def plot_scaling()

in visualization/plotting.py [0:0]

• 51 lines of code
• 13 McCabe index (conditional complexity)

def plot_scaling(save_fname='scaling.pdf'):
    bspn = 256  # batch-size per node
    pdf = pd.DataFrame()

    net_tags = ['eth', 'ib']
    # net_tags = ['ib', 'eth']
    net_columns = {}
    # construct scaling data-frame
    for net_tag in net_tags:
        if net_tag == 'eth':
            nodes, fpaths, tags, legends, colors = get_eth_config()
        elif net_tag == 'ib':
            nodes, fpaths, tags, legends, colors = get_ib_config()
        pdf['nodes'] = nodes
        columns = []
        for alg_tags, alg_fpath, alg_legends in zip(tags, fpaths, legends):  # iterate over algorithms
            alg = alg_legends[0].split(' ')[0]  # algorithm name
            alg_thpt = []
            for i, n in enumerate(nodes):
                df = parse_csv(n, alg_tags[i], alg_fpath)
                tpi = df['time_mean'].dropna().iloc[-1]  # avg time per itr
                thpt = tpi
                # thpt = (bspn * n) / tpi  # throughput (images per second)
                alg_thpt.append(thpt)
            net_label = '(InfiniBand)' if net_tag == 'ib' else '(Ethernet)'
            rtag = alg + ' ' + net_label
            pdf[rtag] = alg_thpt
            columns.append(rtag)
        net_columns[net_tag] = columns

    # plot scaling data
    fig = plt.figure(1)
    ax = fig.add_subplot(111)
    for net_tag in net_tags:
        if net_tag == 'eth':
            _, _, _, _, colors = get_eth_config()
        elif net_tag == 'ib':
            _, _, _, _, colors = get_ib_config()
        columns = net_columns[net_tag]
        print(columns)
        linestyle = '--' if net_tag == 'ib' else '-'
        marker = '1' if net_tag == 'ib' else '2'
        for alg, alg_colors in zip(columns, colors):
            color = alg_colors[-2]
            pdf.plot(x='nodes', y=alg, ax=ax, color=color, grid=True,
                     label=alg, marker=marker, xticks=nodes, ms=15,
                     subplots=True, linestyle=linestyle,
                     fontsize=16)
    ax.set_xlabel('Number of nodes', fontsize=font_size)
    ax.set_ylabel('Time per iteration (s)', fontsize=font_size)
    # ax.set_ylabel('Throughput (Images/sec.)', fontsize=font_size)
    ax.grid(which='both')
    ax.grid(which='minor', alpha=0.2)
    ax.grid(which='major', alpha=0.5)
    plt.legend(prop={'size': 16})
    plt.tight_layout()
    fig.savefig(save_fname)
    plt.show()