Benchmarking TensorFlow on CPUs: More Cost-Effective Deep Learning than GPUs

This R Notebook is the complement to my blog post Benchmarking TensorFlow on CPUs: More Cost-Effective Deep Learning than GPUs.

This notebook is licensed under the MIT License. If you use the code or data visualization designs contained within this notebook, it would be greatly appreciated if proper attribution is given back to this notebook and/or myself. Thanks! :)

library(readr)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(ggplot2)
library(scales)

Attaching package: ‘scales’

The following object is masked from ‘package:readr’:

    col_factor

library(tidyr)
library(RColorBrewer)
sessionInfo()

R version 3.4.0 (2017-04-21)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.5

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] RColorBrewer_1.1-2 tidyr_0.6.3        scales_0.4.1       ggplot2_2.2.1.9000
[5] dplyr_0.7.0        readr_1.1.1       

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.11     assertthat_0.2.0 plyr_1.8.4       grid_3.4.0      
 [5] R6_2.2.2         gtable_0.2.0     magrittr_1.5     rlang_0.1.1     
 [9] lazyeval_0.2.0   tools_3.4.0      glue_1.1.0       munsell_0.4.3   
[13] hms_0.3          compiler_3.4.0   colorspace_1.3-2 knitr_1.16      
[17] tibble_1.3.3    

Set ggplot2 theme.

theme_set(theme_minimal(base_size=9, base_family="Source Sans Pro") +
            theme(plot.title = element_text(size=11, family="Source Sans Pro Bold"),
                  axis.title.x = element_blank(),
                  axis.title.y = element_text(family="Source Sans Pro Semibold"),
                  plot.caption = element_text(size=6, color="#969696"),
                  axis.text.x = element_text(angle = 45, vjust = 0.75, size = 7),
                  legend.position="none"))

relative <- function(x) {
  lab <- paste0(sprintf("%.2f", x), 'x')
}

Set colors according to Brewer palettes for consistent lightness. Ignore first color of palettes since it is too bright.

color_gpu <- brewer.pal(5, "Reds")[5]
colors_pip <- rev(brewer.pal(5, "Blues")[-1])
colors_compiled <- rev(brewer.pal(5, "Greens")[-1])
colors_all <- c(color_gpu, colors_pip[1], colors_compiled[1], colors_pip[2], colors_compiled[2], colors_pip[3], colors_compiled[3], colors_pip[4], colors_compiled[4])

Set known price rates from Google Compute Engine Pricing.

gpu_cost_hr <- 0.745
cpu8_cost_hr <- 0.060
skylake_premium <- 0.0607

Derive the remaining rates, in seconds.

gpu_cost_s <- gpu_cost_hr / 3600
cpu8_cost_s <- (cpu8_cost_hr * (1 + skylake_premium)) / 3600
cpu16_cost_s <- cpu8_cost_s * 2
cpu32_cost_s <- cpu16_cost_s * 2
cpu64_cost_s <- cpu32_cost_s * 2
# works like a Python dict
costs <- c(gpu=gpu_cost_s, cpu8=cpu8_cost_s, cpu16=cpu16_cost_s, cpu32=cpu32_cost_s, cpu64=cpu64_cost_s)

1 Analysis

Create a helpfer function to return the results for all permutations of a given test file name.

tf_types <- c("cpu-compiled", "cpu-pip")
tf_platforms <- c("cpu8","cpu16","cpu32","cpu64")
labels <- c('gpu','cpu64pip', 'cpu64cmp','cpu32pip', 'cpu32cmp','cpu16pip', 'cpu16cmp','cpu8pip', 'cpu8cmp')
process_test_data <- function(file_name) {
  results <- read_csv(sprintf("../logs/gpu/%s", file_name)) %>%
              mutate(type = "gpu", platform = "gpu") %>%
              group_by(type, platform) %>%
              summarize(total_time = sum(elapsed),
                        total_cost = total_time * costs['gpu'])
  
  gpu_total_time <- results %>% pull(total_time)
  gpu_total_cost <- results %>% pull(total_cost)
  
  
  for(tf_type_i in 1:length(tf_types)) {
    tf_type <- tf_types[tf_type_i]
    for(tf_platform_i in 1:length(tf_platforms)) {
      tf_platform <- tf_platforms[tf_platform_i]
      
      temp_df <- read_csv(sprintf("../logs/%s/%s/%s", tf_type, tf_platform, file_name)) %>%
              mutate(type = tf_type, platform = tf_platform) %>%
              group_by(type, platform) %>%
              summarize(total_time = sum(elapsed),
                        total_cost = total_time * costs[tf_platform])
      
      results <- results %>% bind_rows(temp_df)
      
    }
  }
  
  # Normalize
  results <- results %>%
              mutate(total_time_norm = total_time / gpu_total_time,
                     total_cost_norm = total_cost / gpu_total_cost)
  
  # Format/Factorize labels
  
  results <- results %>%
              mutate(label = paste0(
                ifelse(platform == "gpu", '', platform),
                ifelse(type == "cpu-compiled", "cmp", substr(type, nchar(type)-2, nchar(type))))) %>%
              ungroup() %>%
              mutate(label= factor(label, levels=labels)) %>%
              select(label, total_time, total_cost, total_time_norm, total_cost_norm)
  
  return(results)
  
}
process_test_data('cifar10_cnn_tensorflow.csv')

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)

1.1 IMDB Bidirectional LSTM

df_imdb_lstm <- process_test_data("imdb_bidirectional_lstm_tensorflow.csv")

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)

df_imdb_lstm

plot <- ggplot(df_imdb_lstm, aes(x=label, y=total_time_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Time on CPUs w/ Bidirectional LSTMs",
               x = "Platform",
               y = "Total Model Training Time Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-1.png", plot, width=4, height=3)

plot <- ggplot(df_imdb_lstm, aes(x=label, y=total_cost_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Cost on CPUs w/ Bidirectional LSTMs",
               x = "Platform",
               y = "Total Model Training Cost Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-2.png", plot, width=4, height=3)

1.2 IMDB Fasttext

df_imdb_fasttext <- process_test_data("imdb_fasttext_tensorflow.csv")

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)

df_imdb_fasttext

plot <- ggplot(df_imdb_fasttext, aes(x=label, y=total_time_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Time on CPUs w/ fasttext",
               x = "Platform",
               y = "Total Model Training Time Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-3.png", plot, width=4, height=3)

plot <- ggplot(df_imdb_fasttext, aes(x=label, y=total_cost_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Cost on CPUs w/ fasttext",
               x = "Platform",
               y = "Total Model Training Cost Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-4.png", plot, width=4, height=3)

1.3 MNIST MLP

df_mnist_mlp <- process_test_data("mnist_mlp_tensorflow.csv")

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)

df_mnist_mlp

plot <- ggplot(df_mnist_mlp, aes(x=label, y=total_time_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Time on CPUs w/ MLP",
               x = "Platform",
               y = "Total Model Training Time Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-5.png", plot, width=4, height=3)

plot <- ggplot(df_mnist_mlp, aes(x=label, y=total_cost_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Cost on CPUs w/ MLP",
               x = "Platform",
               y = "Total Model Training Cost Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-6.png", plot, width=4, height=3)

1.4 MNIST CNN

df_mnist_cnn <- process_test_data("mnist_cnn_tensorflow.csv")

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)

df_mnist_cnn

plot <- ggplot(df_mnist_cnn, aes(x=label, y=total_time_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Time on CPUs w/ CNN",
               x = "Platform",
               y = "Total Model Training Time Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-7.png", plot, width=4, height=3)

plot <- ggplot(df_mnist_cnn, aes(x=label, y=total_cost_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Cost on CPUs w/ CNN",
               x = "Platform",
               y = "Total Model Training Cost Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-8.png", plot, width=4, height=3)

1.5 CIFAR10 Deep CNN + MLP

df_cifar10_cnn <- process_test_data("cifar10_cnn_tensorflow.csv")

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_double(),
  val_loss = col_double(),
  val_acc = col_double()
)

df_cifar10_cnn

plot <- ggplot(df_cifar10_cnn, aes(x=label, y=total_time_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Time on CPUs w/ Deep CNN + MLP",
               x = "Platform",
               y = "Total Model Training Time Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-9.png", plot, width=4, height=3)

plot <- ggplot(df_cifar10_cnn, aes(x=label, y=total_cost_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Cost on CPUs w/ Deep CNN + MLP",
               x = "Platform",
               y = "Total Model Training Cost Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-10.png", plot, width=4, height=3)

1.6 LSTM Text Generation

df_lstm_text <- process_test_data("lstm_text_generation_tensorflow.csv")

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double(),
  acc = col_character(),
  val_loss = col_character(),
  val_acc = col_character()
)

df_lstm_text

plot <- ggplot(df_lstm_text, aes(x=label, y=total_time_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Time on CPUs w/ LSTM Text Gen",
               x = "Platform",
               y = "Total Model Training Time Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-11.png", plot, width=4, height=3)

plot <- ggplot(df_lstm_text, aes(x=label, y=total_cost_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Cost on CPUs w/ LSTM Text Gen",
               x = "Platform",
               y = "Total Model Training Cost Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-12.png", plot, width=4, height=3)

1.7 Custom Text Generation

df_custom_text <- process_test_data("text_generator_keras_tensorflow.csv")

Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)
Parsed with column specification:
cols(
  epoch = col_integer(),
  elapsed = col_double(),
  loss = col_double()
)

df_custom_text

plot <- ggplot(df_custom_text, aes(x=label, y=total_time_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Time on CPUs w/ Custom Text Gen",
               x = "Platform",
               y = "Total Model Training Time Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-13.png", plot, width=4, height=3)

plot <- ggplot(df_custom_text, aes(x=label, y=total_cost_norm, fill=label)) +
          geom_bar(stat="identity") +
          geom_hline(yintercept = 1, linetype="dashed", color="#1a1a1a") +
          scale_y_continuous(labels = relative) +
          scale_fill_manual(values = colors_all) +
          labs(title = "Training Cost on CPUs w/ Custom Text Gen",
               x = "Platform",
               y = "Total Model Training Cost Relative to GPU",
               caption = "Max Woolf — minimaxir.com")
ggsave("dl-cpu-gpu-14.png", plot, width=4, height=3)

1.8 LICENSE

MIT License

Copyright (c) 2017 Max Woolf

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.