The following page describes the steps to install rlscope using the standard pip python tool so you can use it in your own RL code base. In particular, to install RL-Scope you must enable GPU hardware counter profiling, and install an RL-Scope version that matches the CUDA version used by your DL framework.


Don’t follow these steps if you are trying to reproduce RL-Scope paper artifacts; instead, follow the instructions for running RL-Scope inside a reproducible docker environment: RL-Scope artifact evaluation.

1. NVIDIA driver

By default, the nvidia kernel module doesn’t allow non-root users to access GPU hardware counters. To allow non-root user access, do the following:

  1. Paste the following contents into /etc/modprobe.d/nvidia-profiler.conf:

    options nvidia NVreg_RestrictProfilingToAdminUsers=0
  2. Reboot the machine for the changes to take effect:

    [host]$ sudo reboot now


If you forget to do this, RL-Scope will fail during profiling with an CUPTI_ERROR_INSUFFICIENT_PRIVILEGES error when attempting to read GPU hardware counters.

2. Determine the CUDA version used by your DL framework

RL-Scope does not have dependencies on DL frameworks, but it does have dependencies on different CUDA versions.

In order to host multiple CUDA versions, we provide our own wheel file index instead of hosting packages on PyPi (NOTE: this is the same approach taken by PyTorch).

DL frameworks like TensorFlow and PyTorch have their own CUDA version dependencies. So, depending on which DL framework version you are using, you must choose to install RL-Scope with a matching CUDA version.


For TensorFlow, the CUDA version it uses is determined by your TensorFlow version. For example TensorFlow v2.4.0 uses CUDA 11.0. You can find a full list here.


For PyTorch, multiple CUDA versions are available, but your specific PyTorch installation will only support one CUDA version. You can determine the CUDA version by looking at the version of the installed PyTorch by doing

$ pip freeze | grep torch

In this case the installed CUDA version is “101” which corresponds to 10.1.

3. pip installation

Once you’ve determined your CUDA version, you can use pip to install rlscope. To install RL-Scope version 0.0.1, CUDA 10.1 you can run:

$ pip install rlscope==0.0.1+cu101 -f

More generally, the syntax is:

$ pip install rlscope==${RLSCOPE_VERSION}+cu${CUDA_VERSION}

Where RLSCOPE_VERSION corresponds to a tag on github, and CUDA_VERSION corresponds to a CUDA version with “.” removed (e.g., 10.1 \(\rightarrow\) 101).

For a full list of available releases and CUDA versions, visit the RL-Scope github releases page.

4. requirements.txt

To add RL-Scope to your requirements.txt file, make sure to add two lines to the file:

$ cat requirements.txt

The -f ... line ensures that the rlscope package is fetched using our custom wheel index (otherwise, pip will fail when it attempts to install from the default PyPi index).


pip freeze will not remember to add -f, so avoid generating requirements.txt using its raw output alone.