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# ask-ai
j

Jueming Liu

03/10/2024, 1:20 PM
@Scout how to incorporate mlflow into dagster
s

Scout

03/10/2024, 1:20 PM
Incorporating MLflow into Dagster can be achieved by using the
dagster-mlflow
library, which provides a set of abstractions for using MLflow with Dagster. The documentation mentions that there are API docs available for
dagster-mlflow
which you can refer to for detailed guidance on integration. Here's a high-level overview of how you might incorporate MLflow into a Dagster project: 1. Install the
dagster-mlflow
library
: You need to install the
dagster-mlflow
library to get started. This can be done using pip:
Copy code
shell    pip install dagster-mlflow
2. Configure MLflow within Dagster: You will need to set up MLflow tracking within your Dagster project. This typically involves configuring an MLflow tracking URI and possibly setting up an MLflow experiment. 3. Use MLflow within Dagster assets: Within your Dagster assets, you can use MLflow's API to log parameters, metrics, and artifacts. This can be done by importing MLflow and using its API calls such as
mlflow.log_param
,
mlflow.log_metric
, and
mlflow.log_artifact
. 4. Manage MLflow runs: You can manage MLflow runs within Dagster by starting and ending runs within the asset's execution context. This can be done using
mlflow.start_run
and
mlflow.end_run
. 5. View MLflow tracking UI: MLflow provides a UI to view the experiments and runs. You can use this to monitor the MLflow artifacts and metrics that are logged from your Dagster assets. Please refer to the
dagster-mlflow
API documentation for specific code examples and detailed instructions on how to set up and use MLflow with Dagster. The documentation will provide the most accurate and up-to-date information on how to integrate these tools effectively.
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