<jupyter_start><jupyter_text>Sequence Length<jupyter_code># Columns
keys = hf_pytorch['hf_pt_forward'].keys()

# results
results = []
results.append(list(hf_pytorch['hf_pt_forward'].values()))
results.append(list(hf_tensorflow['hf_tf_forward'].values())[:len(keys)])
results.append(list(tf_transformers_tensorflow['tft_forward'].values())[:len(keys)])

df_sequence_length = pd.DataFrame(results)

# columns and index
df_sequence_length.columns = keys
df_sequence_length.index   = ['hf_pytorch', 'hf_tensorflow', 'tf_transformers_tensorflow']
# Highlight
df_sequence_length.style.highlight_min(color = 'lightgreen', axis = 0)

df_sequence_length.sample(n = 10, axis='columns').style.highlight_min(color = 'lightgreen', axis = 0)<jupyter_output><empty_output><jupyter_text>Find the count of index based on speed<jupyter_code>df_sequence_length.idxmin(axis=0).value_counts()<jupyter_output><empty_output><jupyter_text>Greedy Visualize<jupyter_code>ax = df_sequence_length.transpose().plot.bar(rot=1, figsize=(25,10))
ax.set_xlabel("Batch - Sequence_Length")
ax.set_ylabel("Time in Seconds")<jupyter_output><empty_output>
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