Memory Snapshot (beta)
Memory snapshots can dramatically improve cold start performance for compatible Modal Functions.
During startup, your Python function typically reads many files from the file system, which
is expensive. For example, the torch
package is hundreds of MiB and requires over 20,000 file operations to load! With memory snapshots, Modal
will produce restorable saves of your Function’s container right after startup initialization, and use these when available to lower startup latency. Functions with memory snapshots enabled typically start 1.5-3x faster.
Modal produces snapshots for deployed Functions on demand, creating and maintaining several snapshots to ensure coverage across our diverse worker fleet. Modal will also automatically expire snapshots and create new ones as we make runtime and security updates.
You don’t need to modify CPU Functions to take advantage of snapshotting in most cases. GPU-enabled Functions typically require refactoring to move GPU initialization into post-restore lifecycle functions (see below).
This is a beta feature. Let us know in Modal Slack
if you find any issues. To use memory snapshots, we recommend using Modal client version 0.64.99
or later.
Enabling snapshots
You can enable memory snapshots for your Function with the enable_memory_snapshot=True
parameter:
import modal
app = modal.App("example-memory-snapshot")
@app.function(enable_memory_snapshot=True)
def my_func():
print("hello")
Then deploy the App with modal deploy
.
Keep the following in mind when using memory snapshots:
- Every time a snapshot is created Modal logs
Creating memory snapshot for Function.
. - Modal creates several snapshots for a given version of your Function (see Snapshot compatibility section).
- Redeploying your Function may cause Modal to create new snapshots, as existing snapshots might not be compatible with your updated Function.
- Creating memory snapshots adds latency to a Function’s startup time, so expect your Function to be slower to start during the first invocations.
Updating snapshots
Redeploying your Function with new configuration (e.g. a new GPU type) or new code will cause previous snapshots to become obsolete. Subsequent invocations to the new Function version will automatically create new snapshots from the new configuration and code.
Modal also automatically recreates your snapshots to keep up with platform’s latest runtime and security changes.
Snapshot compatibility
Modal will create memory snapshots for every new version of your Function. Changing your Function or updating its dependencies will trigger a new snapshotting operation when you run your Function anew.
Additionally, you may observe in application logs your Function being memory snapshots multiple times during its first few invocations. This happens because memory snapshots are compatible with the underlying worker type that created them, and Modal Functions run across a handful of worker types.
CPU-only Functions need around 6 snapshots for coverage, and Functions targeting a specific GPU (e.g. A100) need 2-3. The cold boot benefits should greatly outweigh the penalty of creating multiple snapshots.
Using snapshots with lifecycle functions
It’s currently not possible to snapshot GPU memory. We avoid exposing GPU
devices to your Function during the snapshotting stage (e.g. when
@enter(snap=True)
). NVIDIA drivers are available but no GPU devices are.
To work around this limitation, we suggest refactoring your initialization code
to run across two separate @modal.enter
functions: one that runs before
creating the snapshot (snap=True
), and one that runs after restoring from the
snapshot (snap=False
). Load model weights onto CPU memory in the snap=True
method, and then move the weights onto GPU memory in the snap=False
method.
Here’s an example using the sentence-transformers
package:
import modal
image = modal.Image.debian_slim().pip_install("sentence-transformers")
app = modal.App("sentence-transformers", image=image)
with image.imports():
from sentence_transformers import SentenceTransformer
model_vol = modal.Volume.from_name("sentence-transformers-models")
@app.cls(gpu="a10g", volumes={"/models": model_vol}, enable_memory_snapshot=True)
class Embedder:
model_id = "BAAI/bge-small-en-v1.5"
@modal.enter(snap=True)
def load(self):
# Create a memory snapshot with the model loaded in CPU memory.
self.model = SentenceTransformer("/models/{self.model_id}", device="cpu")
@modal.enter(snap=False)
def setup(self):
self.model.to("cuda") # Move the model to a GPU!
@modal.method()
def run(self, sentences:list[str]):
embeddings = self.model.encode(sentences, normalize_embeddings=True)
print(embeddings)
@app.local_entrypoint()
def main():
Embedder().run.remote(sentences=["what is the meaning of life?"])
if __name__ == "__main__":
cls = modal.Cls.lookup("sentence-transformers", "Embedder")
cls().run.remote(sentences=["what is the meaning of life?"])
Snapshotting reduces the time it takes for this App’s Function to startup by about 3x, from ~6 seconds down to just ~2 seconds.
Known limitations
Memory Snapshot is still in beta. Please report any issues on our community Slack server.
Client versions prior to 0.64.99
contain bugs that may cause snapshot restoration to fail.
Caching GPU information
If your program calls functions that check if GPUs are available during snapshotting, they will get a misleading report.
In the following example, GPUs are not available when
no_gpus_available_during_snapshots()
is called, but they are when the app
is restored and gpus_available_following_restore()
is called:
import modal
app = modal.App(image=modal.Image.debian_slim().pip_install("torch"))
@app.cls(enable_memory_snapshot=True, gpu="any")
class GPUAvailability:
@modal.enter(snap=True)
def no_gpus_available_during_snapshots(self):
import torch
print(f"GPUs available: {torch.cuda.is_available()}") # False
@modal.enter(snap=False)
def gpus_available_following_restore(self):
import torch
print(f"GPUs available: {torch.cuda.is_available()}") # True
@modal.method()
def demo(self):
print("Done!")
The torch.cuda
module has multiple functions which, if called during
snapshotting, will initialize CUDA as having zero GPU devices. Such functions
include torch.cuda.is_available
and torch.cuda.get_device_capability
.
If you’re using a framework that calls these methods during its import phase, it may not be compatible with memory snapshots. The problem can manifest as confusing “cuda not available” or “no CUDA-capable device is detected” errors.
In particular, xformers
is known to call torch.cuda.get_device_capability
on
import, so if it is imported during snapshotting it can unhelpfully initialize
CUDA with zero GPUs. The
workaround for this
is to be on version >=0.0.28
and set the XFORMERS_ENABLE_TRITON
environment
variable to 1
in your modal.Image
.
image = modal.Image.debian_slim().env({"XFORMERS_ENABLE_TRITON": "1"})
Setting this variable early-returns from the xformers
function which unhelpfully
initializes CUDA.
Randomness and uniqueness
If your application depends on uniqueness of state, you must evaluate your Function code and verify that it is resilient to snapshotting operations. For example, if a variable is randomly initialized and snapshotted, that variable will be identical after every restore, possibly breaking uniqueness expectations of the proceeding Function code.