Goal

Track R models in the remote MLflow tracking server (running in kubernetes). The model is developed on the local computer from RStudio run in the Docker container.

Setup

Based on my research I need to create RStudio image with conda installed. After that I want to run example from MLflow documentation.

Dockerfile

FROM rocker/rstudio

USER root

ENV PATH="/root/miniconda3/bin:${PATH}"
ARG PATH="/root/miniconda3/bin:${PATH}"
ENV MLFLOW_BIN=/root/miniconda3/bin/mlflow
ENV MLFLOW_PYTHON_BIN=/root/miniconda3/bin/python

RUN apt-get update

RUN apt-get install -y wget && rm -rf /var/lib/apt/lists/*

RUN wget \
    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
    && mkdir /root/.conda \
    && bash Miniconda3-latest-Linux-x86_64.sh -b \
    && rm -f Miniconda3-latest-Linux-x86_64.sh 
RUN conda --version

RUN R -e 'install.packages("mlflow")'
RUN R -e 'install.packages("glmnet")'
RUN R -e 'install.packages("carrier")'

RUN pip install -U mlflow==1.19.0

train.R (adjusted exmple from here)

# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

library(mlflow)
library(glmnet)
library(carrier)

set.seed(40)

# Read the wine-quality csv file
data <- read.csv("wine-quality.csv")

# Split the data into training and test sets. (0.75, 0.25) split.
sampled <- sample(1:nrow(data), 0.75 * nrow(data))
train <- data[sampled, ]
test <- data[-sampled, ]

# The predicted column is "quality" which is a scalar from [3, 9]
train_x <- as.matrix(train[, !(names(train) == "quality")])
test_x <- as.matrix(test[, !(names(train) == "quality")])
train_y <- train[, "quality"]
test_y <- test[, "quality"]

alpha <- mlflow_param("alpha", 0.5, "numeric")
lambda <- mlflow_param("lambda", 0.5, "numeric")

Sys.setenv(MLFLOW_S3_ENDPOINT_URL="<EP>")
Sys.setenv(AWS_ACCESS_KEY_ID="<some_key>")
Sys.setenv(AWS_SECRET_ACCESS_KEY="<some_secret>")

mlflow_set_experiment("Wine R experiment")
mlflow_set_tracking_uri("<http...blabla>")

with(mlflow_start_run(), {
    model <- glmnet(train_x, train_y, alpha = alpha, lambda = lambda, family= "gaussian", standardize = FALSE)
    predictor <- crate(~ glmnet::predict.glmnet(!!model, as.matrix(.x)), !!model)
    predicted <- predictor(test_x)

    rmse <- sqrt(mean((predicted - test_y) ^ 2))
    mae <- mean(abs(predicted - test_y))
    r2 <- as.numeric(cor(predicted, test_y) ^ 2)

    message("Elasticnet model (alpha=", alpha, ", lambda=", lambda, "):")
    message("  RMSE: ", rmse)
    message("  MAE: ", mae)
    message("  R2: ", r2)

    mlflow_log_param("alpha", alpha)
    mlflow_log_param("lambda", lambda)
    mlflow_log_metric("rmse", rmse)
    mlflow_log_metric("r2", r2)
    mlflow_log_metric("mae", mae)

    mlflow_log_model(predictor, "model")
})

Its POC don't mind the unsafe env variables.

I run the container like this:

docker run --rm -p 8787:8787 -e PASSWORD=password --mount type=bind,source=$(pwd)/mlflow/examples/r_wine,target=/home/rstudio rstudio-mlflow

Problem

Everytime I run the file from RStudio I get error on the last line (the mlflow_log_model(predictor, "model")):

cannot start processx process '/root/miniconda3/bin/mlflow' (system error 13, Permission denied) @unix/processx.c:608 (processx_exec)

I am getting permission denied when listing conda bin folder from RStudio terminal. Can you help me how to correctly install conda with RStudio image.