submission_id: rica40325-iter0-3725_v1
developer_uid: rica40325
best_of: 8
celo_rating: 1252.54
display_name: rica40325-iter0-3725_v1
family_friendly_score: 0.0
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}
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'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}
gpu_counts: {'NVIDIA RTX A5000': 1}
is_internal_developer: False
language_model: rica40325/iter0-3725
latencies: [{'batch_size': 1, 'throughput': 0.8693258371401063, 'latency_mean': 1.1502436661720277, 'latency_p50': 1.1485373973846436, 'latency_p90': 1.261863684654236}, {'batch_size': 4, 'throughput': 1.8772859791458893, 'latency_mean': 2.123136668205261, 'latency_p50': 2.105983257293701, 'latency_p90': 2.3540865182876587}, {'batch_size': 5, 'throughput': 2.0205335545122494, 'latency_mean': 2.4566435623168945, 'latency_p50': 2.4600027799606323, 'latency_p90': 2.7956629276275633}, {'batch_size': 8, 'throughput': 2.253563807379607, 'latency_mean': 3.5267786514759063, 'latency_p50': 3.557511806488037, 'latency_p90': 3.994659924507141}, {'batch_size': 10, 'throughput': 2.3108597746882737, 'latency_mean': 4.294466526508331, 'latency_p50': 4.332206845283508, 'latency_p90': 4.861676120758056}, {'batch_size': 12, 'throughput': 2.3305696303368255, 'latency_mean': 5.101506158113479, 'latency_p50': 5.148558497428894, 'latency_p90': 5.68472876548767}, {'batch_size': 15, 'throughput': 2.346547477408201, 'latency_mean': 6.312350606918335, 'latency_p50': 6.313241124153137, 'latency_p90': 7.05810956954956}]
max_input_tokens: 1024
max_output_tokens: 64
model_architecture: LlamaForCausalLM
model_group: rica40325/iter0-3725
model_name: rica40325-iter0-3725_v1
model_num_parameters: 8030261248.0
model_repo: rica40325/iter0-3725
model_size: 8B
num_battles: 69479
num_wins: 35413
ranking_group: single
status: torndown
submission_type: basic
throughput_3p7s: 2.28
timestamp: 2024-09-23T05:43:34+00:00
us_pacific_date: 2024-09-22
win_ratio: 0.5096935764763454
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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 rica40325-iter0-3725-v1-mkmlizer
Waiting for job on rica40325-iter0-3725-v1-mkmlizer to finish
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rica40325-iter0-3725-v1-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
rica40325-iter0-3725-v1-mkmlizer: ║ _____ __ __ ║
rica40325-iter0-3725-v1-mkmlizer: ║ / _/ /_ ___ __/ / ___ ___ / / ║
rica40325-iter0-3725-v1-mkmlizer: ║ / _/ / // / |/|/ / _ \/ -_) -_) / ║
rica40325-iter0-3725-v1-mkmlizer: ║ /_//_/\_, /|__,__/_//_/\__/\__/_/ ║
rica40325-iter0-3725-v1-mkmlizer: ║ /___/ ║
rica40325-iter0-3725-v1-mkmlizer: ║ ║
rica40325-iter0-3725-v1-mkmlizer: ║ Version: 0.10.1 ║
rica40325-iter0-3725-v1-mkmlizer: ║ Copyright 2023 MK ONE TECHNOLOGIES Inc. ║
rica40325-iter0-3725-v1-mkmlizer: ║ https://mk1.ai ║
rica40325-iter0-3725-v1-mkmlizer: ║ ║
rica40325-iter0-3725-v1-mkmlizer: ║ The license key for the current software has been verified as ║
rica40325-iter0-3725-v1-mkmlizer: ║ belonging to: ║
rica40325-iter0-3725-v1-mkmlizer: ║ ║
rica40325-iter0-3725-v1-mkmlizer: ║ Chai Research Corp. ║
rica40325-iter0-3725-v1-mkmlizer: ║ Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f ║
rica40325-iter0-3725-v1-mkmlizer: ║ Expiration: 2024-10-15 23:59:59 ║
rica40325-iter0-3725-v1-mkmlizer: ║ ║
rica40325-iter0-3725-v1-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
rica40325-iter0-3725-v1-mkmlizer: Downloaded to shared memory in 64.117s
rica40325-iter0-3725-v1-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpnzl5jrpy, device:0
rica40325-iter0-3725-v1-mkmlizer: Saving flywheel model at /dev/shm/model_cache
rica40325-iter0-3725-v1-mkmlizer: quantized model in 28.557s
rica40325-iter0-3725-v1-mkmlizer: Processed model rica40325/iter0-3725 in 92.674s
rica40325-iter0-3725-v1-mkmlizer: creating bucket guanaco-mkml-models
rica40325-iter0-3725-v1-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
rica40325-iter0-3725-v1-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/rica40325-iter0-3725-v1
rica40325-iter0-3725-v1-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/rica40325-iter0-3725-v1/config.json
rica40325-iter0-3725-v1-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/rica40325-iter0-3725-v1/special_tokens_map.json
rica40325-iter0-3725-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/rica40325-iter0-3725-v1/tokenizer_config.json
rica40325-iter0-3725-v1-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/rica40325-iter0-3725-v1/tokenizer.json
rica40325-iter0-3725-v1-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/rica40325-iter0-3725-v1/flywheel_model.0.safetensors
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Job rica40325-iter0-3725-v1-mkmlizer completed after 125.6s with status: succeeded
Stopping job with name rica40325-iter0-3725-v1-mkmlizer
Pipeline stage MKMLizer completed in 127.65s
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Inference service rica40325-iter0-3725-v1 ready after 191.2125906944275s
Pipeline stage MKMLDeployer completed in 192.59s
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Received healthy response to inference request in 2.5577878952026367s
Received healthy response to inference request in 1.5738646984100342s
Received healthy response to inference request in 2.5624279975891113s
Received healthy response to inference request in 1.3484630584716797s
Received healthy response to inference request in 1.4701800346374512s
5 requests
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5th percentile: 1.372806453704834
10th percentile: 1.3971498489379883
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mean time: 1.9025447368621826
Pipeline stage StressChecker completed in 11.18s
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Inference service rica40325-iter0-3725-v1-profiler ready after 190.45147466659546s
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kubectl cp /code/guanaco/guanaco_inference_services/src/inference_scripts tenant-chaiml-guanaco/rica40325-iter0-3725-v1-profiler-predictor-00001-deploymenkz868:/code/chaiverse_profiler_1727070781 --namespace tenant-chaiml-guanaco
kubectl exec -it rica40325-iter0-3725-v1-profiler-predictor-00001-deploymenkz868 --namespace tenant-chaiml-guanaco -- sh -c 'cd /code/chaiverse_profiler_1727070781 && 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_1727070781/summary.json'
kubectl exec -it rica40325-iter0-3725-v1-profiler-predictor-00001-deploymenkz868 --namespace tenant-chaiml-guanaco -- bash -c 'cat /code/chaiverse_profiler_1727070781/summary.json'
Pipeline stage MKMLProfilerRunner completed in 787.45s
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Checking if service rica40325-iter0-3725-v1-profiler is running
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rica40325-iter0-3725_v1 status is now inactive due to auto deactivation removed underperforming models
rica40325-iter0-3725_v1 status is now torndown due to DeploymentManager action