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License: MIT License
Dockerized ML Cookiecutter
License: MIT License
manifoldai/docker-ml-cookiecutter.git
. This project no longer exists. It should be manifoldai/docker-cookiecutter
Buggy versions keep getting commited to master. This is somewhat tolerable if we had some semi-regular release cadance. Alongside of this, some structure around releasing/versioning would be helpful.
The default README is rather long, and it's sometimes hard to find project-specific content below all the setup information. I propose moving the setup info to a separate file.
This was causing out-of-the-box CI to fail due to a typo. I am going to put up a bandaid PR to fix this, but we really want out out CI to run the same command as make ci
.
This is related to #28. However, ideally the CI should:
make ci
Jeff and I can fix this up.
Write a paragraph pointing people to right resources.
Right now, everything runs in mounted containers. However, most deployments involve a baked image. We should add a docker build command to the Makefile and CI/CD should run in this.
I was having a nightmare trying to get this up on windows. In the docker compose file you have 127.0.0.1 mapped. If you use 0.0.0.0 instead, it will work.
Because of this:
docker/for-win#204
You have to actually go to the IP returned by docker-machine ip
. And get the port like in the README.
The default documentation is somewhat abstract. Adding a toy dataset and simple prediction model (e.g., linear regression on the radon dataset) would help make things a lot more concrete.
there is conflicts that occur when we have separate compose files for cloud and local.
for orbyter-dl-dev, the following lines need to be added to the develop service of docker-compose.yml
:
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
Python modules cannot have dashes -
in their name. It is recommended to use underscores _
. However, I bunch of paths and made with dashes in the names. A user has to manually edit these.
Would love to see Jenkins integration where a job is created upon repo creation based on cookiecutter input.
Notes from running Orbyter-dl-dev:3.2 in AWS
/etc/docker/daemon.json
file so that the default runtime is set FIRST. If you don’t make it first, then the container will not pick up the runtime. Don’t make that mistake #devops. Here’s what the file should look like after editing:{
"default-runtime": "nvidia",
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
},
}
Hi,
I was wondering if there is a sample output of the cookiecutter - in order to analyze it before setting up the entire project?
isort needs a new "flag" to be compatible with black. It is:
docker exec -t $(CONTAINER_NAME) isort $(PROJ_DIR) --profile black
When cutting a new repository with orbyter 3.5, black complains about how train.py is formatted.
We need to look into how to best test this repo. I'm not familiar with testing cookiecutters, but it looks like we can cookiecut a local repo so this shouldn't be too crazy (just do it in a fresh docker continer):
At the most basic level:
I could probably get to this in 1-2 weeks, but taggin others if they are available @joegoldbeck @jeffmm, @TensorDuck
Add some commands that facilitate easier testing, maybe within a containerized workflow.
took me forever to find this bug, but I get a warning when running docker-compose -f docker/docker-compose.yml config
.
WARNING: The MLFLOW_TRACKING_URI variable is not set. Defaulting to a blank string.
This results in the MLFlow container crashing. I don't know why but reverting from docker-compose version 1.29.2 to 1.27.4 fixed this.
There are a few broken links in the README:
The README could probably also use a few adjustments to come up-to-date.
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