Fix grammar / wording in 2.1

zenml-io · Feb 2, 2023 · 7276ba9 · 7276ba9
1 parent 131bfc1
commit 7276ba9
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diff --git a/1-1_Pipelines.ipynb b/1-1_Pipelines.ipynb
@@ -182,7 +182,7 @@
     "    X_train: np.ndarray,\n",
     "    y_train: np.ndarray,\n",
     ") -> ClassifierMixin:\n",
-    "    \"\"\"Train a sklearn SVC classifier.\"\"\"\n",
+    "    \"\"\"Train an sklearn SVC classifier.\"\"\"\n",
     "    model = SVC(gamma=0.001)\n",
     "    model.fit(X_train, y_train)\n",
     "    return model\n",

diff --git a/1-2_Artifact_Lineage.ipynb b/1-2_Artifact_Lineage.ipynb
@@ -257,7 +257,7 @@
     "    X_train: np.ndarray,\n",
     "    y_train: np.ndarray,\n",
     ") -> ClassifierMixin:\n",
-    "    \"\"\"Train a sklearn decision tree classifier.\"\"\"\n",
+    "    \"\"\"Train an sklearn decision tree classifier.\"\"\"\n",
     "    model = DecisionTreeClassifier()\n",
     "    model.fit(X_train, y_train)\n",
     "    return model\n",

diff --git a/2-1_Experiment_Tracking.ipynb b/2-1_Experiment_Tracking.ipynb
@@ -116,7 +116,7 @@
     "# Register the MLflow experiment tracker\n",
     "!zenml experiment-tracker register mlflow_tracker --flavor=mlflow\n",
     "\n",
-    "# Create a new stack with MLflow experiment tracker in it\n",
+    "# Create a new stack that includes an MLflow experiment\n",
     "!zenml stack register mlflow_exp_tracker_stack -a default -o default -e mlflow_tracker\n",
     "\n",
     "# Set the new stack as active\n",
@@ -152,7 +152,7 @@
     "\n",
     "To integrate the MLFlow experiment tracker into our previously defined ZenML pipeline, we only need to adjust the `svc_trainer` step. Let us define a new `svc_trainer_mlflow` step in which we use MLflow's [`mlflow.sklearn.autolog()`](https://www.mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.autolog) feature to automatically log all relevant attributes and metrics of our model to MLflow. \n",
     "\n",
-    "By adding an `experiment_tracker=mlflow_tracker` parameter in the `@step` decorator, ZenML automatically takes care of correctly initializing MLflow."
+    "By adding an `experiment_tracker=mlflow_tracker` parameter in the `@step` decorator, ZenML automatically takes care of initializing MLflow."
    ]
   },
   {
@@ -172,7 +172,7 @@
     "    X_train: np.ndarray,\n",
     "    y_train: np.ndarray,\n",
     ") -> ClassifierMixin:\n",
-    "    \"\"\"Train a sklearn SVC classifier and log to MLflow.\"\"\"\n",
+    "    \"\"\"Train an sklearn SVC classifier and log to MLflow.\"\"\"\n",
     "    mlflow.sklearn.autolog()  # log all model hparams and metrics to MLflow\n",
     "    model = SVC(gamma=1e-3)\n",
     "    model.fit(X_train, y_train)\n",
@@ -205,7 +205,7 @@
     "    X_train: np.ndarray,\n",
     "    y_train: np.ndarray,\n",
     ") -> ClassifierMixin:\n",
-    "    \"\"\"Train a sklearn decision tree classifier and log to MLflow.\"\"\"\n",
+    "    \"\"\"Train an sklearn decision tree classifier and log to MLflow.\"\"\"\n",
     "    mlflow.sklearn.autolog()  # log all model hparams and metrics to MLflow\n",
     "    model = DecisionTreeClassifier()\n",
     "    model.fit(X_train, y_train)\n",
@@ -319,7 +319,7 @@
     "# Register the W&B experiment tracker\n",
     "!zenml experiment-tracker register wandb_tracker --flavor=wandb --api_key={WANDB_API_KEY} --entity={WANDB_ENTITY} --project_name={WANDB_PROJECT}\n",
     "\n",
-    "# Create a new stack with W&B experiment tracker in it\n",
+    "# Create a new stack that includes a W&B experiment tracker\n",
     "!zenml stack register wandb_stack -a default -o default -e wandb_tracker\n",
     "\n",
     "# Set the new stack as active\n",
@@ -360,7 +360,7 @@
     "    X_train: np.ndarray,\n",
     "    y_train: np.ndarray,\n",
     ") -> ClassifierMixin:\n",
-    "    \"\"\"Train a sklearn SVC classifier and log to W&B.\"\"\"\n",
+    "    \"\"\"Train an sklearn SVC classifier and log to W&B.\"\"\"\n",
     "    gamma = 1e-3\n",
     "    wandb.log({\"gamma\": gamma})  # log gamma hparam to wandb\n",
     "    model = SVC(gamma=gamma)\n",

diff --git a/steps/mlflow_trainer.py b/steps/mlflow_trainer.py
@@ -11,7 +11,7 @@ def svc_trainer_mlflow(
     X_train: np.ndarray,
     y_train: np.ndarray,
 ) -> ClassifierMixin:
-    """Train a sklearn SVC classifier and log to MLflow."""
+    """Train an sklearn SVC classifier and log to MLflow."""
     mlflow.sklearn.autolog()  # log all model hparams and metrics to MLflow
     model = SVC(gamma=0.001)
     model.fit(X_train, y_train)
@@ -23,7 +23,7 @@ def tree_trainer_with_mlflow(
     X_train: np.ndarray,
     y_train: np.ndarray,
 ) -> ClassifierMixin:
-    """Train a sklearn decision tree classifier and log to MLflow."""
+    """Train an sklearn decision tree classifier and log to MLflow."""
     mlflow.sklearn.autolog()  # log all model hparams and metrics to MLflow
     model = DecisionTreeClassifier()
     model.fit(X_train, y_train)

diff --git a/steps/sklearn_trainer.py b/steps/sklearn_trainer.py
@@ -12,7 +12,7 @@ def svc_trainer(
     X_train: np.ndarray,
     y_train: np.ndarray,
 ) -> ClassifierMixin:
-    """Train a sklearn SVC classifier."""
+    """Train an sklearn SVC classifier."""
     model = SVC(gamma=0.001)
     model.fit(X_train, y_train)
     return model
@@ -23,7 +23,7 @@ def tree_trainer(
     X_train: np.ndarray,
     y_train: np.ndarray,
 ) -> ClassifierMixin:
-    """Train a sklearn decision tree classifier."""
+    """Train an sklearn decision tree classifier."""
     model = DecisionTreeClassifier()
     model.fit(X_train, y_train)
     return model