LangPack: A language specific package the encompasses steps to setup, build, and run language-specific algorithms.
LangServer: A server that serve a LangPack's
bin/pipe runner in a way that emulates a light-weight version of the Algorithmia API.
Building LangServer(s) (Partially deprecated)
Disclaimer: The intent was to prototype langserver in rust (because I knew it better), but finally write it in go (lower barrier to entry), but it turned into an official project before the rewrite happened. So, for now: start by installing latest stable rust, and then:
bin/build langserver # just builds the base langserver images (default) bin/build <lang> # builds language-specific image (and deps) bin/build all # builds all images for all langpacks bin/build single-runner # builds 1 image containing the langserver runner and running setup on all langpacks bin/build single-builder # builds 1 image containing the langserver builder and running setup on all langpacks bin/build single # builds the single-runner and single-builder
Note: the initial plan is to NOT use these images, but they are helpful for implementing and testing langpacks locally, as well as provide some "code documentation" for how setup/build/pipe/langserver all fit together.
Building LangServer with Libraries
We're in the process of refactoring the way that images get generated and algorithms are compiled. The initial approach would create an
algorithm.zip that contains a compiled binary (or source for interpreted languages) along with any dependencies needed. Additionally, a number of libraries were installed side-by-side which made it difficult to debug in certain scenarios or independently evolve various languages. In particular, some libraries required certain variables set during install/compilation but not during execution and it was difficult to determine what variables or even system packages were needed for what libraries in particular.
The new process (still experimental) involves templating a Dockerfile based on a set of desired
libraries (which could be language runtimes/buildtimes, services, or deep-learning frameworks) and then building an image with just that subset of libraries. Ideally, libraries' install.sh script should be able to run on an Ubuntu 16.04 host/VM the same as it could during docker build time (this greatly eases creating the install script).
Algorithms no longer have a single
bin/build script but two separate scripts, one to
install-dependencies (which would do an appropriate pip/npm/cargo/etc install/fetch) and one to
install-algorithm which compiles or bundles the algorithm source to
Templating and building a dockerfile:
$ ./bin/build-template --help usage: build-template [-h] [-l LIBRARY] [-p TEMPLATE] -t TAG [-o OUTPUT] [-u USER_ID] Creates a dockerfile, templating in any needed files and environment variables to set up different libraries. Libraries will be installed _in order specified_ so if one needs to be installed before another, then list them in that order on the command line Will then run a docker build and tag operation Library directories should include the following: - install.sh : a script to install the library - config.json (optional): a json file containing configuration such as: - env_variables: dictionary of environment variables to set at the end of execution - install_scripts: list of order to run scripts in to create multiple layers (particularly for testing) optional arguments: -h, --help show this help message and exit -l LIBRARY, --library LIBRARY library directories to include in generating this dockerfile -p TEMPLATE, --template TEMPLATE location of the dockerfile template file -t TAG, --tag TAG tag to label the docker image once produced -o OUTPUT, --output OUTPUT name of file to write output to -u USER_ID, --user_id USER_ID user id to use for the "algo" user, defaults to current user
# Create a langpack consisting only of python2 and tag it as algorithmia/langpack-runner:python2 ./bin/build-template -u 1001 -t algorithmia/langpack-runner:python2 -l python2 -o docker/templated/Dockerfile.python2 # Create a langpack with NVIDIA GPU drivers, python and caffe and tag it as algorithmia/langpack-runner:python2-caffe ./bin/build-template -u 1001 -t algorithmia/langpack-runner:python2-caffe -l gpu-driver -l python2 -l caffe -o Dockerfile.python2-advanced
Building an algorithm
- Bind mount an algorithm working directory to
docker run -it -v \pwd`:/tmp/build algorithmia/langpack-runner:python2`
- Outside of the container commit the image with appropriate entrypoint -
docker commit -c 'ENTRYPOINT /bin/init-langserver' -c 'WORKDIR /opt/algorithm' <container_id> algorithmia/<algorithm_name>
Running an algorithm
docker run --rm -ti -p 9999:9999 algorithmia/<algorithm_name>
Building an algorithm
Bind mount an algorithm working directory to
/tmp/build and start the langbuilder- image. It should create an algorithm.zip that can be served by the init-langserver script (containing
bin/pipe, the algorithm, and any dependencies):
docker run --rm -it -v `pwd`:/tmp/build algorithmia/langbuilder-<lang>
Note, unless using Docker user namespacing, don't be shocked if bind-mount writing results in permission errors.
init-langserver script provides 2 ways to run an algorithm:
Bind mount algorithm.zip to /tmp/algorithm.zip
Note: Make sure you use the absolute path to the algorithm.zip.
docker run --rm -it -v /path/to/algorithm.zip:/tmp/algorithm.zip -p 9999:9999 algorithmia/langserver-<lang>
Bind mount algorithm directory to /tmp/algorithm
docker run --rm -it -v `pwd`:/tmp/algorithm -p 9999:9999 algorithmia/langserver-<lang>
More to come...