Performance Benchmarks

Performance Benchmarks#

Isaac Lab leverages end-to-end GPU training for reinforcement learning workflows, allowing for fast parallel training across thousands of environments. In this section, we provide runtime performance benchmark results for reinforcement learning training of various example environments on different GPU setups. Multi-GPU and multi-node training performance results are also outlined.

Benchmark Results#

All benchmarking results were performed with the RL Games library with --headless flag on Ubuntu 22.04. Isaac-Velocity-Rough-G1-v0 environment benchmarks were performed with the RSL RL library.

Memory Consumption#

Environment Name

# of Environments

RAM (GB)

VRAM (GB)

Isaac-Cartpole-Direct-v0

cartpole

4096

3.7

3.3

Isaac-Cartpole-RGB-Camera-Direct-v0

cartpole-cam

1024

7.5

16.7

Isaac-Velocity-Rough-G1-v0

g1

4096

6.5

6.1

Isaac-Repose-Cube-Shadow-Direct-v0

shadow

8192

6.7

6.4

Single GPU - RTX 4090#

CPU: AMD Ryzen 9 7950X 16-Core Processor

Environment Name

# of Environments

Environment Step FPS

Environment Step and Inference FPS

Environment Step, Inference, and Train FPS

Isaac-Cartpole-Direct-v0

4096

1100000

910000

510000

Isaac-Cartpole-RGB-Camera-Direct-v0

1024

50000

45000

32000

Isaac-Velocity-Rough-G1-v0

4096

94000

88000

82000

Isaac-Repose-Cube-Shadow-Direct-v0

8192

200000

190000

170000

Single GPU - L40#

CPU: Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80GHz

Environment Name

# of Environments

Environment Step FPS

Environment Step and Inference FPS

Environment Step, Inference, and Train FPS

Isaac-Cartpole-Direct-v0

4096

620000

490000

260000

Isaac-Cartpole-RGB-Camera-Direct-v0

1024

30000

28000

21000

Isaac-Velocity-Rough-G1-v0

4096

72000

64000

62000

Isaac-Repose-Cube-Shadow-Direct-v0

8192

170000

140000

120000

Single-Node, 4 x L40 GPUs#

CPU: Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80GHz

Environment Name

# of Environments

Environment Step FPS

Environment Step and Inference FPS

Environment Step, Inference, and Train FPS

Isaac-Cartpole-Direct-v0

4096

2700000

2100000

950000

Isaac-Cartpole-RGB-Camera-Direct-v0

1024

130000

120000

90000

Isaac-Velocity-Rough-G1-v0

4096

290000

270000

250000

Isaac-Repose-Cube-Shadow-Direct-v0

8192

440000

420000

390000

4 Nodes, 4 x L40 GPUs per node#

CPU: Intel(R) Xeon(R) Platinum 8362 CPU @ 2.80GHz

Environment Name

# of Environments

Environment Step FPS

Environment Step and Inference FPS

Environment Step, Inference, and Train FPS

Isaac-Cartpole-Direct-v0

4096

10200000

8200000

3500000

Isaac-Cartpole-RGB-Camera-Direct-v0

1024

530000

490000

260000

Isaac-Velocity-Rough-G1-v0

4096

1200000

1100000

960000

Isaac-Repose-Cube-Shadow-Direct-v0

8192

2400000

2300000

1800000

Benchmark Scripts#

For ease of reproducibility, we provide benchmarking scripts available at source/standalone/benchmarks. This folder contains individual benchmark scripts that resemble the train.py script for RL-Games and RSL RL. In addition, we also provide a benchmarking script that runs only the environment implementation without any reinforcement learning library.

Example scripts can be run similarly to training scripts:

# benchmark with RSL RL
python source/standalone/benchmarks/benchmark_rsl_rl.py --task=Isaac-Cartpole-v0 --headless

# benchmark with RL Games
python source/standalone/benchmarks/benchmark_rlgames.py --task=Isaac-Cartpole-v0 --headless

# benchmark without RL libraries
python source/standalone/benchmarks/benchmark_non_rl.py --task=Isaac-Cartpole-v0 --headless

Each script will generate a set of KPI files at the end of the run, which includes data on the startup times, runtime statistics, such as the time taken for each simulation or rendering step, as well as overall environment FPS for stepping the environment, performing inference during rollout, as well as training.

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