fix

README.md

@@ -70,33 +70,23 @@ You can run benchmarks from the Python API, using the `launch` function from the
 from optimum_benchmark.logging_utils import setup_logging
 from optimum_benchmark.experiment import launch, ExperimentConfig
 from optimum_benchmark.backends.pytorch.config import PyTorchConfig
-from optimum_benchmark.launchers.process.config import ProcessConfig
+from optimum_benchmark.launchers.torchrun.config import TorchrunConfig
 from optimum_benchmark.benchmarks.inference.config import InferenceConfig
 
-
 if __name__ == "__main__":
     setup_logging(level="INFO")
-    benchmark_config = InferenceConfig(latency=False, memory=True, energy=True)
-    launcher_config = ProcessConfig()
-    backend_config = PyTorchConfig(
-        device="cuda",
-        no_weights=True,
-        device_ids="0,1",
-        device_map="auto",
-        model="IlyasMoutawwakil/vicuna-7b-v1.5-awq-gemm",
-    )
+    launcher_config = TorchrunConfig(nproc_per_node=2)
+    benchmark_config = InferenceConfig(latency=True, memory=True)
+    backend_config = PyTorchConfig(model="gpt2", device="cuda", device_ids="0,1", no_weights=True)
     experiment_config = ExperimentConfig(
-        experiment_name="python-api-launch-experiment",
+        experiment_name="api-launch",
         benchmark=benchmark_config,
         launcher=launcher_config,
         backend=backend_config,
     )
     benchmark_report = launch(experiment_config)
-    benchmark_report.log_all()
-    # or
-    print(benchmark_report.to_dict())
-    # or
-    benchmark_report.push_to_hub("IlyasMoutawwakil/vicuna-7b-v1.5-awq-gemm")
+    experiment_config.push_to_hub("IlyasMoutawwakil/benchmarks")
+    benchmark_report.push_to_hub("IlyasMoutawwakil/benchmarks")
 ```
 
 Yep, it's that simple! Check the supported backends, launchers and benchmarks in the [features](#features-) section.

examples/api_launch.py

@@ -0,0 +1,20 @@
+from optimum_benchmark.logging_utils import setup_logging
+from optimum_benchmark.experiment import launch, ExperimentConfig
+from optimum_benchmark.backends.pytorch.config import PyTorchConfig
+from optimum_benchmark.launchers.torchrun.config import TorchrunConfig
+from optimum_benchmark.benchmarks.inference.config import InferenceConfig
+
+if __name__ == "__main__":
+    setup_logging(level="INFO")
+    launcher_config = TorchrunConfig(nproc_per_node=2)
+    benchmark_config = InferenceConfig(latency=True, memory=True)
+    backend_config = PyTorchConfig(model="gpt2", device="cuda", device_ids="0,1", no_weights=True)
+    experiment_config = ExperimentConfig(
+        experiment_name="api-launch",
+        benchmark=benchmark_config,
+        launcher=launcher_config,
+        backend=backend_config,
+    )
+    benchmark_report = launch(experiment_config)
+    experiment_config.push_to_hub("IlyasMoutawwakil/benchmarks")
+    benchmark_report.push_to_hub("IlyasMoutawwakil/benchmarks")

optimum_benchmark/cli.py

@@ -18,7 +18,7 @@
 from .backends.neural_compressor.config import INCConfig
 from .backends.text_generation_inference.config import TGIConfig
 
-from .benchmarks.report import BenchmarkReport
+from .report import BenchmarkReport
 from .experiment import launch, ExperimentConfig
 from .benchmarks.training.config import TrainingConfig
 from .benchmarks.inference.config import InferenceConfig
@@ -74,8 +74,7 @@ def benchmark_cli(experiment_config: DictConfig) -> None:
 
     # Instantiate the experiment configuration and trigger its __post_init__
     experiment_config: ExperimentConfig = OmegaConf.to_object(experiment_config)
-    OmegaConf.save(experiment_config, "experiment_config.yaml", resolve=True)
+    experiment_config.to_json("experiment_config.json")
 
     benchmark_report: BenchmarkReport = launch(experiment_config=experiment_config)
-
     benchmark_report.to_json("benchmark_report.json")