You ran a training job last week. It completed cleanly. Today you rent a fresh pod, run the same script, and something breaks sometimes silently.
Same script.
Same model.
Different pod.
Different result.
This is environment drift and it is one of the most expensive, least documented failure modes in rented GPU compute.
The myth of the standard pod
When you rent a GPU on RunPod or Vast.ai you are not spinning up a standardised environment. You are renting whatever machine a host made available, layered with a base image that varies by provider, instance type, and when it was last updated.
Between two ostensibly identical pods, the following vary without warning:
CUDA version: One pod runs CUDA 12.1, the next runs 12.6, the next runs 12.8
PyTorch build: A cu118 build behaves differently from cu121 and cu128, particularly around custom kernels and FlashAttention
System libraries: gcc version, glibc, NVIDIA driver revision
Pre-installed ML packages: One image ships transformers 4.41, another ships 4.48
None of these differences crash your SSH session. The pod boots fine, nvidia-smi shows the GPU, your /workspace mounts cleanly. The divergence is invisible until your script hits the specific dependency that behaves differently on this image.
How drift breaks training
Environment drift rarely produces a clean early traceback. It fails in three ways:
Silent CPU fallback
If the PyTorch build on the image does not align with the underlying CUDA driver, torch.cuda.is_available() can return False without throwing an exception. Your script resolves device to CPU and keeps running.
I hit this on a real Vast.ai run iteration time jumped from 0.4 seconds to 24 seconds. GPU utilisation: 0%. The script ran to completion at CPU speed while billing at full GPU rates.
Adding an explicit guard prevents this:
python
import torch
if not torch.cuda.is_available():
raise RuntimeError("CUDA unavailable refusing to start training")
Dependency version mismatch
Running ModernBERT requires transformers>=4.48.0. A pod with an older base image ships 4.41. The model may load via remote code but behave incorrectly, or fail partway through the first epoch with an obscure attribute error. The failure is not at import it is 40 minutes into your run.
Accelerate configuration mismatch
Your Accelerate config was created on a 2-GPU node with num_processes: 2. Your new pod has one GPU. The launcher may hang, produce garbage output, or corrupt checkpoints. No error at launch.
Why this is expensive to debug
Environment drift failures share one trait: they are invisible at pod start. The GPU is visible, the script launches, everything looks normal. The failure surfaces at the first forward pass, the first checkpoint save, or not at all if the job completes but produces wrong output.
By the time you notice, you have paid for the compute.
What to check before every new pod
-# Verify PyTorch can see the GPU
python3 -c "import torch; print(torch.version, torch.version.cuda, torch.cuda.is_available())"
-# Check key package versions
pip show transformers accelerate | grep Version
Or run all of this plus HF_HOME path, persistent volume detection, and disk headroom in one command before billing starts:
uvx computefence doctor
The check takes under 30 seconds.
The honest framing
The CPU fallback failure is confirmed from my own Vast.ai runs. The CUDA version variance is confirmed from my A40 session PyTorch 2.8.0+cu128 on that specific pod, not universal. The dependency and Accelerate examples are well-understood engineering realities, not personal war stories.
The point is not that every new pod will break your script. It is that a new pod is a new environment and treating it as identical to the last one is an assumption worth checking before the meter starts running.
Your last pod working is evidence that your setup worked once. It is not proof that your next pod is ready.
GitHub: github.com/Francisco-Booth/ComputeFence
Install: pip install computefence
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