<jupyter_start><jupyter_text>"Sensitivity profile" experiment<jupyter_code>profile1 = swan.SensitivityProfile("6ed71a63-14c0-8e3e-3a65-4da9a11eecf6", slo=500)
profile1.df.head() # or just `profile1`
profile1.latency()
profile1.latency(normalized=False)
profile1.qps()
profile1.qps(normalized=False)
profile1.caffe_batches()<jupyter_output><empty_output><jupyter_text>"Optimal core allocation" experiment<jupyter_code>core = swan.OptimalCoreAllocation("80ad81ec-e6d7-cfc2-de6c-6c60cb300d7f", slo=500)
core.latency() # optionally call with normalized=False to see absolute values
core.qps() # optionally call with normalized=False to see absolute values
core.cpu()<jupyter_output><empty_output><jupyter_text>"Memcached CAT" experiment<jupyter_code>cat1 = swan.CAT("bc1ee530-4e02-b9fd-e845-752eb7545773", 500)
cat1.latency()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12573443008225618, 'fraction_numerical': 0.08578143360752057, 'mean_word_length': 10.36, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '526625', 'license': 'Apache-2.0', 'n_tokens_mistral': 368, 'n_tokens_neox': 334, 'n_words': 43, 'path': 'jupyter/example.ipynb', 'repo_name': 'intelsdi-x/swan', 'size': '851'}