From 7a79b7f62c839810e5fde73ac504d1f2d4d5081b Mon Sep 17 00:00:00 2001
From: shane <incomplete@gmail.com>
Date: Wed, 5 Dec 2018 14:59:07 -0800
Subject: [PATCH] increase container memory and shm to 20G (#3475)

* increase container memory and shm to 20G

* variables are POWERFUL
---
 test/jenkins_tests/run_multi_node_tests.sh | 153 +++++++++++----------
 1 file changed, 78 insertions(+), 75 deletions(-)

diff --git a/test/jenkins_tests/run_multi_node_tests.sh b/test/jenkins_tests/run_multi_node_tests.sh
index 9b8d9295e..9fba8a000 100755
--- a/test/jenkins_tests/run_multi_node_tests.sh
+++ b/test/jenkins_tests/run_multi_node_tests.sh
@@ -6,54 +6,57 @@ set -e
 # Show explicitly which commands are currently running.
 set -x
 
+MEMORY_SIZE="20G"
+SHM_SIZE="20G"
+
 ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE:-$0}")"; pwd)
 
 DOCKER_SHA=$($ROOT_DIR/../../build-docker.sh --output-sha --no-cache)
 echo "Using Docker image" $DOCKER_SHA
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env PongDeterministic-v0 \
     --run A3C \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env Pong-ram-v4 \
     --run A3C \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env PongDeterministic-v0 \
     --run A2C \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v1 \
     --run PPO \
     --stop '{"training_iteration": 2}' \
     --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v1 \
     --run PPO \
     --stop '{"training_iteration": 2}' \
     --config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v1 \
     --run PPO \
     --stop '{"training_iteration": 2}' \
     --config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v1 \
     --run PPO \
@@ -61,180 +64,180 @@ docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
     --config '{"num_gpus": 0.1}' \
     --ray-num-gpus 1
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v1 \
     --run PPO \
     --stop '{"training_iteration": 2}' \
     --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env Pendulum-v0 \
     --run ES \
     --stop '{"training_iteration": 2}' \
     --config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env Pong-v0 \
     --run ES \
     --stop '{"training_iteration": 2}' \
     --config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run A3C \
     --stop '{"training_iteration": 2}' \
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run DQN \
     --stop '{"training_iteration": 2}' \
     --config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run DQN \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run APEX \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env FrozenLake-v0 \
     --run DQN \
     --stop '{"training_iteration": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env FrozenLake-v0 \
     --run PPO \
     --stop '{"training_iteration": 2}' \
     --config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env PongDeterministic-v4 \
     --run DQN \
     --stop '{"training_iteration": 2}' \
     --config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env MontezumaRevenge-v0 \
     --run PPO \
     --stop '{"training_iteration": 2}' \
     --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v1 \
     --run A3C \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2, "model": {"use_lstm": true}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run DQN \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run PG \
     --stop '{"training_iteration": 2}' \
     --config '{"sample_batch_size": 500, "num_workers": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run PG \
     --stop '{"training_iteration": 2}' \
     --config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run PG \
     --stop '{"training_iteration": 2}' \
     --config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env Pong-v0 \
     --run PG \
     --stop '{"training_iteration": 2}' \
     --config '{"sample_batch_size": 500, "num_workers": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env FrozenLake-v0 \
     --run PG \
     --stop '{"training_iteration": 2}' \
     --config '{"sample_batch_size": 500, "num_workers": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env Pendulum-v0 \
     --run DDPG \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run IMPALA \
     --stop '{"training_iteration": 2}' \
     --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run IMPALA \
     --stop '{"training_iteration": 2}' \
     --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run IMPALA \
     --stop '{"training_iteration": 2}' \
     --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_parallel_data_loaders": 2, "replay_proportion": 1.0}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \
     --run IMPALA \
     --stop '{"training_iteration": 2}' \
     --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_parallel_data_loaders": 2, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env MountainCarContinuous-v0 \
     --run DDPG \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     rllib train \
     --env MountainCarContinuous-v0 \
     --run DDPG \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env Pendulum-v0 \
     --run APEX_DDPG \
@@ -242,153 +245,153 @@ docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_local.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_checkpoint_restore.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_policy_evaluator.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_nested_spaces.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_external_env.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=PPO --stop=50
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=DQN --stop=50
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_lstm.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PPO
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PG
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DQN
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DDPG
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_multi_agent_env.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_supported_spaces.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     pytest /ray/python/ray/tune/test/cluster_tests.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/test/test_env_with_subprocess.py
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     /ray/python/ray/rllib/test/test_rollout.sh
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/tune_mnist_ray.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/pbt_example.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/hyperband_example.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/async_hyperband_example.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/tune_mnist_ray_hyperband.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/tune_mnist_async_hyperband.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/hyperopt_example.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/tune_mnist_keras.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/mnist_pytorch.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/mnist_pytorch_trainable.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/genetic_example.py \
     --smoke-test
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/multiagent_cartpole.py --num-iters=2
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2 \
         --batch-size=1 --strategy=simple
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2 \
         --batch-size=1 --strategy=ps
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/experimental/sgd/mnist_example.py --num-iters=1 \
         --num-workers=1 --devices-per-worker=1 --strategy=ps
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/experimental/sgd/mnist_example.py --num-iters=1 \
         --num-workers=1 --devices-per-worker=1 --strategy=ps --tune
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env PongDeterministic-v4 \
     --run A3C \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84, "channel_major": true}, "preprocessor_pref": "rllib"}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v1 \
     --run A3C \
     --stop '{"training_iteration": 2}' \
     --config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
 
-docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA python -m pytest /ray/test/object_manager_test.py
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA python -m pytest /ray/test/object_manager_test.py
 
 python3 $ROOT_DIR/multi_node_docker_test.py \
     --docker-image=$DOCKER_SHA \