Merge pull request rasbt#29 from rasbt/xgboost-cloud-gpu

xgboost-gpu

cloud-resources/xgboost-lightning-gpu/README.md

@@ -0,0 +1,24 @@
+# Training an XGBoost Classifier Using Cloud GPUs Without Worrying About Infrastructure
+
+
+
+Code accompanying the blog article: [Training an XGBoost Classifier Using Cloud GPUs Without Worrying About Infrastructure]()
+
+
+
+Run code as follows
+
+
+
+```pip install lightning
+# run XGBoost classifier locally
+python my_xgboost_classifier.py 
+
+# run XGBoost classifier locally via Lightning (if you have a GPU)
+pip install lightning
+lightning run app xgboost-cloud-gpu.py --setup
+
+# run XGBoost in Lightning cloud on a V100
+lightning run app xgboost-cloud-gpu.py --cloud
+```
+

cloud-resources/xgboost-lightning-gpu/my_xgboost_classifier.py

@@ -0,0 +1,30 @@
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from xgboost import XGBClassifier
+from joblib import dump
+
+
+def run_classifier(save_as="my_model.joblib", use_gpu=False):
+    digits = datasets.load_digits()
+    features, targets = digits.images, digits.target
+    features = features.reshape(-1, 8*8)
+
+    X_train, X_test, y_train, y_test = train_test_split(features, targets, test_size=0.2, random_state=123)
+
+    if use_gpu:
+        model = XGBClassifier(tree_method='gpu_hist', gpu_id=0)
+    else:
+        model = XGBClassifier()
+
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+
+    accuracy = accuracy_score(y_test, y_pred)
+    print(f"Accuracy: {accuracy * 100.0:.2f}%")
+
+    dump(model, filename=save_as)
+
+
+if __name__ == "__main__":
+    run_classifier()

cloud-resources/xgboost-lightning-gpu/xgboost-cloud-gpu.py

@@ -0,0 +1,26 @@
+#!pip install xgboost
+#!pip install scikit-learn
+
+import lightning as L
+from lightning.app.storage import Drive
+from my_xgboost_classifier import run_classifier
+
+
+class RunCode(L.LightningWork):
+    def __init__(self):
+
+        # available GPUs and costs: https://lightning.ai/pricing/consumption-rates
+        super().__init__(cloud_compute=L.CloudCompute("gpu-fast", disk_size=10))
+
+        # storage for outputs
+        self.model_storage = Drive("lit://checkpoints")
+
+    def run(self):
+        # run model code
+        model_path = "my_model.joblib"
+        run_classifier(save_as=model_path, use_gpu=True)
+        self.model_storage.put(model_path)
+
+
+component = RunCode()
+app = L.LightningApp(component)

math/Four-matrix-multiplications.ipynb

@@ -203,7 +203,7 @@
    "id": "72162938",
    "metadata": {},
    "source": [
-    "### 4.1) Columns times rows with dot products (matmuls)"
+    "## 4.1) Columns times rows with dot products (matmuls)"
    ]
   },
   {
@@ -251,9 +251,10 @@
    "id": "5b4ae027-58ce-45e5-81ab-7babb030197d",
    "metadata": {},
    "source": [
-    "### 4.2) Columns times rows with outer products\n",
+    "## 4.2) Columns times rows with outer products\n",
     "\n",
-    "For each row in A and each column in B, compute the outer product. The matrix AB is the sum over all these outer products."
+    "For each row in A and each column in B, compute the outer product. \n",
+    "The matrix AB is the sum over all these outer products."
    ]
   },
   {