Downgrade tf to 2.3 (#152)

And e2e test showed that the TF + GPU integration wasn't working properly, but these unit tests didn't catch it. So I also added a test checking for GPU device.

http://b/177304566

Author: Philmod

Dockerfile

@@ -61,7 +61,7 @@ RUN apt-get install -y libzmq3-dev python-pip default-jdk && \
 # Keras sets up a virtualenv and installs tensorflow
 # in the WORKON_HOME directory, so choose an explicit location for it.
 ENV WORKON_HOME=/usr/local/share/.virtualenvs
-RUN pip install --user virtualenv && R -e 'keras::install_keras(tensorflow = "2.4", extra_packages = c("pandas", "numpy", "pycryptodome"))'
+RUN pip install --user virtualenv && R -e 'keras::install_keras(tensorflow = "2.3", extra_packages = c("pandas", "numpy", "pycryptodome"))'
 
 # Install kaggle libraries.
 # Do this at the end to avoid rebuilding everything when any change is made.

gpu.Dockerfile

@@ -59,7 +59,7 @@ ENV CUDA_HOME=/usr/local/cuda
 ADD ldpaths $R_HOME/etc/ldpaths
 
 # Install tensorflow with GPU support
-RUN R -e 'keras::install_keras(tensorflow = "2.4-gpu")' && \
+RUN R -e 'keras::install_keras(tensorflow = "2.3-gpu")' && \
     rm -rf /tmp/tensorflow_gpu && \
     /tmp/clean-layer.sh
 

tests/test_tensorflow.R

@@ -1,13 +1,19 @@
 context("tensorflow")
 
-test_that("gpu imports", {
+test_that("check gpu device", {
+  check_gpu()
+ 
+  library(tensorflow)
+  gpus = tf$config$experimental$list_physical_devices('GPU')
+  expect_equal(length(gpus), 1)
+})
+
+test_that("tensorflow with gpu", {
   check_gpu()
 
   library(tensorflow)
   with(tf$device("/gpu:0"), {
       const <- tf$constant(42)
       expect_equal(42, as.integer(const))
   })
-
-  expect_true(TRUE)
 })