Spaces:
Running
Running
Commit ·
e7f46d8
1
Parent(s): 8d8acb9
Optimize HF login and silence redundant log notes
Browse files- matrix/Dockerfile +0 -34
- matrix/benchmark.py +18 -34
- matrix/catboost_info/catboost_training.json +201 -201
- matrix/catboost_info/learn/events.out.tfevents +2 -2
- matrix/catboost_info/learn_error.tsv +201 -201
- matrix/catboost_info/time_left.tsv +200 -200
- requirements.txt +0 -2
matrix/Dockerfile
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FROM python:3.11-slim
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# Create user to run the app
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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WORKDIR $HOME/app
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# Install system dependencies (e.g. for lightgbm/xgboost)
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USER root
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential \
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libgomp1 \
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git \
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&& rm -rf /var/lib/apt/lists/*
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USER user
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# Copy the entire project
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COPY --chown=user . $HOME/app/
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# Install python dependencies
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RUN pip install --no-cache-dir --upgrade pip
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RUN pip install --no-cache-dir -r webapp/requirements.txt
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# Install SAP-RPT-1 OSS directly from GitHub (needed for the real model)
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RUN pip install --no-cache-dir git+https://github.com/SAP-samples/sap-rpt-1-oss.git
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# Expose port 7860 (Hugging Face Spaces default port)
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EXPOSE 7860
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# Run the FastAPI app
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CMD ["python", "-m", "uvicorn", "webapp.main:app", "--host", "0.0.0.0", "--port", "7860"]
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matrix/benchmark.py
CHANGED
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@@ -23,6 +23,21 @@ N_FOLDS = int(os.getenv("N_FOLDS", "5"))
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RAND = int(os.getenv("RANDOM_STATE", "42"))
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HF_TOKEN = os.getenv("HUGGING_FACE_HUB_TOKEN", "")
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MODEL_COLORS = {
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"XGBoost": "#f59e0b",
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"LightGBM": "#10b981",
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@@ -70,8 +85,7 @@ class _SAPModel:
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if HF_TOKEN:
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try:
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login(token=HF_TOKEN, add_to_git_credential=False)
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from sap_rpt_oss import SAP_RPT_OSS_Classifier, SAP_RPT_OSS_Regressor
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if task == "classification":
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self._model = SAP_RPT_OSS_Classifier(max_context_size=2048, bagging=1)
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@@ -102,43 +116,13 @@ class _SAPModel:
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return self
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def predict(self, X):
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is_single = len(X) == 1
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if is_single:
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if isinstance(X, pd.DataFrame):
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X_in = pd.concat([X, X])
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else:
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X_in = np.vstack([X, X])
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else:
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X_in = X
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preds = self._model.predict(X_in)
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if is_single:
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# Take the first prediction only
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if isinstance(preds, (list, np.ndarray)):
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preds = preds[:1]
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if not self._real and self.task == "classification":
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preds = self._le.inverse_transform(preds)
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return preds
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def predict_proba(self, X):
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if is_single:
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if isinstance(X, pd.DataFrame):
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X_in = pd.concat([X, X])
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else:
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X_in = np.vstack([X, X])
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else:
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X_in = X
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proba = self._model.predict_proba(X_in)
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if is_single:
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if isinstance(proba, (list, np.ndarray)):
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proba = proba[:1]
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return proba
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@property
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def label(self):
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RAND = int(os.getenv("RANDOM_STATE", "42"))
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HF_TOKEN = os.getenv("HUGGING_FACE_HUB_TOKEN", "")
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# Suppress redundant Hugging Face Hub notes
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import logging
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logging.getLogger("huggingface_hub").setLevel(logging.ERROR)
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_HF_AUTHENTICATED = False
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def _ensure_hf_login():
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global _HF_AUTHENTICATED
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if HF_TOKEN and not _HF_AUTHENTICATED:
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try:
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from huggingface_hub import login
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login(token=HF_TOKEN, add_to_git_credential=False)
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_HF_AUTHENTICATED = True
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except Exception as e:
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print(f"HF Login failed: {e}")
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MODEL_COLORS = {
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"XGBoost": "#f59e0b",
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"LightGBM": "#10b981",
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if HF_TOKEN:
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try:
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_ensure_hf_login()
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from sap_rpt_oss import SAP_RPT_OSS_Classifier, SAP_RPT_OSS_Regressor
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if task == "classification":
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self._model = SAP_RPT_OSS_Classifier(max_context_size=2048, bagging=1)
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return self
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def predict(self, X):
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preds = self._model.predict(X)
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if not self._real and self.task == "classification":
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preds = self._le.inverse_transform(preds)
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return preds
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def predict_proba(self, X):
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return self._model.predict_proba(X)
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@property
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def label(self):
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matrix/catboost_info/catboost_training.json
CHANGED
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{
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"meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"
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"iterations":[
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]}
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{
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"meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":200,"learn_sets":["learn"],"name":"experiment"},
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"iterations":[
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{"learn":[0.4503450769],"iteration":0,"passed_time":0.0003848224741,"remaining_time":0.07657967234},
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{"learn":[0.4282639001],"iteration":1,"passed_time":0.0008716307632,"remaining_time":0.08629144556},
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{"learn":[0.4114120474],"iteration":2,"passed_time":0.004895579214,"remaining_time":0.3214763684},
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{"learn":[0.3920854872],"iteration":3,"passed_time":0.00525987448,"remaining_time":0.2577338495},
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{"learn":[0.3785482607],"iteration":4,"passed_time":0.005615750789,"remaining_time":0.2190142808},
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{"learn":[0.3622837482],"iteration":5,"passed_time":0.005940605537,"remaining_time":0.192079579},
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{"learn":[0.3501451202],"iteration":6,"passed_time":0.006254246347,"remaining_time":0.1724385064},
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{"learn":[0.3395957657],"iteration":7,"passed_time":0.006563753705,"remaining_time":0.1575300889},
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{"learn":[0.3302065251],"iteration":8,"passed_time":0.006874292759,"remaining_time":0.1458877686},
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{"learn":[0.323229957],"iteration":9,"passed_time":0.00810151173,"remaining_time":0.1539287229},
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+
{"learn":[0.3131380379],"iteration":10,"passed_time":0.008442684876,"remaining_time":0.1450606765},
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+
{"learn":[0.3026587395],"iteration":11,"passed_time":0.008767261526,"remaining_time":0.1373537639},
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{"learn":[0.2946229984],"iteration":12,"passed_time":0.008953020094,"remaining_time":0.1287857506},
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requirements.txt
CHANGED
|
@@ -4,8 +4,6 @@ uvicorn[standard]>=0.29.0
|
|
| 4 |
python-multipart>=0.0.9
|
| 5 |
python-dotenv>=1.0.0
|
| 6 |
jinja2>=3.1.3
|
| 7 |
-
hf_xet
|
| 8 |
-
huggingface_hub[hf_xet]
|
| 9 |
|
| 10 |
# Data & Science Stack
|
| 11 |
numpy==1.26.4
|
|
|
|
| 4 |
python-multipart>=0.0.9
|
| 5 |
python-dotenv>=1.0.0
|
| 6 |
jinja2>=3.1.3
|
|
|
|
|
|
|
| 7 |
|
| 8 |
# Data & Science Stack
|
| 9 |
numpy==1.26.4
|