DVC 数据版本管理入门
Ravi Bhadauria
Machine Learning Engineer
定义机器学习流程与依赖的阶段序列
定义于 dvc.yaml 文件
deps)params)cmd)outs)metrics 与 plotsdvc stage add 创建阶段dvc stage add \
-n preprocess \
-p params.yaml:preprocess \
-d raw_data.csv \
-d preprocess.py \
-o processed_data.csv \
python3 preprocess.py
stages:
preprocess:
cmd: python3 preprocess.py
params:
# 来自 params.yaml 的键
- params.yaml
- preprocess
deps:
- preprocess.py
- raw_data.csv
outs:
- processed_data.csv
dvc stage add \
-n train_and_evaluate \
-p train_and_evaluate \
-d train_and_evaluate.py \
-d processed_data.csv \
-o plots.png \
-o metrics.json \
python3 train_and_evaluate.py
stages:
train_and_evaluate:
cmd: python3 train_and_evaluate.py
params:
# 无需指定参数文件
# 默认为 params.yaml
- train_and_evaluate
deps:
- processed_data.csv
- train_and_evaluate.py
outs:
- plots.png
- metrics.json
dvc stage addERROR: Stage 'train_and_evaluate'
already exists in 'dvc.yaml'.
Use '--force' to overwrite.
dvc stage add --forcedvc stage add --force \
-n train_and_evaluate \
-p train_and_evaluate \
-d train_and_evaluate.py \
-d processed_data.csv \
-o plots.png \
-o metrics.json \
python3 train_and_evaluate.py
# 在终端打印 DAG
dvc dag
# 显示至指定步骤的 DAG
dvc dag <target>
+------------+
| preprocess |
+------------+
*
*
*
+--------------------+
| train_and_evaluate |
+--------------------+
# 将步骤输出显示为节点
dvc dag --outs
+-------------------------------+
| processed_dataset/weather.csv |
+-------------------------------+
*** ***
*** ***
** **
+--------------+ +-----------+
| metrics.json | | plots.png |
+--------------+ +-----------+
dvc dag --dot
strict digraph {
"preprocess";
"train_and_evaluate";
"preprocess" -> "train_and_evaluate";
}

DVC 数据版本管理入门