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AI Energy Score Project 20

meta-llama/Llama-3.2-1B

COMPLETED

Text Generation

v0

cardiffnlp/twitter-roberta-base-sentiment-latest

FAILED

Text Classification

v0

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 warnings.warn( Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 2.73MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 201368.48 examples/s] [codecarbon INFO @ 23:20:33] [setup] RAM Tracking... [codecarbon INFO @ 23:20:33] [setup] GPU Tracking... [codecarbon INFO @ 23:20:33] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 23:20:33] GPU available. Starting setup [codecarbon INFO @ 23:20:33] [setup] CPU Tracking... [codecarbon DEBUG @ 23:20:33] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 23:20:33] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 23:20:33] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 23:20:33] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 23:20:34] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 23:20:34] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] >>> Tracker's metadata: [codecarbon INFO @ 23:20:34] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 23:20:34] Python version: 3.9.20 [codecarbon INFO @ 23:20:34] CodeCarbon version: 2.5.1 [codecarbon INFO @ 23:20:34] Available RAM : 186.705 GB [codecarbon INFO @ 23:20:34] CPU count: 48 [codecarbon INFO @ 23:20:34] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] GPU count: 1 [codecarbon INFO @ 23:20:34] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 23:20:35] Not running on AWS [codecarbon DEBUG @ 23:20:36] Not running on Azure [codecarbon DEBUG @ 23:20:37] Not running on GCP [codecarbon INFO @ 23:20:37] Saving emissions data to file /runs/text_classification/cardiffnlp/twitter-roberta-base-sentiment-latest/2024-10-07-23-20-25/codecarbon.csv [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.0031156789045780897, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 159984.13 examples/s] Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2691.27 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2667.25 examples/s] [codecarbon INFO @ 23:20:38] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.27353525161743164 W [codecarbon DEBUG @ 23:20:38] RAM : 0.27 W during 0.41 s [measurement time: 0.0005] [codecarbon INFO @ 23:20:38] Energy consumed for all GPUs : 0.000007 kWh. Total GPU Power : 65.45066620881586 W [codecarbon DEBUG @ 23:20:38] GPU : 65.45 W during 0.41 s [measurement time: 0.0022] [codecarbon INFO @ 23:20:38] Energy consumed for all CPUs : 0.000005 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 23:20:38] CPU : 42.50 W during 0.41 s [measurement time: 0.0000] [codecarbon INFO @ 23:20:38] 0.000012 kWh of electricity used since the beginning. [codecarbon DEBUG @ 23:20:38] last_duration=0.40900541795417666 ------------------------ [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.4121430969098583, emissions=4.5573209828862485e-06, emissions_rate=1.1057618135681164e-05, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.002233574981801212, emissions=4.5573209828862485e-06, emissions_rate=0.002040370715117479, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 1%|▏ | 14/1000 [00:00<00:07, 137.21it/s]/opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [33,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [34,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [35,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [36,0,0] Assertion `srcIndex < srcSelectDimSize` failed. 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launch(experiment_config=experiment_config) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch raise error File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch while not process_context.join(): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error: Traceback (most recent call last): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap fn(i, *args) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint worker_output = worker(*worker_args) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run benchmark.run(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 178, in run self.run_inference_energy_tracking(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 262, in run_inference_energy_tracking inputs = backend.prepare_inputs(inputs) File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 338, in prepare_inputs inputs[key] = value.to(self.config.device) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

cardiffnlp/twitter-roberta-base-sentiment-latest

FAILED

Text Generation

v0

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 warnings.warn( Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 2.73MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 201368.48 examples/s] [codecarbon INFO @ 23:20:33] [setup] RAM Tracking... [codecarbon INFO @ 23:20:33] [setup] GPU Tracking... [codecarbon INFO @ 23:20:33] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 23:20:33] GPU available. Starting setup [codecarbon INFO @ 23:20:33] [setup] CPU Tracking... [codecarbon DEBUG @ 23:20:33] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 23:20:33] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 23:20:33] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 23:20:33] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 23:20:34] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 23:20:34] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] >>> Tracker's metadata: [codecarbon INFO @ 23:20:34] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 23:20:34] Python version: 3.9.20 [codecarbon INFO @ 23:20:34] CodeCarbon version: 2.5.1 [codecarbon INFO @ 23:20:34] Available RAM : 186.705 GB [codecarbon INFO @ 23:20:34] CPU count: 48 [codecarbon INFO @ 23:20:34] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] GPU count: 1 [codecarbon INFO @ 23:20:34] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 23:20:35] Not running on AWS [codecarbon DEBUG @ 23:20:36] Not running on Azure [codecarbon DEBUG @ 23:20:37] Not running on GCP [codecarbon INFO @ 23:20:37] Saving emissions data to file /runs/text_classification/cardiffnlp/twitter-roberta-base-sentiment-latest/2024-10-07-23-20-25/codecarbon.csv [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.0031156789045780897, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 159984.13 examples/s] Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2691.27 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2667.25 examples/s] [codecarbon INFO @ 23:20:38] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.27353525161743164 W [codecarbon DEBUG @ 23:20:38] RAM : 0.27 W during 0.41 s [measurement time: 0.0005] [codecarbon INFO @ 23:20:38] Energy consumed for all GPUs : 0.000007 kWh. Total GPU Power : 65.45066620881586 W [codecarbon DEBUG @ 23:20:38] GPU : 65.45 W during 0.41 s [measurement time: 0.0022] [codecarbon INFO @ 23:20:38] Energy consumed for all CPUs : 0.000005 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 23:20:38] CPU : 42.50 W during 0.41 s [measurement time: 0.0000] [codecarbon INFO @ 23:20:38] 0.000012 kWh of electricity used since the beginning. [codecarbon DEBUG @ 23:20:38] last_duration=0.40900541795417666 ------------------------ [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.4121430969098583, emissions=4.5573209828862485e-06, emissions_rate=1.1057618135681164e-05, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.002233574981801212, emissions=4.5573209828862485e-06, emissions_rate=0.002040370715117479, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 1%|▏ | 14/1000 [00:00<00:07, 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launch(experiment_config=experiment_config) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch raise error File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch while not process_context.join(): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error: Traceback (most recent call last): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap fn(i, *args) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint worker_output = worker(*worker_args) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run benchmark.run(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 178, in run self.run_inference_energy_tracking(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 262, in run_inference_energy_tracking inputs = backend.prepare_inputs(inputs) File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 338, in prepare_inputs inputs[key] = value.to(self.config.device) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

cardiffnlp/twitter-roberta-base-sentiment-latest

FAILED

Text Generation

v0

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 warnings.warn( Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 2.73MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 201368.48 examples/s] [codecarbon INFO @ 23:20:33] [setup] RAM Tracking... [codecarbon INFO @ 23:20:33] [setup] GPU Tracking... [codecarbon INFO @ 23:20:33] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 23:20:33] GPU available. Starting setup [codecarbon INFO @ 23:20:33] [setup] CPU Tracking... [codecarbon DEBUG @ 23:20:33] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 23:20:33] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 23:20:33] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 23:20:33] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 23:20:34] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 23:20:34] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] >>> Tracker's metadata: [codecarbon INFO @ 23:20:34] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 23:20:34] Python version: 3.9.20 [codecarbon INFO @ 23:20:34] CodeCarbon version: 2.5.1 [codecarbon INFO @ 23:20:34] Available RAM : 186.705 GB [codecarbon INFO @ 23:20:34] CPU count: 48 [codecarbon INFO @ 23:20:34] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] GPU count: 1 [codecarbon INFO @ 23:20:34] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 23:20:35] Not running on AWS [codecarbon DEBUG @ 23:20:36] Not running on Azure [codecarbon DEBUG @ 23:20:37] Not running on GCP [codecarbon INFO @ 23:20:37] Saving emissions data to file /runs/text_classification/cardiffnlp/twitter-roberta-base-sentiment-latest/2024-10-07-23-20-25/codecarbon.csv [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.0031156789045780897, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 159984.13 examples/s] Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2691.27 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2667.25 examples/s] [codecarbon INFO @ 23:20:38] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.27353525161743164 W [codecarbon DEBUG @ 23:20:38] RAM : 0.27 W during 0.41 s [measurement time: 0.0005] [codecarbon INFO @ 23:20:38] Energy consumed for all GPUs : 0.000007 kWh. Total GPU Power : 65.45066620881586 W [codecarbon DEBUG @ 23:20:38] GPU : 65.45 W during 0.41 s [measurement time: 0.0022] [codecarbon INFO @ 23:20:38] Energy consumed for all CPUs : 0.000005 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 23:20:38] CPU : 42.50 W during 0.41 s [measurement time: 0.0000] [codecarbon INFO @ 23:20:38] 0.000012 kWh of electricity used since the beginning. [codecarbon DEBUG @ 23:20:38] last_duration=0.40900541795417666 ------------------------ [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.4121430969098583, emissions=4.5573209828862485e-06, emissions_rate=1.1057618135681164e-05, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.002233574981801212, emissions=4.5573209828862485e-06, emissions_rate=0.002040370715117479, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 1%|▏ | 14/1000 [00:00<00:07, 137.21it/s]/opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [33,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [34,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [35,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [36,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [37,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [38,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [39,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [40,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [41,0,0] Assertion `srcIndex < srcSelectDimSize` failed. 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launch(experiment_config=experiment_config) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch raise error File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch while not process_context.join(): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error: Traceback (most recent call last): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap fn(i, *args) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint worker_output = worker(*worker_args) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run benchmark.run(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 178, in run self.run_inference_energy_tracking(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 262, in run_inference_energy_tracking inputs = backend.prepare_inputs(inputs) File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 338, in prepare_inputs inputs[key] = value.to(self.config.device) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

cardiffnlp/twitter-roberta-base-sentiment-latest

FAILED

Text Classification

v0

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 warnings.warn( Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 2.73MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 201368.48 examples/s] [codecarbon INFO @ 23:20:33] [setup] RAM Tracking... [codecarbon INFO @ 23:20:33] [setup] GPU Tracking... [codecarbon INFO @ 23:20:33] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 23:20:33] GPU available. Starting setup [codecarbon INFO @ 23:20:33] [setup] CPU Tracking... [codecarbon DEBUG @ 23:20:33] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 23:20:33] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 23:20:33] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 23:20:33] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 23:20:34] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 23:20:34] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] >>> Tracker's metadata: [codecarbon INFO @ 23:20:34] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 23:20:34] Python version: 3.9.20 [codecarbon INFO @ 23:20:34] CodeCarbon version: 2.5.1 [codecarbon INFO @ 23:20:34] Available RAM : 186.705 GB [codecarbon INFO @ 23:20:34] CPU count: 48 [codecarbon INFO @ 23:20:34] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] GPU count: 1 [codecarbon INFO @ 23:20:34] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 23:20:35] Not running on AWS [codecarbon DEBUG @ 23:20:36] Not running on Azure [codecarbon DEBUG @ 23:20:37] Not running on GCP [codecarbon INFO @ 23:20:37] Saving emissions data to file /runs/text_classification/cardiffnlp/twitter-roberta-base-sentiment-latest/2024-10-07-23-20-25/codecarbon.csv [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.0031156789045780897, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 159984.13 examples/s] Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2691.27 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2667.25 examples/s] [codecarbon INFO @ 23:20:38] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.27353525161743164 W [codecarbon DEBUG @ 23:20:38] RAM : 0.27 W during 0.41 s [measurement time: 0.0005] [codecarbon INFO @ 23:20:38] Energy consumed for all GPUs : 0.000007 kWh. Total GPU Power : 65.45066620881586 W [codecarbon DEBUG @ 23:20:38] GPU : 65.45 W during 0.41 s [measurement time: 0.0022] [codecarbon INFO @ 23:20:38] Energy consumed for all CPUs : 0.000005 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 23:20:38] CPU : 42.50 W during 0.41 s [measurement time: 0.0000] [codecarbon INFO @ 23:20:38] 0.000012 kWh of electricity used since the beginning. [codecarbon DEBUG @ 23:20:38] last_duration=0.40900541795417666 ------------------------ [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.4121430969098583, emissions=4.5573209828862485e-06, emissions_rate=1.1057618135681164e-05, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.002233574981801212, emissions=4.5573209828862485e-06, emissions_rate=0.002040370715117479, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 1%|▏ | 14/1000 [00:00<00:07, 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launch(experiment_config=experiment_config) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch raise error File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch while not process_context.join(): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error: Traceback (most recent call last): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap fn(i, *args) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint worker_output = worker(*worker_args) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run benchmark.run(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 178, in run self.run_inference_energy_tracking(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 262, in run_inference_energy_tracking inputs = backend.prepare_inputs(inputs) File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 338, in prepare_inputs inputs[key] = value.to(self.config.device) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

cardiffnlp/twitter-roberta-base-sentiment-latest

FAILED

Text Classification

v0

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 warnings.warn( Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 2.73MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 201368.48 examples/s] [codecarbon INFO @ 23:20:33] [setup] RAM Tracking... [codecarbon INFO @ 23:20:33] [setup] GPU Tracking... [codecarbon INFO @ 23:20:33] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 23:20:33] GPU available. Starting setup [codecarbon INFO @ 23:20:33] [setup] CPU Tracking... [codecarbon DEBUG @ 23:20:33] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 23:20:33] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 23:20:33] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 23:20:33] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 23:20:34] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 23:20:34] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] >>> Tracker's metadata: [codecarbon INFO @ 23:20:34] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 23:20:34] Python version: 3.9.20 [codecarbon INFO @ 23:20:34] CodeCarbon version: 2.5.1 [codecarbon INFO @ 23:20:34] Available RAM : 186.705 GB [codecarbon INFO @ 23:20:34] CPU count: 48 [codecarbon INFO @ 23:20:34] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] GPU count: 1 [codecarbon INFO @ 23:20:34] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 23:20:35] Not running on AWS [codecarbon DEBUG @ 23:20:36] Not running on Azure [codecarbon DEBUG @ 23:20:37] Not running on GCP [codecarbon INFO @ 23:20:37] Saving emissions data to file /runs/text_classification/cardiffnlp/twitter-roberta-base-sentiment-latest/2024-10-07-23-20-25/codecarbon.csv [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.0031156789045780897, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 159984.13 examples/s] Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2691.27 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2667.25 examples/s] [codecarbon INFO @ 23:20:38] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.27353525161743164 W [codecarbon DEBUG @ 23:20:38] RAM : 0.27 W during 0.41 s [measurement time: 0.0005] [codecarbon INFO @ 23:20:38] Energy consumed for all GPUs : 0.000007 kWh. Total GPU Power : 65.45066620881586 W [codecarbon DEBUG @ 23:20:38] GPU : 65.45 W during 0.41 s [measurement time: 0.0022] [codecarbon INFO @ 23:20:38] Energy consumed for all CPUs : 0.000005 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 23:20:38] CPU : 42.50 W during 0.41 s [measurement time: 0.0000] [codecarbon INFO @ 23:20:38] 0.000012 kWh of electricity used since the beginning. [codecarbon DEBUG @ 23:20:38] last_duration=0.40900541795417666 ------------------------ [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.4121430969098583, emissions=4.5573209828862485e-06, emissions_rate=1.1057618135681164e-05, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.002233574981801212, emissions=4.5573209828862485e-06, emissions_rate=0.002040370715117479, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 1%|▏ | 14/1000 [00:00<00:07, 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/opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [520,0,0], thread: [125,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [520,0,0], thread: [126,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [520,0,0], thread: [127,0,0] Assertion `srcIndex < srcSelectDimSize` failed. 2%|▏ | 22/1000 [00:00<00:07, 124.18it/s] Error executing job with overrides: ['backend.model=cardiffnlp/twitter-roberta-base-sentiment-latest', 'backend.processor=cardiffnlp/twitter-roberta-base-sentiment-latest'] Traceback (most recent call last): File "/optimum-benchmark/optimum_benchmark/cli.py", line 65, in benchmark_cli benchmark_report: BenchmarkReport = launch(experiment_config=experiment_config) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch raise error File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch while not process_context.join(): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error: Traceback (most recent call last): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap fn(i, *args) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint worker_output = worker(*worker_args) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run benchmark.run(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 178, in run self.run_inference_energy_tracking(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 262, in run_inference_energy_tracking inputs = backend.prepare_inputs(inputs) File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 338, in prepare_inputs inputs[key] = value.to(self.config.device) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

cardiffnlp/twitter-roberta-base-sentiment-latest

FAILED

Text Classification

v0

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 warnings.warn( Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 2.73MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.83MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 201368.48 examples/s] [codecarbon INFO @ 23:20:33] [setup] RAM Tracking... [codecarbon INFO @ 23:20:33] [setup] GPU Tracking... [codecarbon INFO @ 23:20:33] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 23:20:33] GPU available. Starting setup [codecarbon INFO @ 23:20:33] [setup] CPU Tracking... [codecarbon DEBUG @ 23:20:33] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 23:20:33] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 23:20:33] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 23:20:33] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 23:20:34] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 23:20:34] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] >>> Tracker's metadata: [codecarbon INFO @ 23:20:34] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 23:20:34] Python version: 3.9.20 [codecarbon INFO @ 23:20:34] CodeCarbon version: 2.5.1 [codecarbon INFO @ 23:20:34] Available RAM : 186.705 GB [codecarbon INFO @ 23:20:34] CPU count: 48 [codecarbon INFO @ 23:20:34] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 23:20:34] GPU count: 1 [codecarbon INFO @ 23:20:34] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 23:20:35] Not running on AWS [codecarbon DEBUG @ 23:20:36] Not running on Azure [codecarbon DEBUG @ 23:20:37] Not running on GCP [codecarbon INFO @ 23:20:37] Saving emissions data to file /runs/text_classification/cardiffnlp/twitter-roberta-base-sentiment-latest/2024-10-07-23-20-25/codecarbon.csv [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.0031156789045780897, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 159984.13 examples/s] Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2691.27 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2667.25 examples/s] [codecarbon INFO @ 23:20:38] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.27353525161743164 W [codecarbon DEBUG @ 23:20:38] RAM : 0.27 W during 0.41 s [measurement time: 0.0005] [codecarbon INFO @ 23:20:38] Energy consumed for all GPUs : 0.000007 kWh. Total GPU Power : 65.45066620881586 W [codecarbon DEBUG @ 23:20:38] GPU : 65.45 W during 0.41 s [measurement time: 0.0022] [codecarbon INFO @ 23:20:38] Energy consumed for all CPUs : 0.000005 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 23:20:38] CPU : 42.50 W during 0.41 s [measurement time: 0.0000] [codecarbon INFO @ 23:20:38] 0.000012 kWh of electricity used since the beginning. [codecarbon DEBUG @ 23:20:38] last_duration=0.40900541795417666 ------------------------ [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.4121430969098583, emissions=4.5573209828862485e-06, emissions_rate=1.1057618135681164e-05, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 23:20:38] EmissionsData(timestamp='2024-10-07T23:20:38', project_name='codecarbon', run_id='0e4bc046-c4df-4e48-866a-9a8d99e426db', duration=0.002233574981801212, emissions=4.5573209828862485e-06, emissions_rate=0.002040370715117479, cpu_power=42.5, gpu_power=65.45066620881586, ram_power=0.27353525161743164, cpu_energy=4.864328597775764e-06, gpu_energy=7.450561515298659e-06, ram_energy=3.1077252325416766e-08, energy_consumed=1.234596736539984e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 1%|▏ | 14/1000 [00:00<00:07, 137.21it/s]/opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [33,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [34,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [35,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [36,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [37,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [38,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [39,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [40,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [41,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [42,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [43,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [44,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [45,0,0] Assertion `srcIndex < srcSelectDimSize` failed. /opt/conda/conda-bld/pytorch_1720538643151/work/aten/src/ATen/native/cuda/Indexing.cu:1284: indexSelectLargeIndex: block: [516,0,0], thread: [46,0,0] Assertion `srcIndex < srcSelectDimSize` failed. 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launch(experiment_config=experiment_config) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch raise error File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch while not process_context.join(): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error: Traceback (most recent call last): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap fn(i, *args) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint worker_output = worker(*worker_args) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run benchmark.run(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 178, in run self.run_inference_energy_tracking(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 262, in run_inference_energy_tracking inputs = backend.prepare_inputs(inputs) File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 338, in prepare_inputs inputs[key] = value.to(self.config.device) RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

Emanuel/twitter-emotion-deberta-v3-base

FAILED

Text Classification

v0

Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 3.49MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.37MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.37MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 210599.72 examples/s] [codecarbon INFO @ 12:58:38] [setup] RAM Tracking... [codecarbon INFO @ 12:58:38] [setup] GPU Tracking... [codecarbon INFO @ 12:58:38] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 12:58:38] GPU available. Starting setup [codecarbon INFO @ 12:58:38] [setup] CPU Tracking... [codecarbon DEBUG @ 12:58:38] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 12:58:38] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 12:58:38] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 12:58:38] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 12:58:39] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 12:58:39] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 12:58:39] >>> Tracker's metadata: [codecarbon INFO @ 12:58:39] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 12:58:39] Python version: 3.9.20 [codecarbon INFO @ 12:58:39] CodeCarbon version: 2.5.1 [codecarbon INFO @ 12:58:39] Available RAM : 186.705 GB [codecarbon INFO @ 12:58:39] CPU count: 48 [codecarbon INFO @ 12:58:39] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 12:58:39] GPU count: 1 [codecarbon INFO @ 12:58:39] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 12:58:40] Not running on AWS [codecarbon DEBUG @ 12:58:41] Not running on Azure [codecarbon DEBUG @ 12:58:42] Not running on GCP [codecarbon INFO @ 12:58:42] Saving emissions data to file /runs/text_classification/Emanuel/twitter-emotion-deberta-v3-base/2024-10-08-12-58-29/codecarbon.csv [codecarbon DEBUG @ 12:58:42] EmissionsData(timestamp='2024-10-08T12:58:42', project_name='codecarbon', run_id='572ec4e9-8ce3-489c-9abe-fc829b7d0905', duration=0.002154106000034517, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 164844.52 examples/s] Error executing job with overrides: ['backend.model=Emanuel/twitter-emotion-deberta-v3-base', 'backend.processor=Emanuel/twitter-emotion-deberta-v3-base'] Traceback (most recent call last): File "/optimum-benchmark/optimum_benchmark/cli.py", line 65, in benchmark_cli benchmark_report: BenchmarkReport = launch(experiment_config=experiment_config) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch raise error File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch while not process_context.join(): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join raise ProcessRaisedException(msg, error_index, failed_process.pid) torch.multiprocessing.spawn.ProcessRaisedException: -- Process 0 terminated with the following error: Traceback (most recent call last): File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap fn(i, *args) File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint worker_output = worker(*worker_args) File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run benchmark.run(backend) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 122, in run self.dataset = preprocess( File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/preprocessing_utils.py", line 28, in preprocess return task_to_preprocessing[task](dataset, config, preprocessor, pretrained_config) File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/preprocessing_utils.py", line 112, in text_classification_preprocessing tokenizer.pad_token = tokenizer.eos_token AttributeError: 'NoneType' object has no attribute 'eos_token' Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

distilbert/distilbert-base-uncased-finetuned-sst-2-english

COMPLETED

Text Classification

v0

/opt/conda/lib/python3.9/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 warnings.warn( Downloading readme: 0%| | 0.00/271 [00:00<?, ?B/s] Downloading readme: 100%|██████████| 271/271 [00:00<00:00, 2.45MB/s] Downloading data: 0%| | 0.00/453k [00:00<?, ?B/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.77MB/s] Downloading data: 100%|██████████| 453k/453k [00:00<00:00, 2.76MB/s] Generating train split: 0%| | 0/1000 [00:00<?, ? examples/s] Generating train split: 100%|██████████| 1000/1000 [00:00<00:00, 211214.83 examples/s] [codecarbon INFO @ 13:01:54] [setup] RAM Tracking... [codecarbon INFO @ 13:01:54] [setup] GPU Tracking... [codecarbon INFO @ 13:01:54] Tracking Nvidia GPU via pynvml [codecarbon DEBUG @ 13:01:54] GPU available. Starting setup [codecarbon INFO @ 13:01:54] [setup] CPU Tracking... [codecarbon DEBUG @ 13:01:54] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget [codecarbon DEBUG @ 13:01:54] Not using the RAPL interface, an exception occurred while instantiating IntelRAPL : Intel RAPL files not found at /sys/class/powercap/intel-rapl on linux [codecarbon DEBUG @ 13:01:54] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics [codecarbon WARNING @ 13:01:54] No CPU tracking mode found. Falling back on CPU constant mode. [codecarbon WARNING @ 13:01:55] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us. [codecarbon INFO @ 13:01:55] CPU Model on constant consumption mode: AMD EPYC 7R32 [codecarbon INFO @ 13:01:55] >>> Tracker's metadata: [codecarbon INFO @ 13:01:55] Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35 [codecarbon INFO @ 13:01:55] Python version: 3.9.20 [codecarbon INFO @ 13:01:55] CodeCarbon version: 2.5.1 [codecarbon INFO @ 13:01:55] Available RAM : 186.705 GB [codecarbon INFO @ 13:01:55] CPU count: 48 [codecarbon INFO @ 13:01:55] CPU model: AMD EPYC 7R32 [codecarbon INFO @ 13:01:55] GPU count: 1 [codecarbon INFO @ 13:01:55] GPU model: 1 x NVIDIA A10G [codecarbon DEBUG @ 13:01:56] Not running on AWS [codecarbon DEBUG @ 13:01:57] Not running on Azure [codecarbon DEBUG @ 13:01:58] Not running on GCP [codecarbon INFO @ 13:01:58] Saving emissions data to file /runs/text_classification/distilbert/distilbert-base-uncased-finetuned-sst-2-english/2024-10-08-13-01-46/codecarbon.csv [codecarbon DEBUG @ 13:01:58] EmissionsData(timestamp='2024-10-08T13:01:58', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.0022171269999944343, emissions=0.0, emissions_rate=0.0, cpu_power=0.0, gpu_power=0.0, ram_power=0.0, cpu_energy=0, gpu_energy=0, ram_energy=0, energy_consumed=0, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) Filter: 0%| | 0/1000 [00:00<?, ? examples/s] Filter: 100%|██████████| 1000/1000 [00:00<00:00, 163744.06 examples/s] Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2480.60 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2463.09 examples/s] [codecarbon INFO @ 13:01:59] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.27339935302734375 W [codecarbon DEBUG @ 13:01:59] RAM : 0.27 W during 0.42 s [measurement time: 0.0005] [codecarbon INFO @ 13:01:59] Energy consumed for all GPUs : 0.000007 kWh. Total GPU Power : 62.61794011262714 W [codecarbon DEBUG @ 13:01:59] GPU : 62.62 W during 0.42 s [measurement time: 0.0022] [codecarbon INFO @ 13:01:59] Energy consumed for all CPUs : 0.000005 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:01:59] CPU : 42.50 W during 0.43 s [measurement time: 0.0000] [codecarbon INFO @ 13:01:59] 0.000012 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:01:59] last_duration=0.423816565999914 ------------------------ [codecarbon DEBUG @ 13:01:59] EmissionsData(timestamp='2024-10-08T13:01:59', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.42697465799994916, emissions=4.598363284616753e-06, emissions_rate=1.0769639833325424e-05, cpu_power=42.5, gpu_power=62.61794011262714, ram_power=0.27339935302734375, cpu_energy=5.039404527083176e-06, gpu_energy=7.385561463999803e-06, ram_energy=3.218663418082601e-08, energy_consumed=1.2457152625263805e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:01:59] EmissionsData(timestamp='2024-10-08T13:01:59', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.0022345670000731843, emissions=4.598363284616753e-06, emissions_rate=0.0020578319130579443, cpu_power=42.5, gpu_power=62.61794011262714, ram_power=0.27339935302734375, cpu_energy=5.039404527083176e-06, gpu_energy=7.385561463999803e-06, ram_energy=3.218663418082601e-08, energy_consumed=1.2457152625263805e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 2%|▎ | 25/1000 [00:00<00:04, 240.73it/s] 5%|▌ | 50/1000 [00:00<00:03, 239.68it/s] 7%|▋ | 74/1000 [00:00<00:03, 239.07it/s] 10%|▉ | 98/1000 [00:00<00:03, 238.38it/s] 12%|█▏ | 123/1000 [00:00<00:03, 240.99it/s] 15%|█▍ | 148/1000 [00:00<00:03, 240.86it/s] 17%|█▋ | 173/1000 [00:00<00:03, 243.66it/s] 20%|█▉ | 198/1000 [00:00<00:03, 243.66it/s] 22%|██▏ | 223/1000 [00:00<00:03, 244.09it/s] 25%|██▍ | 248/1000 [00:01<00:03, 243.10it/s] 27%|██▋ | 273/1000 [00:01<00:03, 241.62it/s] 30%|██▉ | 298/1000 [00:01<00:02, 243.21it/s] 32%|███▎ | 325/1000 [00:01<00:02, 248.72it/s] 35%|███▌ | 352/1000 [00:01<00:02, 254.48it/s] 38%|███▊ | 379/1000 [00:01<00:02, 258.46it/s] 40%|████ | 405/1000 [00:01<00:02, 253.37it/s] 43%|████▎ | 431/1000 [00:01<00:02, 255.03it/s] 46%|████▌ | 457/1000 [00:01<00:02, 255.15it/s] 48%|████▊ | 484/1000 [00:01<00:01, 258.12it/s] 51%|█████ | 510/1000 [00:02<00:01, 257.83it/s] 54%|█████▎ | 536/1000 [00:02<00:01, 258.22it/s] 56%|█████▋ | 563/1000 [00:02<00:01, 259.76it/s] 59%|█████▉ | 589/1000 [00:02<00:01, 258.08it/s] 62%|██████▏ | 616/1000 [00:02<00:01, 258.99it/s] 64%|██████▍ | 642/1000 [00:02<00:01, 258.11it/s] 67%|██████▋ | 669/1000 [00:02<00:01, 259.99it/s] 70%|██████▉ | 696/1000 [00:02<00:01, 261.94it/s] 72%|███████▏ | 723/1000 [00:02<00:01, 257.46it/s] 75%|███████▌ | 750/1000 [00:02<00:00, 258.43it/s] 78%|███████▊ | 777/1000 [00:03<00:00, 261.16it/s] 80%|████████ | 804/1000 [00:03<00:00, 262.30it/s] 83%|████████▎ | 831/1000 [00:03<00:00, 262.46it/s] 86%|████████▌ | 858/1000 [00:03<00:00, 262.74it/s] 89%|████████▊ | 886/1000 [00:03<00:00, 265.29it/s] 91%|█████████▏| 913/1000 [00:03<00:00, 264.31it/s] 94%|█████████▍| 940/1000 [00:03<00:00, 263.50it/s] 97%|█████████▋| 967/1000 [00:03<00:00, 262.46it/s] 99%|█████████▉| 994/1000 [00:03<00:00, 262.85it/s] 100%|██████████| 1000/1000 [00:03<00:00, 254.98it/s] [codecarbon WARNING @ 13:02:03] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:03] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.344088077545166 W [codecarbon DEBUG @ 13:02:03] RAM : 0.34 W during 3.93 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:03] Energy consumed for all GPUs : 0.000187 kWh. Total GPU Power : 164.50826052714714 W [codecarbon DEBUG @ 13:02:03] GPU : 164.51 W during 3.93 s [measurement time: 0.0053] [codecarbon INFO @ 13:02:03] Energy consumed for all CPUs : 0.000051 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:03] CPU : 42.50 W during 3.93 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:03] 0.000239 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:03] last_duration=3.9250225560000445 ------------------------ [codecarbon DEBUG @ 13:02:03] EmissionsData(timestamp='2024-10-08T13:02:03', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.9313579220000747, emissions=8.809180558455209e-05, emissions_rate=2.2407475313195464e-05, cpu_power=42.5, gpu_power=164.50826052714714, ram_power=0.344088077545166, cpu_energy=5.1450096922916725e-05, gpu_energy=0.00018678681609600022, ram_energy=4.073608845729088e-07, energy_consumed=0.00023864427390348986, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:03] EmissionsData(timestamp='2024-10-08T13:02:03', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.0020242140000164, emissions=8.809180558455209e-05, emissions_rate=0.04351901804050282, cpu_power=42.5, gpu_power=164.50826052714714, ram_power=0.344088077545166, cpu_energy=5.1450096922916725e-05, gpu_energy=0.00018678681609600022, ram_energy=4.073608845729088e-07, energy_consumed=0.00023864427390348986, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 3%|▎ | 27/1000 [00:00<00:03, 261.83it/s] 5%|▌ | 54/1000 [00:00<00:03, 259.36it/s] 8%|▊ | 80/1000 [00:00<00:03, 256.36it/s] 11%|█ | 106/1000 [00:00<00:03, 251.00it/s] 13%|█▎ | 133/1000 [00:00<00:03, 255.32it/s] 16%|█▌ | 160/1000 [00:00<00:03, 257.75it/s] 19%|█▊ | 186/1000 [00:00<00:03, 258.25it/s] 21%|██▏ | 213/1000 [00:00<00:03, 260.13it/s] 24%|██▍ | 240/1000 [00:00<00:02, 261.59it/s] 27%|██▋ | 267/1000 [00:01<00:02, 254.67it/s] 29%|██▉ | 294/1000 [00:01<00:02, 258.72it/s] 32%|███▏ | 321/1000 [00:01<00:02, 260.10it/s] 35%|███▍ | 348/1000 [00:01<00:02, 262.56it/s] 38%|███▊ | 375/1000 [00:01<00:02, 264.45it/s] 40%|████ | 402/1000 [00:01<00:02, 261.13it/s] 43%|████▎ | 429/1000 [00:01<00:02, 259.33it/s] 46%|████▌ | 455/1000 [00:01<00:02, 258.04it/s] 48%|████▊ | 482/1000 [00:01<00:01, 260.32it/s] 51%|█████ | 509/1000 [00:01<00:01, 259.16it/s] 54%|█████▎ | 535/1000 [00:02<00:01, 257.88it/s] 56%|█████▌ | 561/1000 [00:02<00:01, 257.45it/s] 59%|█████▊ | 587/1000 [00:02<00:01, 255.24it/s] 61%|██████▏ | 613/1000 [00:02<00:01, 255.02it/s] 64%|██████▍ | 639/1000 [00:02<00:01, 253.69it/s] 67%|██████▋ | 666/1000 [00:02<00:01, 257.24it/s] 69%|██████▉ | 693/1000 [00:02<00:01, 259.29it/s] 72%|███████▏ | 719/1000 [00:02<00:01, 256.61it/s] 74%|███████▍ | 745/1000 [00:02<00:00, 257.58it/s] 77%|███████▋ | 771/1000 [00:02<00:00, 257.89it/s] 80%|███████▉ | 798/1000 [00:03<00:00, 259.12it/s] 82%|████████▏ | 824/1000 [00:03<00:00, 256.32it/s] 85%|████████▌ | 850/1000 [00:03<00:00, 255.23it/s] 88%|████████▊ | 876/1000 [00:03<00:00, 256.48it/s] 90%|█████████ | 903/1000 [00:03<00:00, 257.75it/s] 93%|█████████▎| 930/1000 [00:03<00:00, 259.74it/s] 96%|█████████▌| 956/1000 [00:03<00:00, 258.72it/s] 98%|█████████▊| 983/1000 [00:03<00:00, 259.60it/s] 100%|██████████| 1000/1000 [00:03<00:00, 258.19it/s] [codecarbon WARNING @ 13:02:07] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:07] Energy consumed for RAM : 0.000001 kWh. RAM Power : 0.3441166877746582 W [codecarbon DEBUG @ 13:02:07] RAM : 0.34 W during 3.88 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:07] Energy consumed for all GPUs : 0.000360 kWh. Total GPU Power : 161.22114466323345 W [codecarbon DEBUG @ 13:02:07] GPU : 161.22 W during 3.88 s [measurement time: 0.0022] [codecarbon INFO @ 13:02:07] Energy consumed for all CPUs : 0.000097 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:07] CPU : 42.50 W during 3.88 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:07] 0.000458 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:07] last_duration=3.8756087210000487 ------------------------ [codecarbon DEBUG @ 13:02:07] EmissionsData(timestamp='2024-10-08T13:02:07', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.878755604000048, emissions=0.00016921236807989048, emissions_rate=4.362542664595487e-05, cpu_power=42.5, gpu_power=161.22114466323345, ram_power=0.3441166877746582, cpu_energy=9.723976907152746e-05, gpu_energy=0.0003603855660860005, ram_energy=7.778324010774964e-07, energy_consumed=0.00045840316755860547, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:07] EmissionsData(timestamp='2024-10-08T13:02:07', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.002066713999965941, emissions=0.00016921236807989048, emissions_rate=0.08187507709469188, cpu_power=42.5, gpu_power=161.22114466323345, ram_power=0.3441166877746582, cpu_energy=9.723976907152746e-05, gpu_energy=0.0003603855660860005, ram_energy=7.778324010774964e-07, energy_consumed=0.00045840316755860547, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 3%|▎ | 26/1000 [00:00<00:03, 257.04it/s] 5%|▌ | 52/1000 [00:00<00:03, 249.03it/s] 8%|▊ | 77/1000 [00:00<00:03, 244.75it/s] 10%|█ | 102/1000 [00:00<00:03, 242.10it/s] 13%|█▎ | 128/1000 [00:00<00:03, 246.00it/s] 15%|█▌ | 153/1000 [00:00<00:03, 246.94it/s] 18%|█▊ | 179/1000 [00:00<00:03, 248.80it/s] 20%|██ | 205/1000 [00:00<00:03, 249.85it/s] 23%|██▎ | 231/1000 [00:00<00:03, 250.43it/s] 26%|██▌ | 257/1000 [00:01<00:03, 243.45it/s] 28%|██▊ | 282/1000 [00:01<00:02, 245.33it/s] 31%|███ | 308/1000 [00:01<00:02, 248.70it/s] 33%|███▎ | 334/1000 [00:01<00:02, 249.35it/s] 36%|███▌ | 360/1000 [00:01<00:02, 251.10it/s] 39%|███▊ | 386/1000 [00:01<00:02, 251.26it/s] 41%|████ | 412/1000 [00:01<00:02, 248.87it/s] 44%|████▎ | 437/1000 [00:01<00:02, 248.55it/s] 46%|████▌ | 462/1000 [00:01<00:02, 248.03it/s] 49%|████▉ | 488/1000 [00:01<00:02, 249.33it/s] 51%|█████▏ | 513/1000 [00:02<00:01, 249.12it/s] 54%|█████▍ | 539/1000 [00:02<00:01, 248.68it/s] 56%|█████▋ | 565/1000 [00:02<00:01, 249.15it/s] 59%|█████▉ | 590/1000 [00:02<00:01, 247.38it/s] 62%|██████▏ | 615/1000 [00:02<00:01, 247.13it/s] 64%|██████▍ | 640/1000 [00:02<00:01, 245.57it/s] 67%|██████▋ | 666/1000 [00:02<00:01, 247.18it/s] 69%|██████▉ | 692/1000 [00:02<00:01, 249.23it/s] 72%|███████▏ | 717/1000 [00:02<00:01, 248.36it/s] 74%|███████▍ | 742/1000 [00:02<00:01, 247.85it/s] 77%|███████▋ | 767/1000 [00:03<00:00, 246.47it/s] 79%|███████▉ | 793/1000 [00:03<00:00, 248.62it/s] 82%|████████▏ | 819/1000 [00:03<00:00, 249.28it/s] 84%|████████▍ | 845/1000 [00:03<00:00, 250.82it/s] 87%|████████▋ | 871/1000 [00:03<00:00, 252.75it/s] 90%|████████▉ | 897/1000 [00:03<00:00, 252.70it/s] 92%|█████████▏| 923/1000 [00:03<00:00, 252.33it/s] 95%|█████████▍| 949/1000 [00:03<00:00, 249.49it/s] 98%|█████████▊| 975/1000 [00:03<00:00, 251.17it/s] 100%|██████████| 1000/1000 [00:04<00:00, 248.77it/s] [codecarbon WARNING @ 13:02:11] Background scheduler didn't run for a long period (4s), results might be inaccurate [codecarbon INFO @ 13:02:11] Energy consumed for RAM : 0.000001 kWh. RAM Power : 0.3441195487976074 W [codecarbon DEBUG @ 13:02:11] RAM : 0.34 W during 4.02 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:11] Energy consumed for all GPUs : 0.000547 kWh. Total GPU Power : 166.59459293584817 W [codecarbon DEBUG @ 13:02:11] GPU : 166.59 W during 4.02 s [measurement time: 0.0023] [codecarbon INFO @ 13:02:11] Energy consumed for all CPUs : 0.000145 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:11] CPU : 42.50 W during 4.03 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:11] 0.000692 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:11] last_duration=4.0223104200000535 ------------------------ [codecarbon DEBUG @ 13:02:11] EmissionsData(timestamp='2024-10-08T13:02:11', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=4.025530553999943, emissions=0.0002556195272544281, emissions_rate=6.349958690549085e-05, cpu_power=42.5, gpu_power=166.59459293584817, ram_power=0.3441195487976074, cpu_energy=0.0001447620110951379, gpu_energy=0.0005465593261360004, ram_energy=1.1623287851676462e-06, energy_consumed=0.000692483666016306, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:11] EmissionsData(timestamp='2024-10-08T13:02:11', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.0020257639999954336, emissions=0.0002556195272544281, emissions_rate=0.12618425801574334, cpu_power=42.5, gpu_power=166.59459293584817, ram_power=0.3441195487976074, cpu_energy=0.0001447620110951379, gpu_energy=0.0005465593261360004, ram_energy=1.1623287851676462e-06, energy_consumed=0.000692483666016306, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 2%|▎ | 25/1000 [00:00<00:03, 249.12it/s] 5%|▌ | 50/1000 [00:00<00:03, 244.37it/s] 8%|▊ | 75/1000 [00:00<00:03, 243.61it/s] 10%|█ | 100/1000 [00:00<00:03, 241.96it/s] 13%|█▎ | 126/1000 [00:00<00:03, 246.54it/s] 15%|█▌ | 151/1000 [00:00<00:03, 246.34it/s] 18%|█▊ | 177/1000 [00:00<00:03, 250.17it/s] 20%|██ | 203/1000 [00:00<00:03, 249.16it/s] 23%|██▎ | 229/1000 [00:00<00:03, 249.91it/s] 25%|██▌ | 254/1000 [00:01<00:03, 245.04it/s] 28%|██▊ | 279/1000 [00:01<00:02, 245.16it/s] 30%|███ | 305/1000 [00:01<00:02, 246.77it/s] 33%|███▎ | 331/1000 [00:01<00:02, 248.30it/s] 36%|███▌ | 357/1000 [00:01<00:02, 249.85it/s] 38%|███▊ | 383/1000 [00:01<00:02, 250.67it/s] 41%|████ | 409/1000 [00:01<00:02, 247.40it/s] 43%|████▎ | 434/1000 [00:01<00:02, 246.74it/s] 46%|████▌ | 459/1000 [00:01<00:02, 247.10it/s] 48%|████▊ | 485/1000 [00:01<00:02, 247.60it/s] 51%|█████ | 510/1000 [00:02<00:01, 247.92it/s] 54%|█████▎ | 535/1000 [00:02<00:01, 247.99it/s] 56%|█████▌ | 560/1000 [00:02<00:01, 247.73it/s] 58%|█████▊ | 585/1000 [00:02<00:01, 245.50it/s] 61%|██████ | 610/1000 [00:02<00:01, 246.33it/s] 64%|██████▎ | 635/1000 [00:02<00:01, 246.67it/s] 66%|██████▌ | 660/1000 [00:02<00:01, 247.07it/s] 69%|██████▊ | 686/1000 [00:02<00:01, 249.32it/s] 71%|███████ | 712/1000 [00:02<00:01, 250.57it/s] 74%|███████▍ | 738/1000 [00:02<00:01, 247.79it/s] 76%|███████▋ | 763/1000 [00:03<00:00, 246.53it/s] 79%|███████▉ | 789/1000 [00:03<00:00, 249.12it/s] 81%|████████▏ | 814/1000 [00:03<00:00, 248.38it/s] 84%|████████▍ | 840/1000 [00:03<00:00, 249.67it/s] 86%|████████▋ | 865/1000 [00:03<00:00, 248.89it/s] 89%|████████▉ | 891/1000 [00:03<00:00, 250.65it/s] 92%|█████████▏| 917/1000 [00:03<00:00, 248.36it/s] 94%|█████████▍| 943/1000 [00:03<00:00, 250.04it/s] 97%|█████████▋| 969/1000 [00:03<00:00, 250.44it/s] 100%|█████████▉| 995/1000 [00:04<00:00, 248.36it/s] 100%|██████████| 1000/1000 [00:04<00:00, 247.92it/s] [codecarbon WARNING @ 13:02:15] Background scheduler didn't run for a long period (4s), results might be inaccurate [codecarbon INFO @ 13:02:15] Energy consumed for RAM : 0.000002 kWh. RAM Power : 0.3441195487976074 W [codecarbon DEBUG @ 13:02:15] RAM : 0.34 W during 4.04 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:15] Energy consumed for all GPUs : 0.000728 kWh. Total GPU Power : 161.81244025187812 W [codecarbon DEBUG @ 13:02:15] GPU : 161.81 W during 4.04 s [measurement time: 0.0024] [codecarbon INFO @ 13:02:15] Energy consumed for all CPUs : 0.000192 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:15] CPU : 42.50 W during 4.04 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:15] 0.000922 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:15] last_duration=4.036086341999976 ------------------------ [codecarbon DEBUG @ 13:02:15] EmissionsData(timestamp='2024-10-08T13:02:15', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=4.03931347199989, emissions=0.00034034244004413366, emissions_rate=8.425749633034248e-05, cpu_power=42.5, gpu_power=161.81244025187812, ram_power=0.3441195487976074, cpu_energy=0.00019244719350277567, gpu_energy=0.00072800613796, ram_energy=1.5481412498839128e-06, energy_consumed=0.0009220014727126596, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:15] EmissionsData(timestamp='2024-10-08T13:02:15', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.002023970999971425, emissions=0.00034034244004413366, emissions_rate=0.16815578881759605, cpu_power=42.5, gpu_power=161.81244025187812, ram_power=0.3441195487976074, cpu_energy=0.00019244719350277567, gpu_energy=0.00072800613796, ram_energy=1.5481412498839128e-06, energy_consumed=0.0009220014727126596, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 2%|▎ | 25/1000 [00:00<00:03, 247.32it/s] 5%|▌ | 50/1000 [00:00<00:03, 245.27it/s] 8%|▊ | 75/1000 [00:00<00:03, 242.61it/s] 10%|█ | 100/1000 [00:00<00:03, 241.01it/s] 12%|█▎ | 125/1000 [00:00<00:03, 243.84it/s] 15%|█▌ | 150/1000 [00:00<00:03, 243.68it/s] 18%|█▊ | 176/1000 [00:00<00:03, 246.66it/s] 20%|██ | 201/1000 [00:00<00:03, 245.34it/s] 23%|██▎ | 227/1000 [00:00<00:03, 246.91it/s] 25%|██▌ | 252/1000 [00:01<00:03, 243.73it/s] 28%|██▊ | 279/1000 [00:01<00:02, 249.11it/s] 31%|███ | 306/1000 [00:01<00:02, 253.58it/s] 33%|███▎ | 333/1000 [00:01<00:02, 256.20it/s] 36%|███▌ | 359/1000 [00:01<00:02, 256.61it/s] 38%|███▊ | 385/1000 [00:01<00:02, 256.97it/s] 41%|████ | 411/1000 [00:01<00:02, 254.69it/s] 44%|████▎ | 437/1000 [00:01<00:02, 255.53it/s] 46%|████▋ | 463/1000 [00:01<00:02, 255.22it/s] 49%|████▉ | 490/1000 [00:01<00:01, 257.52it/s] 52%|█████▏ | 517/1000 [00:02<00:01, 258.50it/s] 54%|█████▍ | 543/1000 [00:02<00:01, 257.53it/s] 57%|█████▋ | 569/1000 [00:02<00:01, 255.49it/s] 60%|█████▉ | 595/1000 [00:02<00:01, 253.01it/s] 62%|██████▏ | 622/1000 [00:02<00:01, 254.84it/s] 65%|██████▍ | 648/1000 [00:02<00:01, 254.98it/s] 68%|██████▊ | 675/1000 [00:02<00:01, 257.11it/s] 70%|███████ | 702/1000 [00:02<00:01, 259.58it/s] 73%|███████▎ | 728/1000 [00:02<00:01, 254.25it/s] 76%|███████▌ | 755/1000 [00:02<00:00, 256.17it/s] 78%|███████▊ | 782/1000 [00:03<00:00, 259.33it/s] 81%|████████ | 809/1000 [00:03<00:00, 258.70it/s] 84%|████████▎ | 835/1000 [00:03<00:00, 258.03it/s] 86%|████████▌ | 861/1000 [00:03<00:00, 257.42it/s] 89%|████████▉ | 888/1000 [00:03<00:00, 259.85it/s] 91%|█████████▏| 914/1000 [00:03<00:00, 256.92it/s] 94%|█████████▍| 941/1000 [00:03<00:00, 257.38it/s] 97%|█████████▋| 968/1000 [00:03<00:00, 259.22it/s] 100%|█████████▉| 995/1000 [00:03<00:00, 258.88it/s] 100%|██████████| 1000/1000 [00:03<00:00, 254.27it/s] [codecarbon WARNING @ 13:02:19] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:19] Energy consumed for RAM : 0.000002 kWh. RAM Power : 0.3441195487976074 W [codecarbon DEBUG @ 13:02:19] RAM : 0.34 W during 3.94 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:19] Energy consumed for all GPUs : 0.000913 kWh. Total GPU Power : 169.19881768534145 W [codecarbon DEBUG @ 13:02:19] GPU : 169.20 W during 3.94 s [measurement time: 0.0036] [codecarbon INFO @ 13:02:19] Energy consumed for all CPUs : 0.000239 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:19] CPU : 42.50 W during 3.94 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:19] 0.001154 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:19] last_duration=3.9353076109999847 ------------------------ [codecarbon DEBUG @ 13:02:19] EmissionsData(timestamp='2024-10-08T13:02:19', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.939801181000007, emissions=0.00042593804655264846, emissions_rate=0.00010811155867630258, cpu_power=42.5, gpu_power=169.19881768534145, ram_power=0.3441195487976074, cpu_energy=0.00023895755629652494, gpu_energy=0.0009130015637339997, ram_energy=1.924322054952754e-06, energy_consumed=0.0011538834420854775, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:19] EmissionsData(timestamp='2024-10-08T13:02:19', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.00672881499997402, emissions=0.00042593804655264846, emissions_rate=0.06330060293741067, cpu_power=42.5, gpu_power=169.19881768534145, ram_power=0.3441195487976074, cpu_energy=0.00023895755629652494, gpu_energy=0.0009130015637339997, ram_energy=1.924322054952754e-06, energy_consumed=0.0011538834420854775, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 3%|▎ | 26/1000 [00:00<00:03, 258.57it/s] 5%|▌ | 52/1000 [00:00<00:03, 254.79it/s] 8%|▊ | 78/1000 [00:00<00:03, 248.88it/s] 10%|█ | 103/1000 [00:00<00:03, 239.96it/s] 13%|█▎ | 130/1000 [00:00<00:03, 247.95it/s] 16%|█▌ | 156/1000 [00:00<00:03, 250.76it/s] 18%|█▊ | 182/1000 [00:00<00:03, 252.58it/s] 21%|██ | 209/1000 [00:00<00:03, 255.87it/s] 24%|██▎ | 236/1000 [00:00<00:02, 258.90it/s] 26%|██▌ | 262/1000 [00:01<00:02, 251.53it/s] 29%|██▉ | 289/1000 [00:01<00:02, 255.18it/s] 32%|███▏ | 315/1000 [00:01<00:02, 256.59it/s] 34%|███▍ | 342/1000 [00:01<00:02, 260.56it/s] 37%|███▋ | 369/1000 [00:01<00:02, 262.90it/s] 40%|███▉ | 396/1000 [00:01<00:02, 259.61it/s] 42%|████▏ | 422/1000 [00:01<00:02, 257.92it/s] 45%|████▍ | 448/1000 [00:01<00:02, 258.02it/s] 47%|████▋ | 474/1000 [00:01<00:02, 255.15it/s] 50%|█████ | 500/1000 [00:01<00:01, 254.94it/s] 53%|█████▎ | 526/1000 [00:02<00:01, 256.15it/s] 55%|█████▌ | 553/1000 [00:02<00:01, 259.23it/s] 58%|█████▊ | 579/1000 [00:02<00:01, 257.54it/s] 60%|██████ | 605/1000 [00:02<00:01, 255.94it/s] 63%|██████▎ | 632/1000 [00:02<00:01, 257.35it/s] 66%|██████▌ | 658/1000 [00:02<00:01, 256.41it/s] 68%|██████▊ | 684/1000 [00:02<00:01, 255.76it/s] 71%|███████ | 710/1000 [00:02<00:01, 255.70it/s] 74%|███████▎ | 736/1000 [00:02<00:01, 253.05it/s] 76%|███████▋ | 763/1000 [00:02<00:00, 254.11it/s] 79%|███████▉ | 790/1000 [00:03<00:00, 257.98it/s] 82%|████████▏ | 816/1000 [00:03<00:00, 257.53it/s] 84%|████████▍ | 842/1000 [00:03<00:00, 256.45it/s] 87%|████████▋ | 869/1000 [00:03<00:00, 258.22it/s] 90%|████████▉ | 896/1000 [00:03<00:00, 259.32it/s] 92%|█████████▏| 922/1000 [00:03<00:00, 259.27it/s] 95%|█████████▍| 948/1000 [00:03<00:00, 257.46it/s] 98%|█████████▊| 975/1000 [00:03<00:00, 260.95it/s] 100%|██████████| 1000/1000 [00:03<00:00, 256.43it/s] [codecarbon WARNING @ 13:02:23] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:23] Energy consumed for RAM : 0.000002 kWh. RAM Power : 0.34412240982055664 W [codecarbon DEBUG @ 13:02:23] RAM : 0.34 W during 3.91 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:23] Energy consumed for all GPUs : 0.001094 kWh. Total GPU Power : 166.44713882712838 W [codecarbon DEBUG @ 13:02:23] GPU : 166.45 W during 3.91 s [measurement time: 0.0023] [codecarbon INFO @ 13:02:23] Energy consumed for all CPUs : 0.000285 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:23] CPU : 42.50 W during 3.91 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:23] 0.001381 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:23] last_duration=3.9068080260000215 ------------------------ [codecarbon DEBUG @ 13:02:23] EmissionsData(timestamp='2024-10-08T13:02:23', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.9099457949999987, emissions=0.0005098047109615491, emissions_rate=0.00013038664413544618, cpu_power=42.5, gpu_power=166.44713882712838, ram_power=0.34412240982055664, cpu_energy=0.00028511548199652555, gpu_energy=0.0010936683749340004, ram_energy=2.2977802624369826e-06, energy_consumed=0.001381081637192963, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:23] EmissionsData(timestamp='2024-10-08T13:02:23', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.002026291999982277, emissions=0.0005098047109615491, emissions_rate=0.2515948890712731, cpu_power=42.5, gpu_power=166.44713882712838, ram_power=0.34412240982055664, cpu_energy=0.00028511548199652555, gpu_energy=0.0010936683749340004, ram_energy=2.2977802624369826e-06, energy_consumed=0.001381081637192963, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 3%|▎ | 26/1000 [00:00<00:03, 254.70it/s] 5%|▌ | 52/1000 [00:00<00:03, 254.73it/s] 8%|▊ | 78/1000 [00:00<00:03, 251.95it/s] 10%|█ | 104/1000 [00:00<00:03, 245.93it/s] 13%|█▎ | 130/1000 [00:00<00:03, 250.39it/s] 16%|█▌ | 156/1000 [00:00<00:03, 253.08it/s] 18%|█▊ | 182/1000 [00:00<00:03, 253.07it/s] 21%|██ | 208/1000 [00:00<00:03, 255.23it/s] 24%|██▎ | 235/1000 [00:00<00:02, 259.07it/s] 26%|██▌ | 261/1000 [00:01<00:02, 251.39it/s] 29%|██▉ | 288/1000 [00:01<00:02, 255.49it/s] 32%|███▏ | 315/1000 [00:01<00:02, 257.10it/s] 34%|███▍ | 343/1000 [00:01<00:02, 261.15it/s] 37%|███▋ | 370/1000 [00:01<00:02, 263.21it/s] 40%|███▉ | 397/1000 [00:01<00:02, 260.74it/s] 42%|████▏ | 424/1000 [00:01<00:02, 258.99it/s] 45%|████▌ | 450/1000 [00:01<00:02, 258.60it/s] 48%|████▊ | 477/1000 [00:01<00:02, 259.45it/s] 50%|█████ | 503/1000 [00:01<00:01, 258.53it/s] 53%|█████▎ | 529/1000 [00:02<00:01, 258.74it/s] 56%|█████▌ | 556/1000 [00:02<00:01, 260.32it/s] 58%|█████▊ | 583/1000 [00:02<00:01, 256.77it/s] 61%|██████ | 609/1000 [00:02<00:01, 257.48it/s] 64%|██████▎ | 635/1000 [00:02<00:01, 257.78it/s] 66%|██████▌ | 661/1000 [00:02<00:01, 257.72it/s] 69%|██████▉ | 688/1000 [00:02<00:01, 259.21it/s] 71%|███████▏ | 714/1000 [00:02<00:01, 258.53it/s] 74%|███████▍ | 740/1000 [00:02<00:01, 257.56it/s] 77%|███████▋ | 766/1000 [00:02<00:00, 257.13it/s] 79%|███████▉ | 793/1000 [00:03<00:00, 260.48it/s] 82%|████████▏ | 820/1000 [00:03<00:00, 259.45it/s] 85%|████████▍ | 847/1000 [00:03<00:00, 260.11it/s] 87%|████████▋ | 874/1000 [00:03<00:00, 261.00it/s] 90%|█████████ | 901/1000 [00:03<00:00, 260.67it/s] 93%|█████████▎| 928/1000 [00:03<00:00, 258.07it/s] 95%|█████████▌| 954/1000 [00:03<00:00, 256.35it/s] 98%|█████████▊| 981/1000 [00:03<00:00, 258.44it/s] 100%|██████████| 1000/1000 [00:03<00:00, 257.64it/s] [codecarbon WARNING @ 13:02:27] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:27] Energy consumed for RAM : 0.000003 kWh. RAM Power : 0.34412240982055664 W [codecarbon DEBUG @ 13:02:27] RAM : 0.34 W during 3.88 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:27] Energy consumed for all GPUs : 0.001276 kWh. Total GPU Power : 168.879760235557 W [codecarbon DEBUG @ 13:02:27] GPU : 168.88 W during 3.88 s [measurement time: 0.0025] [codecarbon INFO @ 13:02:27] Energy consumed for all CPUs : 0.000331 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:27] CPU : 42.50 W during 3.89 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:27] 0.001610 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:27] EmissionsData(timestamp='2024-10-08T13:02:27', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.8871041779999587, emissions=0.0005941470596458625, emissions_rate=0.00015285081964321585, cpu_power=42.5, gpu_power=168.879760235557, ram_power=0.34412240982055664, cpu_energy=0.00033100397732916396, gpu_energy=0.0012758954651600008, ram_energy=2.669037804816254e-06, energy_consumed=0.001609568480293981, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon INFO @ 13:02:27] 0.021709 g.CO2eq/s mean an estimation of 684.6247066740339 kg.CO2eq/year [codecarbon DEBUG @ 13:02:27] last_duration=3.883787933000008 ------------------------ [codecarbon DEBUG @ 13:02:27] EmissionsData(timestamp='2024-10-08T13:02:27', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.8874427140000307, emissions=0.0005941470596458625, emissions_rate=0.0001528375087062075, cpu_power=42.5, gpu_power=168.879760235557, ram_power=0.34412240982055664, cpu_energy=0.00033100397732916396, gpu_energy=0.0012758954651600008, ram_energy=2.669037804816254e-06, energy_consumed=0.001609568480293981, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:27] EmissionsData(timestamp='2024-10-08T13:02:27', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.005072793999943315, emissions=0.0005941470596458625, emissions_rate=0.11712422378131297, cpu_power=42.5, gpu_power=168.879760235557, ram_power=0.34412240982055664, cpu_energy=0.00033100397732916396, gpu_energy=0.0012758954651600008, ram_energy=2.669037804816254e-06, energy_consumed=0.001609568480293981, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 3%|▎ | 27/1000 [00:00<00:03, 261.59it/s] 5%|▌ | 54/1000 [00:00<00:03, 256.73it/s] 8%|▊ | 80/1000 [00:00<00:03, 252.77it/s] 11%|█ | 106/1000 [00:00<00:03, 246.51it/s] 13%|█▎ | 131/1000 [00:00<00:03, 246.50it/s] 16%|█▌ | 157/1000 [00:00<00:03, 247.96it/s] 18%|█▊ | 182/1000 [00:00<00:03, 247.01it/s] 21%|██ | 208/1000 [00:00<00:03, 248.47it/s] 24%|██▎ | 235/1000 [00:00<00:03, 254.43it/s] 26%|██▌ | 261/1000 [00:01<00:02, 248.02it/s] 29%|██▉ | 288/1000 [00:01<00:02, 253.45it/s] 32%|███▏ | 315/1000 [00:01<00:02, 255.93it/s] 34%|███▍ | 341/1000 [00:01<00:02, 254.56it/s] 37%|███▋ | 367/1000 [00:01<00:02, 254.44it/s] 39%|███▉ | 393/1000 [00:01<00:02, 252.57it/s] 42%|████▏ | 419/1000 [00:01<00:02, 252.07it/s] 45%|████▍ | 446/1000 [00:01<00:02, 254.71it/s] 47%|████▋ | 472/1000 [00:01<00:02, 254.69it/s] 50%|████▉ | 498/1000 [00:01<00:01, 251.76it/s] 52%|█████▏ | 524/1000 [00:02<00:01, 250.75it/s] 55%|█████▌ | 550/1000 [00:02<00:01, 251.48it/s] 58%|█████▊ | 576/1000 [00:02<00:01, 252.07it/s] 60%|██████ | 602/1000 [00:02<00:01, 250.92it/s] 63%|██████▎ | 628/1000 [00:02<00:01, 253.39it/s] 65%|██████▌ | 654/1000 [00:02<00:01, 253.83it/s] 68%|██████▊ | 681/1000 [00:02<00:01, 256.03it/s] 71%|███████ | 708/1000 [00:02<00:01, 257.99it/s] 73%|███████▎ | 734/1000 [00:02<00:01, 256.19it/s] 76%|███████▌ | 760/1000 [00:03<00:00, 256.90it/s] 79%|███████▊ | 787/1000 [00:03<00:00, 259.13it/s] 81%|████████▏ | 813/1000 [00:03<00:00, 259.11it/s] 84%|████████▍ | 840/1000 [00:03<00:00, 260.96it/s] 87%|████████▋ | 867/1000 [00:03<00:00, 260.58it/s] 89%|████████▉ | 894/1000 [00:03<00:00, 260.70it/s] 92%|█████████▏| 921/1000 [00:03<00:00, 260.56it/s] 95%|█████████▍| 948/1000 [00:03<00:00, 259.03it/s] 98%|█████████▊| 975/1000 [00:03<00:00, 261.68it/s] 100%|██████████| 1000/1000 [00:03<00:00, 254.96it/s] [codecarbon WARNING @ 13:02:31] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:31] Energy consumed for RAM : 0.000003 kWh. RAM Power : 0.34412240982055664 W [codecarbon DEBUG @ 13:02:31] RAM : 0.34 W during 3.93 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:31] Energy consumed for all GPUs : 0.001458 kWh. Total GPU Power : 166.45990558993896 W [codecarbon DEBUG @ 13:02:31] GPU : 166.46 W during 3.93 s [measurement time: 0.0023] [codecarbon INFO @ 13:02:31] Energy consumed for all CPUs : 0.000377 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:31] CPU : 42.50 W during 3.93 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:31] 0.001838 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:31] last_duration=3.9277428439999085 ------------------------ [codecarbon DEBUG @ 13:02:31] EmissionsData(timestamp='2024-10-08T13:02:31', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.930906269999923, emissions=0.000678468780911744, emissions_rate=0.00017259856488813132, cpu_power=42.5, gpu_power=166.45990558993896, ram_power=0.34412240982055664, cpu_energy=0.00037740933536110764, gpu_energy=0.00145754561048, ram_energy=3.0444970408115106e-06, energy_consumed=0.0018379994428819194, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:31] EmissionsData(timestamp='2024-10-08T13:02:31', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.0020576900000151, emissions=0.000678468780911744, emissions_rate=0.3297235156446137, cpu_power=42.5, gpu_power=166.45990558993896, ram_power=0.34412240982055664, cpu_energy=0.00037740933536110764, gpu_energy=0.00145754561048, ram_energy=3.0444970408115106e-06, energy_consumed=0.0018379994428819194, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 3%|▎ | 26/1000 [00:00<00:03, 257.97it/s] 5%|▌ | 52/1000 [00:00<00:03, 250.91it/s] 8%|▊ | 78/1000 [00:00<00:03, 248.24it/s] 10%|█ | 103/1000 [00:00<00:03, 243.31it/s] 13%|█▎ | 130/1000 [00:00<00:03, 250.80it/s] 16%|█▌ | 156/1000 [00:00<00:03, 252.66it/s] 18%|█▊ | 182/1000 [00:00<00:03, 252.87it/s] 21%|██ | 208/1000 [00:00<00:03, 254.80it/s] 24%|██▎ | 235/1000 [00:00<00:02, 258.88it/s] 26%|██▌ | 261/1000 [00:01<00:02, 250.89it/s] 29%|██▉ | 288/1000 [00:01<00:02, 255.34it/s] 32%|███▏ | 315/1000 [00:01<00:02, 257.25it/s] 34%|███▍ | 342/1000 [00:01<00:02, 260.99it/s] 37%|███▋ | 369/1000 [00:01<00:02, 262.99it/s] 40%|███▉ | 396/1000 [00:01<00:02, 258.85it/s] 42%|████▏ | 422/1000 [00:01<00:02, 257.36it/s] 45%|████▍ | 448/1000 [00:01<00:02, 256.77it/s] 47%|████▋ | 474/1000 [00:01<00:02, 254.85it/s] 50%|█████ | 500/1000 [00:01<00:01, 251.99it/s] 53%|█████▎ | 526/1000 [00:02<00:01, 251.64it/s] 55%|█████▌ | 552/1000 [00:02<00:01, 251.02it/s] 58%|█████▊ | 578/1000 [00:02<00:01, 249.20it/s] 60%|██████ | 603/1000 [00:02<00:01, 247.32it/s] 63%|██████▎ | 628/1000 [00:02<00:01, 248.00it/s] 65%|██████▌ | 653/1000 [00:02<00:01, 247.76it/s] 68%|██████▊ | 679/1000 [00:02<00:01, 249.66it/s] 70%|███████ | 705/1000 [00:02<00:01, 251.98it/s] 73%|███████▎ | 731/1000 [00:02<00:01, 247.36it/s] 76%|███████▌ | 756/1000 [00:02<00:00, 248.10it/s] 78%|███████▊ | 782/1000 [00:03<00:00, 249.49it/s] 81%|████████ | 808/1000 [00:03<00:00, 249.74it/s] 83%|████████▎ | 833/1000 [00:03<00:00, 249.70it/s] 86%|████████▌ | 858/1000 [00:03<00:00, 249.68it/s] 88%|████████▊ | 885/1000 [00:03<00:00, 255.48it/s] 91%|█████████ | 911/1000 [00:03<00:00, 254.38it/s] 94%|█████████▎| 937/1000 [00:03<00:00, 252.65it/s] 96%|█████████▋| 963/1000 [00:03<00:00, 253.32it/s] 99%|█████████▉| 989/1000 [00:03<00:00, 252.52it/s] 100%|██████████| 1000/1000 [00:03<00:00, 252.48it/s] [codecarbon WARNING @ 13:02:35] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:35] Energy consumed for RAM : 0.000003 kWh. RAM Power : 0.34412240982055664 W [codecarbon DEBUG @ 13:02:35] RAM : 0.34 W during 3.96 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:35] Energy consumed for all GPUs : 0.001643 kWh. Total GPU Power : 168.260425038011 W [codecarbon DEBUG @ 13:02:35] GPU : 168.26 W during 3.96 s [measurement time: 0.0067] [codecarbon INFO @ 13:02:35] Energy consumed for all CPUs : 0.000424 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:35] CPU : 42.50 W during 3.97 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:35] 0.002071 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:35] last_duration=3.963194380999994 ------------------------ [codecarbon DEBUG @ 13:02:35] EmissionsData(timestamp='2024-10-08T13:02:35', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.9707531710000694, emissions=0.0007643019785847099, emissions_rate=0.00019248287306466186, cpu_power=42.5, gpu_power=168.260425038011, ram_power=0.34412240982055664, cpu_energy=0.00042428511917569123, gpu_energy=0.0016428165920300006, ram_energy=3.423345149084833e-06, energy_consumed=0.002070525056354777, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) [codecarbon DEBUG @ 13:02:35] EmissionsData(timestamp='2024-10-08T13:02:35', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=0.0020522500000197397, emissions=0.0007643019785847099, emissions_rate=0.3724214781714501, cpu_power=42.5, gpu_power=168.260425038011, ram_power=0.34412240982055664, cpu_energy=0.00042428511917569123, gpu_energy=0.0016428165920300006, ram_energy=3.423345149084833e-06, energy_consumed=0.002070525056354777, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0) 0%| | 0/1000 [00:00<?, ?it/s] 3%|▎ | 26/1000 [00:00<00:03, 253.14it/s] 5%|▌ | 52/1000 [00:00<00:03, 253.10it/s] 8%|▊ | 78/1000 [00:00<00:03, 250.61it/s] 10%|█ | 104/1000 [00:00<00:03, 247.09it/s] 13%|█▎ | 131/1000 [00:00<00:03, 252.06it/s] 16%|█▌ | 158/1000 [00:00<00:03, 254.63it/s] 18%|█▊ | 184/1000 [00:00<00:03, 256.07it/s] 21%|██ | 211/1000 [00:00<00:03, 257.36it/s] 24%|██▎ | 237/1000 [00:00<00:02, 257.38it/s] 26%|██▋ | 263/1000 [00:01<00:02, 248.20it/s] 29%|██▉ | 289/1000 [00:01<00:02, 249.11it/s] 32%|███▏ | 315/1000 [00:01<00:02, 249.91it/s] 34%|███▍ | 341/1000 [00:01<00:02, 250.90it/s] 37%|███▋ | 367/1000 [00:01<00:02, 251.46it/s] 39%|███▉ | 393/1000 [00:01<00:02, 250.64it/s] 42%|████▏ | 419/1000 [00:01<00:02, 249.01it/s] 44%|████▍ | 444/1000 [00:01<00:02, 249.15it/s] 47%|████▋ | 470/1000 [00:01<00:02, 251.02it/s] 50%|████▉ | 496/1000 [00:01<00:01, 252.45it/s] 52%|█████▏ | 523/1000 [00:02<00:01, 255.46it/s] 55%|█████▌ | 550/1000 [00:02<00:01, 258.11it/s] 58%|█████▊ | 576/1000 [00:02<00:01, 258.12it/s] 60%|██████ | 602/1000 [00:02<00:01, 254.83it/s] 63%|██████▎ | 628/1000 [00:02<00:01, 256.23it/s] 65%|██████▌ | 654/1000 [00:02<00:01, 253.82it/s] 68%|██████▊ | 680/1000 [00:02<00:01, 253.55it/s] 71%|███████ | 706/1000 [00:02<00:01, 254.06it/s] 73%|███████▎ | 732/1000 [00:02<00:01, 252.53it/s] 76%|███████▌ | 759/1000 [00:02<00:00, 255.13it/s] 79%|███████▊ | 786/1000 [00:03<00:00, 257.97it/s] 81%|████████ | 812/1000 [00:03<00:00, 258.56it/s] 84%|████████▍ | 839/1000 [00:03<00:00, 260.34it/s] 87%|████████▋ | 866/1000 [00:03<00:00, 258.71it/s] 89%|████████▉ | 892/1000 [00:03<00:00, 258.64it/s] 92%|█████████▏| 918/1000 [00:03<00:00, 254.47it/s] 94%|█████████▍| 944/1000 [00:03<00:00, 253.47it/s] 97%|█████████▋| 970/1000 [00:03<00:00, 254.14it/s] 100%|█████████▉| 996/1000 [00:03<00:00, 253.00it/s] 100%|██████████| 1000/1000 [00:03<00:00, 253.87it/s] [codecarbon WARNING @ 13:02:39] Background scheduler didn't run for a long period (3s), results might be inaccurate [codecarbon INFO @ 13:02:39] Energy consumed for RAM : 0.000004 kWh. RAM Power : 0.34412240982055664 W [codecarbon DEBUG @ 13:02:39] RAM : 0.34 W during 3.94 s [measurement time: 0.0004] [codecarbon INFO @ 13:02:39] Energy consumed for all GPUs : 0.001825 kWh. Total GPU Power : 166.0055014943033 W [codecarbon DEBUG @ 13:02:39] GPU : 166.01 W during 3.94 s [measurement time: 0.0022] [codecarbon INFO @ 13:02:39] Energy consumed for all CPUs : 0.000471 kWh. Total CPU Power : 42.5 W [codecarbon DEBUG @ 13:02:39] CPU : 42.50 W during 3.94 s [measurement time: 0.0000] [codecarbon INFO @ 13:02:39] 0.002299 kWh of electricity used since the beginning. [codecarbon DEBUG @ 13:02:39] last_duration=3.941462545000036 ------------------------ [codecarbon DEBUG @ 13:02:39] EmissionsData(timestamp='2024-10-08T13:02:39', project_name='codecarbon', run_id='d9e5d902-455c-48c2-8494-cae66f047761', duration=3.9445968759999914, emissions=0.0008487342972597723, emissions_rate=0.00021516376043993353, cpu_power=42.5, gpu_power=166.0055014943033, ram_power=0.34412240982055664, cpu_energy=0.00047085211187360723, gpu_energy=0.001824603404126001, ram_energy=3.8001158098703305e-06, energy_consumed=0.002299255631809479, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0)

distilbert/distilbert-base-uncased-finetuned-sst-2-english

COMPLETED

Text Classification

v0

openai/whisper-large-v3-turbo

FAILED

automatic speech recognition

v0

CohereForAI/aya-23-8B

FAILED

Text Generation

v0

hugging-quants/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4

FAILED

Text Generation

v0

hugging-quants/Meta-Llama-3.1-8B-Instruct-BNB-NF4

FAILED

Text Generation

v0

HuggingFaceTB/SmolLM-135M

COMPLETED

Text Generation

v0

meta-llama/Llama-3.1-8B-Instruct

FAILED

Text Generation

v0

HuggingFaceTB/SmolLM-360M

COMPLETED

Text Generation

v0

HuggingFaceTB/SmolLM-1.7B

COMPLETED

Text Generation

v0

meta-llama/Llama-3.2-3B

FAILED

Text Generation

v0

meta-llama/Llama-3.2-3B

FAILED

Text Generation

v0

allenai/OLMo-1B-hf

COMPLETED

Text Generation

v0

google/gemma-2-2b

FAILED

Text Generation

v0

google/gemma-2-2b

FAILED

Text Generation

v0

microsoft/phi-2

COMPLETED

Text Generation

v0

microsoft/phi-2

COMPLETED

Text Generation

v0

meta-llama/Llama-3.1-8B-Instruct

FAILED

Text Generation

v0

EleutherAI/pythia-1.4b

COMPLETED

Text Generation

v0

openai-community/gpt2-large

COMPLETED

Text Generation

v0

meta-llama/Llama-3.1-405B-Instruct-FP8

FAILED

Text Generation

v0

meta-llama/Llama-3.1-405B-Instruct-FP8

FAILED

text-generation

v0

meta-llama/Llama-3.1-405B-Instruct-FP8

FAILED

Text Generation

v0

meta-llama/Llama-3.1-405B-Instruct-FP8

FAILED

text-generation

v0

meta-llama/Llama-3.1-405B-Instruct-FP8

FAILED

text-generation

v0

meta-llama/Llama-3.1-405B-Instruct-FP8

FAILED

text-generation

v0

allenai/OLMo-7B-0724-Instruct-hf

FAILED

text-generation

v0

allenai/OLMo-7B-0724-Instruct-hf

FAILED

text-generation

v0

Qwen/Qwen2.5-Coder-32B-Instruct

FAILED

text-generation

v0

Qwen/Qwen2.5-Coder-32B-Instruct

FAILED

text-generation

v0

hugging-quants/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4

FAILED

text-generation

v0

Qwen/QwQ-32B-Preview

FAILED

text-generation

v0

Qwen/Qwen2.5-32B

FAILED

text-generation

v0

sentence-transformers/all-mpnet-base-v2

COMPLETED

sentence-similarity

v0

sentence-transformers/all-MiniLM-L6-v2

COMPLETED

sentence-similarity

v0

Qwen/Qwen2.5-1.5B-Instruct

COMPLETED

text-generation

v0

microsoft/resnet-50

COMPLETED

image-classification

v0

jonatasgrosman/wav2vec2-large-xlsr-53-english

FAILED

automatic-speech-recognition

v0

facebook/opt-1.3b

COMPLETED

text-generation

v0

openai-community/gpt2

COMPLETED

text-generation

v0

bigscience/bloomz-560m

COMPLETED

text-generation

v0

timm/resnet50.a1_in1k

FAILED

image-classification

v0

pyannote/speaker-diarization-3.1

FAILED

automatic-speech-recognition

v0

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

COMPLETED

sentence-similarity

v0

openai/whisper-small

COMPLETED

automatic-speech-recognition

v0

bartowski/Meta-Llama-3.1-8B-Instruct-GGUF

FAILED

text-generation

v0

sentence-transformers/all-MiniLM-L12-v2

COMPLETED

sentence-similarity

v0

sentence-transformers/paraphrase-MiniLM-L6-v2

COMPLETED

sentence-similarity

v0

facebook/opt-125m

COMPLETED

text-generation

v0

meta-llama/Llama-3.1-8B-Instruct

FAILED

text-generation

v0

pyannote/speaker-diarization

FAILED

automatic-speech-recognition

v0

sentence-transformers/multi-qa-MiniLM-L6-cos-v1

COMPLETED

sentence-similarity

v0

facebook/bart-large-cnn

COMPLETED

summarization

v0

cardiffnlp/twitter-roberta-base-sentiment-latest

FAILED

text-classification

v0

WinKawaks/vit-tiny-patch16-224

COMPLETED

image-classification

v0

sentence-transformers/bert-base-nli-mean-tokens

COMPLETED

sentence-similarity

v0

papluca/xlm-roberta-base-language-detection

FAILED

text-classification

v0

distributed/optimized-gpt2-1b

FAILED

text-generation

v0

facebook/fasttext-language-identification

FAILED

text-classification

v0

jonatasgrosman/wav2vec2-large-xlsr-53-portuguese

FAILED

automatic-speech-recognition

v0

Falconsai/nsfw_image_detection

COMPLETED

image-classification

v0

google/vit-base-patch16-224

COMPLETED

image-classification

v0

jonatasgrosman/wav2vec2-large-xlsr-53-russian

PENDING

automatic-speech-recognition

v0

openai/whisper-large-v3

PENDING

automatic-speech-recognition

v0

Minej/bert-base-personality

PENDING

text-classification

v0

BAAI/bge-m3

PENDING

sentence-similarity

v0

microsoft/table-transformer-detection

PENDING

object-detection

v0

Alibaba-NLP/gte-large-en-v1.5

PENDING

sentence-similarity

v0

pyannote/speaker-diarization-3.0

PENDING

automatic-speech-recognition

v0

Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier

PENDING

text-classification

v0

meta-llama/Meta-Llama-3-8B-Instruct

PENDING

text-generation

v0

jonatasgrosman/wav2vec2-large-xlsr-53-dutch

PENDING

automatic-speech-recognition

v0

cross-encoder/ms-marco-MiniLM-L-6-v2

PENDING

text-classification

v0

cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual

PENDING

text-classification

v0

openai/whisper-large-v3-turbo

PENDING

automatic-speech-recognition

v0

lxyuan/distilbert-base-multilingual-cased-sentiments-student

PENDING

text-classification

v0

facebook/roberta-hate-speech-dynabench-r4-target

PENDING

text-classification

v0

jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn

PENDING

automatic-speech-recognition

v0

distilbert/distilgpt2

PENDING

text-generation

v0

timm/resnet18.a1_in1k

PENDING

image-classification

v0

WinKawaks/vit-small-patch16-224

PENDING

image-classification

v0

google/gemma-2-2b

PENDING

text-generation

v0

meta-llama/Llama-3.1-70B-Instruct

PENDING

text-generation

v0

sentence-transformers/paraphrase-multilingual-mpnet-base-v2

PENDING

sentence-similarity

v0

nlpconnect/vit-gpt2-image-captioning

PENDING

image-to-text

v0

cardiffnlp/twitter-roberta-base-sentiment

PENDING

text-classification

v0

TinyLlama/TinyLlama-1.1B-Chat-v1.0

PENDING

text-generation

v0

microsoft/git-base

PENDING

image-to-text

v0

Salesforce/blip-image-captioning-large

PENDING

image-to-text

v0

sentence-transformers/all-distilroberta-v1

PENDING

sentence-similarity

v0

microsoft/beit-base-patch16-224-pt22k-ft22k

PENDING

image-classification

v0

thenlper/gte-large

PENDING

sentence-similarity

v0