vessl.configure()
.vessl.create_dataset()
. Let's create a dataset from the public AWS S3 dataset we have prepared: s3://savvihub-public-apne2/mnist
. You can check that your dataset was created successfully by executing the dataset's variable name.vessl.create_experiment()
. Let's run an experiment using VESSL's managed clusters. First, specify the cluster and resource options. Then, specify the image URL β in this case, we are pulling a Docker image from VESSL's Amazon ECR Public Gallery. Next, we are going to mount the dataset we have created previously. Finally, let's specify the start command that will be executed in the experiment container. Here, we will use the MNIST Keras example from our GitHub repository.git clone
every time you create an experiment. For more information about these features, please refer to the project repository & project dataset page.vessl.read_experiment()
.metrics_summary.latest
as follows.vessl.create_model_repository()
and specifying the repository name.vessl.create_model()
with the name and ID of the destination repository and experiment we just created.sweep_objective
with the target metric name and target value. Note that the metric must be a logged to VESSL using vessl.log()
.parameters
. In this example, the optimizer
is a categorical
type and the option values are listed as an array. The batch_size
is an int value and the search space
is set using max, min, and step.vessl.create_sweep()
. You can see in the code below that the options for cluster, resource, image, dataset, and command options has been set similar to the vessl experiment create
explained above.vessl.get_best_sweep_experiment()
returns the experiment information with the best metric value set in sweep_objective
. In this example, this will return the details of the experiment with the maximum val_accuracy
.vessl.read_model()
and specifying the model repository followed by the model number.