<jupyter_start><jupyter_text>DL Benchmarks Runner* Configuration: Computer vision, <10GB VRAM* Adjust `threads = XX` below according to your CPU (suggested to leave 1C/2T for OS/background tasks)<jupyter_code>threads = 30
threads = str(int(threads))
start_time = time.time()<jupyter_output><empty_output><jupyter_text>Image Classification (ResNet-50 MLPerf model)ResNet-50 identical to the MLPerf reference implementation and is one of the most common benchmarks performed. **No image augmentation (GPU limited)**<jupyter_code>exp_name = "rn50"
print("Running:", exp_name)
results = utils.run_command("python3 run_cnn.py --threads "+threads+" --batch_size 64")
if results[-1].split(",")[0] == "PASS":
    rn50_10gb = exp_name + "," + results[-1]
    print(rn50_10gb)
else:
    print(exp_name, "FAIL")<jupyter_output>Running: rn50
rn50,PASS,2674.6,64,41,4,0.7,55,167,49,9.7,3.2,[]<jupyter_text>**Image augmentation used (CPU limited)**<jupyter_code>exp_name = "rn50_imgaug"
print("Running:", exp_name)
results = utils.run_command("python3 run_cnn.py --threads "+threads+" --batch_size 64 --img_aug")
if results[-1].split(",")[0] == "PASS":
    rn50_imgaug_10gb = exp_name + "," + results[-1]
    print(rn50_imgaug_10gb)
else:
    print(exp_name, "FAIL")<jupyter_output>Running: rn50_imgaug
rn50_imgaug,PASS,2534.3,62,39,5,0.8,53,161,48,9.7,3.0,[]<jupyter_text>Image Classification (CNN Stress-test Model)Large, compute heavy CNN to perform a stress-test of the GPU compute.<jupyter_code>exp_name = "hugecnn-10gb"
print("Running:", exp_name)
results = utils.run_command("python3 run_cnn.py --threads "+threads+" --batch_size 64 --huge_cnn")
if results[-1].split(",")[0] == "PASS":
    hugecnn_10gb = exp_name + "," + results[-1]
    print(hugecnn_10gb)
else:
    print(exp_name, "FAIL")
end_time = time.time()
print("Total time taken:", int(end_time-start_time), "seconds.")<jupyter_output>Total time taken: 599 seconds.<jupyter_text>Export ResultsValues reported are for GPU 0 (first GPU), except for NVLink which is average TX+RX across all GPUs<jupyter_code>cols = ["name", "passed", "avg_fps", "avg_sm_%", "avg_mem_io_%", "avg_pcie_%", "pcie_gbps", "avg_pwr_%", "pwr_watts", "avg_temp", "max_vram", "avg_nvlink", "throttle"]
df = pd.DataFrame([rn50_10gb.split(","), rn50_imgaug_10gb.split(","), hugecnn_10gb.split(",")], 
                  columns=cols) 
df.head()<jupyter_output><empty_output>
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{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '251513', 'license': 'MIT', 'n_tokens_mistral': 964, 'n_tokens_neox': 898, 'n_words': 185, 'path': 'run_cv_10gb.ipynb', 'repo_name': 'tlkh/mini-dlperf', 'size': '2396'}