<jupyter_start><jupyter_text>Benchmarks Results format:`PASS, avg_fps, avg_sm, avg_mem, avg_pcie, pcie_gbps, avg_pwr, pwr_watts, avg_temp, max_vram, avg_nvlink, throttle`  CNN (Synthetic - High Load)<jupyter_code>exp_name = "hugecnn-10gb"
results = utils.run_command("python3 run_cnn.py --threads 38 --batch_size 80 --huge_cnn")
if results[-1].split(",")[0] == "PASS":
    hugecnn_10gb = exp_name + results[-1]
    print(hugecnn_10gb)
else:
    print(exp_name, "FAIL")<jupyter_output>PASS,2045.2,93,35,14,2.3,73,221,52,9.5,12.1,['SwPowerCap']<jupyter_text>CNN (ResNet - Image Classification) **No image augmentation (GPU limited)**<jupyter_code>results = utils.run_command("python3 run_cnn.py --threads 38 --batch_size 144")
print(results[-1])
results = utils.run_command("python3 run_cnn.py --threads 14 --batch_size 144")
print(results[-1])<jupyter_output><empty_output><jupyter_text>**Image augmentation used (CPU limited)**<jupyter_code>results = utils.run_command("python3 run_cnn.py --img_aug  --threads 38 --batch_size 144")
print(results[-1])
results = utils.run_command("python3 run_cnn.py --img_aug  --threads 18 --batch_size 144")
print(results[-1])
results = utils.run_command("python3 run_cnn.py --img_aug  --threads 14 --batch_size 144")
print(results[-1])<jupyter_output><empty_output><jupyter_text>Transformer (Encoder-Decoder)<jupyter_code># download and cache the models locally first
_ = utils.run_command("horovodrun -np 1 python3 run_t5.py --model t5-small --maxseqlen 128 --batch_size 1 --steps 1")
_ = utils.run_command("horovodrun -np 1 python3 run_t5.py --model t5-base --maxseqlen 128 --batch_size 1 --steps 1")
results = utils.run_command("horovodrun -np 4 python3 run_t5.py --model t5-small --xla --maxseqlen 512 --batch_size 16")
print(results[-1])
results = utils.run_command("horovodrun -np 4 python3 run_t5.py --model t5-base --xla --maxseqlen 512 --batch_size 4")
print(results[-1])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15269922879177378, 'fraction_numerical': 0.0570694087403599, 'mean_word_length': 5.574324324324325, 'pattern_counts': {'":': 1, '<': 16, '<?xml version=': 0, '>': 16, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '632294', 'license': 'MIT', 'n_tokens_mistral': 808, 'n_tokens_neox': 744, 'n_words': 152, 'path': 'runner.ipynb', 'repo_name': 'tlkh/mini-dlperf', 'size': '1945'}