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.from_name("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.