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automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/.hydra/config.yaml

@@ -0,0 +1,94 @@
+backend:
+  name: pytorch
+  version: 2.4.0
+  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
+  task: automatic-speech-recognition
+  model: openai/whisper-small
+  processor: openai/whisper-small
+  library: null
+  device: cuda
+  device_ids: '0'
+  seed: 42
+  inter_op_num_threads: null
+  intra_op_num_threads: null
+  hub_kwargs: {}
+  no_weights: true
+  device_map: null
+  torch_dtype: null
+  amp_autocast: false
+  amp_dtype: null
+  eval_mode: true
+  to_bettertransformer: false
+  low_cpu_mem_usage: null
+  attn_implementation: null
+  cache_implementation: null
+  torch_compile: false
+  torch_compile_config: {}
+  quantization_scheme: null
+  quantization_config: {}
+  deepspeed_inference: false
+  deepspeed_inference_config: {}
+  peft_type: null
+  peft_config: {}
+launcher:
+  name: process
+  _target_: optimum_benchmark.launchers.process.launcher.ProcessLauncher
+  device_isolation: true
+  device_isolation_action: warn
+  start_method: spawn
+benchmark:
+  name: energy_star
+  _target_: optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark
+  dataset_name: EnergyStarAI/ASR
+  dataset_config: ''
+  dataset_split: train
+  num_samples: 1000
+  input_shapes:
+    batch_size: 1
+  text_column_name: text
+  truncation: true
+  max_length: -1
+  dataset_prefix1: ''
+  dataset_prefix2: ''
+  t5_task: ''
+  image_column_name: image
+  resize: false
+  question_column_name: question
+  context_column_name: context
+  sentence1_column_name: sentence1
+  sentence2_column_name: sentence2
+  audio_column_name: audio
+  iterations: 10
+  warmup_runs: 10
+  energy: true
+  forward_kwargs: {}
+  generate_kwargs: {}
+  call_kwargs: {}
+experiment_name: automatic_speech_recognition
+environment:
+  cpu: ' AMD EPYC 7R32'
+  cpu_count: 48
+  cpu_ram_mb: 200472.73984
+  system: Linux
+  machine: x86_64
+  platform: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+  processor: x86_64
+  python_version: 3.9.20
+  gpu:
+  - NVIDIA A10G
+  gpu_count: 1
+  gpu_vram_mb: 24146608128
+  optimum_benchmark_version: 0.2.0
+  optimum_benchmark_commit: null
+  transformers_version: 4.44.0
+  transformers_commit: null
+  accelerate_version: 0.33.0
+  accelerate_commit: null
+  diffusers_version: 0.30.0
+  diffusers_commit: null
+  optimum_version: null
+  optimum_commit: null
+  timm_version: null
+  timm_commit: null
+  peft_version: null
+  peft_commit: null

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: /runs/automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58
+  sweep:
+    dir: runs/${experiment_name}/${backend.model}/${now:%Y-%m-%d-%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
+          - %(message)s'
+        log_colors:
+          DEBUG: purple
+          INFO: green
+          WARNING: yellow
+          ERROR: red
+          CRITICAL: red
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.run.dir=/runs/automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58
+    - hydra.mode=RUN
+    task:
+    - backend.model=openai/whisper-small
+    - backend.processor=openai/whisper-small
+  job:
+    name: cli
+    chdir: true
+    override_dirname: backend.model=openai/whisper-small,backend.processor=openai/whisper-small
+    id: ???
+    num: ???
+    config_name: automatic_speech_recognition
+    env_set:
+      OVERRIDE_BENCHMARKS: '1'
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: optimum_benchmark
+      schema: pkg
+      provider: main
+    - path: hydra_plugins.hydra_colorlog.conf
+      schema: pkg
+      provider: hydra-colorlog
+    - path: /optimum-benchmark/examples/energy_star
+      schema: file
+      provider: command-line
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /runs/automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58
+    choices:
+      benchmark: energy_star
+      launcher: process
+      backend: pytorch
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: colorlog
+      hydra/hydra_logging: colorlog
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/.hydra/overrides.yaml

@@ -0,0 +1,2 @@
+- backend.model=openai/whisper-small
+- backend.processor=openai/whisper-small

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/benchmark_report.json

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+}

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/cli.log

@@ -0,0 +1,191 @@
+[2024-12-04 21:42:01,673][launcher][INFO] - ََAllocating process launcher
+[2024-12-04 21:42:01,673][process][INFO] - 	+ Setting multiprocessing start method to spawn.
+[2024-12-04 21:42:01,684][device-isolation][INFO] - 	+ Launched device(s) isolation process 491
+[2024-12-04 21:42:01,685][device-isolation][INFO] - 	+ Isolating device(s) [0]
+[2024-12-04 21:42:01,690][process][INFO] - 	+ Launched benchmark in isolated process 492.
+[PROC-0][2024-12-04 21:42:04,460][datasets][INFO] - PyTorch version 2.4.0 available.
+[PROC-0][2024-12-04 21:42:05,370][backend][INFO] - َAllocating pytorch backend
+[PROC-0][2024-12-04 21:42:05,370][backend][INFO] - 	+ Setting random seed to 42
+[PROC-0][2024-12-04 21:42:06,829][pytorch][INFO] - 	+ Using AutoModel class AutoModelForSpeechSeq2Seq
+[PROC-0][2024-12-04 21:42:06,830][pytorch][INFO] - 	+ Creating backend temporary directory
+[PROC-0][2024-12-04 21:42:06,830][pytorch][INFO] - 	+ Loading model with random weights
+[PROC-0][2024-12-04 21:42:06,830][pytorch][INFO] - 	+ Creating no weights model
+[PROC-0][2024-12-04 21:42:06,830][pytorch][INFO] - 	+ Creating no weights model directory
+[PROC-0][2024-12-04 21:42:06,830][pytorch][INFO] - 	+ Creating no weights model state dict
+[PROC-0][2024-12-04 21:42:06,832][pytorch][INFO] - 	+ Saving no weights model safetensors
+[PROC-0][2024-12-04 21:42:06,833][pytorch][INFO] - 	+ Saving no weights model pretrained config
+[PROC-0][2024-12-04 21:42:06,834][pytorch][INFO] - 	+ Loading no weights AutoModel
+[PROC-0][2024-12-04 21:42:06,834][pytorch][INFO] - 	+ Loading model directly on device: cuda
+[PROC-0][2024-12-04 21:42:07,049][pytorch][INFO] - 	+ Turning on model's eval mode
+[PROC-0][2024-12-04 21:42:07,056][benchmark][INFO] - Allocating energy_star benchmark
+[PROC-0][2024-12-04 21:42:07,056][energy_star][INFO] - 	+ Loading raw dataset
+[PROC-0][2024-12-04 21:42:12,960][energy_star][INFO] - 	+ Updating Text Generation kwargs with default values
+[PROC-0][2024-12-04 21:42:12,960][energy_star][INFO] - 	+ Initializing Text Generation report
+[PROC-0][2024-12-04 21:42:12,961][energy][INFO] - 	+ Tracking GPU energy on devices [0]
+[PROC-0][2024-12-04 21:42:17,167][energy_star][INFO] - 	+ Preprocessing dataset
+[PROC-0][2024-12-04 21:42:32,399][energy][INFO] - 	+ Saving codecarbon emission data to preprocess_codecarbon.json
+[PROC-0][2024-12-04 21:42:32,399][energy_star][INFO] - 	+ Preparing backend for Inference
+[PROC-0][2024-12-04 21:42:32,400][energy_star][INFO] - 	+ Initialising dataloader
+[PROC-0][2024-12-04 21:42:32,400][energy_star][INFO] - 	+ Warming up backend for Inference
+[PROC-0][2024-12-04 21:42:33,923][energy_star][INFO] - 	+ Additional warmup for Text Generation
+[PROC-0][2024-12-04 21:42:34,768][energy_star][INFO] - 	+ Running Text Generation energy tracking for 10 iterations
+[PROC-0][2024-12-04 21:42:34,768][energy_star][INFO] - 	+ Prefill iteration 1/10
+[PROC-0][2024-12-04 21:44:14,704][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:44:14,704][energy_star][INFO] - 	+ Prefill iteration 2/10
+[PROC-0][2024-12-04 21:45:54,695][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:45:54,695][energy_star][INFO] - 	+ Prefill iteration 3/10
+[PROC-0][2024-12-04 21:47:34,694][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:47:34,694][energy_star][INFO] - 	+ Prefill iteration 4/10
+[PROC-0][2024-12-04 21:49:14,695][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:49:14,695][energy_star][INFO] - 	+ Prefill iteration 5/10
+[PROC-0][2024-12-04 21:50:54,698][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:50:54,698][energy_star][INFO] - 	+ Prefill iteration 6/10
+[PROC-0][2024-12-04 21:52:34,697][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:52:34,697][energy_star][INFO] - 	+ Prefill iteration 7/10
+[PROC-0][2024-12-04 21:54:14,695][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:54:14,696][energy_star][INFO] - 	+ Prefill iteration 8/10
+[PROC-0][2024-12-04 21:55:54,694][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:55:54,694][energy_star][INFO] - 	+ Prefill iteration 9/10
+[PROC-0][2024-12-04 21:57:34,694][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:57:34,695][energy_star][INFO] - 	+ Prefill iteration 10/10
+[PROC-0][2024-12-04 21:59:14,798][energy][INFO] - 	+ Saving codecarbon emission data to prefill_codecarbon.json
+[PROC-0][2024-12-04 21:59:14,799][energy_star][INFO] - 	+ Decoding iteration 1/10
+[PROC-0][2024-12-04 22:15:06,041][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-04 22:15:06,041][energy_star][INFO] - 	+ Decoding iteration 2/10
+[PROC-0][2024-12-04 22:30:51,624][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-04 22:30:51,624][energy_star][INFO] - 	+ Decoding iteration 3/10
+[PROC-0][2024-12-04 22:46:36,438][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-04 22:46:36,439][energy_star][INFO] - 	+ Decoding iteration 4/10
+[PROC-0][2024-12-04 23:02:21,205][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-04 23:02:21,206][energy_star][INFO] - 	+ Decoding iteration 5/10
+[PROC-0][2024-12-04 23:18:07,862][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-04 23:18:07,862][energy_star][INFO] - 	+ Decoding iteration 6/10
+[PROC-0][2024-12-04 23:33:50,000][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-04 23:33:50,000][energy_star][INFO] - 	+ Decoding iteration 7/10
+[PROC-0][2024-12-04 23:49:34,542][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-04 23:49:34,542][energy_star][INFO] - 	+ Decoding iteration 8/10
+[PROC-0][2024-12-05 00:05:20,569][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-05 00:05:20,569][energy_star][INFO] - 	+ Decoding iteration 9/10
+[PROC-0][2024-12-05 00:21:06,577][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-05 00:21:06,577][energy_star][INFO] - 	+ Decoding iteration 10/10
+[PROC-0][2024-12-05 00:36:50,431][energy][INFO] - 	+ Saving codecarbon emission data to generate_codecarbon.json
+[PROC-0][2024-12-05 00:36:50,432][energy][INFO] - 		+ prefill energy consumption:
+[PROC-0][2024-12-05 00:36:50,432][energy][INFO] - 			+ CPU: 0.001063 (kWh)
+[PROC-0][2024-12-05 00:36:50,432][energy][INFO] - 			+ GPU: 0.004004 (kWh)
+[PROC-0][2024-12-05 00:36:50,432][energy][INFO] - 			+ RAM: 0.000048 (kWh)
+[PROC-0][2024-12-05 00:36:50,432][energy][INFO] - 			+ total: 0.005114 (kWh)
+[PROC-0][2024-12-05 00:36:50,432][energy][INFO] - 		+ prefill_iteration_1 energy consumption:
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ CPU: 0.001180 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ GPU: 0.004326 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ total: 0.005559 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 		+ prefill_iteration_2 energy consumption:
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ CPU: 0.001180 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ GPU: 0.004583 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ total: 0.005816 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 		+ prefill_iteration_3 energy consumption:
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ CPU: 0.001181 (kWh)
+[PROC-0][2024-12-05 00:36:50,433][energy][INFO] - 			+ GPU: 0.004499 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ total: 0.005733 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 		+ prefill_iteration_4 energy consumption:
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ CPU: 0.001181 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ GPU: 0.004336 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ total: 0.005569 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 		+ prefill_iteration_5 energy consumption:
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ CPU: 0.001181 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ GPU: 0.004438 (kWh)
+[PROC-0][2024-12-05 00:36:50,434][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ total: 0.005672 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 		+ prefill_iteration_6 energy consumption:
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ CPU: 0.001181 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ GPU: 0.004447 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ total: 0.005681 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 		+ prefill_iteration_7 energy consumption:
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ CPU: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ GPU: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ RAM: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 			+ total: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:36:50,435][energy][INFO] - 		+ prefill_iteration_8 energy consumption:
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ CPU: 0.001181 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ GPU: 0.004500 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ total: 0.005734 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 		+ prefill_iteration_9 energy consumption:
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ CPU: 0.001181 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ GPU: 0.004578 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ total: 0.005811 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 		+ prefill_iteration_10 energy consumption:
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ CPU: 0.001182 (kWh)
+[PROC-0][2024-12-05 00:36:50,436][energy][INFO] - 			+ GPU: 0.004334 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ RAM: 0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ total: 0.005569 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 		+ decode energy consumption:
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ CPU: 0.008983 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ GPU: 0.020038 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ RAM: 0.000403 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ total: 0.029424 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 		+ decode_iteration_1 energy consumption:
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ CPU: 0.010050 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ GPU: 0.022509 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ RAM: 0.000451 (kWh)
+[PROC-0][2024-12-05 00:36:50,437][energy][INFO] - 			+ total: 0.033010 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 		+ decode_iteration_2 energy consumption:
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ CPU: 0.009983 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ GPU: 0.022075 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ RAM: 0.000448 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ total: 0.032506 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 		+ decode_iteration_3 energy consumption:
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ CPU: 0.009974 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ GPU: 0.022207 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ RAM: 0.000447 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ total: 0.032627 (kWh)
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 		+ decode_iteration_4 energy consumption:
+[PROC-0][2024-12-05 00:36:50,438][energy][INFO] - 			+ CPU: 0.009973 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ GPU: 0.022316 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ RAM: 0.000447 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ total: 0.032737 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 		+ decode_iteration_5 energy consumption:
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ CPU: -0.001181 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ GPU: -0.004438 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ RAM: -0.000053 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ total: -0.005672 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 		+ decode_iteration_6 energy consumption:
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ CPU: 0.009942 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ GPU: 0.022229 (kWh)
+[PROC-0][2024-12-05 00:36:50,439][energy][INFO] - 			+ RAM: 0.000446 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ total: 0.032617 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 		+ decode_iteration_7 energy consumption:
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ CPU: 0.011151 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ GPU: 0.026727 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ RAM: 0.000500 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ total: 0.038378 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 		+ decode_iteration_8 energy consumption:
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ CPU: 0.009988 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ GPU: 0.022179 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ RAM: 0.000448 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 			+ total: 0.032615 (kWh)
+[PROC-0][2024-12-05 00:36:50,440][energy][INFO] - 		+ decode_iteration_9 energy consumption:
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ CPU: 0.009988 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ GPU: 0.022162 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ RAM: 0.000448 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ total: 0.032597 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 		+ decode_iteration_10 energy consumption:
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ CPU: 0.009961 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ GPU: 0.022418 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ RAM: 0.000447 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ total: 0.032825 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 		+ preprocess energy consumption:
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ CPU: 0.000180 (kWh)
+[PROC-0][2024-12-05 00:36:50,441][energy][INFO] - 			+ GPU: 0.000276 (kWh)
+[PROC-0][2024-12-05 00:36:50,442][energy][INFO] - 			+ RAM: 0.000009 (kWh)
+[PROC-0][2024-12-05 00:36:50,442][energy][INFO] - 			+ total: 0.000465 (kWh)
+[PROC-0][2024-12-05 00:36:50,442][energy][INFO] - 		+ prefill energy efficiency: 195526.556644 (tokens/kWh)
+[PROC-0][2024-12-05 00:36:50,442][energy][INFO] - 		+ decode energy efficiency: 3364586.284552 (tokens/kWh)
+[PROC-0][2024-12-05 00:36:50,442][energy][INFO] - 		+ preprocess energy efficiency: 2150692.219800 (samples/kWh)
+[2024-12-05 00:36:51,389][device-isolation][INFO] - 	+ Closing device(s) isolation process...
+[2024-12-05 00:36:51,437][datasets][INFO] - PyTorch version 2.4.0 available.

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/experiment_config.json

@@ -0,0 +1,107 @@
+{
+    "experiment_name": "automatic_speech_recognition",
+    "backend": {
+        "name": "pytorch",
+        "version": "2.4.0",
+        "_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
+        "task": "automatic-speech-recognition",
+        "model": "openai/whisper-small",
+        "processor": "openai/whisper-small",
+        "library": "transformers",
+        "device": "cuda",
+        "device_ids": "0",
+        "seed": 42,
+        "inter_op_num_threads": null,
+        "intra_op_num_threads": null,
+        "hub_kwargs": {
+            "revision": "main",
+            "force_download": false,
+            "local_files_only": false,
+            "trust_remote_code": true
+        },
+        "no_weights": true,
+        "device_map": null,
+        "torch_dtype": null,
+        "amp_autocast": false,
+        "amp_dtype": null,
+        "eval_mode": true,
+        "to_bettertransformer": false,
+        "low_cpu_mem_usage": null,
+        "attn_implementation": null,
+        "cache_implementation": null,
+        "torch_compile": false,
+        "torch_compile_config": {},
+        "quantization_scheme": null,
+        "quantization_config": {},
+        "deepspeed_inference": false,
+        "deepspeed_inference_config": {},
+        "peft_type": null,
+        "peft_config": {}
+    },
+    "launcher": {
+        "name": "process",
+        "_target_": "optimum_benchmark.launchers.process.launcher.ProcessLauncher",
+        "device_isolation": true,
+        "device_isolation_action": "warn",
+        "start_method": "spawn"
+    },
+    "benchmark": {
+        "name": "energy_star",
+        "_target_": "optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark",
+        "dataset_name": "EnergyStarAI/ASR",
+        "dataset_config": "",
+        "dataset_split": "train",
+        "num_samples": 1000,
+        "input_shapes": {
+            "batch_size": 1
+        },
+        "text_column_name": "text",
+        "truncation": true,
+        "max_length": -1,
+        "dataset_prefix1": "",
+        "dataset_prefix2": "",
+        "t5_task": "",
+        "image_column_name": "image",
+        "resize": false,
+        "question_column_name": "question",
+        "context_column_name": "context",
+        "sentence1_column_name": "sentence1",
+        "sentence2_column_name": "sentence2",
+        "audio_column_name": "audio",
+        "iterations": 10,
+        "warmup_runs": 10,
+        "energy": true,
+        "forward_kwargs": {},
+        "generate_kwargs": {},
+        "call_kwargs": {}
+    },
+    "environment": {
+        "cpu": " AMD EPYC 7R32",
+        "cpu_count": 48,
+        "cpu_ram_mb": 200472.73984,
+        "system": "Linux",
+        "machine": "x86_64",
+        "platform": "Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35",
+        "processor": "x86_64",
+        "python_version": "3.9.20",
+        "gpu": [
+            "NVIDIA A10G"
+        ],
+        "gpu_count": 1,
+        "gpu_vram_mb": 24146608128,
+        "optimum_benchmark_version": "0.2.0",
+        "optimum_benchmark_commit": null,
+        "transformers_version": "4.44.0",
+        "transformers_commit": null,
+        "accelerate_version": "0.33.0",
+        "accelerate_commit": null,
+        "diffusers_version": "0.30.0",
+        "diffusers_commit": null,
+        "optimum_version": null,
+        "optimum_commit": null,
+        "timm_version": null,
+        "timm_commit": null,
+        "peft_version": null,
+        "peft_commit": null
+    }
+}

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/generate_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-12-05T00:36:50",
+    "project_name": "codecarbon",
+    "run_id": "3d018a32-eab0-4bc6-9c8f-b7514a1ee350",
+    "duration": -1732873329.239606,
+    "emissions": 0.014172676342690815,
+    "emissions_rate": 1.5015793619270198e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 102.03599834796752,
+    "ram_power": 1.90618371963501,
+    "cpu_energy": 0.011142711423042254,
+    "gpu_energy": 0.026751879179263582,
+    "ram_energy": 0.0004997643848251186,
+    "energy_consumed": 0.03839435498713095,
+    "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
+}

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/prefill_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-12-04T21:59:14",
+    "project_name": "codecarbon",
+    "run_id": "3d018a32-eab0-4bc6-9c8f-b7514a1ee350",
+    "duration": -1732874172.9901237,
+    "emissions": 0.002055700188499321,
+    "emissions_rate": 2.0536304116236467e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 155.88187673553176,
+    "ram_power": 1.906186580657959,
+    "cpu_energy": 0.0011817679713422378,
+    "gpu_energy": 0.004334233467384152,
+    "ram_energy": 5.29737411139031e-05,
+    "energy_consumed": 0.005568975179840292,
+    "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
+}

automatic_speech_recognition/openai/whisper-small/2024-12-04-21-41-58/preprocess_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-12-04T21:42:32",
+    "project_name": "codecarbon",
+    "run_id": "3d018a32-eab0-4bc6-9c8f-b7514a1ee350",
+    "duration": -1732874257.8619206,
+    "emissions": 0.0001716351482059165,
+    "emissions_rate": 1.1270239894406122e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 65.21603224495975,
+    "ram_power": 2.1929240226745605,
+    "cpu_energy": 0.0001798122039097911,
+    "gpu_energy": 0.00027587827625819017,
+    "ram_energy": 9.276097008200274e-06,
+    "energy_consumed": 0.00046496657717618156,
+    "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
+}

automatic_speech_recognition/pyannote/speaker-diarization-3.1/2024-12-04-21-40-43/.hydra/config.yaml

@@ -0,0 +1,94 @@
+backend:
+  name: pytorch
+  version: 2.4.0
+  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
+  task: automatic-speech-recognition
+  model: pyannote/speaker-diarization-3.1
+  processor: pyannote/speaker-diarization-3.1
+  library: null
+  device: cuda
+  device_ids: '0'
+  seed: 42
+  inter_op_num_threads: null
+  intra_op_num_threads: null
+  hub_kwargs: {}
+  no_weights: true
+  device_map: null
+  torch_dtype: null
+  amp_autocast: false
+  amp_dtype: null
+  eval_mode: true
+  to_bettertransformer: false
+  low_cpu_mem_usage: null
+  attn_implementation: null
+  cache_implementation: null
+  torch_compile: false
+  torch_compile_config: {}
+  quantization_scheme: null
+  quantization_config: {}
+  deepspeed_inference: false
+  deepspeed_inference_config: {}
+  peft_type: null
+  peft_config: {}
+launcher:
+  name: process
+  _target_: optimum_benchmark.launchers.process.launcher.ProcessLauncher
+  device_isolation: true
+  device_isolation_action: warn
+  start_method: spawn
+benchmark:
+  name: energy_star
+  _target_: optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark
+  dataset_name: EnergyStarAI/ASR
+  dataset_config: ''
+  dataset_split: train
+  num_samples: 1000
+  input_shapes:
+    batch_size: 1
+  text_column_name: text
+  truncation: true
+  max_length: -1
+  dataset_prefix1: ''
+  dataset_prefix2: ''
+  t5_task: ''
+  image_column_name: image
+  resize: false
+  question_column_name: question
+  context_column_name: context
+  sentence1_column_name: sentence1
+  sentence2_column_name: sentence2
+  audio_column_name: audio
+  iterations: 10
+  warmup_runs: 10
+  energy: true
+  forward_kwargs: {}
+  generate_kwargs: {}
+  call_kwargs: {}
+experiment_name: automatic_speech_recognition
+environment:
+  cpu: ' AMD EPYC 7R32'
+  cpu_count: 48
+  cpu_ram_mb: 200472.73984
+  system: Linux
+  machine: x86_64
+  platform: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+  processor: x86_64
+  python_version: 3.9.20
+  gpu:
+  - NVIDIA A10G
+  gpu_count: 1
+  gpu_vram_mb: 24146608128
+  optimum_benchmark_version: 0.2.0
+  optimum_benchmark_commit: null
+  transformers_version: 4.44.0
+  transformers_commit: null
+  accelerate_version: 0.33.0
+  accelerate_commit: null
+  diffusers_version: 0.30.0
+  diffusers_commit: null
+  optimum_version: null
+  optimum_commit: null
+  timm_version: null
+  timm_commit: null
+  peft_version: null
+  peft_commit: null

automatic_speech_recognition/pyannote/speaker-diarization-3.1/2024-12-04-21-40-43/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: /runs/automatic_speech_recognition/pyannote/speaker-diarization-3.1/2024-12-04-21-40-43
+  sweep:
+    dir: runs/${experiment_name}/${backend.model}/${now:%Y-%m-%d-%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
+          - %(message)s'
+        log_colors:
+          DEBUG: purple
+          INFO: green
+          WARNING: yellow
+          ERROR: red
+          CRITICAL: red
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.run.dir=/runs/automatic_speech_recognition/pyannote/speaker-diarization-3.1/2024-12-04-21-40-43
+    - hydra.mode=RUN
+    task:
+    - backend.model=pyannote/speaker-diarization-3.1
+    - backend.processor=pyannote/speaker-diarization-3.1
+  job:
+    name: cli
+    chdir: true
+    override_dirname: backend.model=pyannote/speaker-diarization-3.1,backend.processor=pyannote/speaker-diarization-3.1
+    id: ???
+    num: ???
+    config_name: automatic_speech_recognition
+    env_set:
+      OVERRIDE_BENCHMARKS: '1'
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: optimum_benchmark
+      schema: pkg
+      provider: main
+    - path: hydra_plugins.hydra_colorlog.conf
+      schema: pkg
+      provider: hydra-colorlog
+    - path: /optimum-benchmark/examples/energy_star
+      schema: file
+      provider: command-line
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /runs/automatic_speech_recognition/pyannote/speaker-diarization-3.1/2024-12-04-21-40-43
+    choices:
+      benchmark: energy_star
+      launcher: process
+      backend: pytorch
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: colorlog
+      hydra/hydra_logging: colorlog
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

automatic_speech_recognition/pyannote/speaker-diarization-3.1/2024-12-04-21-40-43/.hydra/overrides.yaml

@@ -0,0 +1,2 @@
+- backend.model=pyannote/speaker-diarization-3.1
+- backend.processor=pyannote/speaker-diarization-3.1

automatic_speech_recognition/pyannote/speaker-diarization-3.1/2024-12-04-21-40-43/error.log

@@ -0,0 +1,7 @@
+Error executing job with overrides: ['backend.model=pyannote/speaker-diarization-3.1', 'backend.processor=pyannote/speaker-diarization-3.1']
+Traceback (most recent call last):
+  File "/optimum-benchmark/optimum_benchmark/cli.py", line 62, in benchmark_cli
+    experiment_config: ExperimentConfig = OmegaConf.to_object(experiment_config)
+ValueError: `library` must be either `transformers`, `diffusers` or `timm`, but got pyannote-audio
+
+Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/.hydra/config.yaml

@@ -0,0 +1,94 @@
+backend:
+  name: pytorch
+  version: 2.4.0
+  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
+  task: sentence-similarity
+  model: sentence-transformers/all-MiniLM-L12-v2
+  processor: sentence-transformers/all-MiniLM-L12-v2
+  library: transformers
+  device: cuda
+  device_ids: '0'
+  seed: 42
+  inter_op_num_threads: null
+  intra_op_num_threads: null
+  hub_kwargs: {}
+  no_weights: true
+  device_map: null
+  torch_dtype: null
+  amp_autocast: false
+  amp_dtype: null
+  eval_mode: true
+  to_bettertransformer: false
+  low_cpu_mem_usage: null
+  attn_implementation: null
+  cache_implementation: null
+  torch_compile: false
+  torch_compile_config: {}
+  quantization_scheme: null
+  quantization_config: {}
+  deepspeed_inference: false
+  deepspeed_inference_config: {}
+  peft_type: null
+  peft_config: {}
+launcher:
+  name: process
+  _target_: optimum_benchmark.launchers.process.launcher.ProcessLauncher
+  device_isolation: true
+  device_isolation_action: warn
+  start_method: spawn
+benchmark:
+  name: energy_star
+  _target_: optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark
+  dataset_name: EnergyStarAI/sentence_similarity
+  dataset_config: ''
+  dataset_split: train
+  num_samples: 1000
+  input_shapes:
+    batch_size: 1
+  text_column_name: text
+  truncation: true
+  max_length: -1
+  dataset_prefix1: ''
+  dataset_prefix2: ''
+  t5_task: ''
+  image_column_name: image
+  resize: false
+  question_column_name: question
+  context_column_name: context
+  sentence1_column_name: sentence1
+  sentence2_column_name: sentence2
+  audio_column_name: audio
+  iterations: 10
+  warmup_runs: 10
+  energy: true
+  forward_kwargs: {}
+  generate_kwargs: {}
+  call_kwargs: {}
+experiment_name: sentence_similarity_udever-bloom-7b1
+environment:
+  cpu: ' AMD EPYC 7R32'
+  cpu_count: 48
+  cpu_ram_mb: 200472.73984
+  system: Linux
+  machine: x86_64
+  platform: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+  processor: x86_64
+  python_version: 3.9.20
+  gpu:
+  - NVIDIA A10G
+  gpu_count: 1
+  gpu_vram_mb: 24146608128
+  optimum_benchmark_version: 0.2.0
+  optimum_benchmark_commit: null
+  transformers_version: 4.44.0
+  transformers_commit: null
+  accelerate_version: 0.33.0
+  accelerate_commit: null
+  diffusers_version: 0.30.0
+  diffusers_commit: null
+  optimum_version: null
+  optimum_commit: null
+  timm_version: null
+  timm_commit: null
+  peft_version: null
+  peft_commit: null

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: /runs/sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55
+  sweep:
+    dir: sweeps/${experiment_name}/${now:%Y-%m-%d-%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
+          - %(message)s'
+        log_colors:
+          DEBUG: purple
+          INFO: green
+          WARNING: yellow
+          ERROR: red
+          CRITICAL: red
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.run.dir=/runs/sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55
+    - hydra.mode=RUN
+    task:
+    - backend.model=sentence-transformers/all-MiniLM-L12-v2
+    - backend.processor=sentence-transformers/all-MiniLM-L12-v2
+  job:
+    name: cli
+    chdir: true
+    override_dirname: backend.model=sentence-transformers/all-MiniLM-L12-v2,backend.processor=sentence-transformers/all-MiniLM-L12-v2
+    id: ???
+    num: ???
+    config_name: sentence_similarity
+    env_set:
+      OVERRIDE_BENCHMARKS: '1'
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: optimum_benchmark
+      schema: pkg
+      provider: main
+    - path: hydra_plugins.hydra_colorlog.conf
+      schema: pkg
+      provider: hydra-colorlog
+    - path: /optimum-benchmark/examples/energy_star
+      schema: file
+      provider: command-line
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /runs/sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55
+    choices:
+      benchmark: energy_star
+      launcher: process
+      backend: pytorch
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: colorlog
+      hydra/hydra_logging: colorlog
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/.hydra/overrides.yaml

@@ -0,0 +1,2 @@
+- backend.model=sentence-transformers/all-MiniLM-L12-v2
+- backend.processor=sentence-transformers/all-MiniLM-L12-v2

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/benchmark_report.json

@@ -0,0 +1,107 @@
+{
+    "forward": {
+        "memory": null,
+        "latency": null,
+        "throughput": null,
+        "energy": {
+            "unit": "kWh",
+            "cpu": 5.834651541804407e-05,
+            "ram": 4.690545039945406e-07,
+            "gpu": 0.00011053269953702483,
+            "total": 0.00016934826945906345
+        },
+        "efficiency": {
+            "unit": "samples/kWh",
+            "value": 5904990.958539024
+        },
+        "measures": [
+            {
+                "unit": "kWh",
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+                "ram": 5.339025680179651e-07,
+                "gpu": 0.00012639760111721188,
+                "total": 0.00019336008755375201
+            },
+            {
+                "unit": "kWh",
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+                "ram": 5.183489446487988e-07,
+                "gpu": 0.00012057565201661902,
+                "total": 0.0001855762418311708
+            },
+            {
+                "unit": "kWh",
+                "cpu": 6.420218333065957e-05,
+                "ram": 5.162153570657138e-07,
+                "gpu": 0.00012280454268775998,
+                "total": 0.00018752294137548524
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+            {
+                "unit": "kWh",
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+                "ram": 5.099481002263594e-07,
+                "gpu": 0.00011886037286590323,
+                "total": 0.00018283726311513866
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+            {
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+                "cpu": 6.327727858022425e-05,
+                "ram": 5.087816248549878e-07,
+                "gpu": 0.000121267319236118,
+                "total": 0.00018505337944119723
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+            {
+                "unit": "kWh",
+                "cpu": 6.360978441318567e-05,
+                "ram": 5.114048105494929e-07,
+                "gpu": 0.00012099759679795596,
+                "total": 0.00018511878602169105
+            },
+            {
+                "unit": "kWh",
+                "cpu": 0.0,
+                "ram": 0.0,
+                "gpu": 0.0,
+                "total": 0.0
+            },
+            {
+                "unit": "kWh",
+                "cpu": 6.471016283309536e-05,
+                "ram": 5.203108890100263e-07,
+                "gpu": 0.00012313120961593427,
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+            {
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+                "ram": 5.312737488909054e-07,
+                "gpu": 0.00012466898862317777,
+                "total": 0.0001912731593486732
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+            {
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+                "ram": 5.403589966811575e-07,
+                "gpu": 0.00012662371240956816,
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+        ]
+    },
+    "preprocess": {
+        "memory": null,
+        "latency": null,
+        "throughput": null,
+        "energy": {
+            "unit": "kWh",
+            "cpu": 1.9452090272049344e-06,
+            "ram": 1.193016676754692e-08,
+            "gpu": 3.657225147613019e-06,
+            "total": 5.614364341585501e-06
+        },
+        "efficiency": {
+            "unit": "samples/kWh",
+            "value": 178114553.87620947
+        },
+        "measures": null
+    }
+}

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/cli.log

@@ -0,0 +1,113 @@
+[2024-12-05 00:36:58,469][launcher][INFO] - ََAllocating process launcher
+[2024-12-05 00:36:58,469][process][INFO] - 	+ Setting multiprocessing start method to spawn.
+[2024-12-05 00:36:58,482][device-isolation][INFO] - 	+ Launched device(s) isolation process 822
+[2024-12-05 00:36:58,482][device-isolation][INFO] - 	+ Isolating device(s) [0]
+[2024-12-05 00:36:58,488][process][INFO] - 	+ Launched benchmark in isolated process 823.
+[PROC-0][2024-12-05 00:37:01,111][datasets][INFO] - PyTorch version 2.4.0 available.
+[PROC-0][2024-12-05 00:37:02,059][backend][INFO] - َAllocating pytorch backend
+[PROC-0][2024-12-05 00:37:02,059][backend][INFO] - 	+ Setting random seed to 42
+[PROC-0][2024-12-05 00:37:02,694][pytorch][INFO] - 	+ Using AutoModel class AutoModel
+[PROC-0][2024-12-05 00:37:02,694][pytorch][INFO] - 	+ Creating backend temporary directory
+[PROC-0][2024-12-05 00:37:02,694][pytorch][INFO] - 	+ Loading model with random weights
+[PROC-0][2024-12-05 00:37:02,694][pytorch][INFO] - 	+ Creating no weights model
+[PROC-0][2024-12-05 00:37:02,694][pytorch][INFO] - 	+ Creating no weights model directory
+[PROC-0][2024-12-05 00:37:02,694][pytorch][INFO] - 	+ Creating no weights model state dict
+[PROC-0][2024-12-05 00:37:02,696][pytorch][INFO] - 	+ Saving no weights model safetensors
+[PROC-0][2024-12-05 00:37:02,697][pytorch][INFO] - 	+ Saving no weights model pretrained config
+[PROC-0][2024-12-05 00:37:02,698][pytorch][INFO] - 	+ Loading no weights AutoModel
+[PROC-0][2024-12-05 00:37:02,698][pytorch][INFO] - 	+ Loading model directly on device: cuda
+[PROC-0][2024-12-05 00:37:02,999][pytorch][INFO] - 	+ Turning on model's eval mode
+[PROC-0][2024-12-05 00:37:03,005][benchmark][INFO] - Allocating energy_star benchmark
+[PROC-0][2024-12-05 00:37:03,005][energy_star][INFO] - 	+ Loading raw dataset
+[PROC-0][2024-12-05 00:37:04,014][energy_star][INFO] - 	+ Initializing Inference report
+[PROC-0][2024-12-05 00:37:04,015][energy][INFO] - 	+ Tracking GPU energy on devices [0]
+[PROC-0][2024-12-05 00:37:08,201][energy_star][INFO] - 	+ Preprocessing dataset
+[PROC-0][2024-12-05 00:37:08,367][energy][INFO] - 	+ Saving codecarbon emission data to preprocess_codecarbon.json
+[PROC-0][2024-12-05 00:37:08,367][energy_star][INFO] - 	+ Preparing backend for Inference
+[PROC-0][2024-12-05 00:37:08,368][energy_star][INFO] - 	+ Initialising dataloader
+[PROC-0][2024-12-05 00:37:08,368][energy_star][INFO] - 	+ Warming up backend for Inference
+[PROC-0][2024-12-05 00:37:09,127][energy_star][INFO] - 	+ Running Inference energy tracking for 10 iterations
+[PROC-0][2024-12-05 00:37:09,127][energy_star][INFO] - 	+ Iteration 1/10
+[PROC-0][2024-12-05 00:37:14,755][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:14,755][energy_star][INFO] - 	+ Iteration 2/10
+[PROC-0][2024-12-05 00:37:20,218][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:20,219][energy_star][INFO] - 	+ Iteration 3/10
+[PROC-0][2024-12-05 00:37:25,658][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:25,658][energy_star][INFO] - 	+ Iteration 4/10
+[PROC-0][2024-12-05 00:37:31,035][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:31,035][energy_star][INFO] - 	+ Iteration 5/10
+[PROC-0][2024-12-05 00:37:36,396][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:36,396][energy_star][INFO] - 	+ Iteration 6/10
+[PROC-0][2024-12-05 00:37:41,785][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:41,785][energy_star][INFO] - 	+ Iteration 7/10
+[PROC-0][2024-12-05 00:37:47,170][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:47,170][energy_star][INFO] - 	+ Iteration 8/10
+[PROC-0][2024-12-05 00:37:52,652][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:52,652][energy_star][INFO] - 	+ Iteration 9/10
+[PROC-0][2024-12-05 00:37:58,250][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:37:58,250][energy_star][INFO] - 	+ Iteration 10/10
+[PROC-0][2024-12-05 00:38:03,944][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-05 00:38:03,945][energy][INFO] - 		+ forward energy consumption:
+[PROC-0][2024-12-05 00:38:03,945][energy][INFO] - 			+ CPU: 0.000058 (kWh)
+[PROC-0][2024-12-05 00:38:03,945][energy][INFO] - 			+ GPU: 0.000111 (kWh)
+[PROC-0][2024-12-05 00:38:03,945][energy][INFO] - 			+ RAM: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:38:03,945][energy][INFO] - 			+ total: 0.000169 (kWh)
+[PROC-0][2024-12-05 00:38:03,945][energy][INFO] - 		+ forward_iteration_1 energy consumption:
+[PROC-0][2024-12-05 00:38:03,945][energy][INFO] - 			+ CPU: 0.000066 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ GPU: 0.000126 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ total: 0.000193 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 		+ forward_iteration_2 energy consumption:
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ CPU: 0.000064 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ GPU: 0.000121 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ total: 0.000186 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 		+ forward_iteration_3 energy consumption:
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ CPU: 0.000064 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ GPU: 0.000123 (kWh)
+[PROC-0][2024-12-05 00:38:03,946][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ total: 0.000188 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 		+ forward_iteration_4 energy consumption:
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ CPU: 0.000063 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ GPU: 0.000119 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ total: 0.000183 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 		+ forward_iteration_5 energy consumption:
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ CPU: 0.000063 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ GPU: 0.000121 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ total: 0.000185 (kWh)
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 		+ forward_iteration_6 energy consumption:
+[PROC-0][2024-12-05 00:38:03,947][energy][INFO] - 			+ CPU: 0.000064 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ GPU: 0.000121 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ total: 0.000185 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 		+ forward_iteration_7 energy consumption:
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ CPU: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ GPU: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ RAM: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ total: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 		+ forward_iteration_8 energy consumption:
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ CPU: 0.000065 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ GPU: 0.000123 (kWh)
+[PROC-0][2024-12-05 00:38:03,948][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ total: 0.000188 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 		+ forward_iteration_9 energy consumption:
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ CPU: 0.000066 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ GPU: 0.000125 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ total: 0.000191 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 		+ forward_iteration_10 energy consumption:
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ CPU: 0.000067 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ GPU: 0.000127 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ total: 0.000194 (kWh)
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 		+ preprocess energy consumption:
+[PROC-0][2024-12-05 00:38:03,949][energy][INFO] - 			+ CPU: 0.000002 (kWh)
+[PROC-0][2024-12-05 00:38:03,950][energy][INFO] - 			+ GPU: 0.000004 (kWh)
+[PROC-0][2024-12-05 00:38:03,950][energy][INFO] - 			+ RAM: 0.000000 (kWh)
+[PROC-0][2024-12-05 00:38:03,950][energy][INFO] - 			+ total: 0.000006 (kWh)
+[PROC-0][2024-12-05 00:38:03,950][energy][INFO] - 		+ forward energy efficiency: 5904990.958539 (samples/kWh)
+[PROC-0][2024-12-05 00:38:03,950][energy][INFO] - 		+ preprocess energy efficiency: 178114553.876209 (samples/kWh)
+[2024-12-05 00:38:04,604][device-isolation][INFO] - 	+ Closing device(s) isolation process...
+[2024-12-05 00:38:04,652][datasets][INFO] - PyTorch version 2.4.0 available.

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/error.log

@@ -0,0 +1,170 @@
+/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(
+[codecarbon INFO @ 00:37:04] [setup] RAM Tracking...
+[codecarbon INFO @ 00:37:04] [setup] GPU Tracking...
+[codecarbon INFO @ 00:37:04] Tracking Nvidia GPU via pynvml
+[codecarbon DEBUG @ 00:37:04] GPU available. Starting setup
+[codecarbon INFO @ 00:37:04] [setup] CPU Tracking...
+[codecarbon DEBUG @ 00:37:04] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget
+[codecarbon DEBUG @ 00:37:04] 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 @ 00:37:04] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics
+[codecarbon WARNING @ 00:37:04] No CPU tracking mode found. Falling back on CPU constant mode.
+[codecarbon WARNING @ 00:37:05] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us.
+[codecarbon INFO @ 00:37:05] CPU Model on constant consumption mode: AMD EPYC 7R32
+[codecarbon INFO @ 00:37:05] >>> Tracker's metadata:
+[codecarbon INFO @ 00:37:05]   Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+[codecarbon INFO @ 00:37:05]   Python version: 3.9.20
+[codecarbon INFO @ 00:37:05]   CodeCarbon version: 2.5.1
+[codecarbon INFO @ 00:37:05]   Available RAM : 186.705 GB
+[codecarbon INFO @ 00:37:05]   CPU count: 48
+[codecarbon INFO @ 00:37:05]   CPU model: AMD EPYC 7R32
+[codecarbon INFO @ 00:37:05]   GPU count: 1
+[codecarbon INFO @ 00:37:05]   GPU model: 1 x NVIDIA A10G
+[codecarbon DEBUG @ 00:37:06] Not running on AWS
+[codecarbon DEBUG @ 00:37:07] Not running on Azure
+[codecarbon DEBUG @ 00:37:08] Not running on GCP
+[codecarbon INFO @ 00:37:08] Saving emissions data to file /runs/sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/codecarbon.csv
+[codecarbon DEBUG @ 00:37:08] EmissionsData(timestamp='2024-12-05T00:37:08', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.0022454530117101967, 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)
+
+[codecarbon INFO @ 00:37:08] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.26567602157592773 W
+[codecarbon DEBUG @ 00:37:08] RAM : 0.27 W during 0.16 s [measurement time: 0.0005]
+[codecarbon INFO @ 00:37:08] Energy consumed for all GPUs : 0.000004 kWh. Total GPU Power : 81.04958906810351 W
+[codecarbon DEBUG @ 00:37:08] GPU : 81.05 W during 0.16 s [measurement time: 0.0023]
+[codecarbon INFO @ 00:37:08] Energy consumed for all CPUs : 0.000002 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:08] CPU : 42.50 W during 0.16 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:08] 0.000006 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:08] last_duration=0.16165502701187506
+------------------------
+[codecarbon DEBUG @ 00:37:08] EmissionsData(timestamp='2024-12-05T00:37:08', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.16491737304022536, emissions=2.072454888483118e-06, emissions_rate=1.2566625639723359e-05, cpu_power=42.5, gpu_power=81.04958906810351, ram_power=0.26567602157592773, cpu_energy=1.9452090272049344e-06, gpu_energy=3.657225147613019e-06, ram_energy=1.193016676754692e-08, energy_consumed=5.614364341585501e-06, 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 @ 00:37:09] EmissionsData(timestamp='2024-12-05T00:37:09', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.003446593997068703, emissions=2.072454888483118e-06, emissions_rate=0.0006013051987689069, cpu_power=42.5, gpu_power=81.04958906810351, ram_power=0.26567602157592773, cpu_energy=1.9452090272049344e-06, gpu_energy=3.657225147613019e-06, ram_energy=1.193016676754692e-08, energy_consumed=5.614364341585501e-06, 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)
+
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+[codecarbon WARNING @ 00:37:14] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:14] Energy consumed for RAM : 0.000001 kWh. RAM Power : 0.34178638458251953 W
+[codecarbon DEBUG @ 00:37:14] RAM : 0.34 W during 5.62 s [measurement time: 0.0005]
+[codecarbon INFO @ 00:37:14] Energy consumed for all GPUs : 0.000130 kWh. Total GPU Power : 80.90480266562896 W
+[codecarbon DEBUG @ 00:37:14] GPU : 80.90 W during 5.62 s [measurement time: 0.0026]
+[codecarbon INFO @ 00:37:14] Energy consumed for all CPUs : 0.000068 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:14] CPU : 42.50 W during 5.63 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:14] 0.000199 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:14] last_duration=5.623332002025563
+------------------------
+[codecarbon DEBUG @ 00:37:14] EmissionsData(timestamp='2024-12-05T00:37:14', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.626995744998567, emissions=7.344831051653639e-05, emissions_rate=1.3052846286905286e-05, cpu_power=42.5, gpu_power=80.90480266562896, ram_power=0.34178638458251953, cpu_energy=6.83737928957271e-05, gpu_energy=0.0001300548262648249, ram_energy=5.458327347855121e-07, energy_consumed=0.0001989744518953375, 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 @ 00:37:14] EmissionsData(timestamp='2024-12-05T00:37:14', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.005245147971436381, emissions=7.344831051653639e-05, emissions_rate=0.014003095988238174, cpu_power=42.5, gpu_power=80.90480266562896, ram_power=0.34178638458251953, cpu_energy=6.83737928957271e-05, gpu_energy=0.0001300548262648249, ram_energy=5.458327347855121e-07, energy_consumed=0.0001989744518953375, 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)
+
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+[codecarbon WARNING @ 00:37:20] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:20] Energy consumed for RAM : 0.000001 kWh. RAM Power : 0.3418750762939453 W
+[codecarbon DEBUG @ 00:37:20] RAM : 0.34 W during 5.46 s [measurement time: 0.0004]
+[codecarbon INFO @ 00:37:20] Energy consumed for all GPUs : 0.000251 kWh. Total GPU Power : 79.51430083582915 W
+[codecarbon DEBUG @ 00:37:20] GPU : 79.51 W during 5.46 s [measurement time: 0.0030]
+[codecarbon INFO @ 00:37:20] Energy consumed for all CPUs : 0.000133 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:20] CPU : 42.50 W during 5.46 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:20] 0.000385 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:20] last_duration=5.458206440962385
+------------------------
+[codecarbon DEBUG @ 00:37:20] EmissionsData(timestamp='2024-12-05T00:37:20', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.462129193008877, emissions=0.00014195088109618715, emissions_rate=2.598819546009161e-05, cpu_power=42.5, gpu_power=79.51430083582915, ram_power=0.3418750762939453, cpu_energy=0.00013285603376563004, gpu_energy=0.0002506304782814439, ram_energy=1.064181679434311e-06, energy_consumed=0.0003845506937265083, 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 @ 00:37:20] EmissionsData(timestamp='2024-12-05T00:37:20', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.0020164839806966484, emissions=0.00014195088109618715, emissions_rate=0.07039524362953105, cpu_power=42.5, gpu_power=79.51430083582915, ram_power=0.3418750762939453, cpu_energy=0.00013285603376563004, gpu_energy=0.0002506304782814439, ram_energy=1.064181679434311e-06, energy_consumed=0.0003845506937265083, 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)
+
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+[codecarbon WARNING @ 00:37:25] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:25] Energy consumed for RAM : 0.000002 kWh. RAM Power : 0.34191083908081055 W
+[codecarbon DEBUG @ 00:37:25] RAM : 0.34 W during 5.44 s [measurement time: 0.0004]
+[codecarbon INFO @ 00:37:25] Energy consumed for all GPUs : 0.000373 kWh. Total GPU Power : 81.32767939620143 W
+[codecarbon DEBUG @ 00:37:25] GPU : 81.33 W during 5.44 s [measurement time: 0.0023]
+[codecarbon INFO @ 00:37:25] Energy consumed for all CPUs : 0.000197 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:25] CPU : 42.50 W during 5.44 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:25] 0.000572 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:25] last_duration=5.435178957995959
+------------------------
+[codecarbon DEBUG @ 00:37:25] EmissionsData(timestamp='2024-12-05T00:37:25', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.4384063520119525, emissions=0.0002111720454010686, emissions_rate=3.882976587855465e-05, cpu_power=42.5, gpu_power=81.32767939620143, ram_power=0.34191083908081055, cpu_energy=0.0001970582170962896, gpu_energy=0.0003734350209692039, ram_energy=1.5803970365000247e-06, energy_consumed=0.0005720736351019935, 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 @ 00:37:25] EmissionsData(timestamp='2024-12-05T00:37:25', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.002025774971116334, emissions=0.0002111720454010686, emissions_rate=0.10424259772777182, cpu_power=42.5, gpu_power=81.32767939620143, ram_power=0.34191083908081055, cpu_energy=0.0001970582170962896, gpu_energy=0.0003734350209692039, ram_energy=1.5803970365000247e-06, energy_consumed=0.0005720736351019935, 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)
+
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+[codecarbon WARNING @ 00:37:31] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:31] Energy consumed for RAM : 0.000002 kWh. RAM Power : 0.34191083908081055 W
+[codecarbon DEBUG @ 00:37:31] RAM : 0.34 W during 5.37 s [measurement time: 0.0004]
+[codecarbon INFO @ 00:37:31] Energy consumed for all GPUs : 0.000492 kWh. Total GPU Power : 79.68353647234068 W
+[codecarbon DEBUG @ 00:37:31] GPU : 79.68 W during 5.37 s [measurement time: 0.0060]
+[codecarbon INFO @ 00:37:31] Energy consumed for all CPUs : 0.000261 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:31] CPU : 42.50 W during 5.38 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:31] 0.000755 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:31] last_duration=5.369190519966651
+------------------------
+[codecarbon DEBUG @ 00:37:31] EmissionsData(timestamp='2024-12-05T00:37:31', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.3761263599735685, emissions=0.00027866356477632083, emissions_rate=5.1833522152870464e-05, cpu_power=42.5, gpu_power=79.68353647234068, ram_power=0.34191083908081055, cpu_energy=0.0002605251592452987, gpu_energy=0.0004922953938351071, ram_energy=2.090345136726384e-06, energy_consumed=0.0007549108982171322, 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 @ 00:37:31] EmissionsData(timestamp='2024-12-05T00:37:31', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.002033925033174455, emissions=0.00027866356477632083, emissions_rate=0.13700778555313603, cpu_power=42.5, gpu_power=79.68353647234068, ram_power=0.34191083908081055, cpu_energy=0.0002605251592452987, gpu_energy=0.0004922953938351071, ram_energy=2.090345136726384e-06, energy_consumed=0.0007549108982171322, 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)
+
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+[codecarbon WARNING @ 00:37:36] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:36] Energy consumed for RAM : 0.000003 kWh. RAM Power : 0.34191226959228516 W
+[codecarbon DEBUG @ 00:37:36] RAM : 0.34 W during 5.36 s [measurement time: 0.0004]
+[codecarbon INFO @ 00:37:36] Energy consumed for all GPUs : 0.000614 kWh. Total GPU Power : 81.48378815444873 W
+[codecarbon DEBUG @ 00:37:36] GPU : 81.48 W during 5.36 s [measurement time: 0.0023]
+[codecarbon INFO @ 00:37:36] Energy consumed for all CPUs : 0.000324 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:36] CPU : 42.50 W during 5.36 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:36] 0.000940 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:36] last_duration=5.35688115702942
+------------------------
+[codecarbon DEBUG @ 00:37:36] EmissionsData(timestamp='2024-12-05T00:37:36', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.3600602590013295, emissions=0.0003469731288729262, emissions_rate=6.473306494833571e-05, cpu_power=42.5, gpu_power=81.48378815444873, ram_power=0.34191226959228516, cpu_energy=0.00032380243782552295, gpu_energy=0.0006135627130712251, ram_energy=2.599126761581372e-06, energy_consumed=0.0009399642776583294, 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 @ 00:37:36] EmissionsData(timestamp='2024-12-05T00:37:36', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.0020143139990977943, emissions=0.0003469731288729262, emissions_rate=0.17225374446503103, cpu_power=42.5, gpu_power=81.48378815444873, ram_power=0.34191226959228516, cpu_energy=0.00032380243782552295, gpu_energy=0.0006135627130712251, ram_energy=2.599126761581372e-06, energy_consumed=0.0009399642776583294, 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)
+
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+[codecarbon WARNING @ 00:37:41] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:41] Energy consumed for RAM : 0.000003 kWh. RAM Power : 0.34191226959228516 W
+[codecarbon DEBUG @ 00:37:41] RAM : 0.34 W during 5.38 s [measurement time: 0.0004]
+[codecarbon INFO @ 00:37:41] Energy consumed for all GPUs : 0.000735 kWh. Total GPU Power : 80.88542164475706 W
+[codecarbon DEBUG @ 00:37:41] GPU : 80.89 W during 5.39 s [measurement time: 0.0028]
+[codecarbon INFO @ 00:37:41] Energy consumed for all CPUs : 0.000387 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:41] CPU : 42.50 W during 5.39 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:41] 0.001125 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:41] last_duration=5.38450276304502
+------------------------
+[codecarbon DEBUG @ 00:37:41] EmissionsData(timestamp='2024-12-05T00:37:41', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.38823386002332, emissions=0.00041530683678693217, emissions_rate=7.707661686108308e-05, cpu_power=42.5, gpu_power=80.88542164475706, ram_power=0.34191226959228516, cpu_energy=0.0003874122222387086, gpu_energy=0.0007345603098691811, ram_energy=3.1105315721308648e-06, energy_consumed=0.0011250830636800205, 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 @ 00:37:41] EmissionsData(timestamp='2024-12-05T00:37:41', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.006022343994118273, emissions=0.00041530683678693217, emissions_rate=0.06896099545169487, cpu_power=42.5, gpu_power=80.88542164475706, ram_power=0.34191226959228516, cpu_energy=0.0003874122222387086, gpu_energy=0.0007345603098691811, ram_energy=3.1105315721308648e-06, energy_consumed=0.0011250830636800205, 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)
+
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+[codecarbon WARNING @ 00:37:47] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:47] Energy consumed for RAM : 0.000004 kWh. RAM Power : 0.3419151306152344 W
+[codecarbon DEBUG @ 00:37:47] RAM : 0.34 W during 5.38 s [measurement time: 0.0004]
+[codecarbon INFO @ 00:37:47] Energy consumed for all GPUs : 0.000856 kWh. Total GPU Power : 80.91784923283659 W
+[codecarbon DEBUG @ 00:37:47] GPU : 80.92 W during 5.38 s [measurement time: 0.0030]
+[codecarbon INFO @ 00:37:47] Energy consumed for all CPUs : 0.000451 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:47] CPU : 42.50 W during 5.38 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:47] 0.001310 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:47] EmissionsData(timestamp='2024-12-05T00:37:47', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.383781075011939, emissions=0.0004836005986516783, emissions_rate=8.982545759452262e-05, cpu_power=42.5, gpu_power=80.91784923283659, ram_power=0.3419151306152344, cpu_energy=0.0004509697803620333, gpu_energy=0.000855502351067905, ram_energy=3.6215028043111817e-06, energy_consumed=0.0013100936342342493, 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 @ 00:37:47] 0.012699 g.CO2eq/s mean an estimation of 400.48506857409944 kg.CO2eq/year
+[codecarbon DEBUG @ 00:37:47] last_duration=5.379895540012512
+------------------------
+[codecarbon DEBUG @ 00:37:47] EmissionsData(timestamp='2024-12-05T00:37:47', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.384111588005908, emissions=0.0004836005986516783, emissions_rate=8.98199435035832e-05, cpu_power=42.5, gpu_power=80.91784923283659, ram_power=0.3419151306152344, cpu_energy=0.0004509697803620333, gpu_energy=0.000855502351067905, ram_energy=3.6215028043111817e-06, energy_consumed=0.0013100936342342493, 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 @ 00:37:47] EmissionsData(timestamp='2024-12-05T00:37:47', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.002039326005615294, emissions=0.0004836005986516783, emissions_rate=0.23713746469180588, cpu_power=42.5, gpu_power=80.91784923283659, ram_power=0.3419151306152344, cpu_energy=0.0004509697803620333, gpu_energy=0.000855502351067905, ram_energy=3.6215028043111817e-06, energy_consumed=0.0013100936342342493, 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)
+
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+[codecarbon WARNING @ 00:37:52] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:52] Energy consumed for RAM : 0.000004 kWh. RAM Power : 0.3419151306152344 W
+[codecarbon DEBUG @ 00:37:52] RAM : 0.34 W during 5.48 s [measurement time: 0.0005]
+[codecarbon INFO @ 00:37:52] Energy consumed for all GPUs : 0.000979 kWh. Total GPU Power : 80.90339416228707 W
+[codecarbon DEBUG @ 00:37:52] GPU : 80.90 W during 5.48 s [measurement time: 0.0023]
+[codecarbon INFO @ 00:37:52] Energy consumed for all CPUs : 0.000516 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:52] CPU : 42.50 W during 5.48 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:52] 0.001498 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:52] last_duration=5.478222850011662
+------------------------
+[codecarbon DEBUG @ 00:37:52] EmissionsData(timestamp='2024-12-05T00:37:52', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.481445233977865, emissions=0.0005531313714491181, emissions_rate=0.00010090976883622216, cpu_power=42.5, gpu_power=80.90339416228707, ram_power=0.3419151306152344, cpu_energy=0.0005156799431951287, gpu_energy=0.0009786335606838392, ram_energy=4.141813693321208e-06, energy_consumed=0.0014984553175722889, 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 @ 00:37:52] EmissionsData(timestamp='2024-12-05T00:37:52', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.00202655402245, emissions=0.0005531313714491181, emissions_rate=0.2729418339316761, cpu_power=42.5, gpu_power=80.90339416228707, ram_power=0.3419151306152344, cpu_energy=0.0005156799431951287, gpu_energy=0.0009786335606838392, ram_energy=4.141813693321208e-06, energy_consumed=0.0014984553175722889, 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)
+
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+[codecarbon WARNING @ 00:37:58] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:37:58] Energy consumed for RAM : 0.000005 kWh. RAM Power : 0.3419151306152344 W
+[codecarbon DEBUG @ 00:37:58] RAM : 0.34 W during 5.59 s [measurement time: 0.0005]
+[codecarbon INFO @ 00:37:58] Energy consumed for all GPUs : 0.001103 kWh. Total GPU Power : 80.22327428835422 W
+[codecarbon DEBUG @ 00:37:58] GPU : 80.22 W during 5.59 s [measurement time: 0.0022]
+[codecarbon INFO @ 00:37:58] Energy consumed for all CPUs : 0.000582 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:37:58] CPU : 42.50 W during 5.60 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:37:58] 0.001690 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:37:58] last_duration=5.593650219030678
+------------------------
+[codecarbon DEBUG @ 00:37:58] EmissionsData(timestamp='2024-12-05T00:37:58', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.59686559200054, emissions=0.000623736870132487, emissions_rate=0.00011144396088839054, cpu_power=42.5, gpu_power=80.22327428835422, ram_power=0.3419151306152344, cpu_energy=0.000581752840171733, gpu_energy=0.001103302549307017, ram_energy=4.673087442212113e-06, energy_consumed=0.001689728476920962, 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 @ 00:37:58] EmissionsData(timestamp='2024-12-05T00:37:58', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=0.002028044022154063, emissions=0.000623736870132487, emissions_rate=0.3075558830670708, cpu_power=42.5, gpu_power=80.22327428835422, ram_power=0.3419151306152344, cpu_energy=0.000581752840171733, gpu_energy=0.001103302549307017, ram_energy=4.673087442212113e-06, energy_consumed=0.001689728476920962, 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)
+
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+[codecarbon WARNING @ 00:38:03] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 00:38:03] Energy consumed for RAM : 0.000005 kWh. RAM Power : 0.341916561126709 W
+[codecarbon DEBUG @ 00:38:03] RAM : 0.34 W during 5.69 s [measurement time: 0.0004]
+[codecarbon INFO @ 00:38:03] Energy consumed for all GPUs : 0.001230 kWh. Total GPU Power : 80.11220968397961 W
+[codecarbon DEBUG @ 00:38:03] GPU : 80.11 W during 5.69 s [measurement time: 0.0034]
+[codecarbon INFO @ 00:38:03] Energy consumed for all CPUs : 0.000649 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 00:38:03] CPU : 42.50 W during 5.69 s [measurement time: 0.0000]
+[codecarbon INFO @ 00:38:03] 0.001884 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 00:38:03] last_duration=5.689292910974473
+------------------------
+[codecarbon DEBUG @ 00:38:03] EmissionsData(timestamp='2024-12-05T00:38:03', project_name='codecarbon', run_id='949ebe32-92ae-4a61-b9e4-1c493887be0e', duration=5.693626552005298, emissions=0.0006954888976896771, emissions_rate=0.00012215218039629338, cpu_power=42.5, gpu_power=80.11220968397961, ram_power=0.341916561126709, cpu_energy=0.0006489679213309703, gpu_energy=0.0012299262617165851, ram_energy=5.213446438893271e-06, energy_consumed=0.0018841076294864487, 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)

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/experiment_config.json

@@ -0,0 +1,107 @@
+{
+    "experiment_name": "sentence_similarity_udever-bloom-7b1",
+    "backend": {
+        "name": "pytorch",
+        "version": "2.4.0",
+        "_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
+        "task": "sentence-similarity",
+        "model": "sentence-transformers/all-MiniLM-L12-v2",
+        "processor": "sentence-transformers/all-MiniLM-L12-v2",
+        "library": "transformers",
+        "device": "cuda",
+        "device_ids": "0",
+        "seed": 42,
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+            "local_files_only": false,
+            "trust_remote_code": true
+        },
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+        "amp_autocast": false,
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+        "torch_compile": false,
+        "torch_compile_config": {},
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+        "quantization_config": {},
+        "deepspeed_inference": false,
+        "deepspeed_inference_config": {},
+        "peft_type": null,
+        "peft_config": {}
+    },
+    "launcher": {
+        "name": "process",
+        "_target_": "optimum_benchmark.launchers.process.launcher.ProcessLauncher",
+        "device_isolation": true,
+        "device_isolation_action": "warn",
+        "start_method": "spawn"
+    },
+    "benchmark": {
+        "name": "energy_star",
+        "_target_": "optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark",
+        "dataset_name": "EnergyStarAI/sentence_similarity",
+        "dataset_config": "",
+        "dataset_split": "train",
+        "num_samples": 1000,
+        "input_shapes": {
+            "batch_size": 1
+        },
+        "text_column_name": "text",
+        "truncation": true,
+        "max_length": -1,
+        "dataset_prefix1": "",
+        "dataset_prefix2": "",
+        "t5_task": "",
+        "image_column_name": "image",
+        "resize": false,
+        "question_column_name": "question",
+        "context_column_name": "context",
+        "sentence1_column_name": "sentence1",
+        "sentence2_column_name": "sentence2",
+        "audio_column_name": "audio",
+        "iterations": 10,
+        "warmup_runs": 10,
+        "energy": true,
+        "forward_kwargs": {},
+        "generate_kwargs": {},
+        "call_kwargs": {}
+    },
+    "environment": {
+        "cpu": " AMD EPYC 7R32",
+        "cpu_count": 48,
+        "cpu_ram_mb": 200472.73984,
+        "system": "Linux",
+        "machine": "x86_64",
+        "platform": "Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35",
+        "processor": "x86_64",
+        "python_version": "3.9.20",
+        "gpu": [
+            "NVIDIA A10G"
+        ],
+        "gpu_count": 1,
+        "gpu_vram_mb": 24146608128,
+        "optimum_benchmark_version": "0.2.0",
+        "optimum_benchmark_commit": null,
+        "transformers_version": "4.44.0",
+        "transformers_commit": null,
+        "accelerate_version": "0.33.0",
+        "accelerate_commit": null,
+        "diffusers_version": "0.30.0",
+        "diffusers_commit": null,
+        "optimum_version": null,
+        "optimum_commit": null,
+        "timm_version": null,
+        "timm_commit": null,
+        "peft_version": null,
+        "peft_commit": null
+    }
+}

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/forward_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-12-05T00:38:03",
+    "project_name": "codecarbon",
+    "run_id": "949ebe32-92ae-4a61-b9e4-1c493887be0e",
+    "duration": -1732874267.3993058,
+    "emissions": 7.175202755719016e-05,
+    "emissions_rate": 1.2606656540609707e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 80.11220968397961,
+    "ram_power": 0.341916561126709,
+    "cpu_energy": 6.721508115923723e-05,
+    "gpu_energy": 0.00012662371240956816,
+    "ram_energy": 5.403589966811575e-07,
+    "energy_consumed": 0.00019437915256548667,
+    "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
+}

sentence_similarity/sentence-transformers/all-MiniLM-L12-v2/2024-12-05-00-36-55/preprocess_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-12-05T00:37:08",
+    "project_name": "codecarbon",
+    "run_id": "949ebe32-92ae-4a61-b9e4-1c493887be0e",
+    "duration": -1732874272.9282913,
+    "emissions": 2.072454888483118e-06,
+    "emissions_rate": 1.2740089919144203e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 81.04958906810351,
+    "ram_power": 0.26567602157592773,
+    "cpu_energy": 1.9452090272049344e-06,
+    "gpu_energy": 3.657225147613019e-06,
+    "ram_energy": 1.193016676754692e-08,
+    "energy_consumed": 5.614364341585501e-06,
+    "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
+}

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/.hydra/config.yaml

@@ -0,0 +1,94 @@
+backend:
+  name: pytorch
+  version: 2.4.0
+  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
+  task: sentence-similarity
+  model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  processor: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  library: transformers
+  device: cuda
+  device_ids: '0'
+  seed: 42
+  inter_op_num_threads: null
+  intra_op_num_threads: null
+  hub_kwargs: {}
+  no_weights: true
+  device_map: null
+  torch_dtype: null
+  amp_autocast: false
+  amp_dtype: null
+  eval_mode: true
+  to_bettertransformer: false
+  low_cpu_mem_usage: null
+  attn_implementation: null
+  cache_implementation: null
+  torch_compile: false
+  torch_compile_config: {}
+  quantization_scheme: null
+  quantization_config: {}
+  deepspeed_inference: false
+  deepspeed_inference_config: {}
+  peft_type: null
+  peft_config: {}
+launcher:
+  name: process
+  _target_: optimum_benchmark.launchers.process.launcher.ProcessLauncher
+  device_isolation: true
+  device_isolation_action: warn
+  start_method: spawn
+benchmark:
+  name: energy_star
+  _target_: optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark
+  dataset_name: EnergyStarAI/sentence_similarity
+  dataset_config: ''
+  dataset_split: train
+  num_samples: 1000
+  input_shapes:
+    batch_size: 1
+  text_column_name: text
+  truncation: true
+  max_length: -1
+  dataset_prefix1: ''
+  dataset_prefix2: ''
+  t5_task: ''
+  image_column_name: image
+  resize: false
+  question_column_name: question
+  context_column_name: context
+  sentence1_column_name: sentence1
+  sentence2_column_name: sentence2
+  audio_column_name: audio
+  iterations: 10
+  warmup_runs: 10
+  energy: true
+  forward_kwargs: {}
+  generate_kwargs: {}
+  call_kwargs: {}
+experiment_name: sentence_similarity_udever-bloom-7b1
+environment:
+  cpu: ' AMD EPYC 7R32'
+  cpu_count: 48
+  cpu_ram_mb: 200472.73984
+  system: Linux
+  machine: x86_64
+  platform: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+  processor: x86_64
+  python_version: 3.9.20
+  gpu:
+  - NVIDIA A10G
+  gpu_count: 1
+  gpu_vram_mb: 24146608128
+  optimum_benchmark_version: 0.2.0
+  optimum_benchmark_commit: null
+  transformers_version: 4.44.0
+  transformers_commit: null
+  accelerate_version: 0.33.0
+  accelerate_commit: null
+  diffusers_version: 0.30.0
+  diffusers_commit: null
+  optimum_version: null
+  optimum_commit: null
+  timm_version: null
+  timm_commit: null
+  peft_version: null
+  peft_commit: null

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: /runs/sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46
+  sweep:
+    dir: sweeps/${experiment_name}/${now:%Y-%m-%d-%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
+          - %(message)s'
+        log_colors:
+          DEBUG: purple
+          INFO: green
+          WARNING: yellow
+          ERROR: red
+          CRITICAL: red
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.run.dir=/runs/sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46
+    - hydra.mode=RUN
+    task:
+    - backend.model=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+    - backend.processor=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  job:
+    name: cli
+    chdir: true
+    override_dirname: backend.model=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,backend.processor=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+    id: ???
+    num: ???
+    config_name: sentence_similarity
+    env_set:
+      OVERRIDE_BENCHMARKS: '1'
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: optimum_benchmark
+      schema: pkg
+      provider: main
+    - path: hydra_plugins.hydra_colorlog.conf
+      schema: pkg
+      provider: hydra-colorlog
+    - path: /optimum-benchmark/examples/energy_star
+      schema: file
+      provider: command-line
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /runs/sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46
+    choices:
+      benchmark: energy_star
+      launcher: process
+      backend: pytorch
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: colorlog
+      hydra/hydra_logging: colorlog
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/.hydra/overrides.yaml

@@ -0,0 +1,2 @@
+- backend.model=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+- backend.processor=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/benchmark_report.json

@@ -0,0 +1,107 @@
+{
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+        "energy": {
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+        "efficiency": {
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+            "value": 5841080.015429623
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+        "measures": [
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+            {
+                "unit": "kWh",
+                "cpu": 6.487486111202292e-05,
+                "ram": 6.435263415166126e-07,
+                "gpu": 0.00012489759991840543,
+                "total": 0.00019041598737194504
+            },
+            {
+                "unit": "kWh",
+                "cpu": 6.302952599819241e-05,
+                "ram": 6.254024038295793e-07,
+                "gpu": 0.0001234812098962479,
+                "total": 0.00018713613829826983
+            },
+            {
+                "unit": "kWh",
+                "cpu": 6.371498831091833e-05,
+                "ram": 6.32209506748507e-07,
+                "gpu": 0.00012280398713215845,
+                "total": 0.0001871511849498254
+            }
+        ]
+    },
+    "preprocess": {
+        "memory": null,
+        "latency": null,
+        "throughput": null,
+        "energy": {
+            "unit": "kWh",
+            "cpu": 2.4225458650309725e-06,
+            "ram": 2.014529724960743e-08,
+            "gpu": 3.5700028560015085e-06,
+            "total": 6.012694018282088e-06
+        },
+        "efficiency": {
+            "unit": "samples/kWh",
+            "value": 166314799.4824646
+        },
+        "measures": null
+    }
+}

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/cli.log

@@ -0,0 +1,113 @@
+[2024-12-04 21:40:49,001][launcher][INFO] - ََAllocating process launcher
+[2024-12-04 21:40:49,001][process][INFO] - 	+ Setting multiprocessing start method to spawn.
+[2024-12-04 21:40:49,012][device-isolation][INFO] - 	+ Launched device(s) isolation process 242
+[2024-12-04 21:40:49,013][device-isolation][INFO] - 	+ Isolating device(s) [0]
+[2024-12-04 21:40:49,018][process][INFO] - 	+ Launched benchmark in isolated process 243.
+[PROC-0][2024-12-04 21:40:51,760][datasets][INFO] - PyTorch version 2.4.0 available.
+[PROC-0][2024-12-04 21:40:52,678][backend][INFO] - َAllocating pytorch backend
+[PROC-0][2024-12-04 21:40:52,679][backend][INFO] - 	+ Setting random seed to 42
+[PROC-0][2024-12-04 21:40:54,905][pytorch][INFO] - 	+ Using AutoModel class AutoModel
+[PROC-0][2024-12-04 21:40:54,905][pytorch][INFO] - 	+ Creating backend temporary directory
+[PROC-0][2024-12-04 21:40:54,905][pytorch][INFO] - 	+ Loading model with random weights
+[PROC-0][2024-12-04 21:40:54,905][pytorch][INFO] - 	+ Creating no weights model
+[PROC-0][2024-12-04 21:40:54,905][pytorch][INFO] - 	+ Creating no weights model directory
+[PROC-0][2024-12-04 21:40:54,905][pytorch][INFO] - 	+ Creating no weights model state dict
+[PROC-0][2024-12-04 21:40:54,908][pytorch][INFO] - 	+ Saving no weights model safetensors
+[PROC-0][2024-12-04 21:40:54,908][pytorch][INFO] - 	+ Saving no weights model pretrained config
+[PROC-0][2024-12-04 21:40:54,909][pytorch][INFO] - 	+ Loading no weights AutoModel
+[PROC-0][2024-12-04 21:40:54,909][pytorch][INFO] - 	+ Loading model directly on device: cuda
+[PROC-0][2024-12-04 21:40:55,116][pytorch][INFO] - 	+ Turning on model's eval mode
+[PROC-0][2024-12-04 21:40:55,122][benchmark][INFO] - Allocating energy_star benchmark
+[PROC-0][2024-12-04 21:40:55,122][energy_star][INFO] - 	+ Loading raw dataset
+[PROC-0][2024-12-04 21:40:56,431][energy_star][INFO] - 	+ Initializing Inference report
+[PROC-0][2024-12-04 21:40:56,431][energy][INFO] - 	+ Tracking GPU energy on devices [0]
+[PROC-0][2024-12-04 21:41:00,637][energy_star][INFO] - 	+ Preprocessing dataset
+[PROC-0][2024-12-04 21:41:00,843][energy][INFO] - 	+ Saving codecarbon emission data to preprocess_codecarbon.json
+[PROC-0][2024-12-04 21:41:00,843][energy_star][INFO] - 	+ Preparing backend for Inference
+[PROC-0][2024-12-04 21:41:00,843][energy_star][INFO] - 	+ Initialising dataloader
+[PROC-0][2024-12-04 21:41:00,843][energy_star][INFO] - 	+ Warming up backend for Inference
+[PROC-0][2024-12-04 21:41:01,498][energy_star][INFO] - 	+ Running Inference energy tracking for 10 iterations
+[PROC-0][2024-12-04 21:41:01,498][energy_star][INFO] - 	+ Iteration 1/10
+[PROC-0][2024-12-04 21:41:07,056][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:07,057][energy_star][INFO] - 	+ Iteration 2/10
+[PROC-0][2024-12-04 21:41:12,562][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:12,563][energy_star][INFO] - 	+ Iteration 3/10
+[PROC-0][2024-12-04 21:41:18,071][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:18,071][energy_star][INFO] - 	+ Iteration 4/10
+[PROC-0][2024-12-04 21:41:23,852][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:23,853][energy_star][INFO] - 	+ Iteration 5/10
+[PROC-0][2024-12-04 21:41:29,575][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:29,575][energy_star][INFO] - 	+ Iteration 6/10
+[PROC-0][2024-12-04 21:41:35,002][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:35,003][energy_star][INFO] - 	+ Iteration 7/10
+[PROC-0][2024-12-04 21:41:40,713][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:40,714][energy_star][INFO] - 	+ Iteration 8/10
+[PROC-0][2024-12-04 21:41:46,210][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:46,210][energy_star][INFO] - 	+ Iteration 9/10
+[PROC-0][2024-12-04 21:41:51,550][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:51,550][energy_star][INFO] - 	+ Iteration 10/10
+[PROC-0][2024-12-04 21:41:56,948][energy][INFO] - 	+ Saving codecarbon emission data to forward_codecarbon.json
+[PROC-0][2024-12-04 21:41:56,948][energy][INFO] - 		+ forward energy consumption:
+[PROC-0][2024-12-04 21:41:56,948][energy][INFO] - 			+ CPU: 0.000059 (kWh)
+[PROC-0][2024-12-04 21:41:56,948][energy][INFO] - 			+ GPU: 0.000112 (kWh)
+[PROC-0][2024-12-04 21:41:56,948][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ total: 0.000171 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 		+ forward_iteration_1 energy consumption:
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ CPU: 0.000066 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ GPU: 0.000124 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ total: 0.000191 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 		+ forward_iteration_2 energy consumption:
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ CPU: 0.000065 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ GPU: 0.000122 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 			+ total: 0.000188 (kWh)
+[PROC-0][2024-12-04 21:41:56,949][energy][INFO] - 		+ forward_iteration_3 energy consumption:
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ CPU: 0.000065 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ GPU: 0.000122 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ total: 0.000188 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 		+ forward_iteration_4 energy consumption:
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ CPU: 0.000068 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ GPU: 0.000128 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ total: 0.000197 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 		+ forward_iteration_5 energy consumption:
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ CPU: 0.000068 (kWh)
+[PROC-0][2024-12-04 21:41:56,950][energy][INFO] - 			+ GPU: 0.000128 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ total: 0.000196 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 		+ forward_iteration_6 energy consumption:
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ CPU: 0.000064 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ GPU: 0.000123 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ total: 0.000187 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 		+ forward_iteration_7 energy consumption:
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ CPU: 0.000000 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ GPU: 0.000000 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ RAM: 0.000000 (kWh)
+[PROC-0][2024-12-04 21:41:56,951][energy][INFO] - 			+ total: 0.000000 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 		+ forward_iteration_8 energy consumption:
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ CPU: 0.000065 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ GPU: 0.000125 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ total: 0.000190 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 		+ forward_iteration_9 energy consumption:
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ CPU: 0.000063 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ GPU: 0.000123 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 			+ total: 0.000187 (kWh)
+[PROC-0][2024-12-04 21:41:56,952][energy][INFO] - 		+ forward_iteration_10 energy consumption:
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ CPU: 0.000064 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ GPU: 0.000123 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ RAM: 0.000001 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ total: 0.000187 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 		+ preprocess energy consumption:
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ CPU: 0.000002 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ GPU: 0.000004 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ RAM: 0.000000 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 			+ total: 0.000006 (kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 		+ forward energy efficiency: 5841080.015430 (samples/kWh)
+[PROC-0][2024-12-04 21:41:56,953][energy][INFO] - 		+ preprocess energy efficiency: 166314799.482465 (samples/kWh)
+[2024-12-04 21:41:57,805][device-isolation][INFO] - 	+ Closing device(s) isolation process...
+[2024-12-04 21:41:57,852][datasets][INFO] - PyTorch version 2.4.0 available.

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/error.log

@@ -0,0 +1,178 @@
+/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(
+
+
+
+
+
+
+
+[codecarbon INFO @ 21:40:56] [setup] RAM Tracking...
+[codecarbon INFO @ 21:40:56] [setup] GPU Tracking...
+[codecarbon INFO @ 21:40:56] Tracking Nvidia GPU via pynvml
+[codecarbon DEBUG @ 21:40:56] GPU available. Starting setup
+[codecarbon INFO @ 21:40:56] [setup] CPU Tracking...
+[codecarbon DEBUG @ 21:40:56] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget
+[codecarbon DEBUG @ 21:40:56] 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 @ 21:40:56] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics
+[codecarbon WARNING @ 21:40:56] No CPU tracking mode found. Falling back on CPU constant mode.
+[codecarbon WARNING @ 21:40:57] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us.
+[codecarbon INFO @ 21:40:57] CPU Model on constant consumption mode: AMD EPYC 7R32
+[codecarbon INFO @ 21:40:57] >>> Tracker's metadata:
+[codecarbon INFO @ 21:40:57]   Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+[codecarbon INFO @ 21:40:57]   Python version: 3.9.20
+[codecarbon INFO @ 21:40:57]   CodeCarbon version: 2.5.1
+[codecarbon INFO @ 21:40:57]   Available RAM : 186.705 GB
+[codecarbon INFO @ 21:40:57]   CPU count: 48
+[codecarbon INFO @ 21:40:57]   CPU model: AMD EPYC 7R32
+[codecarbon INFO @ 21:40:57]   GPU count: 1
+[codecarbon INFO @ 21:40:57]   GPU model: 1 x NVIDIA A10G
+[codecarbon DEBUG @ 21:40:58] Not running on AWS
+[codecarbon DEBUG @ 21:40:59] Not running on Azure
+[codecarbon DEBUG @ 21:41:00] Not running on GCP
+[codecarbon INFO @ 21:41:00] Saving emissions data to file /runs/sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/codecarbon.csv
+[codecarbon DEBUG @ 21:41:00] EmissionsData(timestamp='2024-12-04T21:41:00', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.002095217991154641, 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)
+
+
+[codecarbon INFO @ 21:41:00] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.358670711517334 W
+[codecarbon DEBUG @ 21:41:00] RAM : 0.36 W during 0.20 s [measurement time: 0.0005]
+[codecarbon INFO @ 21:41:00] Energy consumed for all GPUs : 0.000004 kWh. Total GPU Power : 63.313183121484805 W
+[codecarbon DEBUG @ 21:41:00] GPU : 63.31 W during 0.20 s [measurement time: 0.0022]
+[codecarbon INFO @ 21:41:00] Energy consumed for all CPUs : 0.000002 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:00] CPU : 42.50 W during 0.21 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:00] 0.000006 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:00] last_duration=0.20219684799667448
+------------------------
+[codecarbon DEBUG @ 21:41:00] EmissionsData(timestamp='2024-12-04T21:41:00', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.20531185902655125, emissions=2.2194920658859325e-06, emissions_rate=1.0810345181273257e-05, cpu_power=42.5, gpu_power=63.313183121484805, ram_power=0.358670711517334, cpu_energy=2.4225458650309725e-06, gpu_energy=3.5700028560015085e-06, ram_energy=2.014529724960743e-08, energy_consumed=6.012694018282088e-06, 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 @ 21:41:01] EmissionsData(timestamp='2024-12-04T21:41:01', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.00529201200697571, emissions=2.2194920658859325e-06, emissions_rate=0.0004194042007010359, cpu_power=42.5, gpu_power=63.313183121484805, ram_power=0.358670711517334, cpu_energy=2.4225458650309725e-06, gpu_energy=3.5700028560015085e-06, ram_energy=2.014529724960743e-08, energy_consumed=6.012694018282088e-06, 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)
+
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+[codecarbon WARNING @ 21:41:07] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:07] Energy consumed for RAM : 0.000001 kWh. RAM Power : 0.4217991828918457 W
+[codecarbon DEBUG @ 21:41:07] RAM : 0.42 W during 5.55 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:07] Energy consumed for all GPUs : 0.000128 kWh. Total GPU Power : 80.5587352712058 W
+[codecarbon DEBUG @ 21:41:07] GPU : 80.56 W during 5.56 s [measurement time: 0.0023]
+[codecarbon INFO @ 21:41:07] Energy consumed for all CPUs : 0.000068 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:07] CPU : 42.50 W during 5.56 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:07] 0.000197 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:07] last_duration=5.554333877982572
+------------------------
+[codecarbon DEBUG @ 21:41:07] EmissionsData(timestamp='2024-12-04T21:41:07', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.557659258018248, emissions=7.25665004257193e-05, emissions_rate=1.305702581910268e-05, cpu_power=42.5, gpu_power=80.5587352712058, ram_power=0.4217991828918457, cpu_energy=6.803259559953101e-05, gpu_energy=0.0001278820467502939, ram_energy=6.709502475631516e-07, energy_consumed=0.00019658559259738806, 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 @ 21:41:07] EmissionsData(timestamp='2024-12-04T21:41:07', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.005300023010931909, emissions=7.25665004257193e-05, emissions_rate=0.013691733087958773, cpu_power=42.5, gpu_power=80.5587352712058, ram_power=0.4217991828918457, cpu_energy=6.803259559953101e-05, gpu_energy=0.0001278820467502939, ram_energy=6.709502475631516e-07, energy_consumed=0.00019658559259738806, 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)
+
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+[codecarbon WARNING @ 21:41:12] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:12] Energy consumed for RAM : 0.000001 kWh. RAM Power : 0.42191505432128906 W
+[codecarbon DEBUG @ 21:41:12] RAM : 0.42 W during 5.50 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:12] Energy consumed for all GPUs : 0.000250 kWh. Total GPU Power : 80.02536311863172 W
+[codecarbon DEBUG @ 21:41:12] GPU : 80.03 W during 5.50 s [measurement time: 0.0057]
+[codecarbon INFO @ 21:41:12] Energy consumed for all CPUs : 0.000133 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:12] CPU : 42.50 W during 5.50 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:12] 0.000384 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:12] last_duration=5.4962086220039055
+------------------------
+[codecarbon DEBUG @ 21:41:12] EmissionsData(timestamp='2024-12-04T21:41:12', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.502865254005883, emissions=0.00014189053092958443, emissions_rate=2.5784845599534417e-05, cpu_power=42.5, gpu_power=80.02536311863172, ram_power=0.42191505432128906, cpu_energy=0.0001329957828916021, gpu_energy=0.0002500763111727977, ram_energy=1.3151086132727109e-06, energy_consumed=0.00038438720267767253, 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 @ 21:41:12] EmissionsData(timestamp='2024-12-04T21:41:12', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.0020321760093793273, emissions=0.00014189053092958443, emissions_rate=0.06982196929532744, cpu_power=42.5, gpu_power=80.02536311863172, ram_power=0.42191505432128906, cpu_energy=0.0001329957828916021, gpu_energy=0.0002500763111727977, ram_energy=1.3151086132727109e-06, energy_consumed=0.00038438720267767253, 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)
+
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+[codecarbon WARNING @ 21:41:18] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:18] Energy consumed for RAM : 0.000002 kWh. RAM Power : 0.4219307899475098 W
+[codecarbon DEBUG @ 21:41:18] RAM : 0.42 W during 5.50 s [measurement time: 0.0005]
+[codecarbon INFO @ 21:41:18] Energy consumed for all GPUs : 0.000373 kWh. Total GPU Power : 80.0877431989967 W
+[codecarbon DEBUG @ 21:41:18] GPU : 80.09 W during 5.50 s [measurement time: 0.0023]
+[codecarbon INFO @ 21:41:18] Energy consumed for all CPUs : 0.000198 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:18] CPU : 42.50 W during 5.51 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:18] 0.000573 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:18] last_duration=5.503680415975396
+------------------------
+[codecarbon DEBUG @ 21:41:18] EmissionsData(timestamp='2024-12-04T21:41:18', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.5071164420223795, emissions=0.00021133107931143805, emissions_rate=3.837418030584276e-05, cpu_power=42.5, gpu_power=80.0877431989967, ram_power=0.4219307899475098, cpu_energy=0.0001980087208722883, gpu_energy=0.0003725355758064097, ram_energy=1.960167736127847e-06, energy_consumed=0.0005725044644148259, 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 @ 21:41:18] EmissionsData(timestamp='2024-12-04T21:41:18', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.006641042011324316, emissions=0.00021133107931143805, emissions_rate=0.031821975971703824, cpu_power=42.5, gpu_power=80.0877431989967, ram_power=0.4219307899475098, cpu_energy=0.0001980087208722883, gpu_energy=0.0003725355758064097, ram_energy=1.960167736127847e-06, energy_consumed=0.0005725044644148259, 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)
+
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+[codecarbon WARNING @ 21:41:23] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:23] Energy consumed for RAM : 0.000003 kWh. RAM Power : 0.421933650970459 W
+[codecarbon DEBUG @ 21:41:23] RAM : 0.42 W during 5.78 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:23] Energy consumed for all GPUs : 0.000501 kWh. Total GPU Power : 79.87532674888328 W
+[codecarbon DEBUG @ 21:41:23] GPU : 79.88 W during 5.78 s [measurement time: 0.0023]
+[codecarbon INFO @ 21:41:23] Energy consumed for all CPUs : 0.000266 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:23] CPU : 42.50 W during 5.78 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:23] 0.000770 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:23] last_duration=5.7772260020137765
+------------------------
+[codecarbon DEBUG @ 21:41:23] EmissionsData(timestamp='2024-12-04T21:41:23', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.780409655009862, emissions=0.0002840937397998374, emissions_rate=4.9147682734495175e-05, cpu_power=42.5, gpu_power=79.87532674888328, ram_power=0.421933650970459, cpu_energy=0.0002662484930160847, gpu_energy=0.0005007356783659489, ram_energy=2.637291195278228e-06, energy_consumed=0.0007696214625773118, 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 @ 21:41:23] EmissionsData(timestamp='2024-12-04T21:41:23', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.005203137989155948, emissions=0.0002840937397998374, emissions_rate=0.05460046233483095, cpu_power=42.5, gpu_power=79.87532674888328, ram_power=0.421933650970459, cpu_energy=0.0002662484930160847, gpu_energy=0.0005007356783659489, ram_energy=2.637291195278228e-06, energy_consumed=0.0007696214625773118, 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)
+
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+[codecarbon WARNING @ 21:41:29] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:29] Energy consumed for RAM : 0.000003 kWh. RAM Power : 0.421933650970459 W
+[codecarbon DEBUG @ 21:41:29] RAM : 0.42 W during 5.72 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:29] Energy consumed for all GPUs : 0.000629 kWh. Total GPU Power : 80.62234383570966 W
+[codecarbon DEBUG @ 21:41:29] GPU : 80.62 W during 5.72 s [measurement time: 0.0059]
+[codecarbon INFO @ 21:41:29] Energy consumed for all CPUs : 0.000334 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:29] CPU : 42.50 W during 5.72 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:29] 0.000966 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:29] last_duration=5.714988129970152
+------------------------
+[codecarbon DEBUG @ 21:41:29] EmissionsData(timestamp='2024-12-04T21:41:29', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.7217791639850475, emissions=0.00035652631813954327, emissions_rate=6.23103947079344e-05, cpu_power=42.5, gpu_power=80.62234383570966, ram_power=0.421933650970459, cpu_energy=0.0003337960765023228, gpu_energy=0.0006287410585477105, ram_energy=3.3071197624280633e-06, energy_consumed=0.0009658442548124613, 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 @ 21:41:29] EmissionsData(timestamp='2024-12-04T21:41:29', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.0020339550101198256, emissions=0.00035652631813954327, emissions_rate=0.17528721941521183, cpu_power=42.5, gpu_power=80.62234383570966, ram_power=0.421933650970459, cpu_energy=0.0003337960765023228, gpu_energy=0.0006287410585477105, ram_energy=3.3071197624280633e-06, energy_consumed=0.0009658442548124613, 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)
+
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+[codecarbon WARNING @ 21:41:34] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:34] Energy consumed for RAM : 0.000004 kWh. RAM Power : 0.4219350814819336 W
+[codecarbon DEBUG @ 21:41:34] RAM : 0.42 W during 5.42 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:35] Energy consumed for all GPUs : 0.000752 kWh. Total GPU Power : 81.4907511602875 W
+[codecarbon DEBUG @ 21:41:35] GPU : 81.49 W during 5.42 s [measurement time: 0.0023]
+[codecarbon INFO @ 21:41:35] Energy consumed for all CPUs : 0.000398 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:35] CPU : 42.50 W during 5.43 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:35] 0.001153 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:35] last_duration=5.423253223998472
+------------------------
+[codecarbon DEBUG @ 21:41:35] EmissionsData(timestamp='2024-12-04T21:41:35', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.426443868025672, emissions=0.00042573062954880483, emissions_rate=7.845481127287518e-05, cpu_power=42.5, gpu_power=81.4907511602875, ram_power=0.4219350814819336, cpu_energy=0.00039785707053577226, gpu_energy=0.0007515217123286178, ram_energy=3.942758145850851e-06, energy_consumed=0.0011533215410102409, 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 @ 21:41:35] EmissionsData(timestamp='2024-12-04T21:41:35', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.002038365986663848, emissions=0.00042573062954880483, emissions_rate=0.20885877822440976, cpu_power=42.5, gpu_power=81.4907511602875, ram_power=0.4219350814819336, cpu_energy=0.00039785707053577226, gpu_energy=0.0007515217123286178, ram_energy=3.942758145850851e-06, energy_consumed=0.0011533215410102409, 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)
+
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+[codecarbon WARNING @ 21:41:40] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:40] Energy consumed for RAM : 0.000005 kWh. RAM Power : 0.4219350814819336 W
+[codecarbon DEBUG @ 21:41:40] RAM : 0.42 W during 5.71 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:40] Energy consumed for all GPUs : 0.000880 kWh. Total GPU Power : 80.97259072749019 W
+[codecarbon DEBUG @ 21:41:40] GPU : 80.97 W during 5.71 s [measurement time: 0.0023]
+[codecarbon INFO @ 21:41:40] Energy consumed for all CPUs : 0.000465 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:40] CPU : 42.50 W during 5.71 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:40] 0.001350 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:40] EmissionsData(timestamp='2024-12-04T21:41:40', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.709941094974056, emissions=0.0004982483443375706, emissions_rate=8.725980461972427e-05, cpu_power=42.5, gpu_power=80.97259072749019, ram_power=0.4219350814819336, cpu_energy=0.00046526514224654725, gpu_energy=0.0008798982039177616, ram_energy=4.611625174304834e-06, energy_consumed=0.0013497749713386137, 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 @ 21:41:40] 0.012705 g.CO2eq/s mean an estimation of 400.6583086926967 kg.CO2eq/year
+[codecarbon DEBUG @ 21:41:40] last_duration=5.706764361995738
+------------------------
+[codecarbon DEBUG @ 21:41:40] EmissionsData(timestamp='2024-12-04T21:41:40', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.710274618992116, emissions=0.0004982483443375706, emissions_rate=8.725470797506288e-05, cpu_power=42.5, gpu_power=80.97259072749019, ram_power=0.4219350814819336, cpu_energy=0.00046526514224654725, gpu_energy=0.0008798982039177616, ram_energy=4.611625174304834e-06, energy_consumed=0.0013497749713386137, 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 @ 21:41:40] EmissionsData(timestamp='2024-12-04T21:41:40', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.0022437340230681, emissions=0.0004982483443375706, emissions_rate=0.2220621246613989, cpu_power=42.5, gpu_power=80.97259072749019, ram_power=0.4219350814819336, cpu_energy=0.00046526514224654725, gpu_energy=0.0008798982039177616, ram_energy=4.611625174304834e-06, energy_consumed=0.0013497749713386137, 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)
+
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+[codecarbon WARNING @ 21:41:46] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:46] Energy consumed for RAM : 0.000005 kWh. RAM Power : 0.4219350814819336 W
+[codecarbon DEBUG @ 21:41:46] RAM : 0.42 W during 5.49 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:46] Energy consumed for all GPUs : 0.001005 kWh. Total GPU Power : 81.8805876937801 W
+[codecarbon DEBUG @ 21:41:46] GPU : 81.88 W during 5.49 s [measurement time: 0.0039]
+[codecarbon INFO @ 21:41:46] Energy consumed for all CPUs : 0.000530 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:46] CPU : 42.50 W during 5.50 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:46] 0.001540 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:46] last_duration=5.490556205040775
+------------------------
+[codecarbon DEBUG @ 21:41:46] EmissionsData(timestamp='2024-12-04T21:41:46', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.495387717033736, emissions=0.0005685374313765645, emissions_rate=0.00010345720095677723, cpu_power=42.5, gpu_power=81.8805876937801, ram_power=0.4219350814819336, cpu_energy=0.0005301400033585702, gpu_energy=0.001004795803836167, ram_energy=5.255151515821446e-06, energy_consumed=0.0015401909587105587, 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 @ 21:41:46] EmissionsData(timestamp='2024-12-04T21:41:46', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.0020296149887144566, emissions=0.0005685374313765645, emissions_rate=0.28012082810674943, cpu_power=42.5, gpu_power=81.8805876937801, ram_power=0.4219350814819336, cpu_energy=0.0005301400033585702, gpu_energy=0.001004795803836167, ram_energy=5.255151515821446e-06, energy_consumed=0.0015401909587105587, 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)
+
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+[codecarbon WARNING @ 21:41:51] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:51] Energy consumed for RAM : 0.000006 kWh. RAM Power : 0.4219365119934082 W
+[codecarbon DEBUG @ 21:41:51] RAM : 0.42 W during 5.34 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:51] Energy consumed for all GPUs : 0.001128 kWh. Total GPU Power : 83.2974686771808 W
+[codecarbon DEBUG @ 21:41:51] GPU : 83.30 W during 5.34 s [measurement time: 0.0023]
+[codecarbon INFO @ 21:41:51] Energy consumed for all CPUs : 0.000593 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:51] CPU : 42.50 W during 5.34 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:51] 0.001727 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:51] last_duration=5.335901124984957
+------------------------
+[codecarbon DEBUG @ 21:41:51] EmissionsData(timestamp='2024-12-04T21:41:51', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.339070877991617, emissions=0.0006376158133681719, emissions_rate=0.00011942448938007394, cpu_power=42.5, gpu_power=83.2974686771808, ram_power=0.4219365119934082, cpu_energy=0.0005931695293567626, gpu_energy=0.001128277013732415, ram_energy=5.880553919651026e-06, energy_consumed=0.0017273270970088286, 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 @ 21:41:51] EmissionsData(timestamp='2024-12-04T21:41:51', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=0.002015664998907596, emissions=0.0006376158133681719, emissions_rate=0.3163302501723909, cpu_power=42.5, gpu_power=83.2974686771808, ram_power=0.4219365119934082, cpu_energy=0.0005931695293567626, gpu_energy=0.001128277013732415, ram_energy=5.880553919651026e-06, energy_consumed=0.0017273270970088286, 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)
+
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+[codecarbon WARNING @ 21:41:56] Background scheduler didn't run for a long period (5s), results might be inaccurate
+[codecarbon INFO @ 21:41:56] Energy consumed for RAM : 0.000007 kWh. RAM Power : 0.4219365119934082 W
+[codecarbon DEBUG @ 21:41:56] RAM : 0.42 W during 5.39 s [measurement time: 0.0004]
+[codecarbon INFO @ 21:41:56] Energy consumed for all GPUs : 0.001251 kWh. Total GPU Power : 81.94900456059085 W
+[codecarbon DEBUG @ 21:41:56] GPU : 81.95 W during 5.39 s [measurement time: 0.0023]
+[codecarbon INFO @ 21:41:56] Energy consumed for all CPUs : 0.000657 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 21:41:56] CPU : 42.50 W during 5.40 s [measurement time: 0.0000]
+[codecarbon INFO @ 21:41:56] 0.001914 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 21:41:56] last_duration=5.393976785999257
+------------------------
+[codecarbon DEBUG @ 21:41:56] EmissionsData(timestamp='2024-12-04T21:41:56', project_name='codecarbon', run_id='67a1e19b-dda1-45b5-badc-b2407c0fbf10', duration=5.397134728031233, emissions=0.0007066997495961405, emissions_rate=0.0001309398014331079, cpu_power=42.5, gpu_power=81.94900456059085, ram_power=0.4219365119934082, cpu_energy=0.0006568845176676809, gpu_energy=0.0012510810008645734, ram_energy=6.5127634263995326e-06, energy_consumed=0.001914478281958654, 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)

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/experiment_config.json

@@ -0,0 +1,107 @@
+{
+    "experiment_name": "sentence_similarity_udever-bloom-7b1",
+    "backend": {
+        "name": "pytorch",
+        "version": "2.4.0",
+        "_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
+        "task": "sentence-similarity",
+        "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+        "processor": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+        "library": "transformers",
+        "device": "cuda",
+        "device_ids": "0",
+        "seed": 42,
+        "inter_op_num_threads": null,
+        "intra_op_num_threads": null,
+        "hub_kwargs": {
+            "revision": "main",
+            "force_download": false,
+            "local_files_only": false,
+            "trust_remote_code": true
+        },
+        "no_weights": true,
+        "device_map": null,
+        "torch_dtype": null,
+        "amp_autocast": false,
+        "amp_dtype": null,
+        "eval_mode": true,
+        "to_bettertransformer": false,
+        "low_cpu_mem_usage": null,
+        "attn_implementation": null,
+        "cache_implementation": null,
+        "torch_compile": false,
+        "torch_compile_config": {},
+        "quantization_scheme": null,
+        "quantization_config": {},
+        "deepspeed_inference": false,
+        "deepspeed_inference_config": {},
+        "peft_type": null,
+        "peft_config": {}
+    },
+    "launcher": {
+        "name": "process",
+        "_target_": "optimum_benchmark.launchers.process.launcher.ProcessLauncher",
+        "device_isolation": true,
+        "device_isolation_action": "warn",
+        "start_method": "spawn"
+    },
+    "benchmark": {
+        "name": "energy_star",
+        "_target_": "optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark",
+        "dataset_name": "EnergyStarAI/sentence_similarity",
+        "dataset_config": "",
+        "dataset_split": "train",
+        "num_samples": 1000,
+        "input_shapes": {
+            "batch_size": 1
+        },
+        "text_column_name": "text",
+        "truncation": true,
+        "max_length": -1,
+        "dataset_prefix1": "",
+        "dataset_prefix2": "",
+        "t5_task": "",
+        "image_column_name": "image",
+        "resize": false,
+        "question_column_name": "question",
+        "context_column_name": "context",
+        "sentence1_column_name": "sentence1",
+        "sentence2_column_name": "sentence2",
+        "audio_column_name": "audio",
+        "iterations": 10,
+        "warmup_runs": 10,
+        "energy": true,
+        "forward_kwargs": {},
+        "generate_kwargs": {},
+        "call_kwargs": {}
+    },
+    "environment": {
+        "cpu": " AMD EPYC 7R32",
+        "cpu_count": 48,
+        "cpu_ram_mb": 200472.73984,
+        "system": "Linux",
+        "machine": "x86_64",
+        "platform": "Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35",
+        "processor": "x86_64",
+        "python_version": "3.9.20",
+        "gpu": [
+            "NVIDIA A10G"
+        ],
+        "gpu_count": 1,
+        "gpu_vram_mb": 24146608128,
+        "optimum_benchmark_version": "0.2.0",
+        "optimum_benchmark_commit": null,
+        "transformers_version": "4.44.0",
+        "transformers_commit": null,
+        "accelerate_version": "0.33.0",
+        "accelerate_commit": null,
+        "diffusers_version": "0.30.0",
+        "diffusers_commit": null,
+        "optimum_version": null,
+        "optimum_commit": null,
+        "timm_version": null,
+        "timm_commit": null,
+        "peft_version": null,
+        "peft_commit": null
+    }
+}

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/forward_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-12-04T21:41:56",
+    "project_name": "codecarbon",
+    "run_id": "67a1e19b-dda1-45b5-badc-b2407c0fbf10",
+    "duration": -1732874267.695807,
+    "emissions": 6.908393622796858e-05,
+    "emissions_rate": 1.2804895577066277e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 81.94900456059085,
+    "ram_power": 0.4219365119934082,
+    "cpu_energy": 6.371498831091833e-05,
+    "gpu_energy": 0.00012280398713215845,
+    "ram_energy": 6.32209506748507e-07,
+    "energy_consumed": 0.0001871511849498254,
+    "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
+}

sentence_similarity/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/2024-12-04-21-40-46/preprocess_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-12-04T21:41:00",
+    "project_name": "codecarbon",
+    "run_id": "67a1e19b-dda1-45b5-badc-b2407c0fbf10",
+    "duration": -1732874272.8877585,
+    "emissions": 2.2194920658859325e-06,
+    "emissions_rate": 1.0921802735137906e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 63.313183121484805,
+    "ram_power": 0.358670711517334,
+    "cpu_energy": 2.4225458650309725e-06,
+    "gpu_energy": 3.5700028560015085e-06,
+    "ram_energy": 2.014529724960743e-08,
+    "energy_consumed": 6.012694018282088e-06,
+    "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
+}

text_generation/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/2024-12-05-00-36-52/.hydra/config.yaml

@@ -0,0 +1,96 @@
+backend:
+  name: pytorch
+  version: 2.4.0
+  _target_: optimum_benchmark.backends.pytorch.backend.PyTorchBackend
+  task: text-generation
+  model: bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
+  processor: bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
+  library: null
+  device: cuda
+  device_ids: '0'
+  seed: 42
+  inter_op_num_threads: null
+  intra_op_num_threads: null
+  hub_kwargs: {}
+  no_weights: true
+  device_map: null
+  torch_dtype: null
+  amp_autocast: false
+  amp_dtype: null
+  eval_mode: true
+  to_bettertransformer: false
+  low_cpu_mem_usage: null
+  attn_implementation: null
+  cache_implementation: null
+  torch_compile: false
+  torch_compile_config: {}
+  quantization_scheme: null
+  quantization_config: {}
+  deepspeed_inference: false
+  deepspeed_inference_config: {}
+  peft_type: null
+  peft_config: {}
+launcher:
+  name: process
+  _target_: optimum_benchmark.launchers.process.launcher.ProcessLauncher
+  device_isolation: false
+  device_isolation_action: warn
+  start_method: spawn
+benchmark:
+  name: energy_star
+  _target_: optimum_benchmark.benchmarks.energy_star.benchmark.EnergyStarBenchmark
+  dataset_name: EnergyStarAI/text_generation
+  dataset_config: ''
+  dataset_split: train
+  num_samples: 1000
+  input_shapes:
+    batch_size: 1
+  text_column_name: text
+  truncation: true
+  max_length: -1
+  dataset_prefix1: ''
+  dataset_prefix2: ''
+  t5_task: ''
+  image_column_name: image
+  resize: false
+  question_column_name: question
+  context_column_name: context
+  sentence1_column_name: sentence1
+  sentence2_column_name: sentence2
+  audio_column_name: audio
+  iterations: 10
+  warmup_runs: 10
+  energy: true
+  forward_kwargs: {}
+  generate_kwargs:
+    max_new_tokens: 10
+    min_new_tokens: 10
+  call_kwargs: {}
+experiment_name: text_generation
+environment:
+  cpu: ' AMD EPYC 7R32'
+  cpu_count: 48
+  cpu_ram_mb: 200472.73984
+  system: Linux
+  machine: x86_64
+  platform: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+  processor: x86_64
+  python_version: 3.9.20
+  gpu:
+  - NVIDIA A10G
+  gpu_count: 1
+  gpu_vram_mb: 24146608128
+  optimum_benchmark_version: 0.2.0
+  optimum_benchmark_commit: null
+  transformers_version: 4.44.0
+  transformers_commit: null
+  accelerate_version: 0.33.0
+  accelerate_commit: null
+  diffusers_version: 0.30.0
+  diffusers_commit: null
+  optimum_version: null
+  optimum_commit: null
+  timm_version: null
+  timm_commit: null
+  peft_version: null
+  peft_commit: null

text_generation/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/2024-12-05-00-36-52/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: /runs/text_generation/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/2024-12-05-00-36-52
+  sweep:
+    dir: sweeps/${experiment_name}/${backend.model}/${now:%Y-%m-%d-%H-%M-%S}
+    subdir: ${hydra.job.num}
+  launcher:
+    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
+  sweeper:
+    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
+    max_batch_size: null
+    params: null
+  help:
+    app_name: ${hydra.job.name}
+    header: '${hydra.help.app_name} is powered by Hydra.
+
+      '
+    footer: 'Powered by Hydra (https://hydra.cc)
+
+      Use --hydra-help to view Hydra specific help
+
+      '
+    template: '${hydra.help.header}
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (group=option)
+
+
+      $APP_CONFIG_GROUPS
+
+
+      == Config ==
+
+      Override anything in the config (foo.bar=value)
+
+
+      $CONFIG
+
+
+      ${hydra.help.footer}
+
+      '
+  hydra_help:
+    template: 'Hydra (${hydra.runtime.version})
+
+      See https://hydra.cc for more info.
+
+
+      == Flags ==
+
+      $FLAGS_HELP
+
+
+      == Configuration groups ==
+
+      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
+      to command line)
+
+
+      $HYDRA_CONFIG_GROUPS
+
+
+      Use ''--cfg hydra'' to Show the Hydra config.
+
+      '
+    hydra_help: ???
+  hydra_logging:
+    version: 1
+    formatters:
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s'
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+    root:
+      level: INFO
+      handlers:
+      - console
+    disable_existing_loggers: false
+  job_logging:
+    version: 1
+    formatters:
+      simple:
+        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+      colorlog:
+        (): colorlog.ColoredFormatter
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s]
+          - %(message)s'
+        log_colors:
+          DEBUG: purple
+          INFO: green
+          WARNING: yellow
+          ERROR: red
+          CRITICAL: red
+    handlers:
+      console:
+        class: logging.StreamHandler
+        formatter: colorlog
+        stream: ext://sys.stdout
+      file:
+        class: logging.FileHandler
+        formatter: simple
+        filename: ${hydra.job.name}.log
+    root:
+      level: INFO
+      handlers:
+      - console
+      - file
+    disable_existing_loggers: false
+  env: {}
+  mode: RUN
+  searchpath: []
+  callbacks: {}
+  output_subdir: .hydra
+  overrides:
+    hydra:
+    - hydra.run.dir=/runs/text_generation/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/2024-12-05-00-36-52
+    - hydra.mode=RUN
+    task:
+    - backend.model=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
+    - backend.processor=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
+  job:
+    name: cli
+    chdir: true
+    override_dirname: backend.model=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF,backend.processor=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
+    id: ???
+    num: ???
+    config_name: text_generation
+    env_set:
+      OVERRIDE_BENCHMARKS: '1'
+    env_copy: []
+    config:
+      override_dirname:
+        kv_sep: '='
+        item_sep: ','
+        exclude_keys: []
+  runtime:
+    version: 1.3.2
+    version_base: '1.3'
+    cwd: /
+    config_sources:
+    - path: hydra.conf
+      schema: pkg
+      provider: hydra
+    - path: optimum_benchmark
+      schema: pkg
+      provider: main
+    - path: hydra_plugins.hydra_colorlog.conf
+      schema: pkg
+      provider: hydra-colorlog
+    - path: /optimum-benchmark/examples/energy_star
+      schema: file
+      provider: command-line
+    - path: ''
+      schema: structured
+      provider: schema
+    output_dir: /runs/text_generation/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/2024-12-05-00-36-52
+    choices:
+      benchmark: energy_star
+      launcher: process
+      backend: pytorch
+      hydra/env: default
+      hydra/callbacks: null
+      hydra/job_logging: colorlog
+      hydra/hydra_logging: colorlog
+      hydra/hydra_help: default
+      hydra/help: default
+      hydra/sweeper: basic
+      hydra/launcher: basic
+      hydra/output: default
+  verbose: false

text_generation/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/2024-12-05-00-36-52/.hydra/overrides.yaml

@@ -0,0 +1,2 @@
+- backend.model=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
+- backend.processor=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF

text_generation/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/2024-12-05-00-36-52/error.log

@@ -0,0 +1,7 @@
+Error executing job with overrides: ['backend.model=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF', 'backend.processor=bartowski/Meta-Llama-3.1-8B-Instruct-GGUF']
+Traceback (most recent call last):
+  File "/optimum-benchmark/optimum_benchmark/cli.py", line 62, in benchmark_cli
+    experiment_config: ExperimentConfig = OmegaConf.to_object(experiment_config)
+KeyError: 'Could not find the proper library name for bartowski/Meta-Llama-3.1-8B-Instruct-GGUF.'
+
+Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.