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package.py — Build model.tar.gz for SageMaker
================================================
Run this script once before deploying.
It bundles your code/ folder into the exact archive structure SageMaker expects.
Usage:
cd "RL Envir"
python sagemaker/package.py
Output:
sagemaker/model.tar.gz ← upload this to S3, then point SageMaker at it
What goes inside model.tar.gz:
code/
├── inference.py ← SageMaker entry point (the 4 handlers)
└── classifier.py ← your rule-based classifier logic
SageMaker unpacks the archive and looks for code/inference.py automatically
when you use the SKLearn or generic Python containers.
"""
import os
import tarfile
# ── Paths — all relative to this script's location ───────────────────────────
HERE = os.path.dirname(os.path.abspath(__file__))
OUTPUT_TAR = os.path.join(HERE, "model.tar.gz")
# Files to include — add more here if you create extra helper modules
FILES_TO_PACK = {
"code/inference.py": os.path.join(HERE, "inference.py"),
"code/classifier.py": os.path.join(HERE, "classifier.py"),
}
def build():
print("\n Building model.tar.gz ...")
print(f" Output → {OUTPUT_TAR}\n")
# Verify all source files exist before starting
missing = [src for src in FILES_TO_PACK.values() if not os.path.exists(src)]
if missing:
print(" ❌ Missing files:")
for f in missing:
print(f" {f}")
raise FileNotFoundError("Fix missing files then re-run.")
# Build the archive
with tarfile.open(OUTPUT_TAR, "w:gz") as tar:
for archive_name, source_path in FILES_TO_PACK.items():
tar.add(source_path, arcname=archive_name)
size_kb = os.path.getsize(source_path) / 1024
print(f" + {archive_name:<30} ({size_kb:.1f} KB)")
# Verify and report
tar_size_kb = os.path.getsize(OUTPUT_TAR) / 1024
print(f"\n ✅ Done! model.tar.gz = {tar_size_kb:.1f} KB")
# Show contents as confirmation
print("\n Contents of model.tar.gz:")
with tarfile.open(OUTPUT_TAR, "r:gz") as tar:
for member in tar.getmembers():
print(f" {member.name:<35} {member.size / 1024:.1f} KB")
print(f"\n Next step — upload to S3:")
print(f" aws s3 cp {OUTPUT_TAR} s3://YOUR-BUCKET/email-gatekeeper/model.tar.gz\n")
if __name__ == "__main__":
build()
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