Comments (6)
For now we are building docker images only for serving, so server option is required and run command is mlem serve
.
That can be changed, but for that we first need to understand what it means to build docker for batch scoring
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For the reference, there is Airflow extension that adds support for DVC operations: https://github.com/covid-genomics/airflow-dvc
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UPD: we can take a look at other orchestration tools (dagster, prefect, etc.)
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Posting here a short discussion with @mike0sv:
mlem apply model hdfs://…
should work already thanks to fsspec - need to check this though- if map-reduce saved many files that should be merged, the above command may not work. We may implement special
Reader
in MLEM that will receive many artifacts, will merge them in a single dataframe beforeapply
(or will output each file as a batch) - to
apply
the models in spark, we can create a UDF for MLEM models - we can think about supporting
sparkml
in MLEM - we can implement export to spark UDF model that will start
spark
and run the data through it when you domlem apply spark-udf-model …
(could have a wow-effect) - with hadoop we can implement
Reader
s as well, that will startspark
and read data in batches
To my mind, we need to get the working demo as simple as possible first. If we can work with mlem apply model hdfs://…
- nice, this can be used. If not, downloading data as csv and using mlem apply model data.csv
can work as well. @mnrozhkov mentioned they have a project with batch scoring in GitLab, we can start with using MLEM there.
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One issue I found: when you want to build a docker image for Batch Scoring scenario, you absolutely need to specify --server
option. Why? Let's either specify fastapi
as default so people won't need to confuse themselves about this, or allow to build without any server to skip installing extra dependencies like FastAPI people may not need.
UPD: it was an issue with my local MLEM installation. We don't need to specify --server
. By default it's FASTAPI. Maybe good to give an option to skip installing it anyway, but this is not a priority now.
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For reference, a product similar to MLEM that does export to Airflow Pipelines. Need to take a deeper look at it https://docs.lineapy.org/en/latest/guide/build_pipelines/pipeline_basics.html
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Related Issues (20)
- flyctl deployment failure: org slug must be specified when not running interactively HOT 5
- `link` won't accept --rev flag / unexpected 404 HOT 5
- Expose custom information in FastAPI's `interface.json` HOT 1
- Expose GTO's model version in FastAPI's `interface.json` HOT 2
- Requirements aren't found when using `FunctionTransformer` HOT 1
- Confusing error in `deploy run flyio` when app exists HOT 2
- Bug: Validation error when loading linked model
- Pin flyctl version to <0.1.0 in MLEM
- Bug: image_name is missing from command arguments
- Use `classify-imports` instead of `isort` for import classification HOT 1
- Bug: Mlem couldn’t fetch your accounts information HOT 2
- Get started feedback
- How to use callbacks HOT 1
- fast.ai support HOT 2
- Fixing version collection HOT 2
- No clear way to overwrite requirements inferred by MLEM
- Uploading docker image is suboptimal
- Inferencing types at saving is not feasible sometimes
- Right way to add authentification middleware
- NameError: name 'UUID' is not defined HOT 3
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