In [6]:

    

import pickle
import pandas as pd
import tabulate


    

In [2]:

    

def pickle_load(path):
    with open(path, 'rb') as f:
        return pickle.load(f)

    
def load(t3f_cpu_path, t3f_gpu_path, ttpy_path=None):
    cpu = pickle_load(t3f_cpu_path)
    gpu = pickle_load(t3f_gpu_path)
    d = {}
    op_list = ['matvec', 'matmul', 'norm', 'round', 'gram', 'project_rank100']
    
    one_cpu = []
    for op in op_list:
        if op == 'gram':
            # Single element Gram matrix is flat inner.
            curr_time = cpu['flatinner']['wall_time'] * 1000
        else:
            curr_time = cpu[op]['wall_time'] * 1000
        one_cpu.append(curr_time)
    d['one on CPU'] = one_cpu
    
    one_gpu = []
    for op in op_list:
        if op == 'gram':
            # Single element Gram matrix is flat inner.
            curr_time = gpu['flatinner']['wall_time'] * 1000
        else:
            curr_time = gpu[op]['wall_time'] * 1000
        one_gpu.append(curr_time)
    d['one on GPU'] = one_gpu
    
    batch_cpu = []
    for op in op_list:
        if op == 'gram':
            curr_time = cpu['batch_gram']['wall_time'] * 1000 / 100**2
        else:
            curr_time = cpu['batch_' + op]['wall_time'] * 1000 / 100
        batch_cpu.append(curr_time)
    d['batch on CPU'] = batch_cpu
    
    batch_gpu = []
    for op in op_list:
        if op == 'gram':
            curr_time = gpu['batch_gram']['wall_time'] * 1000 / 100.**2
        else:
            curr_time = gpu['batch_' + op]['wall_time'] * 1000 / 100.
#         curr_time = gpu['batch_' + op + '_time'] * 1000
        batch_gpu.append(curr_time)
    d['batch on GPU'] = batch_gpu
    
    if ttpy_path:
        ttpy = pickle_load(ttpy_path)
        ttpy_col = []
        for op in op_list:
            if op == 'gram':
                # Single element Gram matrix is flat inner.
                curr_time = ttpy['flatinner_time'] * 1000
            else:
                curr_time = ttpy[op + '_time'] * 1000
            ttpy_col.append(curr_time)
    d['ttpy, one on CPU'] = ttpy_col
    
    df = pd.DataFrame(d, index=op_list)
    regime_list = ['one on CPU', 'one on GPU', 'batch on CPU', 'batch on GPU']
    if ttpy_path:
        regime_list = ['ttpy, one on CPU'] + regime_list
    df = df[regime_list]
    return df


    

In [3]:

    

df = load('logs_cpu.pkl', 'logs_gpu.pkl', ttpy_path='logs_ttpy.pkl')


    

In [4]:

    

df.round(decimals=3)


    

    
Out[4]:







  

    

      

      
ttpy, one on CPU
      
one on CPU
      
one on GPU
      
batch on CPU
      
batch on GPU
    
  
  

    

      
matvec
      
11.142
      
1.190
      
0.744
      
1.885
      
0.140
    
    

      
matmul
      
86.191
      
9.849
      
0.950
      
17.483
      
1.461
    
    

      
norm
      
3.790
      
2.136
      
1.019
      
0.253
      
0.044
    
    

      
round
      
73.027
      
86.040
      
165.969
      
8.234
      
161.102
    
    

      
gram
      
0.145
      
0.606
      
0.973
      
0.021
      
0.001
    
    

      
project_rank100
      
116.868
      
3.001
      
13.239
      
1.645
      
0.226
    
  

In [5]:

    

print(df.round(decimals=3).to_latex())


    

    




\begin{tabular}{lrrrrr}
\toprule
{} &  ttpy, one on CPU &  one on CPU &  one on GPU &  batch on CPU &  batch on GPU \\
\midrule
matvec          &            11.142 &       1.190 &       0.744 &         1.885 &         0.140 \\
matmul          &            86.191 &       9.849 &       0.950 &        17.483 &         1.461 \\
norm            &             3.790 &       2.136 &       1.019 &         0.253 &         0.044 \\
round           &            73.027 &      86.040 &     165.969 &         8.234 &       161.102 \\
gram            &             0.145 &       0.606 &       0.973 &         0.021 &         0.001 \\
project\_rank100 &           116.868 &       3.001 &      13.239 &         1.645 &         0.226 \\
\bottomrule
\end{tabular}

In [10]:

    

print(tabulate.tabulate(df.round(decimals=3), headers=df.keys(), tablefmt="rst"))


    

    




===============  ==================  ============  ============  ==============  ==============
..                 ttpy, one on CPU    one on CPU    one on GPU    batch on CPU    batch on GPU
===============  ==================  ============  ============  ==============  ==============
matvec                       11.142         1.19          0.744           1.885           0.14
matmul                       86.191         9.849         0.95           17.483           1.461
norm                          3.79          2.136         1.019           0.253           0.044
round                        73.027        86.04        165.969           8.234         161.102
gram                          0.145         0.606         0.973           0.021           0.001
project_rank100             116.868         3.001        13.239           1.645           0.226
===============  ==================  ============  ============  ==============  ==============

In [ ]: