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1 . MLflow with R
Javier Luraschi
September 2018
2 .Overview
What is MLflow?
What is R?
MLflow with R
4 . Background
Spark Summit from Andrej Karpathy at Tesla
The toolchain for the (so ware) 2.0 tack does not exist.
5 . MLflow
“Helps teams manage their machine learning lifecycle.”
Tracking : Track experiments to record and compare params and results.
Projects : Reuse and reproduce code to share or transfer to production.
Models : Manage and deploy models from across libraries and platforms.
7 . R Language
R is a programming language and free so ware environment
for statistical computing and graphics.
Interface language diagram by John Chambers - Rick Becker useR 2016.
8 . R Community
Provides a rich package archive provided in CRAN and Bioconductor:
dplyr to manipulate data, cluster to analyze clusters, ggplot2 to
visualize data, etc.
Daily downloads of CRAN packages.
9 . R Language
Language features I would highlighting:
2.1.1 Vectors
2.1.4 Expression objects
2.1.8 Promise objects
2.1.9 Dot-dot-dot
3.1.4 Operators
cran.r project.org/doc/manuals/R-lang.html
10 . Use Case
Select the cyl and hp columns and add 2 and 20:
mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
11 . How to NOT write R code
This is how I would have written R code as a so ware engineer before
knowing R:
# Select columns subset
data = data.frame(mtcars$cyl, mtcars$hp)
colnames(data) = c("cyl", "hp")
# Transform each row
for (idx in 1:nrow(data)) {
data$cyl[idx] = data$cyl[idx] + 2
}
# One column at a time to use the CPU cache efficiently
for (idx in 1:nrow(data)) {
data$hp[idx] = data$hp[idx] + 20
}
12 . 2.1.1 Vectors
Everything is a vector in R:
# Select columns subset
data = data.frame(mtcars$cyl, mtcars$hp)
colnames(data) = c("cyl", "hp")
# Transform each row
data$cyl = data$cyl + 2
# One column at a time to use the CPU cache efficiently
data$hp = data$hp + 20
13 . 2.1.9 Dot-dot-dot
Dynamic parameters using the ... parameter:
# Select columns subset
data = data.frame(cyl = mtcars$cyl, hp = mtcars$hp)
# Transform each row
data$cyl = data$cyl + 2
# One column at a time to use the CPU cache efficiently
data$hp = data$hp + 20
14 .2.1.4/2.1.8 Expression and Promise objects
One can lazily evaluate operations and operate over expressions:
# Select columns subset
data = select(mtcars, cyl, hp)
# Transform each row
data = mutate(data, cyl = cyl + 2)
# One column at a time to use the CPU cache efficiently
data = mutate(data, hp = hp + 20)
15 . 3.1.4 Operators
Use <- for assignment, or the newer %>% pipe:
data mtcars %>%
select(mtcars, cyl, hp) %>%
mutate(data, cyl = cyl + 2) %>%
mutate(data, hp = hp + 20)
16 . Linear Models
lm(mpg ~ cyl + hp, mtcars) %>% plot()
18 . Principles
Parity with Python API.
Designed for the R user.
19 . Installing
Install Anaconda or miniconda.
Today…
git clone https: github.com/mlflow/mlflow
devtools install_github("mlflow/mlflow", subdir = "R/mlflow")
mlflow mlflow_install()
reticulate conda_install("r mlflow", "<local github repo>", pip = TRUE)
Soon…
install.packages("mlflow")
mlflow mlflow_install()
20 . Tracking - Implicit
Implicit MLflow run:
library(mlflow)
# Log a parameter (key value pair)
mlflow_log_param("param1", 5)
# Log a metric; metrics can be updated throughout the run
mlflow_log_metric("foo", 1)
mlflow_log_metric("foo", 2)
mlflow_log_metric("foo", 3)
# Log an artifact (output file)
writeLines("Hello world!", "output.txt")
mlflow_log_artifact("output.txt")
Run terminates when the R session finishes or by running:
mlflow_end_run()
Useful when sourcing files.
21 . Tracking - Explicit
Explicit MLflow run:
library(mlflow)
with(mlflow_start_run(), {
# Log a parameter (key value pair)
mlflow_log_param("param1", 5)
# Log a metric; metrics can be updated throughout the run
mlflow_log_metric("foo", 1)
mlflow_log_metric("foo", 2)
mlflow_log_metric("foo", 3)
# Log an artifact (output file)
writeLines("Hello world!", "output.txt")
mlflow_log_artifact("output.txt")
})
22 . Tracking - Sources
mlflow_run("R/tracking.R")
Or adding the following to tracking.R in RStudio 1.2:
# !source mlflow mlflow_run(entry_point = .file)
23 . Tracking - UI
mlflow_ui()
24 . Projects - Snapshots
Create dependencies snapshot:
mlflow_snapshot()
Then restore snapshot:
mlflow_restore_snapshot()
25 . Projects - Consuming
mlflow_run(
"train.R",
"https: github.com/rstudio/mlflow example",
param_list = list(alpha = 0.2)
)
Elasticnet model (alpha=0.2, lambda=0.5):
RMSE: 0.827574750159859
MAE: 0.632070002076146
R2: 0.227227498131926
Or from bash:
mlflow run entry point train.R https: github.com/rstudio/mlflow example
26 . Models - Saving
mlflow_save_model(model)
Generic functions are serialized with crate:
column mlflow_log_param("column", 1)
model lm(
Sepal.Width ~ x,
data.frame(Sepal.Width = iris$Sepal.Width, x = iris[,column])
)
mlflow_save_model(
crate(~ stats predict(model, .x), model)
)
However, mlflow_save_model() can be extended by packages:
#' @export
mlflow_save_flavor.tensorflow function( ) {}
mlflow_load_flavor.tensorflow function( ) {}
mlflow_predict_flavor.tensorflow function( ) {}
27 . Models - Predictions
mlflow_rfunc_predict(
"model",
data = data.frame(x = c(0.3, 0.2))
)
1 2
3.400381 3.406570
Or from bash,
mlflow rfunc predic model path model input path data.csv
28 . Models - Serving
mlflow_rfunc_serve("model")
mlflow rfunc serve model path model
curl -X POST "http: 127.0.0.1 8090/predict/" -H "accept: application/json" -H "Content-Type:
application/json" d '[{"x": [0.3, 0.2]}]'
29 . Future Work
Currently merged, various github issues pending:
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