999302864075}, {'batch_size': 6, 'throughput': 1.6512644530825658, 'latency_mean': 3.6211861193180086, 'latency_p50': 3.628666639328003, 'latency_p90': 4.119210362434387}, {'batch_size': 8, 'throughput': 1.6276445502439343, 'latency_mean': 4.876935040950775, 'latency_p50': 4.910666227340698, 'latency_p90': 5.478620505332946}, {'batch_size': 10, 'throughput': 1.573848682362742, 'latency_mean': 6.315571975708008, 'latency_p50': 6.359361171722412, 'latency_p90': 7.178284931182861}]
gpu_counts: {'NVIDIA RTX A5000': 1}
display_name: auto
is_internal_developer: False
language_model: cycy233/nemo-p-v1-c1
model_size: 13B
ranking_group: single
throughput_3p7s: 1.66
us_pacific_date: 2024-09-16
win_ratio: 0.47419416730621644
generation_params: {'temperature': 1.0, 'top_p': 0.9, 'min_p': 0.05, 'top_k': 80, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n', '</s>', '###', 'Bot:', 'User:', 'You:', '<|im_end|>'], 'max_input_tokens': 512, '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 cycy233-nemo-p-v1-c1-v1-mkmlizer
Waiting for job on cycy233-nemo-p-v1-c1-v1-mkmlizer to finish
cycy233-nemo-p-v1-c1-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ _____ __ __ ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ /___/ ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ Version: 0.10.1 ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ https://mk1.ai ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ The license key for the current software has been verified as ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ belonging to: ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ Chai Research Corp. ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ Expiration: 2024-10-15 23:59:59 ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ║ ║
cycy233-nemo-p-v1-c1-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
cycy233-nemo-p-v1-c1-v1-mkmlizer: Downloaded to shared memory in 51.056s
cycy233-nemo-p-v1-c1-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpzlfbyal0, device:0
cycy233-nemo-p-v1-c1-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
cycy233-nemo-p-v1-c1-v1-mkmlizer: quantized model in 35.589s
cycy233-nemo-p-v1-c1-v1-mkmlizer: Processed model cycy233/nemo-p-v1-c1 in 86.645s
cycy233-nemo-p-v1-c1-v1-mkmlizer: creating bucket guanaco-mkml-models
cycy233-nemo-p-v1-c1-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/cycy233-nemo-p-v1-c1-v1/special_tokens_map.json
cycy233-nemo-p-v1-c1-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/cycy233-nemo-p-v1-c1-v1/config.json
cycy233-nemo-p-v1-c1-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/cycy233-nemo-p-v1-c1-v1/tokenizer_config.json
cycy233-nemo-p-v1-c1-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/cycy233-nemo-p-v1-c1-v1/tokenizer.json
cycy233-nemo-p-v1-c1-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/cycy233-nemo-p-v1-c1-v1/flywheel_model.0.safetensors
cycy233-nemo-p-v1-c1-v1-mkmlizer: Loading 0: 0%| | 0/363 [00:00<?, ?it/s] Loading 0: 1%| | 4/363 [00:00<00:10, 34.77it/s] Loading 0: 4%|▎ | 13/363 [00:00<00:06, 55.99it/s] Loading 0: 6%|▌ | 22/363 [00:00<00:05, 66.45it/s] Loading 0: 9%|▊ | 31/363 [00:00<00:04, 71.59it/s] Loading 0: 11%|█ | 40/363 [00:00<00:04, 75.10it/s] Loading 0: 13%|█▎ | 49/363 [00:00<00:03, 78.95it/s] Loading 0: 16%|█▌ | 58/363 [00:00<00:03, 80.72it/s] Loading 0: 18%|█▊ | 67/363 [00:02<00:15, 19.26it/s] Loading 0: 21%|██ | 76/363 [00:02<00:11, 25.40it/s] Loading 0: 23%|██▎ | 85/363 [00:02<00:08, 32.43it/s] Loading 0: 26%|██▌ | 94/363 [00:02<00:06, 40.20it/s] Loading 0: 28%|██▊ | 103/363 [00:02<00:05, 45.36it/s] Loading 0: 32%|███▏ | 115/363 [00:02<00:04, 55.04it/s] Loading 0: 36%|███▌ | 130/363 [00:02<00:03, 69.11it/s] Loading 0: 39%|███▉ | 142/363 [00:03<00:08, 24.91it/s] Loading 0: 43%|████▎ | 157/363 [00:04<00:05, 34.50it/s] Loading 0: 46%|████▌ | 166/363 [00:04<00:04, 39.93it/s] Loading 0: 48%|████▊ | 175/363 [00:04<00:04, 45.56it/s] Loading 0: 51%|█████ | 184/363 [00:04<00:03, 52.19it/s] Loading 0: 54%|█████▍ | 196/363 [00:04<00:02, 59.50it/s] Loading 0: 56%|█████▋ | 205/363 [00:04<00:02, 65.01it/s] Loading 0: 59%|█████▉ | 214/363 [00:04<00:02, 68.85it/s] Loading 0: 61%|██████▏ | 223/363 [00:05<00:06, 22.68it/s] Loading 0: 64%|██████▍ | 233/363 [00:05<00:04, 29.78it/s] Loading 0: 66%|██████▋ | 241/363 [00:06<00:03, 35.12it/s] Loading 0: 71%|███████ | 256/363 [00:06<00:02, 49.38it/s] Loading 0: 74%|███████▍ | 268/363 [00:06<00:01, 57.36it/s] Loading 0: 76%|███████▋ | 277/363 [00:06<00:01, 63.10it/s] Loading 0: 79%|███████▉ | 286/363 [00:06<00:01, 66.87it/s] Loading 0: 83%|████████▎ | 301/363 [00:06<00:00, 80.13it/s] Loading 0: 86%|████████▌ | 311/363 [00:07<00:01, 26.56it/s] Loading 0: 89%|████████▊ | 322/363 [00:07<00:01, 33.36it/s] Loading 0: 93%|█████████▎| 337/363 [00:07<00:00, 43.64it/s] Loading 0: 96%|█████████▌| 348/363 [00:08<00:00, 52.31it/s] Loading 0: 98%|█████████▊| 357/363 [00:08<00:00, 57.16it/s]
Job cycy233-nemo-p-v1-c1-v1-mkmlizer completed after 115.18s with status: succeeded
Stopping job with name cycy233-nemo-p-v1-c1-v1-mkmlizer
Pipeline stage MKMLizer completed in 116.49s
run pipeline stage %s
Running pipeline stage MKMLTemplater
Pipeline stage MKMLTemplater completed in 0.25s
run pipeline stage %s
Running pipeline stage MKMLDeployer
Creating inference service cycy233-nemo-p-v1-c1-v1
Waiting for inference service cycy233-nemo-p-v1-c1-v1 to be ready
Connection pool is full, discarding connection: %s. Connection pool size: %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
Inference service cycy233-nemo-p-v1-c1-v1 ready after 171.76439571380615s
Pipeline stage MKMLDeployer completed in 172.28s
run pipeline stage %s
Running pipeline stage StressChecker
Received healthy response to inference request in 3.374891519546509s
Received healthy response to inference request in 1.9376940727233887s
Received healthy response to inference request in 1.8772897720336914s
Received healthy response to inference request in 1.852114200592041s
Received healthy response to inference request in 2.0117321014404297s
5 requests
0 failed requests
5th percentile: 1.857149314880371
10th percentile: 1.862184429168701
20th percentile: 1.8722546577453614
30th percentile: 1.8893706321716308
40th percentile: 1.9135323524475099
50th percentile: 1.9376940727233887
60th percentile: 1.9673092842102051
70th percentile: 1.9969244956970216
80th percentile: 2.2843639850616455
90th percentile: 2.829627752304077
95th percentile: 3.102259635925293
99th percentile: 3.3203651428222654
mean time: 2.210744333267212
Pipeline stage StressChecker completed in 11.94s
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 5.05s
Shutdown handler de-registered
cycy233-nemo-p-v1-c1_v1 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 0.12s
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 cycy233-nemo-p-v1-c1-v1-profiler
Waiting for inference service cycy233-nemo-p-v1-c1-v1-profiler to be ready
Inference service cycy233-nemo-p-v1-c1-v1-profiler ready after 170.38764214515686s
Pipeline stage MKMLProfilerDeployer completed in 174.37s
run pipeline stage %s
Running pipeline stage MKMLProfilerRunner
kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/cycy233-nemo-p-v1-c1-v1-profiler-predictor-00001-deploymenfl9cs:/code/chaiverse_profiler_1726478841 --namespace tenant-chaiml-guanaco
kubectl exec -it cycy233-nemo-p-v1-c1-v1-profiler-predictor-00001-deploymenfl9cs --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1726478841 && 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 512 --output_tokens 64 --summary /code/chaiverse_profiler_1726478841/summary.json'
kubectl exec -it cycy233-nemo-p-v1-c1-v1-profiler-predictor-00001-deploymenfl9cs --namespace tenant-chaiml-guanaco -- bash -c 'cat /code/chaiverse_profiler_1726478841/summary.json'
Pipeline stage MKMLProfilerRunner completed in 937.61s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Checking if service cycy233-nemo-p-v1-c1-v1-profiler is running
Tearing down inference service cycy233-nemo-p-v1-c1-v1-profiler
Service cycy233-nemo-p-v1-c1-v1-profiler has been torndown
Pipeline stage MKMLProfilerDeleter completed in 10.49s
Shutdown handler de-registered
cycy233-nemo-p-v1-c1_v1 status is now inactive due to auto deactivation removed underperforming models
cycy233-nemo-p-v1-c1_v1 status is now torndown due to DeploymentManager action
Pipeline stage MKMLDeleter completed in 4.97s
Running pipeline stage MKMLDeleter
Pipeline stage %s skipped, reason=%s
cycy233-nemo-p-v0-c3_v2 status is now torndown due to DeploymentManager action
run pipeline %s
run pipeline stage %s
run pipeline %s
Shutdown handler not registered because Python interpreter is not running in the main thread
Pipeline stage MKMLModelDeleter completed in 5.00s
Pipeline stage %s skipped, reason=%s
admin requested tearing down of chaiml-0916-intent-suppo_6584_v2
Shutdown handler not registered because Python interpreter is not running in the main thread
Running pipeline stage MKMLModelDeleter
admin requested tearing down of cycy233-nemo-p-v3-c4_v1
cycy233-nemo-p-v1-c1_v1 status is now torndown due to DeploymentManager action
run pipeline stage %s
Pipeline stage %s skipped, reason=%s
Pipeline stage MKMLDeleter completed in 6.15s
run pipeline stage %s
run pipeline stage %s