Running pipeline stage MKMLizer
Starting job with name nousresearch-meta-llama-4941-v54-mkmlizer
Waiting for job on nousresearch-meta-llama-4941-v54-mkmlizer to finish
nousresearch-meta-llama-4941-v54-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
nousresearch-meta-llama-4941-v54-mkmlizer: ║     _____            __           __                                ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║       /___/                                                         ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║                                                                     ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  Version: 0.8.14                                                    ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  https://mk1.ai                                                     ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║                                                                     ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  The license key for the current software has been verified as      ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  belonging to:                                                      ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║                                                                     ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  Chai Research Corp.                                                ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║  Expiration: 2024-07-15 23:59:59                                    ║
nousresearch-meta-llama-4941-v54-mkmlizer: ║                                                                     ║
nousresearch-meta-llama-4941-v54-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
nousresearch-meta-llama-4941-v54-mkmlizer: /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'list_files_info' (from 'huggingface_hub.hf_api') is deprecated and will be removed from version '0.23'. Use `list_repo_tree` and `get_paths_info` instead.
nousresearch-meta-llama-4941-v54-mkmlizer:   warnings.warn(warning_message, FutureWarning)
nousresearch-meta-llama-4941-v54-mkmlizer: Downloaded to shared memory in 22.348s
nousresearch-meta-llama-4941-v54-mkmlizer: quantizing model to /dev/shm/model_cache
nousresearch-meta-llama-4941-v54-mkmlizer: Saving flywheel model at /dev/shm/model_cache
nousresearch-meta-llama-4941-v54-mkmlizer: 
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Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
nousresearch-meta-llama-4941-v54-mkmlizer: quantized model in 17.471s
nousresearch-meta-llama-4941-v54-mkmlizer: Processed model NousResearch/Meta-Llama-3-8B-Instruct in 40.798s
nousresearch-meta-llama-4941-v54-mkmlizer: creating bucket guanaco-mkml-models
nousresearch-meta-llama-4941-v54-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
nousresearch-meta-llama-4941-v54-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54
nousresearch-meta-llama-4941-v54-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54/tokenizer_config.json
nousresearch-meta-llama-4941-v54-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54/special_tokens_map.json
nousresearch-meta-llama-4941-v54-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54/config.json
nousresearch-meta-llama-4941-v54-mkmlizer: cp /dev/shm/model_cache/tokenizer.json s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54/tokenizer.json
nousresearch-meta-llama-4941-v54-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54/flywheel_model.0.safetensors
nousresearch-meta-llama-4941-v54-mkmlizer: loading reward model from ChaiML/reward_gpt2_medium_preference_24m_e2
nousresearch-meta-llama-4941-v54-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:913: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
nousresearch-meta-llama-4941-v54-mkmlizer:   warnings.warn(
nousresearch-meta-llama-4941-v54-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py:757: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
nousresearch-meta-llama-4941-v54-mkmlizer:   warnings.warn(
nousresearch-meta-llama-4941-v54-mkmlizer: /opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:468: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.
nousresearch-meta-llama-4941-v54-mkmlizer:   warnings.warn(
Job nousresearch-meta-llama-4941-v54-mkmlizer completed after 69.52s with status: succeeded
Stopping job with name nousresearch-meta-llama-4941-v54-mkmlizer
Pipeline stage MKMLizer completed in 71.10s
Running pipeline stage MKMLKubeTemplater
Pipeline stage MKMLKubeTemplater completed in 0.43s
Running pipeline stage ISVCDeployer
Creating inference service nousresearch-meta-llama-4941-v54
Waiting for inference service nousresearch-meta-llama-4941-v54 to be ready
Inference service nousresearch-meta-llama-4941-v54 ready after 40.63352823257446s
Pipeline stage ISVCDeployer completed in 47.13s
Running pipeline stage StressChecker
HTTP Request: %s %s "%s %d %s"
Received healthy response to inference request in 2.4288899898529053s
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Received healthy response to inference request in 1.6834449768066406s
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Received healthy response to inference request in 1.802610158920288s
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Received healthy response to inference request in 1.6268060207366943s
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Received healthy response to inference request in 1.775407075881958s
5 requests
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5th percentile: 1.6381338119506836
10th percentile: 1.649461603164673
20th percentile: 1.6721171855926513
30th percentile: 1.7018373966217042
40th percentile: 1.738622236251831
50th percentile: 1.775407075881958
60th percentile: 1.78628830909729
70th percentile: 1.7971695423126222
80th percentile: 1.9278661251068117
90th percentile: 2.1783780574798586
95th percentile: 2.303634023666382
99th percentile: 2.4038387966156005
mean time: 1.8634316444396972
Pipeline stage StressChecker completed in 12.74s
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Pipeline stage DaemonicModelEvalScorer completed in 0.15s
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Running M-Eval for topic stay_in_character
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nousresearch-meta-llama_4941_v54 status is now deployed due to DeploymentManager action
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nousresearch-meta-llama_4941_v54 status is now inactive due to auto deactivation removed underperforming models
admin requested tearing down of nousresearch-meta-llama_4941_v54
Running pipeline stage ISVCDeleter
Checking if service nousresearch-meta-llama-4941-v54 is running
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Toredown service nousresearch-meta-llama-4941-v54
Pipeline stage ISVCDeleter completed in 3.16s
Running pipeline stage MKMLModelDeleter
Cleaning model data from S3
Cleaning model data from model cache
Deleting key nousresearch-meta-llama-4941-v54/config.json from bucket guanaco-mkml-models
Deleting key nousresearch-meta-llama-4941-v54/flywheel_model.0.safetensors from bucket guanaco-mkml-models
Deleting key nousresearch-meta-llama-4941-v54/special_tokens_map.json from bucket guanaco-mkml-models
Deleting key nousresearch-meta-llama-4941-v54/tokenizer.json from bucket guanaco-mkml-models
Deleting key nousresearch-meta-llama-4941-v54/tokenizer_config.json from bucket guanaco-mkml-models
Cleaning model data from model cache
Deleting key nousresearch-meta-llama-4941-v54_reward/config.json from bucket guanaco-reward-models
Deleting key nousresearch-meta-llama-4941-v54_reward/merges.txt from bucket guanaco-reward-models
Deleting key nousresearch-meta-llama-4941-v54_reward/reward.tensors from bucket guanaco-reward-models
Deleting key nousresearch-meta-llama-4941-v54_reward/special_tokens_map.json from bucket guanaco-reward-models
Deleting key nousresearch-meta-llama-4941-v54_reward/tokenizer.json from bucket guanaco-reward-models
Deleting key nousresearch-meta-llama-4941-v54_reward/tokenizer_config.json from bucket guanaco-reward-models
Deleting key nousresearch-meta-llama-4941-v54_reward/vocab.json from bucket guanaco-reward-models
Pipeline stage MKMLModelDeleter completed in 1.91s
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clean up pipeline due to error=DeploymentError("No matches found for {'api_version': 'serving.kserve.io/v1beta1', 'kind': 'InferenceService'}")
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Creating inference service nousresearch-meta-llama-4941-v54
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Inference service nousresearch-meta-llama-4941-v54 ready after 162.3812074661255s
Pipeline stage MKMLDeployer completed in 164.91s
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clean up pipeline due to error=DeploymentError('Timeout to start the InferenceService nousresearch-meta-llama-4941-v54-profiler.                                The InferenceService is as following: {\'apiVersion\': \'serving.kserve.io/v1beta1\', \'kind\': \'InferenceService\', \'metadata\': {\'annotations\': {\'autoscaling.knative.dev/class\': \'hpa.autoscaling.knative.dev\', \'autoscaling.knative.dev/container-concurrency-target-percentage\': \'70\', \'autoscaling.knative.dev/initial-scale\': \'1\', \'autoscaling.knative.dev/max-scale-down-rate\': \'1.1\', \'autoscaling.knative.dev/max-scale-up-rate\': \'2\', \'autoscaling.knative.dev/metric\': \'mean_pod_latency_ms_v2\', \'autoscaling.knative.dev/panic-threshold-percentage\': \'650\', \'autoscaling.knative.dev/panic-window-percentage\': \'35\', \'autoscaling.knative.dev/scale-down-delay\': \'30s\', \'autoscaling.knative.dev/scale-to-zero-grace-period\': \'10m\', \'autoscaling.knative.dev/stable-window\': \'180s\', \'autoscaling.knative.dev/target\': \'3700\', \'autoscaling.knative.dev/target-burst-capacity\': \'-1\', \'autoscaling.knative.dev/tick-interval\': \'15s\', \'features.knative.dev/http-full-duplex\': \'Enabled\', \'networking.knative.dev/ingress-class\': \'istio.ingress.networking.knative.dev\'}, \'creationTimestamp\': \'2025-04-10T16:09:17Z\', \'finalizers\': [\'inferenceservice.finalizers\'], \'generation\': 1, \'labels\': {\'knative.coreweave.cloud/ingress\': \'istio.ingress.networking.knative.dev\', \'prometheus.k.chaiverse.com\': \'true\', \'qos.coreweave.cloud/latency\': \'low\'}, \'managedFields\': [{\'apiVersion\': \'serving.kserve.io/v1beta1\', \'fieldsType\': \'FieldsV1\', \'fieldsV1\': {\'f:metadata\': {\'f:annotations\': {\'.\': {}, \'f:autoscaling.knative.dev/class\': {}, \'f:autoscaling.knative.dev/container-concurrency-target-percentage\': {}, \'f:autoscaling.knative.dev/initial-scale\': {}, \'f:autoscaling.knative.dev/max-scale-down-rate\': {}, \'f:autoscaling.knative.dev/max-scale-up-rate\': {}, \'f:autoscaling.knative.dev/metric\': {}, \'f:autoscaling.knative.dev/panic-threshold-percentage\': {}, \'f:autoscaling.knative.dev/panic-window-percentage\': {}, \'f:autoscaling.knative.dev/scale-down-delay\': {}, \'f:autoscaling.knative.dev/scale-to-zero-grace-period\': {}, \'f:autoscaling.knative.dev/stable-window\': {}, \'f:autoscaling.knative.dev/target\': {}, \'f:autoscaling.knative.dev/target-burst-capacity\': {}, \'f:autoscaling.knative.dev/tick-interval\': {}, \'f:features.knative.dev/http-full-duplex\': {}, \'f:networking.knative.dev/ingress-class\': {}}, \'f:labels\': {\'.\': {}, \'f:knative.coreweave.cloud/ingress\': {}, \'f:prometheus.k.chaiverse.com\': {}, \'f:qos.coreweave.cloud/latency\': {}}}, \'f:spec\': {\'.\': {}, \'f:predictor\': {\'.\': {}, \'f:affinity\': {\'.\': {}, \'f:nodeAffinity\': {\'.\': {}, \'f:tion\': {}, \'f:requiredDuringSchedulingIgnoredDuringExecution\': {}}}, \'f:containerConcurrency\': {}, \'f:containers\': {}, \'f:imagePullSecrets\': {}, \'f:maxReplicas\': {}, \'f:minReplicas\': {}, \'f:timeout\': {}, \'f:volumes\': {}}}}, \'manager\': \'OpenAPI-Generator\', \'operation\': \'Update\', \'time\': \'2025-04-10T16:09:17Z\'}, {\'apiVersion\': \'serving.kserve.io/v1beta1\', \'fieldsType\': \'FieldsV1\', \'fieldsV1\': {\'f:metadata\': {\'f:finalizers\': {\'.\': {}, \'v:"inferenceservice.finalizers"\': {}}}}, \'manager\': \'manager\', \'operation\': \'Update\', \'time\': \'2025-04-10T16:09:17Z\'}, {\'apiVersion\': \'serving.kserve.io/v1beta1\', \'fieldsType\': \'FieldsV1\', \'fieldsV1\': {\'f:status\': {\'.\': {}, \'f:components\': {\'.\': {}, \'f:predictor\': {\'.\': {}, \'f:latestCreatedRevision\': {}}}, \'f:conditions\': {}, \'f:modelStatus\': {\'.\': {}, \'f:lastFailureInfo\': {\'.\': {}, \'f:exitCode\': {}, \'f:message\': {}, \'f:reason\': {}}, \'f:states\': {\'.\': {}, \'f:activeModelState\': {}, \'f:targetModelState\': {}}, \'f:transitionStatus\': {}}, \'f:observedGeneration\': {}}}, \'manager\': \'manager\', \'operation\': \'Update\', \'subresource\': \'status\', \'time\': \'2025-04-10T16:11:48Z\'}], \'name\': \'nousresearch-meta-llama-4941-v54-profiler\', \'namespace\': \'tenant-chaiml-guanaco\', \'resourceVersion\': \'341498608\', \'uid\': \'2bb133cc-5d8c-4c3e-9fb3-8427debe1d45\'}, \'spec\': {\'predictor\': {\'affinity\': {\'nodeAffinity\': {\'tion\': [{\'preference\': {\'matchExpressions\': [{\'key\': \'topology.kubernetes.io/region\', \'operator\': \'In\', \'values\': [\'ORD1\']}]}, \'weight\': 5}], \'requiredDuringSchedulingIgnoredDuringExecution\': {\'nodeSelectorTerms\': [{\'matchExpressions\': [{\'key\': \'gpu.nvidia.com/class\', \'operator\': \'In\', \'values\': [\'RTX_A5000\']}]}]}}}, \'containerConcurrency\': 0, \'containers\': [{\'env\': [{\'name\': \'MAX_TOKEN_INPUT\', \'value\': \'1024\'}, {\'name\': \'BEST_OF\', \'value\': \'4\'}, {\'name\': \'TEMPERATURE\', \'value\': \'1.0\'}, {\'name\': \'PRESENCE_PENALTY\', \'value\': \'0.0\'}, {\'name\': \'FREQUENCY_PENALTY\', \'value\': \'0.0\'}, {\'name\': \'TOP_P\', \'value\': \'1.0\'}, {\'name\': \'MIN_P\', \'value\': \'0.0\'}, {\'name\': \'TOP_K\', \'value\': \'40\'}, {\'name\': \'STOPPING_WORDS\', \'value\': \'["\\\\\\\\n"]\'}, {\'name\': \'MAX_TOKENS\', \'value\': \'300\'}, {\'name\': \'MAX_BATCH_SIZE\', \'value\': \'128\'}, {\'name\': \'MAX_CACHED_RESPONSES\', \'value\': \'-1\'}, {\'name\': \'URL_ROUTE\', \'value\': \'GPT-J-6B-lit-v2\'}, {\'name\': \'OBJ_ACCESS_KEY_ID\', \'value\': \'LETMTTRMLFFAMTBK\'}, {\'name\': \'OBJ_SECRET_ACCESS_KEY\', \'value\': \'VwwZaqefOOoaouNxUk03oUmK9pVEfruJhjBHPGdgycK\'}, {\'name\': \'OBJ_ENDPOINT\', \'value\': \'https://accel-object.ord1.coreweave.com\'}, {\'name\': \'TENSORIZER_URI\', \'value\': \'s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54\'}, {\'name\': \'RESERVE_MEMORY\', \'value\': \'2048\'}, {\'name\': \'DOWNLOAD_TO_LOCAL\', \'value\': \'/dev/shm/model_cache\'}, {\'name\': \'NUM_GPUS\', \'value\': \'1\'}, {\'name\': \'MK1_MKML_LICENSE_KEY\', \'valueFrom\': {\'secretKeyRef\': {\'key\': \'key\', \'name\': \'mkml-license-key\'}}}], \'image\': \'gcr.io/chai-959f8/chai-guanaco/mkml:v1.17.5\', \'imagePullPolicy\': \'IfNotPresent\', \'name\': \'kserve-container\', \'readinessProbe\': {\'exec\': {\'command\': [\'cat\', \'/tmp/ready\']}, \'failureThreshold\': 1, \'initialDelaySeconds\': 10, \'periodSeconds\': 10, \'successThreshold\': 1, \'timeoutSeconds\': 5}, \'resources\': {\'limits\': {\'cpu\': \'2\', \'memory\': \'12Gi\', \'nvidia.com/gpu\': \'1\'}, \'requests\': {\'cpu\': \'2\', \'memory\': \'12Gi\', \'nvidia.com/gpu\': \'1\'}}, \'volumeMounts\': [{\'mountPath\': \'/dev/shm\', \'name\': \'shared-memory-cache\'}]}], \'imagePullSecrets\': [{\'name\': \'docker-creds\'}], \'maxReplicas\': 1, \'minReplicas\': 1, \'timeout\': 60, \'volumes\': [{\'emptyDir\': {\'medium\': \'Memory\'}, \'name\': \'shared-memory-cache\'}]}}, \'status\': {\'components\': {\'predictor\': {\'latestCreatedRevision\': \'nousresearch-meta-llama-4941-v54-profiler-predictor-00001\'}}, \'conditions\': [{\'lastTransitionTime\': \'2025-04-10T16:11:48Z\', \'reason\': \'PredictorConfigurationReady not ready\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'LatestDeploymentReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:11:48Z\', \'message\': \'Revision "nousresearch-meta-llama-4941-v54-profiler-predictor-00001" failed with message: Container failed with: �═╝\\n\\nINFO:datasets:PyTorch version 2.3.0 available.\\nInference config: InferenceConfig(server_num_workers=1, server_port=8080, max_batch_size=128, log_level=0, reserve_memory=2048, num_gpus=1, quantization_profile=s0, all_reduce_profile=None, kv_cache_profile=None, calibration_samples=-1, max_cached_responses=-1, sampling=SamplingParameters(temperature=1.0, top_p=1.0, min_p=0.0, top_k=40, max_input_tokens=1024, max_tokens=300, stop=[\\\'\\\\n\\\'], eos_token_ids=[], frequency_penalty=0.0, presence_penalty=0.0, reward_enabled=True, num_samples=4, reward_max_token_input=256, profile=False), url_route=GPT-J-6B-lit-v2, tensorizer_uri=s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54, s3_creds=S3Credentials(s3_access_key_id=\\\'LETMTTRMLFFAMTBK\\\', s3_secret_access_key=\\\'VwwZaqefOOoaouNxUk03oUmK9pVEfruJhjBHPGdgycK\\\', s3_endpoint=\\\'https://accel-object.ord1.coreweave.com\\\', s3_uncached_endpoint=\\\'https://object.ord1.coreweave.com\\\'), local_folder=/dev/shm/model_cache)\\nTraceback (most recent call last):\\n  File "/code/mkml_inference_service/main.py", line 95, in <module>\\n    model.load()\\n  File "/code/mkml_inference_service/main.py", line 31, in load\\n    self.engine = mkml_backend.AsyncInferenceService.from_folder(settings, settings.local_folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 56, in from_folder\\n    return service._from_folder(settings, folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 83, in _from_folder\\n    engine = mkml.ModelForInference.from_pretrained(\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/inference.py", line 68, in from_pretrained\\n    manifold = TensorManifold(model_path, tensor_parallel_size, batching_config, profile, s3_config)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 116, in __init__\\n    self.validate_schema(model_path)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 175, in validate_schema\\n    raise ValueError(f"Unexpected flywheel schema.")\\nValueError: Unexpected flywheel schema.\\n.\', \'reason\': \'RevisionFailed\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'PredictorConfigurationReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:11:48Z\', \'message\': \'Configuration "nousresearch-meta-llama-4941-v54-profiler-predictor" does not have any ready Revision.\', \'reason\': \'RevisionMissing\', \'status\': \'False\', \'type\': \'PredictorReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:11:48Z\', \'message\': \'Configuration "nousresearch-meta-llama-4941-v54-profiler-predictor" does not have any ready Revision.\', \'reason\': \'RevisionMissing\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'PredictorRouteReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:11:48Z\', \'message\': \'Configuration "nousresearch-meta-llama-4941-v54-profiler-predictor" does not have any ready Revision.\', \'reason\': \'RevisionMissing\', \'status\': \'False\', \'type\': \'Ready\'}, {\'lastTransitionTime\': \'2025-04-10T16:11:48Z\', \'reason\': \'PredictorRouteReady not ready\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'RoutesReady\'}], \'modelStatus\': {\'lastFailureInfo\': {\'exitCode\': 1, \'message\': \'�═╝\\n\\nINFO:datasets:PyTorch version 2.3.0 available.\\nInference config: InferenceConfig(server_num_workers=1, server_port=8080, max_batch_size=128, log_level=0, reserve_memory=2048, num_gpus=1, quantization_profile=s0, all_reduce_profile=None, kv_cache_profile=None, calibration_samples=-1, max_cached_responses=-1, sampling=SamplingParameters(temperature=1.0, top_p=1.0, min_p=0.0, top_k=40, max_input_tokens=1024, max_tokens=300, stop=[\\\'\\\\n\\\'], eos_token_ids=[], frequency_penalty=0.0, presence_penalty=0.0, reward_enabled=True, num_samples=4, reward_max_token_input=256, profile=False), url_route=GPT-J-6B-lit-v2, tensorizer_uri=s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54, s3_creds=S3Credentials(s3_access_key_id=\\\'LETMTTRMLFFAMTBK\\\', s3_secret_access_key=\\\'VwwZaqefOOoaouNxUk03oUmK9pVEfruJhjBHPGdgycK\\\', s3_endpoint=\\\'https://accel-object.ord1.coreweave.com\\\', s3_uncached_endpoint=\\\'https://object.ord1.coreweave.com\\\'), local_folder=/dev/shm/model_cache)\\nTraceback (most recent call last):\\n  File "/code/mkml_inference_service/main.py", line 95, in <module>\\n    model.load()\\n  File "/code/mkml_inference_service/main.py", line 31, in load\\n    self.engine = mkml_backend.AsyncInferenceService.from_folder(settings, settings.local_folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 56, in from_folder\\n    return service._from_folder(settings, folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 83, in _from_folder\\n    engine = mkml.ModelForInference.from_pretrained(\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/inference.py", line 68, in from_pretrained\\n    manifold = TensorManifold(model_path, tensor_parallel_size, batching_config, profile, s3_config)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 116, in __init__\\n    self.validate_schema(model_path)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 175, in validate_schema\\n    raise ValueError(f"Unexpected flywheel schema.")\\nValueError: Unexpected flywheel schema.\\n\', \'reason\': \'ModelLoadFailed\'}, \'states\': {\'activeModelState\': \'\', \'targetModelState\': \'FailedToLoad\'}, \'transitionStatus\': \'BlockedByFailedLoad\'}, \'observedGeneration\': 1}}')
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Skipping teardown as no inference service was successfully deployed
Pipeline stage MKMLProfilerDeleter completed in 0.29s
Shutdown handler de-registered
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 0.30s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.24s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service nousresearch-meta-llama-4941-v54-profiler
Waiting for inference service nousresearch-meta-llama-4941-v54-profiler to be ready
Tearing down inference service nousresearch-meta-llama-4941-v54-profiler
%s, retrying in %s seconds...
Creating inference service nousresearch-meta-llama-4941-v54-profiler
Waiting for inference service nousresearch-meta-llama-4941-v54-profiler to be ready
Tearing down inference service nousresearch-meta-llama-4941-v54-profiler
%s, retrying in %s seconds...
Creating inference service nousresearch-meta-llama-4941-v54-profiler
Waiting for inference service nousresearch-meta-llama-4941-v54-profiler to be ready
Tearing down inference service nousresearch-meta-llama-4941-v54-profiler
clean up pipeline due to error=DeploymentError('Timeout to start the InferenceService nousresearch-meta-llama-4941-v54-profiler.                                The InferenceService is as following: {\'apiVersion\': \'serving.kserve.io/v1beta1\', \'kind\': \'InferenceService\', \'metadata\': {\'annotations\': {\'autoscaling.knative.dev/class\': \'hpa.autoscaling.knative.dev\', \'autoscaling.knative.dev/container-concurrency-target-percentage\': \'70\', \'autoscaling.knative.dev/initial-scale\': \'1\', \'autoscaling.knative.dev/max-scale-down-rate\': \'1.1\', \'autoscaling.knative.dev/max-scale-up-rate\': \'2\', \'autoscaling.knative.dev/metric\': \'mean_pod_latency_ms_v2\', \'autoscaling.knative.dev/panic-threshold-percentage\': \'650\', \'autoscaling.knative.dev/panic-window-percentage\': \'35\', \'autoscaling.knative.dev/scale-down-delay\': \'30s\', \'autoscaling.knative.dev/scale-to-zero-grace-period\': \'10m\', \'autoscaling.knative.dev/stable-window\': \'180s\', \'autoscaling.knative.dev/target\': \'3700\', \'autoscaling.knative.dev/target-burst-capacity\': \'-1\', \'autoscaling.knative.dev/tick-interval\': \'15s\', \'features.knative.dev/http-full-duplex\': \'Enabled\', \'networking.knative.dev/ingress-class\': \'istio.ingress.networking.knative.dev\'}, \'creationTimestamp\': \'2025-04-10T16:40:19Z\', \'finalizers\': [\'inferenceservice.finalizers\'], \'generation\': 1, \'labels\': {\'knative.coreweave.cloud/ingress\': \'istio.ingress.networking.knative.dev\', \'prometheus.k.chaiverse.com\': \'true\', \'qos.coreweave.cloud/latency\': \'low\'}, \'managedFields\': [{\'apiVersion\': \'serving.kserve.io/v1beta1\', \'fieldsType\': \'FieldsV1\', \'fieldsV1\': {\'f:metadata\': {\'f:annotations\': {\'.\': {}, \'f:autoscaling.knative.dev/class\': {}, \'f:autoscaling.knative.dev/container-concurrency-target-percentage\': {}, \'f:autoscaling.knative.dev/initial-scale\': {}, \'f:autoscaling.knative.dev/max-scale-down-rate\': {}, \'f:autoscaling.knative.dev/max-scale-up-rate\': {}, \'f:autoscaling.knative.dev/metric\': {}, \'f:autoscaling.knative.dev/panic-threshold-percentage\': {}, \'f:autoscaling.knative.dev/panic-window-percentage\': {}, \'f:autoscaling.knative.dev/scale-down-delay\': {}, \'f:autoscaling.knative.dev/scale-to-zero-grace-period\': {}, \'f:autoscaling.knative.dev/stable-window\': {}, \'f:autoscaling.knative.dev/target\': {}, \'f:autoscaling.knative.dev/target-burst-capacity\': {}, \'f:autoscaling.knative.dev/tick-interval\': {}, \'f:features.knative.dev/http-full-duplex\': {}, \'f:networking.knative.dev/ingress-class\': {}}, \'f:labels\': {\'.\': {}, \'f:knative.coreweave.cloud/ingress\': {}, \'f:prometheus.k.chaiverse.com\': {}, \'f:qos.coreweave.cloud/latency\': {}}}, \'f:spec\': {\'.\': {}, \'f:predictor\': {\'.\': {}, \'f:affinity\': {\'.\': {}, \'f:nodeAffinity\': {\'.\': {}, \'f:tion\': {}, \'f:requiredDuringSchedulingIgnoredDuringExecution\': {}}}, \'f:containerConcurrency\': {}, \'f:containers\': {}, \'f:imagePullSecrets\': {}, \'f:maxReplicas\': {}, \'f:minReplicas\': {}, \'f:timeout\': {}, \'f:volumes\': {}}}}, \'manager\': \'OpenAPI-Generator\', \'operation\': \'Update\', \'time\': \'2025-04-10T16:40:19Z\'}, {\'apiVersion\': \'serving.kserve.io/v1beta1\', \'fieldsType\': \'FieldsV1\', \'fieldsV1\': {\'f:metadata\': {\'f:finalizers\': {\'.\': {}, \'v:"inferenceservice.finalizers"\': {}}}}, \'manager\': \'manager\', \'operation\': \'Update\', \'time\': \'2025-04-10T16:40:19Z\'}, {\'apiVersion\': \'serving.kserve.io/v1beta1\', \'fieldsType\': \'FieldsV1\', \'fieldsV1\': {\'f:status\': {\'.\': {}, \'f:components\': {\'.\': {}, \'f:predictor\': {\'.\': {}, \'f:latestCreatedRevision\': {}}}, \'f:conditions\': {}, \'f:modelStatus\': {\'.\': {}, \'f:lastFailureInfo\': {\'.\': {}, \'f:exitCode\': {}, \'f:message\': {}, \'f:reason\': {}}, \'f:states\': {\'.\': {}, \'f:activeModelState\': {}, \'f:targetModelState\': {}}, \'f:transitionStatus\': {}}, \'f:observedGeneration\': {}}}, \'manager\': \'manager\', \'operation\': \'Update\', \'subresource\': \'status\', \'time\': \'2025-04-10T16:50:21Z\'}], \'name\': \'nousresearch-meta-llama-4941-v54-profiler\', \'namespace\': \'tenant-chaiml-guanaco\', \'resourceVersion\': \'341532160\', \'uid\': \'d928f903-aa0b-4a95-a146-946233c5433d\'}, \'spec\': {\'predictor\': {\'affinity\': {\'nodeAffinity\': {\'tion\': [{\'preference\': {\'matchExpressions\': [{\'key\': \'topology.kubernetes.io/region\', \'operator\': \'In\', \'values\': [\'ORD1\']}]}, \'weight\': 5}], \'requiredDuringSchedulingIgnoredDuringExecution\': {\'nodeSelectorTerms\': [{\'matchExpressions\': [{\'key\': \'gpu.nvidia.com/class\', \'operator\': \'In\', \'values\': [\'RTX_A5000\']}]}]}}}, \'containerConcurrency\': 0, \'containers\': [{\'env\': [{\'name\': \'MAX_TOKEN_INPUT\', \'value\': \'1024\'}, {\'name\': \'BEST_OF\', \'value\': \'4\'}, {\'name\': \'TEMPERATURE\', \'value\': \'1.0\'}, {\'name\': \'PRESENCE_PENALTY\', \'value\': \'0.0\'}, {\'name\': \'FREQUENCY_PENALTY\', \'value\': \'0.0\'}, {\'name\': \'TOP_P\', \'value\': \'1.0\'}, {\'name\': \'MIN_P\', \'value\': \'0.0\'}, {\'name\': \'TOP_K\', \'value\': \'40\'}, {\'name\': \'STOPPING_WORDS\', \'value\': \'["\\\\\\\\n"]\'}, {\'name\': \'MAX_TOKENS\', \'value\': \'300\'}, {\'name\': \'MAX_BATCH_SIZE\', \'value\': \'128\'}, {\'name\': \'MAX_CACHED_RESPONSES\', \'value\': \'-1\'}, {\'name\': \'URL_ROUTE\', \'value\': \'GPT-J-6B-lit-v2\'}, {\'name\': \'OBJ_ACCESS_KEY_ID\', \'value\': \'LETMTTRMLFFAMTBK\'}, {\'name\': \'OBJ_SECRET_ACCESS_KEY\', \'value\': \'VwwZaqefOOoaouNxUk03oUmK9pVEfruJhjBHPGdgycK\'}, {\'name\': \'OBJ_ENDPOINT\', \'value\': \'https://accel-object.ord1.coreweave.com\'}, {\'name\': \'TENSORIZER_URI\', \'value\': \'s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54\'}, {\'name\': \'RESERVE_MEMORY\', \'value\': \'2048\'}, {\'name\': \'DOWNLOAD_TO_LOCAL\', \'value\': \'/dev/shm/model_cache\'}, {\'name\': \'NUM_GPUS\', \'value\': \'1\'}, {\'name\': \'MK1_MKML_LICENSE_KEY\', \'valueFrom\': {\'secretKeyRef\': {\'key\': \'key\', \'name\': \'mkml-license-key\'}}}], \'image\': \'gcr.io/chai-959f8/chai-guanaco/mkml:v1.17.5\', \'imagePullPolicy\': \'IfNotPresent\', \'name\': \'kserve-container\', \'readinessProbe\': {\'exec\': {\'command\': [\'cat\', \'/tmp/ready\']}, \'failureThreshold\': 1, \'initialDelaySeconds\': 10, \'periodSeconds\': 10, \'successThreshold\': 1, \'timeoutSeconds\': 5}, \'resources\': {\'limits\': {\'cpu\': \'2\', \'memory\': \'12Gi\', \'nvidia.com/gpu\': \'1\'}, \'requests\': {\'cpu\': \'2\', \'memory\': \'12Gi\', \'nvidia.com/gpu\': \'1\'}}, \'volumeMounts\': [{\'mountPath\': \'/dev/shm\', \'name\': \'shared-memory-cache\'}]}], \'imagePullSecrets\': [{\'name\': \'docker-creds\'}], \'maxReplicas\': 1, \'minReplicas\': 1, \'timeout\': 60, \'volumes\': [{\'emptyDir\': {\'medium\': \'Memory\'}, \'name\': \'shared-memory-cache\'}]}}, \'status\': {\'components\': {\'predictor\': {\'latestCreatedRevision\': \'nousresearch-meta-llama-4941-v54-profiler-predictor-00001\'}}, \'conditions\': [{\'lastTransitionTime\': \'2025-04-10T16:50:21Z\', \'reason\': \'PredictorConfigurationReady not ready\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'LatestDeploymentReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:50:21Z\', \'message\': \'Revision "nousresearch-meta-llama-4941-v54-profiler-predictor-00001" failed with message: Container failed with: �═╝\\n\\nINFO:datasets:PyTorch version 2.3.0 available.\\nInference config: InferenceConfig(server_num_workers=1, server_port=8080, max_batch_size=128, log_level=0, reserve_memory=2048, num_gpus=1, quantization_profile=s0, all_reduce_profile=None, kv_cache_profile=None, calibration_samples=-1, max_cached_responses=-1, sampling=SamplingParameters(temperature=1.0, top_p=1.0, min_p=0.0, top_k=40, max_input_tokens=1024, max_tokens=300, stop=[\\\'\\\\n\\\'], eos_token_ids=[], frequency_penalty=0.0, presence_penalty=0.0, reward_enabled=True, num_samples=4, reward_max_token_input=256, profile=False), url_route=GPT-J-6B-lit-v2, tensorizer_uri=s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54, s3_creds=S3Credentials(s3_access_key_id=\\\'LETMTTRMLFFAMTBK\\\', s3_secret_access_key=\\\'VwwZaqefOOoaouNxUk03oUmK9pVEfruJhjBHPGdgycK\\\', s3_endpoint=\\\'https://accel-object.ord1.coreweave.com\\\', s3_uncached_endpoint=\\\'https://object.ord1.coreweave.com\\\'), local_folder=/dev/shm/model_cache)\\nTraceback (most recent call last):\\n  File "/code/mkml_inference_service/main.py", line 95, in <module>\\n    model.load()\\n  File "/code/mkml_inference_service/main.py", line 31, in load\\n    self.engine = mkml_backend.AsyncInferenceService.from_folder(settings, settings.local_folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 56, in from_folder\\n    return service._from_folder(settings, folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 83, in _from_folder\\n    engine = mkml.ModelForInference.from_pretrained(\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/inference.py", line 68, in from_pretrained\\n    manifold = TensorManifold(model_path, tensor_parallel_size, batching_config, profile, s3_config)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 116, in __init__\\n    self.validate_schema(model_path)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 175, in validate_schema\\n    raise ValueError(f"Unexpected flywheel schema.")\\nValueError: Unexpected flywheel schema.\\n.\', \'reason\': \'RevisionFailed\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'PredictorConfigurationReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:50:21Z\', \'message\': \'Configuration "nousresearch-meta-llama-4941-v54-profiler-predictor" does not have any ready Revision.\', \'reason\': \'RevisionMissing\', \'status\': \'False\', \'type\': \'PredictorReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:50:21Z\', \'message\': \'Configuration "nousresearch-meta-llama-4941-v54-profiler-predictor" does not have any ready Revision.\', \'reason\': \'RevisionMissing\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'PredictorRouteReady\'}, {\'lastTransitionTime\': \'2025-04-10T16:50:21Z\', \'message\': \'Configuration "nousresearch-meta-llama-4941-v54-profiler-predictor" does not have any ready Revision.\', \'reason\': \'RevisionMissing\', \'status\': \'False\', \'type\': \'Ready\'}, {\'lastTransitionTime\': \'2025-04-10T16:50:21Z\', \'reason\': \'PredictorRouteReady not ready\', \'severity\': \'Info\', \'status\': \'False\', \'type\': \'RoutesReady\'}], \'modelStatus\': {\'lastFailureInfo\': {\'exitCode\': 1, \'message\': \'�═╝\\n\\nINFO:datasets:PyTorch version 2.3.0 available.\\nInference config: InferenceConfig(server_num_workers=1, server_port=8080, max_batch_size=128, log_level=0, reserve_memory=2048, num_gpus=1, quantization_profile=s0, all_reduce_profile=None, kv_cache_profile=None, calibration_samples=-1, max_cached_responses=-1, sampling=SamplingParameters(temperature=1.0, top_p=1.0, min_p=0.0, top_k=40, max_input_tokens=1024, max_tokens=300, stop=[\\\'\\\\n\\\'], eos_token_ids=[], frequency_penalty=0.0, presence_penalty=0.0, reward_enabled=True, num_samples=4, reward_max_token_input=256, profile=False), url_route=GPT-J-6B-lit-v2, tensorizer_uri=s3://guanaco-mkml-models/nousresearch-meta-llama-4941-v54, s3_creds=S3Credentials(s3_access_key_id=\\\'LETMTTRMLFFAMTBK\\\', s3_secret_access_key=\\\'VwwZaqefOOoaouNxUk03oUmK9pVEfruJhjBHPGdgycK\\\', s3_endpoint=\\\'https://accel-object.ord1.coreweave.com\\\', s3_uncached_endpoint=\\\'https://object.ord1.coreweave.com\\\'), local_folder=/dev/shm/model_cache)\\nTraceback (most recent call last):\\n  File "/code/mkml_inference_service/main.py", line 95, in <module>\\n    model.load()\\n  File "/code/mkml_inference_service/main.py", line 31, in load\\n    self.engine = mkml_backend.AsyncInferenceService.from_folder(settings, settings.local_folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 56, in from_folder\\n    return service._from_folder(settings, folder)\\n  File "/code/mkml_inference_service/mkml_backend.py", line 83, in _from_folder\\n    engine = mkml.ModelForInference.from_pretrained(\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/inference.py", line 68, in from_pretrained\\n    manifold = TensorManifold(model_path, tensor_parallel_size, batching_config, profile, s3_config)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 116, in __init__\\n    self.validate_schema(model_path)\\n  File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/manifold.py", line 175, in validate_schema\\n    raise ValueError(f"Unexpected flywheel schema.")\\nValueError: Unexpected flywheel schema.\\n\', \'reason\': \'ModelLoadFailed\'}, \'states\': {\'activeModelState\': \'\', \'targetModelState\': \'FailedToLoad\'}, \'transitionStatus\': \'BlockedByFailedLoad\'}, \'observedGeneration\': 1}}')
run pipeline stage %s
Running pipeline stage MKMLProfilerDeleter
Skipping teardown as no inference service was successfully deployed
Pipeline stage MKMLProfilerDeleter completed in 0.30s
Shutdown handler de-registered