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let x, y;
x = 5;
y = 6; 

document.getElementById("demo").innerHTML = "The value of y is " + y + ".";

joannapurosto

from airflow.operators.valohai import ValohaiSubmitExecutionOperator

train = ValohaiSubmitExecutionOperator(
      task_id='train_model',
      # Specify the project and step from Valohai
      project_name='tensorflow-example',
      step='Train model (MNIST)',
      # Associate a task to a previously created DAG
      dag=dag,
)

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$ vh exec info
$ vh exec logs
$ vh exec watch

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[  1] mlpmnist-single-layer
...
Which project would you like to link with /path/to/mlpmnist-dl4j-example?
Enter [n] to create a new project.:

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$ cd mlpmnist-dl4j-example
$ vh project --help   ### to see all the project-specific options we have for Valohai
$ vh project link 

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    public void iterationDone(Model model, int iteration, int epoch) {
        if (printIterations <= 0)
            printIterations = 1;
        if (iteration % printIterations == 0) {
            double score = model.score();
            System.out.println(String.format(
                    "{\"epoch\": %d, \"iteration\": %d, \"score (loss function)\": %f}",
                    epoch,
                    iteration,
                    score)

joannapurosto

[<--- snipped --->]
11:17:05 {"epoch": 0, "iteration": 0, "score (loss function)": 2.410047}
11:17:07 {"epoch": 0, "iteration": 100, "score (loss function)": 0.613774}
11:17:09 {"epoch": 0, "iteration": 200, "score (loss function)": 0.528494}
11:17:11 {"epoch": 0, "iteration": 300, "score (loss function)": 0.400291}
11:17:13 {"epoch": 0, "iteration": 400, "score (loss function)": 0.357800}
11:17:14 o.d.o.l.CheckpointListener - Model checkpoint saved: epoch 0, iteration 469, path: /valohai/outputs/checkpoint_0_MultiLayerNetwork.zip
[<--- snipped --->]

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configuration.json
coefficients.bin
updaterState.bin

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[<--- snipped --->]
11:17:14 o.d.o.l.CheckpointListener - Model checkpoint saved: epoch 0, iteration 469, path: /valohai/outputs/checkpoint_0_MultiLayerNetwork.zip
[<--- snipped --->]

joannapurosto

[<--- snipped --->]
11:17:05 =========================================================================
11:17:05 LayerName (LayerType) nIn,nOut TotalParams ParamsShape
11:17:05 =========================================================================
11:17:05 layer0 (DenseLayer) 784,1000 785000 W:{784,1000}, b:{1,1000}
11:17:05 layer1 (OutputLayer) 1000,10 10010 W:{1000,10}, b:{1,10}
11:17:05 -------------------------------------------------------------------------
11:17:05  Total Parameters: 795010
11:17:05  Trainable Parameters: 795010
11:17:05  Frozen Parameters: 0