cashet 0.1.2

Content-addressable compute cache with git semantics

cashet

Content-addressable compute cache with git semantics
Run a function once. Get the same result instantly every time after that.

Install · Quick Start · Why · Use Cases · CLI · API · How It Works

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Install

Global CLI tool (recommended):

uv tool install cashet
# or
pipx install cashet

Then use the CLI anywhere:

cashet --help

In a project (library + CLI):

uv add cashet
# or
pip install cashet

This installs cashet as both an importable Python library (from cashet import Client) and a project-local CLI (uv run cashet).

Develop / contribute:

git clone https://github.com/jolovicdev/cashet.git
cd cashet
uv sync
uv run pytest

Quick Start

from cashet import Client

client = Client()  # creates .cashet/ in current directory

def expensive_transform(data, scale=1.0):
    # imagine this takes 10 minutes
    return [x * scale for x in data]

# First call: runs the function
ref = client.submit(expensive_transform, [1, 2, 3], scale=2.0)
print(ref.load())  # [2.0, 4.0, 6.0]

# Second call with same args: instant — returns cached result
ref2 = client.submit(expensive_transform, [1, 2, 3], scale=2.0)
print(ref2.load())  # [2.0, 4.0, 6.0] — no re-computation

You can also use Client as a context manager to ensure the store connection is closed cleanly:

with Client() as client:
    ref = client.submit(expensive_transform, [1, 2, 3], scale=2.0)
    print(ref.load())

Chain tasks into a pipeline where each step's output feeds into the next:

from cashet import Client

client = Client()

def load_dataset(path):
    return list(range(100))

def normalize(data):
    max_val = max(data)
    return [x / max_val for x in data]

def train_model(data, lr=0.01):
    return {"loss": 0.05, "lr": lr, "samples": len(data)}

# Step 1: load
raw = client.submit(load_dataset, "data/train.csv")

# Step 2: normalize (receives raw output as input)
normalized = client.submit(normalize, raw)

# Step 3: train (receives normalized output)
model = client.submit(train_model, normalized, lr=0.001)

print(model.load())  # {'loss': 0.05, 'lr': 0.001, 'samples': 100}

Re-run the script — everything returns instantly from cache. Change one argument and only that step (and downstream) re-runs.

Why

You already have caches (functools.lru_cache, joblib.Memory). Here's what's different:

	lru_cache	joblib.Memory	cashet
AST-normalized hashing	No	No	Yes (comments/formatting don't break cache)
DAG resolution (chain outputs)	No	No	Yes
Content-addressable storage	No	No	Yes (like git blobs)
CLI to inspect history	No	No	Yes
Diff two runs	No	No	Yes
Garbage collection / eviction	No	No	Yes
Pluggable serialization	No	No	Yes
Explicit cache opt-out	No	Partial	Yes
Pluggable store / executor	No	No	Yes
Persists across restarts	No	Yes	Yes

The core idea: hash the function's AST-normalized source + arguments = unique cache key. Comments, docstrings, and formatting changes don't invalidate the cache — only semantic changes do. Same function + same args = same result, stored immutably on disk. The result is a git-like blob you can inspect, diff, and chain.

Use Cases

1. ML Experiment Tracking Without the Bloat

You run 200 hyperparameter sweeps overnight. Half crash. You fix a bug and re-run. Without cashet, you re-process the dataset 200 times. With cashet:

from cashet import Client, TaskRef

client = Client()

def preprocess(dataset_path, image_size):
    # 45 minutes of image resizing
    ...

def train(data, learning_rate, dropout):
    ...

# Batch submit with topological ordering
# TaskRef(0) refers to the first task's output
results = client.submit_many([
    (preprocess, ("s3://my-bucket/images", 224)),
    (train, (TaskRef(0), 0.01, 0.2)),
    (train, (TaskRef(0), 0.01, 0.5)),
    (train, (TaskRef(0), 0.001, 0.2)),
    (train, (TaskRef(0), 0.001, 0.5)),
    (train, (TaskRef(0), 0.0001, 0.2)),
    (train, (TaskRef(0), 0.0001, 0.5)),
])

preprocess runs once — all 6 training jobs reuse its cached output. Re-run the script tomorrow and even the training results come from cache (same function + same args = instant).

2. Data Pipeline Debugging

Your ETL pipeline fails at step 5. You fix a typo. Now you need to re-run steps 5-7 but steps 1-4 are unchanged and expensive:

from cashet import Client

client = Client()

raw = client.submit(load_s3, "s3://logs/2024-05-01/")
clean = client.submit(remove_pii, raw)
enriched = client.submit(join_crm, clean, "select * from users")
report = client.submit(generate_report, enriched)

Fix the join_crm function and re-run the script. Steps 1-2 return instantly from cache. Only step 3 onward re-executes. This works because cashet tracks which function produced which output — changing a function's source code changes its hash, invalidating downstream cache entries.

3. Reproducible Notebook Results

cashet is designed to work in Jupyter notebooks and IPython sessions. Share a result with a colleague and they can verify exactly how it was produced:

# your notebook
ref = client.submit(generate_forecast, date="2024-01-01", model="v3")
print(f"Result hash: {ref.hash}")

# their terminal — inspect provenance
cashet show <hash>

# Output:
# Hash:     a3b4c5d6...
# Function: generate_forecast
# Source:   def generate_forecast(date, model): ...
# Args:     (('2024-01-01',), {'model': 'v3'})
# Created:  2024-05-01T10:32:17

# Retrieve the actual result
cashet get <hash> -o forecast.csv

4. Incremental Computation

Process a large dataset in chunks. Already-processed chunks return instantly:

from cashet import Client

client = Client()

def process_chunk(chunk_id, source_file):
    # expensive per-chunk processing
    ...

results = []
for chunk_id in range(100):
    ref = client.submit(process_chunk, chunk_id, "huge_file.parquet")
    results.append(ref)

First run processes all 100 chunks. Second run (even after restarting Python) returns all 100 results instantly. Add a new chunk? Only that one runs.

CLI

# Show commit history
cashet log

# Filter by function name
cashet log --func "preprocess"

# Filter by tag
cashet log --tag env=prod --tag experiment=run-1

# Show full commit details (source code, args, error)
cashet show <hash>

# Retrieve a result (pretty-prints strings/dicts/lists)
cashet get <hash>

# Write a result to file
cashet get <hash> -o output.bin

# Compare two commits
cashet diff <hash_a> <hash_b>

# Show lineage of a result (same function+args over time)
cashet history <hash>

# Delete a specific commit
cashet rm <hash>

# Evict old cache entries and orphaned blobs
cashet gc --older-than 30

# Clear everything (alias for gc --older-than 0)
cashet clear

# Storage statistics (includes disk size)
cashet stats

API

Client

from cashet import Client

client = Client(
    store_dir=".cashet",       # where to store blobs + metadata (SQLiteStore)
                               # falls back to $CASHET_DIR env var if set
    store=None,                # or inject any Store implementation
    executor=None,             # or inject any Executor implementation
    serializer=None,           # defaults to PickleSerializer
)

Pluggable Backends

Everything is protocol-based. Swap the store, executor, or serializer without touching your task code:

from cashet import Client, Store, Executor, Serializer
from cashet.store import SQLiteStore
from cashet.executor import LocalExecutor

# These are equivalent (the defaults):
client = Client(store_dir=".cashet")

# Explicit injection:
client = Client(
    store=SQLiteStore(Path(".cashet")),
    executor=LocalExecutor(),
)

Store protocol — implement this to use RocksDB, Redis, S3, or anything else:

from cashet.protocols import Store

class RedisStore:
    def put_blob(self, data: bytes) -> ObjectRef: ...
    def get_blob(self, ref: ObjectRef) -> bytes: ...
    def put_commit(self, commit: Commit) -> None: ...
    def get_commit(self, hash: str) -> Commit | None: ...
    def find_by_fingerprint(self, fingerprint: str) -> Commit | None: ...
    def list_commits(self, ...) -> list[Commit]: ...
    def get_history(self, hash: str) -> list[Commit]: ...
    def stats(self) -> dict[str, int]: ...
    def evict(self, older_than: datetime) -> int: ...
    def close(self) -> None: ...

client = Client(store=RedisStore("redis://localhost"))
# Everything else works identically

Executor protocol — implement this for distributed execution (Celery, Kafka, RQ):

from cashet.protocols import Executor

class CeleryExecutor:
    def submit(self, func, args, kwargs, task_def, store, serializer):
        # Push to Celery, poll for result
        ...

client = Client(
    store=RedisStore("redis://localhost"),
    executor=CeleryExecutor(),
)

Serializer protocol — already covered below.

client.submit(func, *args, **kwargs) -> ResultRef

Submit a function for execution. Returns a ResultRef — a lazy handle to the result.

ref = client.submit(my_func, arg1, arg2, key="value")
ref.hash         # content hash of the result blob
ref.commit_hash  # commit hash (use this for show/history/rm/get)
ref.size         # size in bytes
ref.load()       # deserialize and return the result

If the same function + same arguments have been submitted before, returns the cached result without re-executing.

client.clear()

Remove all cache entries and orphaned blobs. Equivalent to client.gc(timedelta(days=0)).

client.clear()

client.submit_many(tasks) -> list[ResultRef]

Submit a batch of tasks with automatic topological ordering. Use TaskRef(index) to wire outputs between tasks in the batch.

from cashet import TaskRef

refs = client.submit_many([
    step1_func,
    (step2_func, (TaskRef(0),)),
    (step3_func, (TaskRef(1), "extra_arg")),
])

This enables parallel fan-out and ensures each task only runs after its dependencies.

Opt out of caching:

# Per-call
ref = client.submit(non_deterministic_func, _cache=False)

# Per-function via decorator
@client.task(cache=False)
def random_score():
    return random.random()

Tag commits:

# Per-call
ref = client.submit(train, data, lr=0.01, _tags={"experiment": "v1"})

# Per-function via decorator
@client.task(tags={"team": "ml"})
def preprocess(raw):
    ...

Tags are not part of the cache key — they are metadata for organization and filtering.

@client.task

Register a function with cashet metadata and make it directly callable:

@client.task
def my_func(x):
    return x * 2

ref = my_func(5)  # Returns ResultRef, same as client.submit(my_func, 5)
ref.load()        # 10

@client.task(cache=False, name="custom_task_name", tags={"env": "prod"})
def other_func(x):
    return x + 1

client.submit(my_func, 5) still works identically.

client.log(), client.show(), client.get(), client.diff(), client.history(), client.rm(), client.gc()

# List commits
commits = client.log(func_name="preprocess", limit=10)

# Filter by tags
commits = client.log(tags={"experiment": "v1"})

# Get commit details
commit = client.show(hash)
commit.task_def.func_source  # the source code
commit.task_def.args_snapshot  # the serialized args
commit.parent_hash  # previous commit for same func+args
commit.created_at

# Load a result by commit hash
result = client.get(hash)

# Diff two commits
diff = client.diff(hash_a, hash_b)
# {'func_changed': True, 'args_changed': False, 'output_changed': True, ...}

# Get lineage (all runs of same func+args)
history = client.history(hash)

# Evict old entries (default: 30 days)
evicted = client.gc()
# Evict entries older than 7 days
from datetime import timedelta
evicted = client.gc(older_than=timedelta(days=7))

# Storage stats
stats = client.stats()
# {'total_commits': 42, 'completed_commits': 40, 'stored_objects': 38, 'disk_bytes': 10485760}

Jupyter & Notebook Support

cashet works seamlessly in Jupyter notebooks, IPython, and the Python REPL. It uses a tiered source-resolution strategy:

1. inspect.getsource() — for normal .py files
2. dill.source.getsource() — for interactive sessions with live history
3. dis.Bytecode fallback — for any live function, even after a kernel restart

This means you can define functions in a notebook cell, rerun the cell with changes, and cashet will correctly invalidate the cache based on the new code.

# In a notebook cell
client = Client()

def preprocess(data):
    return [x * 2 for x in data]

ref = client.submit(preprocess, [1, 2, 3])

Change the cell body and rerun — the cache invalidates automatically.

Thread Safety

cashet is safe to use from multiple threads (and processes sharing the same store directory). Concurrent submissions of the same uncached task are deduplicated: the function executes exactly once and all callers receive the same cached result.

import threading

def worker():
    c = Client()  # separate Client instance, same store
    c.submit(expensive_func, arg)

threads = [threading.Thread(target=worker) for _ in range(10)]
for t in threads:
    t.start()
for t in threads:
    t.join()
# expensive_func ran only once

ResultRef

A lazy reference to a stored result. Pass it as an argument to chain tasks:

step1 = client.submit(func_a, input_data)
step2 = client.submit(func_b, step1)  # step1 auto-resolves to its output

Custom Serialization

from cashet import Client, PickleSerializer, SafePickleSerializer, JsonSerializer

# Default: pickle (handles arbitrary Python objects)
client = Client(serializer=PickleSerializer())

# Safe pickle: restricts deserialization to an allowlist of known types
client = Client(serializer=SafePickleSerializer())

# Allow custom classes through the allowlist
client = Client(serializer=SafePickleSerializer(extra_classes=[MyClass]))

# For JSON-safe data (dicts, lists, primitives)
client = Client(serializer=JsonSerializer())

# Or implement the Serializer protocol
from cashet.hashing import Serializer

class MySerializer:
    def dumps(self, obj) -> bytes:
        ...
    def loads(self, data: bytes):
        ...

How It Works

client.submit(func, arg1, arg2)
         │
         ▼
  ┌─────────────────┐
  │  Hash function   │  SHA256(AST-normalized source + dep versions + referenced user helpers)
  │  Hash arguments  │  SHA256(canonical repr of args/kwargs)
  └────────┬────────┘
           │
           ▼
  ┌─────────────────┐
  │  Fingerprint     │  func_hash:args_hash
  │  cache lookup    │  ← Store protocol (SQLiteStore, RedisStore, ...)
  └────────┬────────┘
           │
     ┌─────┴─────┐
     │            │
  CACHED       MISS
     │            │
     ▼            ▼
  Return ref   ← Executor protocol (LocalExecutor, CeleryExecutor, ...)
               Execute function
               Store result as blob → Store protocol
               Record commit with parent lineage
               Return ref

Architecture (protocol-based):

Protocol	Default	Implement for
Store	SQLiteStore	RocksDB, Redis, S3, Postgres
Executor	LocalExecutor	Celery, Kafka, RQ, subprocess
Serializer	PickleSerializer	JSON, MessagePack, custom formats

Storage layout (in .cashet/):

.cashet/
├── objects/          # content-addressable blobs (like git objects)
│   ├── a3/
│   │   └── b4c5d6... # compressed result blob
│   └── e7/
│       └── f8g9h0...
└── meta.db           # SQLite: commits, fingerprints, provenance

Key design decisions:

• Closure variables are not hashed and emit a ClosureWarning if present. Function identity is source code, not runtime state. If you need cache invalidation based on a value, pass it as an explicit argument.
• Referenced user-defined helper functions are hashed recursively. Change an imported helper in your own code and the caller's cache invalidates correctly. Builtin and third-party library functions are skipped.
• Blobs are deduplicated by content hash. Identical results share one blob on disk.
• Source is hashed as an AST. Comments, docstrings, and whitespace changes don't invalidate the cache.
• Non-cached tasks get unique commit hashes (timestamp salt) so they always re-execute but still record lineage.
• Parent tracking: Each commit records the hash of the previous commit for the same function+args, forming a history chain you can traverse.

Project Status

Experimental. The core (hashing, DAG resolution, fingerprint dedup) is stable. The defaults work reliably for single-machine workflows. The protocol layer (Store, Executor, Serializer) is ready for alternative backends — implementing a Redis store or Celery executor is a single-file job.

Built-in: SQLiteStore + LocalExecutor + PickleSerializer. Not yet built: Redis, RocksDB, S3 stores; Celery/Kafka executors. PRs welcome.

License

MIT