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text_generation/google/gemma-2-2b/2024-10-24-19-05-37/.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: google/gemma-2-2b
+  processor: google/gemma-2-2b
+  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/google/gemma-2-2b/2024-10-24-19-05-37/.hydra/hydra.yaml

@@ -0,0 +1,175 @@
+hydra:
+  run:
+    dir: ./runs/text_generation/google/gemma-2-2b/2024-10-24-19-05-37
+  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/google/gemma-2-2b/2024-10-24-19-05-37
+    - hydra.mode=RUN
+    task:
+    - backend.model=google/gemma-2-2b
+    - backend.processor=google/gemma-2-2b
+  job:
+    name: cli
+    chdir: true
+    override_dirname: backend.model=google/gemma-2-2b,backend.processor=google/gemma-2-2b
+    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/google/gemma-2-2b/2024-10-24-19-05-37
+    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/google/gemma-2-2b/2024-10-24-19-05-37/.hydra/overrides.yaml

@@ -0,0 +1,2 @@
+- backend.model=google/gemma-2-2b
+- backend.processor=google/gemma-2-2b

text_generation/google/gemma-2-2b/2024-10-24-19-05-37/cli.log

@@ -0,0 +1,31 @@
+[2024-10-24 19:05:40,348][launcher][INFO] - ََAllocating process launcher
+[2024-10-24 19:05:40,349][process][INFO] - 	+ Setting multiprocessing start method to spawn.
+[2024-10-24 19:05:40,359][process][INFO] - 	+ Launched benchmark in isolated process 180.
+[PROC-0][2024-10-24 19:05:42,915][datasets][INFO] - PyTorch version 2.4.0 available.
+[PROC-0][2024-10-24 19:05:43,814][backend][INFO] - َAllocating pytorch backend
+[PROC-0][2024-10-24 19:05:43,814][backend][INFO] - 	+ Setting random seed to 42
+[PROC-0][2024-10-24 19:05:45,769][pytorch][INFO] - 	+ Using AutoModel class AutoModelForCausalLM
+[PROC-0][2024-10-24 19:05:45,769][pytorch][INFO] - 	+ Creating backend temporary directory
+[PROC-0][2024-10-24 19:05:45,769][pytorch][INFO] - 	+ Loading model with random weights
+[PROC-0][2024-10-24 19:05:45,769][pytorch][INFO] - 	+ Creating no weights model
+[PROC-0][2024-10-24 19:05:45,769][pytorch][INFO] - 	+ Creating no weights model directory
+[PROC-0][2024-10-24 19:05:45,769][pytorch][INFO] - 	+ Creating no weights model state dict
+[PROC-0][2024-10-24 19:05:45,793][pytorch][INFO] - 	+ Saving no weights model safetensors
+[PROC-0][2024-10-24 19:05:45,793][pytorch][INFO] - 	+ Saving no weights model pretrained config
+[PROC-0][2024-10-24 19:05:45,794][pytorch][INFO] - 	+ Loading no weights AutoModel
+[PROC-0][2024-10-24 19:05:45,794][pytorch][INFO] - 	+ Loading model directly on device: cuda
+[PROC-0][2024-10-24 19:05:46,061][pytorch][INFO] - 	+ Turning on model's eval mode
+[PROC-0][2024-10-24 19:05:46,067][benchmark][INFO] - Allocating energy_star benchmark
+[PROC-0][2024-10-24 19:05:46,068][energy_star][INFO] - 	+ Loading raw dataset
+[PROC-0][2024-10-24 19:05:47,074][energy_star][INFO] - 	+ Updating Text Generation kwargs with default values
+[PROC-0][2024-10-24 19:05:47,074][energy_star][INFO] - 	+ Initializing Text Generation report
+[PROC-0][2024-10-24 19:05:47,074][energy][INFO] - 	+ Tracking GPU energy on devices [0]
+[PROC-0][2024-10-24 19:05:51,277][energy_star][INFO] - 	+ Preprocessing dataset
+[PROC-0][2024-10-24 19:05:52,517][energy][INFO] - 	+ Saving codecarbon emission data to preprocess_codecarbon.json
+[PROC-0][2024-10-24 19:05:52,517][energy_star][INFO] - 	+ Preparing backend for Inference
+[PROC-0][2024-10-24 19:05:52,517][energy_star][INFO] - 	+ Initialising dataloader
+[PROC-0][2024-10-24 19:05:52,517][energy_star][INFO] - 	+ Warming up backend for Inference
+[PROC-0][2024-10-24 19:05:54,536][energy_star][INFO] - 	+ Additional warmup for Text Generation
+[PROC-0][2024-10-24 19:05:54,956][energy_star][INFO] - 	+ Running Text Generation energy tracking for 10 iterations
+[PROC-0][2024-10-24 19:05:54,956][energy_star][INFO] - 	+ Prefill iteration 1/10
+[2024-10-24 19:06:10,217][experiment][ERROR] - Error during experiment

text_generation/google/gemma-2-2b/2024-10-24-19-05-37/error.log

@@ -0,0 +1,90 @@
+
+
+
+
+
+[codecarbon INFO @ 19:05:47] [setup] RAM Tracking...
+[codecarbon INFO @ 19:05:47] [setup] GPU Tracking...
+[codecarbon INFO @ 19:05:47] Tracking Nvidia GPU via pynvml
+[codecarbon DEBUG @ 19:05:47] GPU available. Starting setup
+[codecarbon INFO @ 19:05:47] [setup] CPU Tracking...
+[codecarbon DEBUG @ 19:05:47] Not using PowerGadget, an exception occurred while instantiating IntelPowerGadget : Platform not supported by Intel Power Gadget
+[codecarbon DEBUG @ 19:05:47] 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 @ 19:05:47] Not using PowerMetrics, an exception occurred while instantiating Powermetrics : Platform not supported by Powermetrics
+[codecarbon WARNING @ 19:05:47] No CPU tracking mode found. Falling back on CPU constant mode.
+[codecarbon WARNING @ 19:05:48] We saw that you have a AMD EPYC 7R32 but we don't know it. Please contact us.
+[codecarbon INFO @ 19:05:48] CPU Model on constant consumption mode: AMD EPYC 7R32
+[codecarbon INFO @ 19:05:48] >>> Tracker's metadata:
+[codecarbon INFO @ 19:05:48]   Platform system: Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35
+[codecarbon INFO @ 19:05:48]   Python version: 3.9.20
+[codecarbon INFO @ 19:05:48]   CodeCarbon version: 2.5.1
+[codecarbon INFO @ 19:05:48]   Available RAM : 186.705 GB
+[codecarbon INFO @ 19:05:48]   CPU count: 48
+[codecarbon INFO @ 19:05:48]   CPU model: AMD EPYC 7R32
+[codecarbon INFO @ 19:05:48]   GPU count: 1
+[codecarbon INFO @ 19:05:48]   GPU model: 1 x NVIDIA A10G
+[codecarbon DEBUG @ 19:05:49] Not running on AWS
+[codecarbon DEBUG @ 19:05:50] Not running on Azure
+[codecarbon DEBUG @ 19:05:51] Not running on GCP
+[codecarbon INFO @ 19:05:51] Saving emissions data to file /runs/text_generation/google/gemma-2-2b/2024-10-24-19-05-37/codecarbon.csv
+[codecarbon DEBUG @ 19:05:51] EmissionsData(timestamp='2024-10-24T19:05:51', project_name='codecarbon', run_id='af8cf9cd-3ff9-45b1-87f5-8bd44c5df4f0', duration=0.0021458529954543337, 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 @ 19:05:52] Energy consumed for RAM : 0.000000 kWh. RAM Power : 0.36616086959838867 W
+[codecarbon DEBUG @ 19:05:52] RAM : 0.37 W during 1.24 s [measurement time: 0.0005]
+[codecarbon INFO @ 19:05:52] Energy consumed for all GPUs : 0.000024 kWh. Total GPU Power : 71.15502348245434 W
+[codecarbon DEBUG @ 19:05:52] GPU : 71.16 W during 1.24 s [measurement time: 0.0023]
+[codecarbon INFO @ 19:05:52] Energy consumed for all CPUs : 0.000015 kWh. Total CPU Power : 42.5 W
+[codecarbon DEBUG @ 19:05:52] CPU : 42.50 W during 1.24 s [measurement time: 0.0000]
+[codecarbon INFO @ 19:05:52] 0.000039 kWh of electricity used since the beginning.
+[codecarbon DEBUG @ 19:05:52] last_duration=1.2355880289978813
+------------------------
+[codecarbon DEBUG @ 19:05:52] EmissionsData(timestamp='2024-10-24T19:05:52', project_name='codecarbon', run_id='af8cf9cd-3ff9-45b1-87f5-8bd44c5df4f0', duration=1.238806013003341, emissions=1.4465120447672696e-05, emissions_rate=1.1676663089972978e-05, cpu_power=42.5, gpu_power=71.15502348245434, ram_power=0.36616086959838867, cpu_energy=1.4623404627743892e-05, gpu_energy=2.4437519550057907e-05, ram_energy=1.2567374983479889e-07, energy_consumed=3.9186597927636596e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0)
+[codecarbon DEBUG @ 19:05:54] EmissionsData(timestamp='2024-10-24T19:05:54', project_name='codecarbon', run_id='af8cf9cd-3ff9-45b1-87f5-8bd44c5df4f0', duration=0.0022436440049204975, emissions=1.4465120447672696e-05, emissions_rate=0.006447154903340051, cpu_power=42.5, gpu_power=71.15502348245434, ram_power=0.36616086959838867, cpu_energy=1.4623404627743892e-05, gpu_energy=2.4437519550057907e-05, ram_energy=1.2567374983479889e-07, energy_consumed=3.9186597927636596e-05, country_name='United States', country_iso_code='USA', region='virginia', cloud_provider='', cloud_region='', os='Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35', python_version='3.9.20', codecarbon_version='2.5.1', cpu_count=48, cpu_model='AMD EPYC 7R32', gpu_count=1, gpu_model='1 x NVIDIA A10G', longitude=-77.4903, latitude=39.0469, ram_total_size=186.7047882080078, tracking_mode='process', on_cloud='N', pue=1.0)
+
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+Error executing job with overrides: ['backend.model=google/gemma-2-2b', 'backend.processor=google/gemma-2-2b']
+Traceback (most recent call last):
+  File "/optimum-benchmark/optimum_benchmark/cli.py", line 65, in benchmark_cli
+    benchmark_report: BenchmarkReport = launch(experiment_config=experiment_config)
+  File "/optimum-benchmark/optimum_benchmark/experiment.py", line 102, in launch
+    raise error
+  File "/optimum-benchmark/optimum_benchmark/experiment.py", line 90, in launch
+    report = launcher.launch(run, experiment_config.benchmark, experiment_config.backend)
+  File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 47, in launch
+    while not process_context.join():
+  File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 189, in join
+    raise ProcessRaisedException(msg, error_index, failed_process.pid)
+torch.multiprocessing.spawn.ProcessRaisedException: 
+
+-- Process 0 terminated with the following error:
+Traceback (most recent call last):
+  File "/opt/conda/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 76, in _wrap
+    fn(i, *args)
+  File "/optimum-benchmark/optimum_benchmark/launchers/process/launcher.py", line 63, in entrypoint
+    worker_output = worker(*worker_args)
+  File "/optimum-benchmark/optimum_benchmark/experiment.py", line 62, in run
+    benchmark.run(backend)
+  File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 174, in run
+    self.run_text_generation_energy_tracking(backend)
+  File "/optimum-benchmark/optimum_benchmark/benchmarks/energy_star/benchmark.py", line 198, in run_text_generation_energy_tracking
+    _ = backend.prefill(inputs, prefill_kwargs)
+  File "/opt/conda/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/optimum-benchmark/optimum_benchmark/backends/pytorch/backend.py", line 350, in prefill
+    return self.pretrained_model.generate(**inputs, **kwargs)
+  File "/opt/conda/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+    return func(*args, **kwargs)
+  File "/opt/conda/lib/python3.9/site-packages/transformers/generation/utils.py", line 2024, in generate
+    result = self._sample(
+  File "/opt/conda/lib/python3.9/site-packages/transformers/generation/utils.py", line 2982, in _sample
+    outputs = self(**model_inputs, return_dict=True)
+  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/opt/conda/lib/python3.9/site-packages/transformers/models/gemma2/modeling_gemma2.py", line 1015, in forward
+    logits = logits / self.config.final_logit_softcapping
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 6.02 GiB. GPU 0 has a total capacity of 22.19 GiB of which 2.78 GiB is free. Process 139203 has 19.40 GiB memory in use. Of the allocated memory 17.07 GiB is allocated by PyTorch, and 2.04 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+
+
+Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

text_generation/google/gemma-2-2b/2024-10-24-19-05-37/experiment_config.json

@@ -0,0 +1,110 @@
+{
+    "experiment_name": "text_generation",
+    "backend": {
+        "name": "pytorch",
+        "version": "2.4.0",
+        "_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
+        "task": "text-generation",
+        "model": "google/gemma-2-2b",
+        "processor": "google/gemma-2-2b",
+        "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": 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": {}
+    },
+    "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/google/gemma-2-2b/2024-10-24-19-05-37/preprocess_codecarbon.json

@@ -0,0 +1,33 @@
+{
+    "timestamp": "2024-10-24T19:05:52",
+    "project_name": "codecarbon",
+    "run_id": "af8cf9cd-3ff9-45b1-87f5-8bd44c5df4f0",
+    "duration": -1729710570.0952306,
+    "emissions": 1.4465120447672696e-05,
+    "emissions_rate": 1.1696924438465332e-05,
+    "cpu_power": 42.5,
+    "gpu_power": 71.15502348245434,
+    "ram_power": 0.36616086959838867,
+    "cpu_energy": 1.4623404627743892e-05,
+    "gpu_energy": 2.4437519550057907e-05,
+    "ram_energy": 1.2567374983479889e-07,
+    "energy_consumed": 3.9186597927636596e-05,
+    "country_name": "United States",
+    "country_iso_code": "USA",
+    "region": "virginia",
+    "cloud_provider": "",
+    "cloud_region": "",
+    "os": "Linux-5.10.192-183.736.amzn2.x86_64-x86_64-with-glibc2.35",
+    "python_version": "3.9.20",
+    "codecarbon_version": "2.5.1",
+    "cpu_count": 48,
+    "cpu_model": "AMD EPYC 7R32",
+    "gpu_count": 1,
+    "gpu_model": "1 x NVIDIA A10G",
+    "longitude": -77.4903,
+    "latitude": 39.0469,
+    "ram_total_size": 186.7047882080078,
+    "tracking_mode": "process",
+    "on_cloud": "N",
+    "pue": 1.0
+}