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When building a template that installs large Python packages like PyTorch with CUDA dependencies, pip install can fail with one of two errors:
  • MemoryError — pip tries to serialize large wheel files into memory for caching, exceeding available RAM.
  • OSError: [Errno 28] No space left on device — downloaded wheels fill up the /tmp directory.
Example errors 
File "pip/_vendor/cachecontrol/serialize.py", line 70, in dumps
    return b",".join([b"cc=4", msgpack.dumps(data, use_bin_type=True)])
MemoryError

ERROR: Could not install packages due to an OSError: [Errno 28] No space left on device

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Cause

The build environment mounts /tmp as a tmpfs — a RAM-backed filesystem capped at ~3.9 GB. pip downloads all wheels to /tmp/pip-* before installing them. PyTorch with CUDA dependencies totals ~4.1 GB of downloads, which exceeds this limit. Set the TMPDIR environment variable to a disk-backed path so pip downloads don’t go through the RAM-backed /tmp. Combined with --no-cache-dir, this avoids both the disk space and memory issues. 
const template = Template()
  .runCmd('TMPDIR=/var/tmp pip install --no-cache-dir torch sentence-transformers')

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Solution 2: Install CPU-only PyTorch

E2B sandboxes don’t have GPUs, so there’s no reason to download CUDA dependencies. Installing the CPU-only variant of PyTorch reduces the download from ~4.1 GB to ~189 MB, avoiding the /tmp size limit entirely. 
const template = Template()
  .runCmd('pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cpu')
  .runCmd('echo "torch" > /tmp/constraints.txt && pip install --no-cache-dir -c /tmp/constraints.txt sentence-transformers')
The constraints file in the second step prevents pip from replacing the CPU-only torch with the CUDA version when installing packages that depend on PyTorch.