90 lines
2.9 KiB
Markdown
90 lines
2.9 KiB
Markdown
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## the basic path
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- [X] make a jupyterlab docker container!
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- [ ] volume mount for notebooks
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- [ ] volume for training data?
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- [ ] get a model working in jupyter notebook
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- [ ] make a dockerfile to set up the base image
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- [ ] script to train and validate the model, packaging up the trained model alongside a report of its accuracy etc.
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- [ ] include the package requirements in the artifact
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- [ ] taking that trained model and putting it in a flask or fastAPI container
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- [ ] how about using my laptop-docker-compose pytest script thing? that's pretty great for what i need
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## next steps
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- [ ] get the data into redis for training -- or another data store?
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- [ ] how about a clean path from jupyter to docker?
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- [ ] CI/CD: get it working with git and webhooks
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- [ ] git workflow with webhooks
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- [ ] what is a good project model? here's a guess:
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- base dockerfiles for conda and build scripts (?)
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- model definition
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- dataset definitions
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- rest API
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- test container
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- [ ] whylogs to track model drift?
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## questions
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- [ ] how do we get from jupyter to docker? what's a clean path?
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- [ ] what's a good practice for making training data available? Just have a redis container?
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- [ ] does it make sense to have each component be a separate CI/CD managed project?
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- data ingest (for each dataset?)
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- feature extraction
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- labeling
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- jupyter notebook for experimentation
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- converter for jupyter-> app model (or do we just rely on people copypasting)
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- model trainer (+verification?)
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- [ ] maybe we just define a production application and worry about the rest later?
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- model + API (or messaging system)
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- [ ] what's a good way to get project dependencies into a jupyter container?
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- [ ] does a setup like above help with parameter optimization?
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- [ ] should an optimizer be its own containerized service?
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- [ ] how can we reuse feature extraction for regular model input?
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- [ ] should a feature extractor be a service?
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## What needs doing?
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- [ ] step one is writing out all these steps!
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- [ ] docker image build pipeline:
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- [ ] miniconda image
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- [ ] how do we pull in the requirements for a particular model?
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- honestly just ship a Dockerfile alongside the model
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- but it needs to pull from a manifest that an individual developer using something like jupyter would use
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- [ ] verify this part of the pipeline according to the training materials
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- [ ] feature extraction
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- [ ] training phase
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- [ ] packaging up a pre-trained model artifact
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- [ ] CI/CD
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[ ] webhook plugin for gitea? https://github.com/adnanh/webhook
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- [ ] missing from the course????
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- [ ] training data store!
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- [ ] labeling phase (is that the right term?) -- unattended?
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- [ ] feature extraction phase
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## refer:
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- https://github.com/docker-science/cookiecutter-docker-science?tab=readme-ov-file
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- https://docker-science.github.io/
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