mistralai-mistral-nemo_9330

submission_id: mistralai-mistral-nemo_9330_v121
developer_uid: immaculate_possum_03470
best_of: 8
celo_rating: 1235.59
display_name: nemo_base_1
family_friendly_score: 0.5624935280107695
family_friendly_standard_error: 0.008724393949547217
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': 0.95, 'top_p': 1.0, 'min_p': 0.075, 'top_k': 60, 'presence_penalty': 0.0, 'frequency_penalty': 0.0, 'stopping_words': ['\n', '<|eot_id|>'], 'max_input_tokens': 1024, 'best_of': 8, 'max_output_tokens': 64}
ineligible_reason: num_battles<5000
is_internal_developer: False
language_model: mistralai/Mistral-Nemo-Instruct-2407
max_input_tokens: 1024
max_output_tokens: 64
model_architecture: MistralForCausalLM
model_group: mistralai/Mistral-Nemo-I
model_name: nemo_base_1
model_num_parameters: 12772070400.0
model_repo: mistralai/Mistral-Nemo-Instruct-2407
model_size: 13B
num_battles: 3325
num_wins: 1592
ranking_group: single
status: torndown
submission_type: basic
timestamp: 2024-09-26T23:39:46+00:00
us_pacific_date: 2024-09-26
win_ratio: 0.478796992481203
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run pipeline %s
run pipeline stage %s
Running pipeline stage MKMLizer
Starting job with name mistralai-mistral-nemo-9330-v121-mkmlizer
Waiting for job on mistralai-mistral-nemo-9330-v121-mkmlizer to finish
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║     _____            __           __                                ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║       /___/                                                         ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║                                                                     ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  Version: 0.11.12                                                   ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  https://mk1.ai                                                     ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║                                                                     ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  The license key for the current software has been verified as      ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  belonging to:                                                      ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║                                                                     ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  Chai Research Corp.                                                ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ║                                                                     ║
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
mistralai-mistral-nemo-9330-v121-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
mistralai-mistral-nemo-9330-v121-mkmlizer: ║     _____            __           __                                ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║       /___/                                                         ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║                                                                     ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  Version: 0.11.12                                                   ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  https://mk1.ai                                                     ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║                                                                     ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  The license key for the current software has been verified as      ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  belonging to:                                                      ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║                                                                     ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  Chai Research Corp.                                                ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ║                                                                     ║
mistralai-mistral-nemo-9330-v121-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
mistralai-mistral-nemo-9330-v121-mkmlizer: Downloaded to shared memory in 48.849s
mistralai-mistral-nemo-9330-v121-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpt_mjcyku, device:0
mistralai-mistral-nemo-9330-v121-mkmlizer: Saving flywheel model at /dev/shm/model_cache
mistralai-mistral-nemo-9330-v121-mkmlizer: quantized model in 37.829s
mistralai-mistral-nemo-9330-v121-mkmlizer: Processed model mistralai/Mistral-Nemo-Instruct-2407 in 86.678s
mistralai-mistral-nemo-9330-v121-mkmlizer: creating bucket guanaco-mkml-models
mistralai-mistral-nemo-9330-v121-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
mistralai-mistral-nemo-9330-v121-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/mistralai-mistral-nemo-9330-v121
mistralai-mistral-nemo-9330-v121-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/mistralai-mistral-nemo-9330-v121/config.json
mistralai-mistral-nemo-9330-v121-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/mistralai-mistral-nemo-9330-v121/special_tokens_map.json
mistralai-mistral-nemo-9330-v121-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/mistralai-mistral-nemo-9330-v121/tokenizer_config.json
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: Downloaded to shared memory in 86.887s
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpd7yxdy7f, device:0
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: Saving flywheel model at /dev/shm/model_cache
mistralai-mistral-nemo-9330-v121-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/mistralai-mistral-nemo-9330-v121/flywheel_model.0.safetensors
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Job mistralai-mistral-nemo-9330-v121-mkmlizer completed after 123.34s with status: succeeded
Stopping job with name mistralai-mistral-nemo-9330-v121-mkmlizer
Pipeline stage MKMLizer completed in 123.68s
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 mistralai-mistral-nemo-9330-v121
Waiting for inference service mistralai-mistral-nemo-9330-v121 to be ready
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: quantized model in 43.858s
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: Processed model ChaiML/0926-nemo-virgo-top-safe-bot-1edit in 130.745s
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: creating bucket guanaco-mkml-models
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: Bucket 's3://guanaco-mkml-models/' created
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: uploading /dev/shm/model_cache to s3://guanaco-mkml-models/chaiml-0926-nemo-virgo-t-3956-v7
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: cp /dev/shm/model_cache/config.json s3://guanaco-mkml-models/chaiml-0926-nemo-virgo-t-3956-v7/config.json
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: cp /dev/shm/model_cache/special_tokens_map.json s3://guanaco-mkml-models/chaiml-0926-nemo-virgo-t-3956-v7/special_tokens_map.json
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: cp /dev/shm/model_cache/tokenizer_config.json s3://guanaco-mkml-models/chaiml-0926-nemo-virgo-t-3956-v7/tokenizer_config.json
chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer: cp /dev/shm/model_cache/flywheel_model.0.safetensors s3://guanaco-mkml-models/chaiml-0926-nemo-virgo-t-3956-v7/flywheel_model.0.safetensors
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Job chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer completed after 227.39s with status: succeeded
Stopping job with name chaiml-0926-nemo-virgo-t-3956-v7-mkmlizer
Pipeline stage MKMLizer completed in 227.73s
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 chaiml-0926-nemo-virgo-t-3956-v7
Waiting for inference service chaiml-0926-nemo-virgo-t-3956-v7 to be ready
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 arushimgupta-peft-save-1-v5-mkmlizer
Waiting for job on arushimgupta-peft-save-1-v5-mkmlizer to finish
arushimgupta-peft-save-1-v5-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
arushimgupta-peft-save-1-v5-mkmlizer: ║     _____            __           __                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
arushimgupta-peft-save-1-v5-mkmlizer: ║       /___/                                                         ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Version: 0.11.12                                                   ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  https://mk1.ai                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  The license key for the current software has been verified as      ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  belonging to:                                                      ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Chai Research Corp.                                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
arushimgupta-peft-save-1-v5-mkmlizer: Downloaded to shared memory in 15.327s
arushimgupta-peft-save-1-v5-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmph2e7k95s, device:0
arushimgupta-peft-save-1-v5-mkmlizer: Saving flywheel model at /dev/shm/model_cache
arushimgupta-peft-save-1-v5-mkmlizer: 
Loading 0:   0%|          | 0/1203 [00:00<?, ?it/s]Traceback (most recent call last):
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 151, in <module>
arushimgupta-peft-save-1-v5-mkmlizer:     cli()
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
arushimgupta-peft-save-1-v5-mkmlizer:     return self.main(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1078, in main
arushimgupta-peft-save-1-v5-mkmlizer:     rv = self.invoke(ctx)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1688, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return _process_result(sub_ctx.command.invoke(sub_ctx))
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return ctx.invoke(self.callback, **ctx.params)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 783, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return __callback(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 42, in quantize
arushimgupta-peft-save-1-v5-mkmlizer:     quantize_model(temp_folder, output_path, profile, device)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 135, in quantize_model
arushimgupta-peft-save-1-v5-mkmlizer:     flywheel.instrument(
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/instrument.py", line 93, in instrument
arushimgupta-peft-save-1-v5-mkmlizer:     compiler.save_pretrained(input_model_path, output_model_path, storage_format)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 23, in save_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     self.save_st_pretrained(input_model_path, output_model_path)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 38, in save_st_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in model_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/models/mistral.py", line 241, in tensor_merger
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in tensor_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/loader.py", line 217, in tensor_compiler
arushimgupta-peft-save-1-v5-mkmlizer:     compiled_tensor = runtime.instrument(tensor, profile.value)
arushimgupta-peft-save-1-v5-mkmlizer: RuntimeError: CUDA error: invalid configuration argument
arushimgupta-peft-save-1-v5-mkmlizer: CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
arushimgupta-peft-save-1-v5-mkmlizer: For debugging consider passing CUDA_LAUNCH_BLOCKING=1
arushimgupta-peft-save-1-v5-mkmlizer: Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
arushimgupta-peft-save-1-v5-mkmlizer: Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
arushimgupta-peft-save-1-v5-mkmlizer: frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x71a4490cbf86 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x71a44907ad10 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x71a4491a6f08 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #3: void at::native::gpu_kernel_impl<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x4de (0x71a3fbb0aa2e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #4: void at::native::gpu_kernel<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x34b (0x71a3fbb0b01b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #5: at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&) + 0x38c (0x71a3fbac9a0c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #6: at::native::copy_device_to_device(at::TensorIterator&, bool, bool) + 0xb25 (0x71a3fbaca715 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #7: <unknown function> + 0x1910312 (0x71a3fbacc312 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #8: <unknown function> + 0x1cbebff (0x71a431099bff in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #9: at::native::copy_(at::Tensor&, at::Tensor const&, bool) + 0x62 (0x71a43109b5a2 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #10: at::_ops::copy_::call(at::Tensor&, at::Tensor const&, bool) + 0x15c (0x71a431e5635c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #11: at::native::_to_copy(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1e01 (0x71a4313b96b1 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #12: <unknown function> + 0x2e19f8b (0x71a4321f4f8b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #13: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x71a4318fdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #14: <unknown function> + 0x2c58a33 (0x71a432033a33 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #15: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x71a4318fdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #16: <unknown function> + 0x470df1f (0x71a433ae8f1f in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #17: <unknown function> + 0x470e35e (0x71a433ae935e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #18: at::_ops::_to_copy::call(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1eb (0x71a43198d68b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #19: at::native::to(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0xa2 (0x71a4313b6182 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #20: <unknown function> + 0x301e3b0 (0x71a4323f93b0 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #21: at::_ops::to_dtype::call(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0x178 (0x71a431b3d258 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #22: mkodec::instrument(at::Tensor, int) + 0x4b (0x71a36d9c81ab in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #23: <unknown function> + 0x981e2 (0x71a36d9b01e2 in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #24: <unknown function> + 0xa7b9b (0x71a36d9bfb9b in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #25: python3() [0x4fd907]
arushimgupta-peft-save-1-v5-mkmlizer: <omitting python frames>
arushimgupta-peft-save-1-v5-mkmlizer: frame #28: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #30: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #43: python3() [0x5095ce]
arushimgupta-peft-save-1-v5-mkmlizer: frame #50: python3() [0x509857]
arushimgupta-peft-save-1-v5-mkmlizer: frame #54: python3() [0x5cf913]
arushimgupta-peft-save-1-v5-mkmlizer: frame #57: python3() [0x5951c2]
arushimgupta-peft-save-1-v5-mkmlizer: frame #59: python3() [0x5c5ef7]
arushimgupta-peft-save-1-v5-mkmlizer: frame #60: python3() [0x5c1030]
arushimgupta-peft-save-1-v5-mkmlizer: frame #61: python3() [0x459781]
arushimgupta-peft-save-1-v5-mkmlizer: 
                                                   
arushimgupta-peft-save-1-v5-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
arushimgupta-peft-save-1-v5-mkmlizer: ║     _____            __           __                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
arushimgupta-peft-save-1-v5-mkmlizer: ║       /___/                                                         ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Version: 0.11.12                                                   ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  https://mk1.ai                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  The license key for the current software has been verified as      ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  belonging to:                                                      ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Chai Research Corp.                                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
Job arushimgupta-peft-save-1-v5-mkmlizer completed after 52.27s with status: failed
Stopping job with name arushimgupta-peft-save-1-v5-mkmlizer
%s, retrying in %s seconds...
Starting job with name arushimgupta-peft-save-1-v5-mkmlizer
Waiting for job on arushimgupta-peft-save-1-v5-mkmlizer to finish
arushimgupta-peft-save-1-v5-mkmlizer: ╔═════════════════════════════════════════════════════════════════════╗
arushimgupta-peft-save-1-v5-mkmlizer: ║     _____            __           __                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║    / _/ /_ ___    __/ /  ___ ___ / /                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║   / _/ / // / |/|/ / _ \/ -_) -_) /                                 ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  /_//_/\_, /|__,__/_//_/\__/\__/_/                                  ║
arushimgupta-peft-save-1-v5-mkmlizer: ║       /___/                                                         ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Version: 0.11.12                                                   ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Copyright 2023 MK ONE TECHNOLOGIES Inc.                            ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  https://mk1.ai                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  The license key for the current software has been verified as      ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  belonging to:                                                      ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Chai Research Corp.                                                ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Account ID: 7997a29f-0ceb-4cc7-9adf-840c57b4ae6f                   ║
arushimgupta-peft-save-1-v5-mkmlizer: ║  Expiration: 2024-10-15 23:59:59                                    ║
arushimgupta-peft-save-1-v5-mkmlizer: ║                                                                     ║
arushimgupta-peft-save-1-v5-mkmlizer: ╚═════════════════════════════════════════════════════════════════════╝
arushimgupta-peft-save-1-v5-mkmlizer: Downloaded to shared memory in 16.231s
arushimgupta-peft-save-1-v5-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpjwz23o0t, device:0
arushimgupta-peft-save-1-v5-mkmlizer: Saving flywheel model at /dev/shm/model_cache
arushimgupta-peft-save-1-v5-mkmlizer: 
Loading 0:   0%|          | 0/1203 [00:00<?, ?it/s]Traceback (most recent call last):
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 151, in <module>
arushimgupta-peft-save-1-v5-mkmlizer:     cli()
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
arushimgupta-peft-save-1-v5-mkmlizer:     return self.main(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1078, in main
arushimgupta-peft-save-1-v5-mkmlizer:     rv = self.invoke(ctx)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1688, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return _process_result(sub_ctx.command.invoke(sub_ctx))
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return ctx.invoke(self.callback, **ctx.params)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 783, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return __callback(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 42, in quantize
arushimgupta-peft-save-1-v5-mkmlizer:     quantize_model(temp_folder, output_path, profile, device)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 135, in quantize_model
arushimgupta-peft-save-1-v5-mkmlizer:     flywheel.instrument(
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/instrument.py", line 93, in instrument
arushimgupta-peft-save-1-v5-mkmlizer:     compiler.save_pretrained(input_model_path, output_model_path, storage_format)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 23, in save_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     self.save_st_pretrained(input_model_path, output_model_path)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 38, in save_st_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in model_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/models/mistral.py", line 241, in tensor_merger
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in tensor_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/loader.py", line 217, in tensor_compiler
arushimgupta-peft-save-1-v5-mkmlizer:     compiled_tensor = runtime.instrument(tensor, profile.value)
arushimgupta-peft-save-1-v5-mkmlizer: RuntimeError: CUDA error: invalid configuration argument
arushimgupta-peft-save-1-v5-mkmlizer: CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
arushimgupta-peft-save-1-v5-mkmlizer: For debugging consider passing CUDA_LAUNCH_BLOCKING=1
arushimgupta-peft-save-1-v5-mkmlizer: Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
arushimgupta-peft-save-1-v5-mkmlizer: Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
arushimgupta-peft-save-1-v5-mkmlizer: frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7e235e377f86 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7e235e326d10 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7e235e770f08 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #3: void at::native::gpu_kernel_impl<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x4de (0x7e2310d0aa2e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #4: void at::native::gpu_kernel<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x34b (0x7e2310d0b01b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #5: at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&) + 0x38c (0x7e2310cc9a0c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #6: at::native::copy_device_to_device(at::TensorIterator&, bool, bool) + 0xb25 (0x7e2310cca715 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #7: <unknown function> + 0x1910312 (0x7e2310ccc312 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #8: <unknown function> + 0x1cbebff (0x7e2346299bff in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #9: at::native::copy_(at::Tensor&, at::Tensor const&, bool) + 0x62 (0x7e234629b5a2 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #10: at::_ops::copy_::call(at::Tensor&, at::Tensor const&, bool) + 0x15c (0x7e234705635c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #11: at::native::_to_copy(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1e01 (0x7e23465b96b1 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #12: <unknown function> + 0x2e19f8b (0x7e23473f4f8b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #13: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x7e2346afdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #14: <unknown function> + 0x2c58a33 (0x7e2347233a33 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #15: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x7e2346afdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #16: <unknown function> + 0x470df1f (0x7e2348ce8f1f in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #17: <unknown function> + 0x470e35e (0x7e2348ce935e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #18: at::_ops::_to_copy::call(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1eb (0x7e2346b8d68b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #19: at::native::to(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0xa2 (0x7e23465b6182 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #20: <unknown function> + 0x301e3b0 (0x7e23475f93b0 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #21: at::_ops::to_dtype::call(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0x178 (0x7e2346d3d258 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #22: mkodec::instrument(at::Tensor, int) + 0x4b (0x7e2282bc81ab in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #23: <unknown function> + 0x981e2 (0x7e2282bb01e2 in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #24: <unknown function> + 0xa7b9b (0x7e2282bbfb9b in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #25: python3() [0x4fd907]
arushimgupta-peft-save-1-v5-mkmlizer: <omitting python frames>
arushimgupta-peft-save-1-v5-mkmlizer: frame #28: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #30: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #43: python3() [0x5095ce]
arushimgupta-peft-save-1-v5-mkmlizer: frame #50: python3() [0x509857]
arushimgupta-peft-save-1-v5-mkmlizer: frame #54: python3() [0x5cf913]
arushimgupta-peft-save-1-v5-mkmlizer: frame #57: python3() [0x5951c2]
arushimgupta-peft-save-1-v5-mkmlizer: frame #59: python3() [0x5c5ef7]
arushimgupta-peft-save-1-v5-mkmlizer: frame #60: python3() [0x5c1030]
arushimgupta-peft-save-1-v5-mkmlizer: frame #61: python3() [0x459781]
arushimgupta-peft-save-1-v5-mkmlizer: 
                                                   
arushimgupta-peft-save-1-v5-mkmlizer: 
Loading 0:   0%|          | 0/1203 [00:00<?, ?it/s]Traceback (most recent call last):
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 151, in <module>
arushimgupta-peft-save-1-v5-mkmlizer:     cli()
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
arushimgupta-peft-save-1-v5-mkmlizer:     return self.main(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1078, in main
arushimgupta-peft-save-1-v5-mkmlizer:     rv = self.invoke(ctx)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1688, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return _process_result(sub_ctx.command.invoke(sub_ctx))
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return ctx.invoke(self.callback, **ctx.params)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 783, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return __callback(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 42, in quantize
arushimgupta-peft-save-1-v5-mkmlizer:     quantize_model(temp_folder, output_path, profile, device)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 135, in quantize_model
arushimgupta-peft-save-1-v5-mkmlizer:     flywheel.instrument(
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/instrument.py", line 93, in instrument
arushimgupta-peft-save-1-v5-mkmlizer:     compiler.save_pretrained(input_model_path, output_model_path, storage_format)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 23, in save_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     self.save_st_pretrained(input_model_path, output_model_path)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 38, in save_st_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in model_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/models/mistral.py", line 241, in tensor_merger
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in tensor_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/loader.py", line 217, in tensor_compiler
arushimgupta-peft-save-1-v5-mkmlizer:     compiled_tensor = runtime.instrument(tensor, profile.value)
arushimgupta-peft-save-1-v5-mkmlizer: RuntimeError: CUDA error: invalid configuration argument
arushimgupta-peft-save-1-v5-mkmlizer: CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
arushimgupta-peft-save-1-v5-mkmlizer: For debugging consider passing CUDA_LAUNCH_BLOCKING=1
arushimgupta-peft-save-1-v5-mkmlizer: Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
arushimgupta-peft-save-1-v5-mkmlizer: Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
arushimgupta-peft-save-1-v5-mkmlizer: frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x797ea60cbf86 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x797ea607ad10 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x797ea61a6f08 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #3: void at::native::gpu_kernel_impl<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x4de (0x797e58b0aa2e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #4: void at::native::gpu_kernel<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x34b (0x797e58b0b01b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #5: at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&) + 0x38c (0x797e58ac9a0c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #6: at::native::copy_device_to_device(at::TensorIterator&, bool, bool) + 0xb25 (0x797e58aca715 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #7: <unknown function> + 0x1910312 (0x797e58acc312 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #8: <unknown function> + 0x1cbebff (0x797e8e099bff in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #9: at::native::copy_(at::Tensor&, at::Tensor const&, bool) + 0x62 (0x797e8e09b5a2 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #10: at::_ops::copy_::call(at::Tensor&, at::Tensor const&, bool) + 0x15c (0x797e8ee5635c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #11: at::native::_to_copy(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1e01 (0x797e8e3b96b1 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #12: <unknown function> + 0x2e19f8b (0x797e8f1f4f8b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #13: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x797e8e8fdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #14: <unknown function> + 0x2c58a33 (0x797e8f033a33 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #15: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x797e8e8fdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #16: <unknown function> + 0x470df1f (0x797e90ae8f1f in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #17: <unknown function> + 0x470e35e (0x797e90ae935e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #18: at::_ops::_to_copy::call(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1eb (0x797e8e98d68b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #19: at::native::to(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0xa2 (0x797e8e3b6182 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #20: <unknown function> + 0x301e3b0 (0x797e8f3f93b0 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #21: at::_ops::to_dtype::call(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0x178 (0x797e8eb3d258 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #22: mkodec::instrument(at::Tensor, int) + 0x4b (0x797dca9c81ab in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #23: <unknown function> + 0x981e2 (0x797dca9b01e2 in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #24: <unknown function> + 0xa7b9b (0x797dca9bfb9b in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #25: python3() [0x4fd907]
arushimgupta-peft-save-1-v5-mkmlizer: <omitting python frames>
arushimgupta-peft-save-1-v5-mkmlizer: frame #28: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #30: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #43: python3() [0x5095ce]
arushimgupta-peft-save-1-v5-mkmlizer: frame #50: python3() [0x509857]
arushimgupta-peft-save-1-v5-mkmlizer: frame #54: python3() [0x5cf913]
arushimgupta-peft-save-1-v5-mkmlizer: frame #57: python3() [0x5951c2]
arushimgupta-peft-save-1-v5-mkmlizer: frame #59: python3() [0x5c5ef7]
arushimgupta-peft-save-1-v5-mkmlizer: frame #60: python3() [0x5c1030]
arushimgupta-peft-save-1-v5-mkmlizer: frame #61: python3() [0x459781]
arushimgupta-peft-save-1-v5-mkmlizer: 
                                                   
Job arushimgupta-peft-save-1-v5-mkmlizer completed after 64.89s with status: failed
Stopping job with name arushimgupta-peft-save-1-v5-mkmlizer
%s, retrying in %s seconds...
Starting job with name arushimgupta-peft-save-1-v5-mkmlizer
Waiting for job on arushimgupta-peft-save-1-v5-mkmlizer to finish
arushimgupta-peft-save-1-v5-mkmlizer: Downloaded to shared memory in 16.662s
arushimgupta-peft-save-1-v5-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmpz0mzyzqk, device:0
arushimgupta-peft-save-1-v5-mkmlizer: Saving flywheel model at /dev/shm/model_cache
arushimgupta-peft-save-1-v5-mkmlizer: Downloaded to shared memory in 15.827s
arushimgupta-peft-save-1-v5-mkmlizer: quantizing model to /dev/shm/model_cache, profile:s0, folder:/tmp/tmp9c4qp7od, device:0
arushimgupta-peft-save-1-v5-mkmlizer: Saving flywheel model at /dev/shm/model_cache
arushimgupta-peft-save-1-v5-mkmlizer: 
Loading 0:   0%|          | 0/1203 [00:00<?, ?it/s]Traceback (most recent call last):
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 151, in <module>
arushimgupta-peft-save-1-v5-mkmlizer:     cli()
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
arushimgupta-peft-save-1-v5-mkmlizer:     return self.main(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1078, in main
arushimgupta-peft-save-1-v5-mkmlizer:     rv = self.invoke(ctx)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1688, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return _process_result(sub_ctx.command.invoke(sub_ctx))
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return ctx.invoke(self.callback, **ctx.params)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/click/core.py", line 783, in invoke
arushimgupta-peft-save-1-v5-mkmlizer:     return __callback(*args, **kwargs)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 42, in quantize
arushimgupta-peft-save-1-v5-mkmlizer:     quantize_model(temp_folder, output_path, profile, device)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/code/uploading/mkmlize.py", line 135, in quantize_model
Inference service mistralai-mistral-nemo-9330-v121 ready after 220.55023670196533s
arushimgupta-peft-save-1-v5-mkmlizer:     flywheel.instrument(
Pipeline stage MKMLDeployer completed in 221.10s
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/instrument.py", line 93, in instrument
run pipeline stage %s
arushimgupta-peft-save-1-v5-mkmlizer:     compiler.save_pretrained(input_model_path, output_model_path, storage_format)
Running pipeline stage StressChecker
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 23, in save_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     self.save_st_pretrained(input_model_path, output_model_path)
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/compiler.py", line 38, in save_st_pretrained
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in model_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/models/mistral.py", line 241, in tensor_merger
arushimgupta-peft-save-1-v5-mkmlizer:     for name, tensor in tensor_iterator:
arushimgupta-peft-save-1-v5-mkmlizer:   File "/opt/conda/lib/python3.10/site-packages/mk1/flywheel/functional/loader.py", line 217, in tensor_compiler
arushimgupta-peft-save-1-v5-mkmlizer:     compiled_tensor = runtime.instrument(tensor, profile.value)
arushimgupta-peft-save-1-v5-mkmlizer: RuntimeError: CUDA error: invalid configuration argument
arushimgupta-peft-save-1-v5-mkmlizer: CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
arushimgupta-peft-save-1-v5-mkmlizer: For debugging consider passing CUDA_LAUNCH_BLOCKING=1
arushimgupta-peft-save-1-v5-mkmlizer: Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
arushimgupta-peft-save-1-v5-mkmlizer: Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:43 (most recent call first):
arushimgupta-peft-save-1-v5-mkmlizer: frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x751b9ef77f86 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x751b9ef26d10 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
Received healthy response to inference request in 2.363326072692871s
arushimgupta-peft-save-1-v5-mkmlizer: frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x751b9f3a2f08 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #3: void at::native::gpu_kernel_impl<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x4de (0x751b5190aa2e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #4: void at::native::gpu_kernel<__nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> >(at::TensorIteratorBase&, __nv_hdl_wrapper_t<false, true, false, __nv_dl_tag<void (*)(at::TensorIteratorBase&), &at::native::direct_copy_kernel_cuda, 18u>, c10::Half (c10::Half)> const&) + 0x34b (0x751b5190b01b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #5: at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&) + 0x38c (0x751b518c9a0c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #6: at::native::copy_device_to_device(at::TensorIterator&, bool, bool) + 0xb25 (0x751b518ca715 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #7: <unknown function> + 0x1910312 (0x751b518cc312 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #8: <unknown function> + 0x1cbebff (0x751b86e99bff in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #9: at::native::copy_(at::Tensor&, at::Tensor const&, bool) + 0x62 (0x751b86e9b5a2 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #10: at::_ops::copy_::call(at::Tensor&, at::Tensor const&, bool) + 0x15c (0x751b87c5635c in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #11: at::native::_to_copy(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1e01 (0x751b871b96b1 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #12: <unknown function> + 0x2e19f8b (0x751b87ff4f8b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
Received healthy response to inference request in 1.7707421779632568s
arushimgupta-peft-save-1-v5-mkmlizer: frame #13: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x751b876fdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #14: <unknown function> + 0x2c58a33 (0x751b87e33a33 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #15: at::_ops::_to_copy::redispatch(c10::DispatchKeySet, at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0xf5 (0x751b876fdc25 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #16: <unknown function> + 0x470df1f (0x751b898e8f1f in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #17: <unknown function> + 0x470e35e (0x751b898e935e in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #18: at::_ops::_to_copy::call(at::Tensor const&, std::optional<c10::ScalarType>, std::optional<c10::Layout>, std::optional<c10::Device>, std::optional<bool>, bool, std::optional<c10::MemoryFormat>) + 0x1eb (0x751b8778d68b in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #19: at::native::to(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0xa2 (0x751b871b6182 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #20: <unknown function> + 0x301e3b0 (0x751b881f93b0 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #21: at::_ops::to_dtype::call(at::Tensor const&, c10::ScalarType, bool, bool, std::optional<c10::MemoryFormat>) + 0x178 (0x751b8793d258 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #22: mkodec::instrument(at::Tensor, int) + 0x4b (0x751ac37c81ab in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #23: <unknown function> + 0x981e2 (0x751ac37b01e2 in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
Received healthy response to inference request in 1.785046100616455s
arushimgupta-peft-save-1-v5-mkmlizer: frame #24: <unknown function> + 0xa7b9b (0x751ac37bfb9b in /opt/conda/lib/python3.10/site-packages/mk1/flywheel/runtime.cpython-310-x86_64-linux-gnu.so)
arushimgupta-peft-save-1-v5-mkmlizer: frame #25: python3() [0x4fd907]
arushimgupta-peft-save-1-v5-mkmlizer: <omitting python frames>
arushimgupta-peft-save-1-v5-mkmlizer: frame #28: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #30: python3() [0x5112cf]
arushimgupta-peft-save-1-v5-mkmlizer: frame #43: python3() [0x5095ce]
arushimgupta-peft-save-1-v5-mkmlizer: frame #50: python3() [0x509857]
arushimgupta-peft-save-1-v5-mkmlizer: frame #54: python3() [0x5cf913]
arushimgupta-peft-save-1-v5-mkmlizer: frame #57: python3() [0x5951c2]
arushimgupta-peft-save-1-v5-mkmlizer: frame #59: python3() [0x5c5ef7]
arushimgupta-peft-save-1-v5-mkmlizer: frame #60: python3() [0x5c1030]
Received healthy response to inference request in 1.944331169128418s
arushimgupta-peft-save-1-v5-mkmlizer: frame #61: python3() [0x459781]
arushimgupta-peft-save-1-v5-mkmlizer: 
                                                   
Received healthy response to inference request in 1.7631661891937256s
5 requests
0 failed requests
5th percentile: 1.7646813869476319
10th percentile: 1.7661965847015382
20th percentile: 1.7692269802093505
30th percentile: 1.7736029624938965
40th percentile: 1.7793245315551758
50th percentile: 1.785046100616455
60th percentile: 1.8487601280212402
70th percentile: 1.9124741554260254
80th percentile: 2.0281301498413087
90th percentile: 2.1957281112670897
95th percentile: 2.2795270919799804
99th percentile: 2.346566276550293
mean time: 1.9253223419189454
Pipeline stage StressChecker completed in 13.86s
run pipeline stage %s
Running pipeline stage TriggerMKMLProfilingPipeline
run_pipeline:run_in_cloud %s
starting trigger_guanaco_pipeline args=%s
Job arushimgupta-peft-save-1-v5-mkmlizer completed after 54.6s with status: failed
Stopping job with name arushimgupta-peft-save-1-v5-mkmlizer
clean up pipeline due to error=MKMLizerError('')
Shutdown handler de-registered
MKMLizerError('')
arushimgupta-peft-save-1_v5 status is now failed due to DeploymentManager action
Pipeline stage TriggerMKMLProfilingPipeline completed in 8.56s
Shutdown handler de-registered
mistralai-mistral-nemo_9330_v121 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 0.29s
run pipeline stage %s
Running pipeline stage MKMLProfilerTemplater
Pipeline stage MKMLProfilerTemplater completed in 0.21s
run pipeline stage %s
Running pipeline stage MKMLProfilerDeployer
Creating inference service mistralai-mistral-nemo-9330-v121-profiler
Waiting for inference service mistralai-mistral-nemo-9330-v121-profiler to be ready
Tearing down inference service mistralai-mistral-nemo-9330-v121-profiler
%s, retrying in %s seconds...
Creating inference service mistralai-mistral-nemo-9330-v121-profiler
Waiting for inference service mistralai-mistral-nemo-9330-v121-profiler to be ready
Tearing down inference service mistralai-mistral-nemo-9330-v121-profiler
%s, retrying in %s seconds...
Creating inference service mistralai-mistral-nemo-9330-v121-profiler
Waiting for inference service mistralai-mistral-nemo-9330-v121-profiler to be ready
Tearing down inference service mistralai-mistral-nemo-9330-v121-profiler
clean up pipeline due to error=%s
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
mistralai-mistral-nemo_9330_v121 status is now inactive due to auto deactivation removed underperforming models
Shutdown handler de-registered
admin requested tearing down of blend_hanik_2024-09-27
Tearing down inference service mistralai-mistral-nemo-9330-v112
Service mistralai-mistral-nemo-9330-v111 has been torndown
Checking if service mistralai-mistral-nemo-9330-v114 is running
Pipeline stage MKMLDeleter completed in 131.35s
run pipeline stage %s
Running pipeline stage MKMLDeleter
run pipeline stage %s
Connection pool is full, discarding connection: %s. Connection pool size: %s
run pipeline %s
Shutdown handler de-registered
Shutdown handler not registered because Python interpreter is not running in the main thread
run pipeline %s
admin requested tearing down of mistralai-mistral-nemo_9330_v121
run pipeline stage %s
blend_fulat_2024-09-27 status is now torndown due to DeploymentManager action
Shutdown handler not registered because Python interpreter is not running in the main thread
admin requested tearing down of blend_kufab_2024-09-27
Service mistralai-mistral-nemo-9330-v112 has been torndown
Pipeline stage MKMLDeleter completed in 129.81s
run pipeline stage %s
Running pipeline stage MKMLModelDeleter
Tearing down inference service mistralai-mistral-nemo-9330-v114
Checking if service mistralai-mistral-nemo-9330-v115 is running
Running pipeline stage MKMLDeleter
admin requested tearing down of blend_rofur_2024-10-03
admin requested tearing down of blend_rofur_2024-10-03
admin requested tearing down of blend_dones_2024-09-27
run pipeline stage %s
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