mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-22 02:25:12 -05:00
update samples from Release-58 as a part of SDK release
This commit is contained in:
amlrelsa-ms
@@ -204,108 +204,9 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Option 1: Provision as a run based compute target\n",
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"### Option 1: Provision a compute target (Basic)\n",
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"\n",
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"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.runconfig import RunConfiguration\n",
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"from azureml.core.conda_dependencies import CondaDependencies\n",
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"from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
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"\n",
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"# create a new runconfig object\n",
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"run_config = RunConfiguration()\n",
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"\n",
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"# signal that you want to use AmlCompute to execute script.\n",
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"run_config.target = \"amlcompute\"\n",
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"\n",
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"# AmlCompute will be created in the same region as workspace\n",
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"# Set vm size for AmlCompute\n",
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"run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
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"\n",
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"# enable Docker \n",
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"run_config.environment.docker.enabled = True\n",
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"\n",
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"# set Docker base image to the default CPU-based image\n",
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"run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
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"\n",
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"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
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"run_config.environment.python.user_managed_dependencies = False\n",
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"\n",
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"azureml_pip_packages = [\n",
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" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
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" 'azureml-interpret', 'sklearn-pandas', 'azureml-dataprep'\n",
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"]\n",
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"\n",
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"# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
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"# In production scenario user would choose their dependencies\n",
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"import pkg_resources\n",
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"available_packages = pkg_resources.working_set\n",
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"sklearn_ver = None\n",
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"pandas_ver = None\n",
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"for dist in available_packages:\n",
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" if dist.key == 'scikit-learn':\n",
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" sklearn_ver = dist.version\n",
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" elif dist.key == 'pandas':\n",
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" pandas_ver = dist.version\n",
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"sklearn_dep = 'scikit-learn'\n",
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"pandas_dep = 'pandas'\n",
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"if sklearn_ver:\n",
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" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
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"if pandas_ver:\n",
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" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
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"# specify CondaDependencies obj\n",
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"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
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"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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"# cause errors. Please take extra care when specifying your dependencies in a production environment.\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[sklearn_dep, pandas_dep],\n",
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" pip_packages=azureml_pip_packages)\n",
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"\n",
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"# Now submit a run on AmlCompute\n",
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"from azureml.core.script_run_config import ScriptRunConfig\n",
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"\n",
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"script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
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" script='train_explain.py',\n",
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" run_config=run_config)\n",
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"\n",
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"run = experiment.submit(script_run_config)\n",
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"\n",
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"# Show run details\n",
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"run"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Shows output of the run on stdout.\n",
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"run.wait_for_completion(show_output=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"### Option 2: Provision as a persistent compute target (Basic)\n",
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"\n",
|
||||
"You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
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"\n",
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"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
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"* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
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@@ -351,13 +252,13 @@
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"from azureml.core.runconfig import RunConfiguration\n",
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"from azureml.core.conda_dependencies import CondaDependencies\n",
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"\n",
|
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"# create a new RunConfig object\n",
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"# Create a new RunConfig object\n",
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"run_config = RunConfiguration(framework=\"python\")\n",
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"\n",
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"# Set compute target to AmlCompute target created in previous step\n",
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"run_config.target = cpu_cluster.name\n",
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"\n",
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"# enable Docker \n",
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"# Enable Docker \n",
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"run_config.environment.docker.enabled = True\n",
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"\n",
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"azureml_pip_packages = [\n",
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@@ -382,7 +283,7 @@
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" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
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"if pandas_ver:\n",
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" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
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"# specify CondaDependencies obj\n",
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"# Specify CondaDependencies obj\n",
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"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
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"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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@@ -400,6 +301,13 @@
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"run"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@@ -424,7 +332,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Option 3: Provision as a persistent compute target (Advanced)\n",
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"### Option 2: Provision a compute target (Advanced)\n",
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"\n",
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"You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
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"\n",
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@@ -483,13 +391,13 @@
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"from azureml.core.runconfig import RunConfiguration\n",
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"from azureml.core.conda_dependencies import CondaDependencies\n",
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"\n",
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"# create a new RunConfig object\n",
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"# Create a new RunConfig object\n",
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"run_config = RunConfiguration(framework=\"python\")\n",
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"\n",
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"# Set compute target to AmlCompute target created in previous step\n",
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"run_config.target = cpu_cluster.name\n",
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"\n",
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"# enable Docker \n",
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"# Enable Docker \n",
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"run_config.environment.docker.enabled = True\n",
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"\n",
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"azureml_pip_packages = [\n",
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@@ -516,7 +424,7 @@
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" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
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"if pandas_ver:\n",
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" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
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"# specify CondaDependencies obj\n",
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||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
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||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
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@@ -554,19 +462,6 @@
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"run.get_metrics()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
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"\n",
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"client = ExplanationClient.from_run(run)\n",
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"# Get the top k (e.g., 4) most important features with their importance values\n",
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"explanation = client.download_model_explanation(top_k=4)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@@ -682,7 +577,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# retrieve model for visualization and deployment\n",
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"# Retrieve model for visualization and deployment\n",
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"from azureml.core.model import Model\n",
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"import joblib\n",
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"original_model = Model(ws, 'model_explain_model_on_amlcomp')\n",
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@@ -703,7 +598,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# retrieve x_test for visualization\n",
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"# Retrieve x_test for visualization\n",
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"import joblib\n",
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"x_test_path = './x_test_boston_housing.pkl'\n",
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"run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)"
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@@ -122,7 +122,7 @@
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"# get the IBM employee attrition dataset\n",
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"# Get the IBM employee attrition dataset\n",
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"outdirname = 'dataset.6.21.19'\n",
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"try:\n",
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" from urllib import urlretrieve\n",
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@@ -163,7 +163,7 @@
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"from sklearn.model_selection import train_test_split\n",
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"x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
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||||
" target, \n",
|
||||
" test_size = 0.2,\n",
|
||||
" test_size=0.2,\n",
|
||||
" random_state=0,\n",
|
||||
" stratify=target)"
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]
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@@ -223,7 +223,7 @@
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"# Append classifier to preprocessing pipeline.\n",
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"# Now we have a full prediction pipeline.\n",
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"clf = Pipeline(steps=[('preprocessor', transformations),\n",
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" ('classifier', SVC(kernel='linear', C = 1.0, probability=True))])"
|
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" ('classifier', SVC(C=1.0, probability=True))])"
|
||||
]
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||||
},
|
||||
{
|
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@@ -249,7 +249,7 @@
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"# Append classifier to preprocessing pipeline.\n",
|
||||
"# Now we have a full prediction pipeline.\n",
|
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"clf = Pipeline(steps=[('preprocessor', transformations),\n",
|
||||
" ('classifier', SVC(kernel='linear', C = 1.0, probability=True))]) \n",
|
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" ('classifier', SVC(C=1.0, probability=True))]) \n",
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"\n",
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"\n",
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"\n",
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@@ -393,7 +393,7 @@
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"# feature shap values for all features and all data points in the training data\n",
|
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"# Feature shap values for all features and all data points in the training data\n",
|
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"print('local importance values: {}'.format(global_explanation.local_importance_values))"
|
||||
]
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},
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@@ -450,8 +450,7 @@
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||||
"outputs": [],
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||||
"source": [
|
||||
"import azureml.core\n",
|
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"from azureml.core import Workspace, Experiment, Run\n",
|
||||
"from interpret.ext.blackbox import TabularExplainer\n",
|
||||
"from azureml.core import Workspace, Experiment\n",
|
||||
"from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
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@@ -576,6 +575,23 @@
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"ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
|
||||
]
|
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"## End\n",
|
||||
"Complete the run"
|
||||
]
|
||||
},
|
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{
|
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"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.complete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
||||
@@ -141,7 +141,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get IBM attrition data\n",
|
||||
"# Get IBM attrition data\n",
|
||||
"import os\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
@@ -218,17 +218,17 @@
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n",
|
||||
" target,\n",
|
||||
" test_size = 0.2,\n",
|
||||
" test_size=0.2,\n",
|
||||
" random_state=0,\n",
|
||||
" stratify=target)\n",
|
||||
"\n",
|
||||
"# preprocess the data and fit the classification model\n",
|
||||
"# Preprocess the data and fit the classification model\n",
|
||||
"clf.fit(x_train, y_train)\n",
|
||||
"model = clf.steps[-1][1]\n",
|
||||
"\n",
|
||||
"model_file_name = 'log_reg.pkl'\n",
|
||||
"\n",
|
||||
"# save model in the outputs folder so it automatically get uploaded\n",
|
||||
"# Save model in the outputs folder so it automatically get uploaded\n",
|
||||
"with open(model_file_name, 'wb') as file:\n",
|
||||
" joblib.dump(value=clf, filename=os.path.join('./outputs/',\n",
|
||||
" model_file_name))"
|
||||
@@ -345,7 +345,7 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
@@ -368,7 +368,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"# retrieve scoring explainer for deployment\n",
|
||||
"# Retrieve scoring explainer for deployment\n",
|
||||
"scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
|
||||
]
|
||||
},
|
||||
@@ -416,11 +416,11 @@
|
||||
"\n",
|
||||
"headers = {'Content-Type':'application/json'}\n",
|
||||
"\n",
|
||||
"# send request to service\n",
|
||||
"# Send request to service\n",
|
||||
"print(\"POST to url\", service.scoring_uri)\n",
|
||||
"resp = requests.post(service.scoring_uri, sample_data, headers=headers)\n",
|
||||
"\n",
|
||||
"# can covert back to Python objects from json string if desired\n",
|
||||
"# Can covert back to Python objects from json string if desired\n",
|
||||
"print(\"prediction:\", resp.text)\n",
|
||||
"result = json.loads(resp.text)"
|
||||
]
|
||||
@@ -431,7 +431,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#plot the feature importance for the prediction\n",
|
||||
"# Plot the feature importance for the prediction\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt; plt.rcdefaults()\n",
|
||||
"\n",
|
||||
|
||||
@@ -156,7 +156,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Submit an AmlCompute run in a few different ways\n",
|
||||
"## Submit an AmlCompute run\n",
|
||||
"\n",
|
||||
"First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
|
||||
"\n",
|
||||
@@ -202,9 +202,43 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Provision as a run based compute target\n",
|
||||
"### Provision a compute target\n",
|
||||
"\n",
|
||||
"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
|
||||
"You can provision an AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
|
||||
"\n",
|
||||
"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
|
||||
"* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your CPU cluster\n",
|
||||
"cpu_cluster_name = \"cpu-cluster\"\n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
|
||||
" max_nodes=4)\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
"cpu_cluster.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure & Run"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -217,28 +251,21 @@
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# create a new runconfig object\n",
|
||||
"# Create a new runconfig object\n",
|
||||
"run_config = RunConfiguration()\n",
|
||||
"\n",
|
||||
"# signal that you want to use AmlCompute to execute script.\n",
|
||||
"run_config.target = \"amlcompute\"\n",
|
||||
"# Set compute target to AmlCompute target created in previous step\n",
|
||||
"run_config.target = cpu_cluster.name\n",
|
||||
"\n",
|
||||
"# AmlCompute will be created in the same region as workspace\n",
|
||||
"# Set vm size for AmlCompute\n",
|
||||
"run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
|
||||
"\n",
|
||||
"# enable Docker \n",
|
||||
"# Enable Docker \n",
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"# set Docker base image to the default CPU-based image\n",
|
||||
"# Set Docker base image to the default CPU-based image\n",
|
||||
"run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
|
||||
"# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
|
||||
"run_config.environment.python.user_managed_dependencies = False\n",
|
||||
"\n",
|
||||
"# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
|
||||
"run_config.auto_prepare_environment = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'azureml-dataprep'\n",
|
||||
@@ -263,7 +290,7 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
@@ -327,7 +354,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve model for visualization and deployment\n",
|
||||
"# Retrieve model for visualization and deployment\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"import joblib\n",
|
||||
"original_model = Model(ws, 'amlcompute_deploy_model')\n",
|
||||
@@ -341,7 +368,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve global explanation for visualization\n",
|
||||
"# Retrieve global explanation for visualization\n",
|
||||
"from azureml.contrib.interpret.explanation.explanation_client import ExplanationClient\n",
|
||||
"\n",
|
||||
"# get model explanation data\n",
|
||||
@@ -355,7 +382,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve x_test for visualization\n",
|
||||
"# Retrieve x_test for visualization\n",
|
||||
"import joblib\n",
|
||||
"x_test_path = './x_test.pkl'\n",
|
||||
"run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
|
||||
@@ -435,7 +462,7 @@
|
||||
" sklearn_dep = 'scikit-learn=={}'.format(sklearn_ver)\n",
|
||||
"if pandas_ver:\n",
|
||||
" pandas_dep = 'pandas=={}'.format(pandas_ver)\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# Specify CondaDependencies obj\n",
|
||||
"# The CondaDependencies specifies the conda and pip packages that are installed in the environment\n",
|
||||
"# the submitted job is run in. Note the remote environment(s) needs to be similar to the local\n",
|
||||
"# environment, otherwise if a model is trained or deployed in a different environment this can\n",
|
||||
@@ -457,7 +484,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# retrieve scoring explainer for deployment\n",
|
||||
"# Retrieve scoring explainer for deployment\n",
|
||||
"scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
|
||||
]
|
||||
},
|
||||
@@ -496,17 +523,17 @@
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"\n",
|
||||
"# create data to test service with\n",
|
||||
"# Create data to test service with\n",
|
||||
"examples = x_test[:4]\n",
|
||||
"input_data = examples.to_json()\n",
|
||||
"\n",
|
||||
"headers = {'Content-Type':'application/json'}\n",
|
||||
"\n",
|
||||
"# send request to service\n",
|
||||
"# Send request to service\n",
|
||||
"print(\"POST to url\", service.scoring_uri)\n",
|
||||
"resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
|
||||
"\n",
|
||||
"# can covert back to Python objects from json string if desired\n",
|
||||
"# Can covert back to Python objects from json string if desired\n",
|
||||
"print(\"prediction:\", resp.text)"
|
||||
]
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user