* 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.
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.
If a VERSION is not specified, this will generate a version string that
represents the state of the repo. For example `0.1.21-12-gffaf52e-dirty`
representing 12 commits away from 0.1.21 tag, on commit gffaf52e
and the tree is dirty.
The linux build now support parallel CPU builds to speed things up.
This also exposes AMD GPU targets as an optional setting for advaced
users who want to alter our default set.
This renames Dockerfile.build to Dockerfile, and adds some new stages
to support 2 modes of building - the build_linux.sh script uses
intermediate stages to extract the artifacts for ./dist, and the default
build generates a container image usable by both cuda and rocm cards.
This required transitioniing the x86 base to the rocm image to avoid
layer bloat.
This reduces the built-in linux version to not use any vector extensions
which enables the resulting builds to run under Rosetta on MacOS in
Docker. Then at runtime it checks for the actual CPU vector
extensions and loads the best CPU library available
This can help speed up incremental builds when you're only testing one
archicture, like amd64. E.g.
BUILD_ARCH=amd64 ./scripts/build_linux.sh && scp ./dist/ollama-linux-amd64 test-system:
By default builds will now produce non-debug and non-verbose binaries.
To enable verbose logs in llama.cpp and debug symbols in the
native code, set `CGO_CFLAGS=-g`
