The rocm CI step for RCs was incorrectly tagging them as the latest rocm build.
The multiarch manifest was incorrectly tagged twice (with and without the
prefix "v"). Static windows artifacts weren't being carried between build
jobs. This also fixes the latest tagging script.
* Optimize container images for startup
This change adjusts how to handle runner payloads to support
container builds where we keep them extracted in the filesystem.
This makes it easier to optimize the cpu/cuda vs cpu/rocm images for
size, and should result in faster startup times for container images.
* Refactor payload logic and add buildx support for faster builds
* Move payloads around
* Review comments
* Converge to buildx based helper scripts
* Use docker buildx action for release
We're over budget for github's maximum release artifact size with rocm + 2 cuda
versions. This splits rocm back out as a discrete artifact, but keeps the layout so it can
be extracted into the same location as the main bundle.
This adjusts linux to follow a similar model to windows with a discrete archive
(zip/tgz) to cary the primary executable, and dependent libraries. Runners are
still carried as payloads inside the main binary
Darwin retain the payload model where the go binary is fully self contained.
If we detect an NVIDIA GPU, but nvidia doesn't support the os/arch,
this will report a better error for the user and point them to docs
to self-install the drivers if possible.
We update the PATH on windows to get the CLI mapped, but this has
an unintended side effect of causing other apps that may use our bundled
DLLs to get terminated when we upgrade.
* ensure kernel modules are loaded in `install.sh` script and at startup
* indentation
* use `SUDO` variable
* restart if nouveau is detected
* consistent success message for AMD
Now that the llm runner is an executable and not just a dll, more users are facing
problems with security policy configurations on windows that prevent users
writing to directories and then executing binaries from the same location.
This change removes payloads from the main executable on windows and shifts them
over to be packaged in the installer and discovered based on the executables location.
This also adds a new zip file for people who want to "roll their own" installation model.
This refines where we extract the LLM libraries to by adding a new
OLLAMA_HOME env var, that defaults to `~/.ollama` The logic was already
idempotenent, so this should speed up startups after the first time a
new release is deployed. It also cleans up after itself.
We now build only a single ROCm version (latest major) on both windows
and linux. Given the large size of ROCms tensor files, we split the
dependency out. It's bundled into the installer on windows, and a
separate download on windows. The linux install script is now smart and
detects the presence of AMD GPUs and looks to see if rocm v6 is already
present, and if not, then downloads our dependency tar file.
For Linux discovery, we now use sysfs and check each GPU against what
ROCm supports so we can degrade to CPU gracefully instead of having
llama.cpp+rocm assert/crash on us. For Windows, we now use go's windows
dynamic library loading logic to access the amdhip64.dll APIs to query
the GPU information.
