![]() Log_dir: "output/tblogs" # relative path of Tensorboard logs (same as in your training script) Values are "all", or compute node index (for ex. If `nodes` are not selected, by default, interactive applications are only enabled on the head node. Nodes: all # For distributed jobs, use the `nodes` property to pick which node you want to enable interactive services on. # you can add a command like "sleep 1h" to reserve the compute resource is reserved after the script finishes running.Įnvironment: azureml:AzureML-tensorflow-2.4-ubuntu18.04-p圓7-cuda11-gpu:41 ![]() If you want to use custom environment, follow the examples in this tutorial to create a custom environment. Make sure to replace your compute name with your own value. For more details on how to train with the Python SDKv2, check out this article.Ĭreate a job yaml job.yaml with below sample content. You can also use the sleep infinity command that would keep the job alive indefinitely. You can put sleep at the end of your command to specify the amount of time you want to reserve the compute resource. The services section specifies the training applications you want to interact with. Returned_job = ml_or_update(command_job) Log_dir="output/tblogs" # relative path of Tensorboard logs (same as in your training script) Nodes="all" # For distributed jobs, use the `nodes` property to pick which node you want to enable interactive services on. command_job = command(.Ĭode="./src", # local path where the code is storedĬommand="python main.py", # you can add a command like "sleep 1h" to reserve the compute resource is reserved after the script finishes JupyterLabJobService( Note that you have to import the JobService class from the azure.ai.ml.entities package to configure interactive services via the SDKv2. If you want to use your own custom environment, follow the examples in this tutorial to create a custom environment.
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