433af5c25d
- Docker: Delete 23 deprecated Dockerfiles, fix CI/CD to use Dockerfile.foxhunt-build - Config: Remove 36 .env files, keep 4 essential, delete config/environments/ - Docs: Archive 614 Wave D files to docs/archive/wave_d/, 95% reduction in root - Scripts: Delete 56 deprecated scripts, keep 58 production-critical (49% reduction) - Python: Organize 37 scripts into scripts/python/ subdirectories, delete ml/python/ - Build: Remove 1GB artifacts, delete old venvs, clean Python cache from git - Migrations: Delete deprecated directory (4,432 lines), remove duplicate database/migrations/ - Infrastructure: Delete deployment/ (61 files), docs/scripts/ (8 files) Total impact: ~2,500 files cleaned, 750MB+ space freed, zero production impact All deleted scripts backed up to archives. runpod/ and tests/runpod/ preserved. data_acquisition_service retained per user request.
87 lines
3.3 KiB
Plaintext
87 lines
3.3 KiB
Plaintext
# =============================================================================
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# RUNPOD S3 CONFIGURATION TEMPLATE
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# =============================================================================
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# Copy this file to .env.runpod and fill in your Runpod credentials
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#
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# How to get credentials:
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# 1. S3_ENDPOINT: Datacenter-specific endpoint
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# - US-CA-1: https://s3api-us-ca-1.runpod.io
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# - EU-RO-1: https://s3api-eu-ro-1.runpod.io
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# - Find yours: https://docs.runpod.io/storage/s3-api#endpoint-urls
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#
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# 2. S3_BUCKET: Your Network Volume ID
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# - Go to: https://www.runpod.io/console/storage
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# - Click on your Network Volume
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# - Copy the Volume ID (e.g., abc123xyz456)
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#
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# 3. AWS_ACCESS_KEY_ID: Your Runpod User ID
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# - Go to: https://www.runpod.io/console/settings
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# - Copy your User ID (shown at top of page)
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#
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# 4. AWS_SECRET_ACCESS_KEY: Your Runpod API Key
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# - Go to: https://www.runpod.io/console/settings
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# - Click "API Keys" tab
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# - Create new API key with "Read/Write" permissions
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# - Copy the secret key (only shown once!)
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# =============================================================================
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# Runpod S3 Endpoint (datacenter-specific)
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# Examples:
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# US-CA-1: https://s3api-us-ca-1.runpod.io
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# EU-RO-1: https://s3api-eu-ro-1.runpod.io
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S3_ENDPOINT=https://s3api-DATACENTER.runpod.io
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# Network Volume ID (acts as S3 bucket name)
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# Find this in: https://www.runpod.io/console/storage
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S3_BUCKET=your-network-volume-id
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# Runpod User ID (acts as AWS_ACCESS_KEY_ID)
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# Find this in: https://www.runpod.io/console/settings
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AWS_ACCESS_KEY_ID=your-runpod-user-id
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# Runpod API Key (acts as AWS_SECRET_ACCESS_KEY)
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# Create this in: https://www.runpod.io/console/settings -> API Keys
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AWS_SECRET_ACCESS_KEY=your-runpod-api-key
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# AWS Region (default: us-east-1)
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# This is used by AWS CLI but Runpod doesn't enforce specific regions
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AWS_REGION=us-east-1
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# =============================================================================
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# TRAINING CONFIGURATION (for Docker deployment)
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# =============================================================================
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# Binary to execute (downloaded from s3://${S3_BUCKET}/binaries/)
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BINARY_NAME=train_tft_parquet
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# Data file to download (optional - can mount as volume instead)
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# Downloaded from s3://${S3_BUCKET}/data/
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DATA_NAME=ES_FUT_180d.parquet
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# Training arguments (passed to binary)
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# Override these in Runpod container settings or docker run command
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TRAINING_ARGS=--epochs 50 --batch-size 32 --lookback-window 60 --forecast-horizon 10
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# =============================================================================
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# USAGE INSTRUCTIONS
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# =============================================================================
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#
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# 1. Copy this file:
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# cp .env.runpod.template .env.runpod
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#
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# 2. Fill in your credentials (see instructions above)
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#
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# 3. Upload binaries and data to Runpod S3:
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# ./upload_to_runpod_s3.sh
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#
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# 4. Build and push Docker image:
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# docker build -f Dockerfile.runpod.s3 -t yourusername/foxhunt-runpod-s3:latest .
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# docker push yourusername/foxhunt-runpod-s3:latest
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#
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# 5. Deploy on Runpod:
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# - Use Docker image: yourusername/foxhunt-runpod-s3:latest
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# - Set environment variables from this file
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# - GPU: RTX 4090 or similar (24GB VRAM recommended)
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#
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# =============================================================================
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