Unable to run efficient machine learning model without internet (+ GPU support)

I'm looking to run a simple neural network on a Linux RHEL8. I copied the code from the cifar-10 example, but have run into a few snags.
Initially, the system was running the network fine but crashing after 1-10 epochs. I was able to install an NVIDIA GPU core on the computer, and that seems to have resolved the crashes. I'm still facing a few problems, however:

  1. It seems that I can't get the system to use more than 5% of my system's GPU core, no more than 61MiB of memory. With a still slow network (when it works), it would be nice to maximize this resource.
  2. The computer I'm running the code on does not have internet access. Where the code works on an identical computer with network access,
#code to load packages to run the model
library(reticulate)
library(keras)
install_keras(method = c("conda", conda = "auto", version = "default", tensorflow = "gpu")

comes up with an error:

EnvironmentLocationNotFound: Not a conda environment: /home/megan/anaconda3/envs/r-reticulate

Error: installation of 'python=3.6' into environment 'r-reticulate' failed [error code 1]

Evidence points to this problem being a lack of internet connection. Is there a way to locally install keras so that I can use it for a network?

  1. When #2 shows up as an error, I also receive the following warnings:
2020-02-13  I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-02-13 I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3312000000 Hz
2020-02-13  I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55efd1b83680 executing computations on platform Host. Devices:
2020-02-13 : I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version

Would love advice on how to optimize this system. I have been looking for all sorts of solutions but am new to both machine learning and linux (not as new to R).