From f52c4985bfc0bc10f016795fe17ee0f48f31beb6 Mon Sep 17 00:00:00 2001 From: jeff-shepherd <39775772+jeff-shepherd@users.noreply.github.com> Date: Thu, 18 Jul 2024 11:24:18 -0700 Subject: [PATCH] Upgrade to sklearn-1.5 (#3293) --- sdk/python/jobs/single-step/pytorch/iris/pytorch-iris.ipynb | 2 +- .../scikit-learn/diabetes/sklearn-diabetes.ipynb | 4 ++-- .../single-step/scikit-learn/iris/iris-scikit-learn.ipynb | 6 +++--- .../jobs/single-step/scikit-learn/mnist/sklearn-mnist.ipynb | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/sdk/python/jobs/single-step/pytorch/iris/pytorch-iris.ipynb b/sdk/python/jobs/single-step/pytorch/iris/pytorch-iris.ipynb index 70599edb3..2b40fdde1 100644 --- a/sdk/python/jobs/single-step/pytorch/iris/pytorch-iris.ipynb +++ b/sdk/python/jobs/single-step/pytorch/iris/pytorch-iris.ipynb @@ -119,7 +119,7 @@ " \"epochs\": 10,\n", " \"lr\": 0.1,\n", " },\n", - " environment=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest\",\n", + " environment=\"AzureML-sklearn-1.5@latest\",\n", " display_name=\"pytorch-iris-example\",\n", " description=\"Train a neural network with PyTorch on the Iris dataset.\",\n", ")" diff --git a/sdk/python/jobs/single-step/scikit-learn/diabetes/sklearn-diabetes.ipynb b/sdk/python/jobs/single-step/scikit-learn/diabetes/sklearn-diabetes.ipynb index e29a14363..adb92d9db 100644 --- a/sdk/python/jobs/single-step/scikit-learn/diabetes/sklearn-diabetes.ipynb +++ b/sdk/python/jobs/single-step/scikit-learn/diabetes/sklearn-diabetes.ipynb @@ -127,7 +127,7 @@ " - Azure ML `data`/`dataset` or `datastore` are of type `uri_folder`. To use `data`/`dataset` as input, you can use registered dataset in the workspace using the format ':'. For e.g Input(type='uri_folder', path='my_dataset:1')\n", " - `mode` - \tMode of how the data should be delivered to the compute target. Allowed values are `ro_mount`, `rw_mount` and `download`. Default is `ro_mount`\n", "- `environment` - This is the environment needed for the command to run. Curated or custom environments from the workspace can be used. Or a custom environment can be created and used as well. Check out the [environment](../../../../assets/environment/environment.ipynb) notebook for more examples.\n", - "- `compute` - The compute on which the command will run. In this example we are using [serverless compute (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python) so there is no need to specify any compute. You can also replace serverless with any other compute in the workspace. You can run it on the local machine by using `local` for the compute. This will run the command on the local machine and all the run details and output of the job will be uploaded to the Azure ML workspace.\n", + "- `compute` - The compute on which the command will run. In this example we are using [serverless compute (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python) so there is no need to specify any compute. You can also replace serverless with any other compute in the workspace. You can run it on the local machine by using `local` for the compute. This will run the command on the local machine and all the run details and output of the job will be uploaded to the Azure ML workspace.\n", "- `distribution` - Distribution configuration for distributed training scenarios. Azure Machine Learning supports PyTorch, TensorFlow, and MPI-based distributed training. The allowed values are `PyTorch`, `TensorFlow` or `Mpi`.\n", "- `display_name` - The display name of the Job\n", "- `description` - The description of the experiment\n" @@ -163,7 +163,7 @@ " path=\"https://azuremlexamples.blob.core.windows.net/datasets/diabetes.csv\",\n", " )\n", " },\n", - " environment=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest\",\n", + " environment=\"AzureML-sklearn-1.5@latest\",\n", " display_name=\"sklearn-diabetes-example\",\n", " # description,\n", " # experiment_name\n", diff --git a/sdk/python/jobs/single-step/scikit-learn/iris/iris-scikit-learn.ipynb b/sdk/python/jobs/single-step/scikit-learn/iris/iris-scikit-learn.ipynb index 845228561..7ac39a2a4 100644 --- a/sdk/python/jobs/single-step/scikit-learn/iris/iris-scikit-learn.ipynb +++ b/sdk/python/jobs/single-step/scikit-learn/iris/iris-scikit-learn.ipynb @@ -127,8 +127,8 @@ " - `path` - The path to the file or folder. These can be local or remote files or folders. For remote files - http/https, wasb are supported. \n", " - Azure ML `data`/`dataset` or `datastore` are of type `uri_folder`. To use `data`/`dataset` as input, you can use registered dataset in the workspace using the format ':'. For e.g Input(type='uri_folder', path='my_dataset:1')\n", " - `mode` - \tMode of how the data should be delivered to the compute target. Allowed values are `ro_mount`, `rw_mount` and `download`. Default is `ro_mount`\n", - "- `environment` - This is the environment needed for the command to run. Curated or custom environments from the workspace can be used. Or a custom environment can be created and used as well. Check out the [environment](../../../../assets/environment/environment.ipynb) notebook for more examples.\n", - "- `compute` - The compute on which the command will run. In this example we are using [serverless compute (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python) so there is no need to specify any compute. You can also replace serverless with any other compute in the workspace. You can run it on the local machine by using `local` for the compute. This will run the command on the local machine and all the run details and output of the job will be uploaded to the Azure ML workspace.\n", + "- `environment` - This is the environment needed for the command to run. Curated or custom environments from the workspace can be used. Or a custom environment can be created and used as well. Check out the [environment](../../../../assets/environment/environment.ipynb) notebook for more examples.\n", + "- `compute` - The compute on which the command will run. In this example we are using [serverless compute (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python) so there is no need to specify any compute. You can also replace serverless with any other compute in the workspace. You can run it on the local machine by using `local` for the compute. This will run the command on the local machine and all the run details and output of the job will be uploaded to the Azure ML workspace.\n", "- `distribution` - Distribution configuration for distributed training scenarios. Azure Machine Learning supports PyTorch, TensorFlow, and MPI-based distributed training. The allowed values are `PyTorch`, `TensorFlow` or `Mpi`.\n", "- `display_name` - The display name of the Job\n", "- `description` - The description of the experiment" @@ -177,7 +177,7 @@ " # }\n", " # ) # uncomment add SSH Public Key to access job container via SSH\n", " },\n", - " environment=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest\",\n", + " environment=\"AzureML-sklearn-1.5@latest\",\n", " display_name=\"sklearn-iris-example\",\n", " # experiment_name\n", " # description\n", diff --git a/sdk/python/jobs/single-step/scikit-learn/mnist/sklearn-mnist.ipynb b/sdk/python/jobs/single-step/scikit-learn/mnist/sklearn-mnist.ipynb index 1a7b951b1..7724b3ebd 100644 --- a/sdk/python/jobs/single-step/scikit-learn/mnist/sklearn-mnist.ipynb +++ b/sdk/python/jobs/single-step/scikit-learn/mnist/sklearn-mnist.ipynb @@ -107,7 +107,7 @@ " - Azure ML `data`/`dataset` or `datastore` are of type `uri_folder`. To use `data`/`dataset` as input, you can use registered dataset in the workspace using the format ':'. For e.g Input(type='uri_folder', path='my_dataset:1')\n", " - `mode` - \tMode of how the data should be delivered to the compute target. Allowed values are `ro_mount`, `rw_mount` and `download`. Default is `ro_mount`\n", "- `environment` - This is the environment needed for the command to run. Curated or custom environments from the workspace can be used. Or a custom environment can be created and used as well. Check out the [environment](../../../../assets/environment/environment.ipynb) notebook for more examples.\n", - "- `compute` - The compute on which the command will run. In this example we are using [serverless compute (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python) so there is no need to specify any compute. You can also replace serverless with any other compute in the workspace. You can run it on the local machine by using `local` for the compute. This will run the command on the local machine and all the run details and output of the job will be uploaded to the Azure ML workspace.\n", + "- `compute` - The compute on which the command will run. In this example we are using [serverless compute (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python) so there is no need to specify any compute. You can also replace serverless with any other compute in the workspace. You can run it on the local machine by using `local` for the compute. This will run the command on the local machine and all the run details and output of the job will be uploaded to the Azure ML workspace.\n", "- `distribution` - Distribution configuration for distributed training scenarios. Azure Machine Learning supports PyTorch, TensorFlow, and MPI-based distributed training. The allowed values are `PyTorch`, `TensorFlow` or `Mpi`.\n", "- `display_name` - The display name of the Job\n", "- `description` - The description of the experiment" @@ -128,7 +128,7 @@ " code=\"./src\", # local path where the code is stored\n", " command=\"pip install -r requirements.txt && python main.py --C ${{inputs.C}} --penalty ${{inputs.penalty}}\",\n", " inputs={\"C\": 0.8, \"penalty\": \"l2\"},\n", - " environment=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest\",\n", + " environment=\"AzureML-sklearn-1.5@latest\",\n", " display_name=\"sklearn-mnist-example\"\n", " # experiment_name: sklearn-mnist-example\n", " # description: Train a scikit-learn LogisticRegression model on the MNSIT dataset.\n",