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This means you don't have sufficient privilege on your machine. Run it with sudo:
sudo ./bin/setup
When Conda complains about certain variables should not be in your PATH:
CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'. If your shell is Bash or a Bourne variant, enable conda for the current user with
$ echo ". /home/ubuntu/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc
or, for all users, enable conda with
$ sudo ln -s /home/ubuntu/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh
The options above will permanently enable the 'conda' command, but they do NOT put conda's base (root) environment on PATH. To do so, run
$ conda activate
in your terminal, or to put the base environment on PATH permanently, run
$ echo "conda activate" >> ~/.bashrc
Previous to conda 4.4, the recommended way to activate conda was to modify PATH in your ~/.bashrc file. You should manually remove the line that looks like
export PATH="/home/ubuntu/miniconda3/bin:$PATH"
^^^ The above line should NO LONGER be in your ~/.bashrc file! ^^^
To fix it, do the first thing it recommends and refresh your terminal session:
echo ". /home/ubuntu/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrcsource ~/.bashrc
You encounter libgcc errors like:
ImportError: /home/deploy/miniconda3/envs/lab/lib/python3.6/site-packages/torch/../../.././libstdc++.so.6: version `GLIBCXX_3.4.21' not found (required by /home/deploy/miniconda3/envs/lab/lib/python3.6/site-packages/ray/pyarrow_files/pyarrow/lib.cpython-36m-x86_64-linux-gnu.so)
Try installing libgcc in Conda:
conda install libgcc
If you receive errors similar to the following when trying to use GPU:
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver
Reinstall your NVIDIA GPU driver using this instruction.
If you build your own desktop and want a quick and smooth setup for a Ubuntu GPU server, refer to this gist.
Make sure you also install the packages after updating the repo. Run:
git pull./bin/setup
Manually install it:
conda activate labsudo pip install vizdoom
You can see the running processes using tools like glances. Use the following commands to kill processes by their names. You may need to use sudo.
pkill -f run_labpkill -f slm-envpkill -f ipykernelpkill -f raypkill -f orcapkill -f Xvfbps aux | grep -i Unity | awk '{print $2}' | xargs sudo kill -9
When running SLM Lab on a remote server, you may get NoSuchDisplayException: Cannot connect to "None". Or your graphs may not be generated. This is because servers are typically headless, i.e. without a display. This error occurs when you're trying to render without a headless display.
Install Xvfb, and prepend your command with xvfb-run -a. For example:
xvfb-run -a python run_lab.py slm_lab/spec/demo.json dqn_cartpole train
If you are running via ssh and want GUI forwarding from a server, do:
install OpenGL and/or configure Nvidia driver on your server. Follow instructions here.​
do
sshwith a-Xflag, e.g.ssh -X [email protected].
SLM Lab produces a lot of data which are then zipped for our convenience of transferring/syncing them. We use Dropbox to upload these zip files. Follow this instruction to install Dropbox CLI.
SLM stands for Strange Loop Machine, in homage to Hofstadter’s iconic book Gödel, Escher, Bach: An Eternal Golden Braid. This lab is created as part of a long term project to try out AI ideas heavily influenced by it.
Can't find the issues you encountered? Report new issues on Github; it helps all of us.
