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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Environment Test - FBMC Chronos 2 Forecasting\n",
"\n",
"**Purpose**: Verify GPU, Python 3.11, Chronos 2, and dataset access\n",
"\n",
"**Date**: 2025-11-12 \n",
"**Space**: evgueni-p/fbmc-chronos2-forecast"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Check GPU Availability"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"print(\"PyTorch Version:\", torch.__version__)\n",
"print(\"CUDA Available:\", torch.cuda.is_available())\n",
"\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n",
" print(f\"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n",
" print(f\"CUDA Version: {torch.version.cuda}\")\n",
"else:\n",
" print(\"[!] WARNING: No GPU detected!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Check Python Version"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"print(f\"Python: {sys.version}\")\n",
"print(f\"Version Info: {sys.version_info}\")\n",
"\n",
"# Verify Python 3.11+\n",
"assert sys.version_info >= (3, 11), \"Python 3.11+ required for Chronos 2\"\n",
"print(\"[+] Python version check passed!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Test Chronos 2 Import"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" from chronos import Chronos2Pipeline\n",
" import chronos\n",
" print(f\"Chronos version: {chronos.__version__}\")\n",
" print(\"[+] Chronos 2 imported successfully!\")\n",
"except ImportError as e:\n",
" print(f\"[!] ERROR: Could not import Chronos 2: {e}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Load HuggingFace Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"import os\n",
"\n",
"# Check if HF_TOKEN is available\n",
"hf_token = os.getenv('HF_TOKEN')\n",
"print(f\"HF_TOKEN available: {hf_token is not None}\")\n",
"\n",
"# Load dataset\n",
"print(\"\\nLoading dataset...\")\n",
"dataset = load_dataset(\"evgueni-p/fbmc-features-24month\", split=\"train\")\n",
"\n",
"print(f\"[+] Dataset loaded: {len(dataset)} rows\")\n",
"print(f\" Columns: {len(dataset.column_names)} features\")\n",
"print(f\" First 5 columns: {dataset.column_names[:5]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Load Chronos 2 Model on GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Loading Chronos 2 Large model...\")\n",
"print(\"(This may take 2-3 minutes on first load)\\n\")\n",
"\n",
"pipeline = Chronos2Pipeline.from_pretrained(\n",
" \"amazon/chronos-2-large\",\n",
" device_map=\"cuda\",\n",
" torch_dtype=torch.float32\n",
")\n",
"\n",
"print(\"[+] Chronos 2 Large loaded successfully on GPU!\")\n",
"print(f\" Model device: {next(pipeline.model.parameters()).device}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Quick Inference Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from datetime import datetime, timedelta\n",
"\n",
"# Create minimal test data\n",
"print(\"Creating test data...\")\n",
"timestamps = pd.date_range(start='2024-01-01', periods=100, freq='H')\n",
"test_context = pd.DataFrame({\n",
" 'timestamp': timestamps,\n",
" 'border': ['TEST'] * len(timestamps),\n",
" 'target': np.random.randn(len(timestamps)) * 100 + 1000 # Random values around 1000 MW\n",
"})\n",
"\n",
"future_timestamps = pd.date_range(start=timestamps[-1] + timedelta(hours=1), periods=24, freq='H')\n",
"test_future = pd.DataFrame({\n",
" 'timestamp': future_timestamps,\n",
" 'border': ['TEST'] * len(future_timestamps)\n",
"})\n",
"\n",
"print(f\"Context shape: {test_context.shape}\")\n",
"print(f\"Future shape: {test_future.shape}\")\n",
"\n",
"# Run inference\n",
"print(\"\\nRunning inference (24-hour forecast)...\")\n",
"forecasts = pipeline.predict_df(\n",
" context_df=test_context,\n",
" future_df=test_future,\n",
" prediction_length=24,\n",
" id_column='border',\n",
" timestamp_column='timestamp',\n",
" num_samples=50\n",
")\n",
"\n",
"print(f\"[+] Inference complete! Forecast shape: {forecasts.shape}\")\n",
"print(f\" Forecast columns: {list(forecasts.columns)}\")\n",
"\n",
"if 'mean' in forecasts.columns:\n",
" print(f\"\\nForecast statistics:\")\n",
" print(f\" Mean: {forecasts['mean'].mean():.2f}\")\n",
" print(f\" Min: {forecasts['mean'].min():.2f}\")\n",
" print(f\" Max: {forecasts['mean'].max():.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ✅ Summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"=\"*60)\n",
"print(\"ENVIRONMENT TEST SUMMARY\")\n",
"print(\"=\"*60)\n",
"print(\"[+] GPU: Available and working\")\n",
"print(\"[+] Python: 3.11+\")\n",
"print(\"[+] Chronos 2: Installed and imported\")\n",
"print(\"[+] Dataset: Loaded successfully\")\n",
"print(\"[+] Model: Chronos 2 Large on GPU\")\n",
"print(\"[+] Inference: Working correctly\")\n",
"print(\"=\"*60)\n",
"print(\"\\n[+] Environment is READY for full inference run!\")\n",
"print(\"=\"*60)"
]
}
],
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
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"nbformat": 4,
"nbformat_minor": 4
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