Directories
¶
| Path | Synopsis |
|---|---|
|
Package builtin is Hermod's built-in ML engine: datasets, training, versions and predictions in Hermod's own process, in pure Go, with no hermod-ml worker to run.
|
Package builtin is Hermod's built-in ML engine: datasets, training, versions and predictions in Hermod's own process, in pure Go, with no hermod-ml worker to run. |
|
learn
Package learn holds the built-in ML engine's algorithms, in pure Go: ridge and logistic regression, a random forest, gradient-boosted trees and a small multilayer perceptron, over the same preprocessing the hermod-ml worker applies (ml/worker/hermod_ml/training.py build_pipeline): a missing number gets the training median, numbers are standardised for the linear model and the network, and text is one-hot encoded.
|
Package learn holds the built-in ML engine's algorithms, in pure Go: ridge and logistic regression, a random forest, gradient-boosted trees and a small multilayer perceptron, over the same preprocessing the hermod-ml worker applies (ml/worker/hermod_ml/training.py build_pipeline): a missing number gets the training median, numbers are standardised for the linear model and the network, and text is one-hot encoded. |
|
table
Package table is the built-in ML engine's dataset in memory: typed columns, read from JSON rows, CSV or Excel, with the hermod-ml worker's typing rules (ml/worker/hermod_ml/datasets.py), so the same file gives the same columns on either engine.
|
Package table is the built-in ML engine's dataset in memory: typed columns, read from JSON rows, CSV or Excel, with the hermod-ml worker's typing rules (ml/worker/hermod_ml/datasets.py), so the same file gives the same columns on either engine. |
|
Package inference calls a model server and turns its answer back into rows.
|
Package inference calls a model server and turns its answer back into rows. |
|
Package onnxscore evaluates small tabular ONNX models in pure Go: the linear models and tree ensembles scikit-learn and XGBoost export through skl2onnx and onnxmltools, with the preprocessing glue those exports carry.
|
Package onnxscore evaluates small tabular ONNX models in pure Go: the linear models and tree ensembles scikit-learn and XGBoost export through skl2onnx and onnxmltools, with the preprocessing glue those exports carry. |
|
Package profile describes a dataset for someone deciding what to do with it: per column its kind, how much is missing, how many different values it has, and its numbers, top values or dates with a small chart; the data's problems in plain words, each with how to fix it; and the numeric columns that move together.
|
Package profile describes a dataset for someone deciding what to do with it: per column its kind, how much is missing, how many different values it has, and its numbers, top values or dates with a small chart; the data's problems in plain words, each with how to fix it; and the numeric columns that move together. |
|
Package recommend serves the recommendation models the hermod-ml worker trains (ml/worker/hermod_ml/recommend.py) in-process: it parses a version's artifact once and answers "which items next for this person" and "which items are like this one" from it, without a call to the worker.
|
Package recommend serves the recommendation models the hermod-ml worker trains (ml/worker/hermod_ml/recommend.py) in-process: it parses a version's artifact once and answers "which items next for this person" and "which items are like this one" from it, without a call to the worker. |
|
Package worker is Hermod's client for its hermod-ml worker: the Python process that holds datasets, trains models on them, exports each one to ONNX and serves it over the Open Inference Protocol.
|
Package worker is Hermod's client for its hermod-ml worker: the Python process that holds datasets, trains models on them, exports each one to ONNX and serves it over the Open Inference Protocol. |
Click to show internal directories.
Click to hide internal directories.