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In this example, we train a Pytorch Lightning model to predict handwritten digits, leveraging early stopping.
The code, adapted from this repository, is almost entirely dedicated to model training, with the addition of a single mlflow.pytorch.autolog()
call to enable automatic logging of params, metrics, and models,
including the best model from early stopping.
To run the example via MLflow, navigate to the mlflow/examples/pytorch/MNIST/example1
directory and run the command
mlflow run .
This will run mnist_autolog_example1.py
with the default set of parameters such as --max_epochs=5
. You can see the default value in the MLproject
file.
In order to run the file with custom parameters, run the command
mlflow run . -P max_epochs=X
where X
is your desired value for max_epochs
.
If you have the required modules for the file and would like to skip the creation of a conda environment, add the argument --no-conda
.
mlflow run . --no-conda
Once the code is finished executing, you can view the run's metrics, parameters, and details by running the command
mlflow ui
and navigating to http://localhost:5000.
For more details on MLflow tracking, see the docs.
The parameters can be overridden via the command line:
For example:
mlflow run . -P max_epochs=5 -P gpus=1 -P batch_size=32 -P num_workers=2 -P learning_rate=0.01 -P accelerator="ddp" -P patience=5 -P mode="min" -P monitor="val_loss" -P verbose=True
Or to run the training script directly with custom parameters:
python mnist_autolog_example1.py \
--max_epochs 5 \
--gpus 1 \
--accelerator "ddp" \
--batch_size 64 \
--num_workers 3 \
--lr 0.001 \
--es_patience 5 \
--es_mode "min" \
--es_monitor "val_loss" \
--es_verbose True
To configure MLflow to log to a custom (non-default) tracking location, set the MLFLOW_TRACKING_URI environment variable, e.g. via export MLFLOW_TRACKING_URI=http://localhost:5000/. For more details, see the docs.
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Are you sure you want to delete this access key?
Are you sure you want to delete this access key?