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Remote GPU
The recommended way to setup a Linux server for Avatarify is Docker. This sections explains a native installation.
This guide will assume the OS is Ubuntu 18.04 and latest NVIDIA drivers installed.
See also provisioning a cloud-based GPU instance.
- Download Miniconda Python 3.7 and install it:
bash Miniconda3-latest-Linux-x86_64.sh
- Clone
avatarify
and install its dependencies:
git clone https://github.com/alievk/avatarify.git
cd avatarify
bash scripts/install.sh --no-vcam
-
Download network weights and place
vox-adv-cpk.pth.tar
file in theavatarify
directory (don't unpack it).
This guide will assume the OS is Windows 10 and latest NVIDIA drivers installed.
- Install Miniconda Python 3.7.
- Install Git.
- Press Windows button and type "miniconda". Run suggested Anaconda Prompt.
- Download and install Avatarify (please copy-paste these commands and don't change them):
git clone https://github.com/alievk/avatarify.git
cd avatarify
scripts\install_windows.bat
-
Download network weights and place
vox-adv-cpk.pth.tar
file in theavatarify
directory (don't unpack it).
Docker images are only availabe on Linux.
- Install Docker following the Documentation. Then run this step to make docker available for your user.
- For using the gpu (hardly recommended): Install nvidia drivers and nvidia docker.
- Clone
avatarify
:
git clone https://github.com/alievk/avatarify.git
- Build the Dockerfile:
docker build -t avatarify .
Linux (native and with Docker):
bash run.sh --is-worker
Add the flag --no-gpus
if you don't have a GPU installed. Add the flag --docker
to use Docker.
Windows:
run_windows.bat --is-worker
Clients are supported on all platforms. Use this command to run a client:
run_mac.sh --is-client --in-addr tcp://server_address:5557 --out-addr tcp://server_address:5558
where server_address
is the IP address or the hostname of the server. Use run_windows.bat
for Windows and run.sh
for Linux.
If you want the client to run with docker add the --docker
flag. If you run the server with Docker and the client is on the same machine add the --is-local-client
flag.