From 231ce40dc6f998f36dd9c5729ab5b5e59e554da3 Mon Sep 17 00:00:00 2001 From: Tyler Hutcherson Date: Tue, 21 Jan 2025 14:07:45 -0500 Subject: [PATCH] initial commit --- .github/release-drafter-config.yml | 47 + .github/workflows/lint.yml | 47 + .github/workflows/release-drafter.yml | 24 + .github/workflows/release.yml | 65 + .github/workflows/run_tests.yml | 62 + .gitignore | 5 + LICENSE | 21 + README.md | 2 + docs/redis_model_store.ipynb | 596 +++++ model_store/__init__.py | 1 + model_store/registry.py | 294 +++ model_store/serialize.py | 30 + model_store/shard_manager.py | 105 + model_store/store.py | 227 ++ model_store/utils.py | 53 + poetry.lock | 3354 +++++++++++++++++++++++++ pyproject.toml | 78 + scripts.py | 42 + 18 files changed, 5053 insertions(+) create mode 100644 .github/release-drafter-config.yml create mode 100644 .github/workflows/lint.yml create mode 100644 .github/workflows/release-drafter.yml create mode 100644 .github/workflows/release.yml create mode 100644 .github/workflows/run_tests.yml create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md create mode 100644 docs/redis_model_store.ipynb create mode 100644 model_store/__init__.py create mode 100644 model_store/registry.py create mode 100644 model_store/serialize.py create mode 100644 model_store/shard_manager.py create mode 100644 model_store/store.py create mode 100644 model_store/utils.py create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 scripts.py diff --git a/.github/release-drafter-config.yml b/.github/release-drafter-config.yml new file mode 100644 index 0000000..80dc899 --- /dev/null +++ b/.github/release-drafter-config.yml @@ -0,0 +1,47 @@ +name-template: '$NEXT_MINOR_VERSION' +tag-template: 'v$NEXT_MINOR_VERSION' +autolabeler: + - label: 'maintenance' + files: + - '*.md' + - '.github/*' + - label: 'bug' + branch: + - '/bug-.+' + - label: 'maintenance' + branch: + - '/maintenance-.+' + - label: 'feature' + branch: + - '/feature-.+' +categories: + - title: 'Breaking Changes' + labels: + - 'breakingchange' + - title: '๐Ÿงช Experimental Features' + labels: + - 'experimental' + - title: '๐Ÿš€ New Features' + labels: + - 'feature' + - 'enhancement' + - title: '๐Ÿ› Bug Fixes' + labels: + - 'fix' + - 'bugfix' + - 'bug' + - 'BUG' + - title: '๐Ÿงฐ Maintenance' + label: 'maintenance' +change-template: '- $TITLE (#$NUMBER)' +exclude-labels: + - 'skip-changelog' +template: | + # Changes + + $CHANGES + + ## Contributors + We'd like to thank all the contributors who worked on this release! + + $CONTRIBUTORS \ No newline at end of file diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 0000000..d112694 --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,47 @@ +name: Lint + +on: + pull_request: + push: + branches: + - main + +jobs: + check: + name: Style-check ${{ matrix.python-version }} + runs-on: ubuntu-latest + strategy: + matrix: + # Only lint on the min and max supported Python versions. + # It's extremely unlikely that there's a lint issue on any version in between + # that doesn't show up on the min or max versions. + # + # GitHub rate-limits how many jobs can be running at any one time. + # Starting new jobs is also relatively slow, + # so linting on fewer versions makes CI faster. + python-version: + - "3.9" + - "3.11" + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install Poetry + uses: snok/install-poetry@v1 + with: + version: 1.8.3 + - name: Install dependencies + run: | + poetry install --all-extras + - name: check-sort-import + run: | + poetry run check-sort-imports + - name: check-black-format + run: | + poetry run check-format + - name: check-mypy + run: | + poetry run check-mypy \ No newline at end of file diff --git a/.github/workflows/release-drafter.yml b/.github/workflows/release-drafter.yml new file mode 100644 index 0000000..796c2ed --- /dev/null +++ b/.github/workflows/release-drafter.yml @@ -0,0 +1,24 @@ +name: Release Drafter + +on: + push: + # branches to consider in the event; optional, defaults to all + branches: + - main + +permissions: {} +jobs: + update_release_draft: + permissions: + pull-requests: write # to add label to PR (release-drafter/release-drafter) + contents: write # to create a github release (release-drafter/release-drafter) + + runs-on: ubuntu-latest + steps: + # Drafts your next Release notes as Pull Requests are merged into "master" + - uses: release-drafter/release-drafter@v5 + with: + # (Optional) specify config name to use, relative to .github/. Default: release-drafter.yml + config-name: release-drafter-config.yml + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} \ No newline at end of file diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..244ddd8 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,65 @@ +name: Publish Release + +on: + release: + types: [published] + +env: + PYTHON_VERSION: "3.11" + POETRY_VERSION: "1.8.3" + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ env.PYTHON_VERSION }} + + - name: Install Poetry + uses: snok/install-poetry@v1 + with: + version: 1.8.3 + + - name: Set Version + run: poetry version ${{ github.event.inputs.version }} + + - name: Build package + run: poetry build + + - name: Upload build + uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + publish: + needs: build + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ env.PYTHON_VERSION }} + + - name: Install Poetry + uses: snok/install-poetry@v1 + with: + version: 1.8.3 + + - uses: actions/download-artifact@v4 + with: + name: dist + path: dist/ + + - name: Publish to PyPI + env: + POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI }} + run: poetry publish diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml new file mode 100644 index 0000000..fff5dff --- /dev/null +++ b/.github/workflows/run_tests.yml @@ -0,0 +1,62 @@ +name: Test Suite + +on: + pull_request: + + push: + branches: + - main + +jobs: + test: + name: Python ${{ matrix.python-version }} - ${{ matrix.connection }} [redis-stack ${{matrix.redis-stack-version}}] + runs-on: ubuntu-latest + + strategy: + fail-fast: false + matrix: + python-version: [3.9, 3.10, 3.11] + connection: ['hiredis', 'plain'] + redis-stack-version: ['6.2.6-v9', 'latest', 'edge'] + + services: + redis: + image: redis/redis-stack-server:${{matrix.redis-stack-version}} + ports: + - 6379:6379 + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' + + - name: Install Poetry + uses: snok/install-poetry@v1 + with: + version: 1.8.3 + + - name: Install dependencies + run: | + poetry install --all-extras + + - name: Install hiredis if needed + if: matrix.connection == 'hiredis' + run: | + poetry add hiredis + + - name: Set Redis version + run: | + echo "REDIS_VERSION=${{ matrix.redis-stack-version }}" >> $GITHUB_ENV + + - name: Run tests + run: | + poetry run test-verbose + + - name: Run notebook tests + run: | + cd docs/ && poetry run treon -v + + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..56f914b --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +docs/old_redis_model_store.ipynb +docs/test.py + +__pycache__/ +.mypy_cache/ \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..7563931 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Redis, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..5620a5c --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# Redis Model Store + diff --git a/docs/redis_model_store.ipynb b/docs/redis_model_store.ipynb new file mode 100644 index 0000000..88e13ec --- /dev/null +++ b/docs/redis_model_store.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cbba56a9", + "metadata": {}, + "source": [ + "![Redis](https://redis.io/wp-content/uploads/2024/04/Logotype.svg?auto=webp&quality=85,75&width=120)\n", + "\n", + "# ML Model Serialization in Redis\n", + "\n", + "The `ModelStore` class below implements the following logic:\n", + "- Builds a model metadata index for model version management\n", + "- Handles model chunking, serialization, and deserialization to/from Redis using Pickle\n", + "\n", + "Then we test with various Python ML-native data types and models.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c3ead6c", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install scikit-learn torch tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "307c25ed", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import redis\n", + "\n", + "from model_store import ModelStore\n", + "\n", + "# Replace values below with your own if using Redis Cloud instance\n", + "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n", + "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\") # ex: 18374\n", + "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\") # ex: \"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n", + "\n", + "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n", + "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\"\n", + "\n", + "# Initialize Redis client\n", + "redis_client = redis.Redis.from_url(REDIS_URL)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dfe10ba9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12:28:23 redisvl.index.index INFO Index already exists, not overwriting.\n" + ] + } + ], + "source": [ + "# Initialize the ModelStore\n", + "model_store = ModelStore(redis_client)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "79463e37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_store.model_registry.clear()" + ] + }, + { + "cell_type": "markdown", + "id": "14a8c59f", + "metadata": {}, + "source": [ + "## Test with simple Scikit-Learn model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "74cc62af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model accuracy: 1.00\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Load a simple dataset and train a RandomForest model\n", + "iris = load_iris()\n", + "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)\n", + "\n", + "# Train a RandomForestClassifier\n", + "model = RandomForestClassifier()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Evaluate the model\n", + "y_pred = model.predict(X_test)\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Model accuracy: {accuracy:.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "51a8f83f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:28:49.913 - model_store.store - INFO - Starting save operation for model 'random_forest'\n", + "2025-01-21 12:28:49.925 - model_store.store - INFO - Added version record (0.0116s)\n", + "2025-01-21 12:28:49.926 - model_store.store - INFO - Starting model serialization and storage\n", + "2025-01-21 12:28:49.930 - model_store.store - INFO - Stored model in 1 shards (0.0044s)\n", + "2025-01-21 12:28:49.932 - model_store.store - INFO - Set shards (0.0012s)\n", + "2025-01-21 12:28:49.932 - model_store.store - INFO - Total save operation completed in 0.0191s\n" + ] + } + ], + "source": [ + "# Save the trained model to Redis\n", + "model_name = \"random_forest\"\n", + "version = model_store.save_model(model, model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d0ce2b03", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:28:49.936 - model_store.store - INFO - Starting load operation for model 'random_forest'\n", + "2025-01-21 12:28:49.938 - model_store.store - INFO - Retrieved version metadata (0.0013s)\n", + "2025-01-21 12:28:49.938 - model_store.store - INFO - Starting model reconstruction from shards\n", + "2025-01-21 12:28:49.943 - model_store.store - INFO - Loaded model from 1 shards (0.0041s)\n", + "2025-01-21 12:28:49.943 - model_store.store - INFO - Total load operation completed (0.0070s)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded model accuracy: 1.00\n" + ] + } + ], + "source": [ + "# Load the model from Redis\n", + "loaded_model = model_store.load_model(model_name)\n", + "\n", + "# Verify that the loaded model works\n", + "y_pred_loaded = loaded_model.predict(X_test)\n", + "loaded_accuracy = accuracy_score(y_test, y_pred_loaded)\n", + "print(f\"Loaded model accuracy: {loaded_accuracy:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c743a48d", + "metadata": {}, + "source": [ + "## Test with 1Gb numpy array\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dd894c4e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "desired_size_bytes = 1024*1024*1024\n", + "num_elements = desired_size_bytes // 8\n", + "large_array = np.random.rand(num_elements).astype(np.float64)\n", + "\n", + "model_name = \"numpy_array\"" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bcc6abd7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:29:36.338 - model_store.store - INFO - Starting save operation for model 'numpy_array'\n", + "2025-01-21 12:29:36.343 - model_store.store - INFO - Added version record (0.0046s)\n", + "2025-01-21 12:29:36.344 - model_store.store - INFO - Starting model serialization and storage\n", + "2025-01-21 12:29:44.056 - model_store.store - INFO - Stored model in 1025 shards (7.7123s)\n", + "2025-01-21 12:29:44.060 - model_store.store - INFO - Set shards (0.0037s)\n", + "2025-01-21 12:29:44.061 - model_store.store - INFO - Total save operation completed in 7.7231s\n" + ] + } + ], + "source": [ + "version = model_store.save_model(large_array, model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "12f57740", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:30:09.563 - model_store.store - INFO - Starting load operation for model 'numpy_array'\n", + "2025-01-21 12:30:09.568 - model_store.store - INFO - Retrieved version metadata (0.0038s)\n", + "2025-01-21 12:30:09.568 - model_store.store - INFO - Starting model reconstruction from shards\n", + "2025-01-21 12:30:13.246 - model_store.store - INFO - Loaded model from 1025 shards (3.6776s)\n", + "2025-01-21 12:30:13.340 - model_store.store - INFO - Total load operation completed (3.7763s)\n" + ] + } + ], + "source": [ + "# Load the model from Redis\n", + "loaded_model = model_store.load_model(model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e29ce236", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.True_" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check if all elements match in the array\n", + "sum(loaded_model == large_array) == num_elements" + ] + }, + { + "cell_type": "markdown", + "id": "3c452718", + "metadata": {}, + "source": [ + "## Test with pytorch model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ccc06981", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "\n", + "# Define a simple model\n", + "class SimpleModel(nn.Module):\n", + " def __init__(self):\n", + " super(SimpleModel, self).__init__()\n", + " self.fc = nn.Linear(1, 1)\n", + "\n", + " def forward(self, x):\n", + " return self.fc(x)\n", + "\n", + "# Create model, define loss and optimizer\n", + "model = SimpleModel()\n", + "criterion = nn.MSELoss()\n", + "optimizer = optim.SGD(model.parameters(), lr=0.01)\n", + "\n", + "# Dummy data\n", + "x = torch.tensor([[1.0], [2.0], [3.0]], requires_grad=True)\n", + "y = torch.tensor([[2.0], [4.0], [6.0]])\n", + "\n", + "# Training loop\n", + "for epoch in range(100):\n", + " optimizer.zero_grad()\n", + " output = model(x)\n", + " loss = criterion(output, y)\n", + " loss.backward()\n", + " optimizer.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f5e2dc3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction for input 4.0: 6.903447151184082\n" + ] + } + ], + "source": [ + "# Make a simple prediction\n", + "x_test = torch.tensor([[4.0]])\n", + "prediction = model(x_test).item()\n", + "print(f\"Prediction for input 4.0: {prediction}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "86dc9f9d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:32:58.244 - model_store.store - INFO - Starting save operation for model 'pytorch'\n", + "2025-01-21 12:32:58.247 - model_store.store - INFO - Added version record (0.0028s)\n", + "2025-01-21 12:32:58.248 - model_store.store - INFO - Starting model serialization and storage\n", + "2025-01-21 12:32:58.250 - model_store.store - INFO - Stored model in 1 shards (0.0019s)\n", + "2025-01-21 12:32:58.252 - model_store.store - INFO - Set shards (0.0018s)\n", + "2025-01-21 12:32:58.252 - model_store.store - INFO - Total save operation completed in 0.0083s\n" + ] + }, + { + "data": { + "text/plain": [ + "'1.0'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Save the trained model to Redis\n", + "model_name = \"pytorch\"\n", + "version = \"1.0\"\n", + "model_store.save_model(model, model_name, version=version)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "670f353f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:33:00.741 - model_store.store - INFO - Starting load operation for model 'pytorch'\n", + "2025-01-21 12:33:00.744 - model_store.store - INFO - Retrieved version metadata (0.0018s)\n", + "2025-01-21 12:33:00.745 - model_store.store - INFO - Starting model reconstruction from shards\n", + "2025-01-21 12:33:00.747 - model_store.store - INFO - Loaded model from 1 shards (0.0019s)\n", + "2025-01-21 12:33:00.747 - model_store.store - INFO - Total load operation completed (0.0055s)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction for input 4.0 with loaded model: 6.903447151184082\n" + ] + } + ], + "source": [ + "# Load the model from Redis\n", + "loaded_model = model_store.load_model(model_name)\n", + "\n", + "prediction = loaded_model(x_test).item()\n", + "print(f\"Prediction for input 4.0 with loaded model: {prediction}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c6644d75", + "metadata": {}, + "source": [ + "## Test with tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e8bd2de2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tyler.hutcherson/Library/Caches/pypoetry/virtualenvs/redis-model-store-__eJJx5C-py3.11/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Define a simple model\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Dense(1, input_shape=(1,))\n", + "])\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='sgd', loss='mse')\n", + "\n", + "# Dummy data\n", + "x = tf.constant([[1.0], [2.0], [3.0]])\n", + "y = tf.constant([[2.0], [4.0], [6.0]])\n", + "\n", + "# Train the model\n", + "model.fit(x, y, epochs=100, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c26f35ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction for input 4.0: 7.460770606994629\n" + ] + } + ], + "source": [ + "# Make a simple prediction\n", + "x_test = tf.constant([[4.0]])\n", + "prediction = model(x_test).numpy()[0, 0]\n", + "print(f\"Prediction for input 4.0: {prediction}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5aeaf0ac", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:33:24.276 - model_store.store - INFO - Starting save operation for model 'tensorflow'\n", + "2025-01-21 12:33:24.280 - model_store.store - INFO - Added version record (0.0028s)\n", + "2025-01-21 12:33:24.280 - model_store.store - INFO - Starting model serialization and storage\n", + "2025-01-21 12:33:24.291 - model_store.store - INFO - Stored model in 1 shards (0.0107s)\n", + "2025-01-21 12:33:24.292 - model_store.store - INFO - Set shards (0.0014s)\n", + "2025-01-21 12:33:24.293 - model_store.store - INFO - Total save operation completed in 0.0163s\n" + ] + }, + { + "data": { + "text/plain": [ + "'1.0'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Save the trained model to Redis\n", + "model_name = \"tensorflow\"\n", + "version = \"1.0\"\n", + "model_store.save_model(model, model_name, version=version)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "117255d6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-21 12:33:24.297 - model_store.store - INFO - Starting load operation for model 'tensorflow'\n", + "2025-01-21 12:33:24.300 - model_store.store - INFO - Retrieved version metadata (0.0027s)\n", + "2025-01-21 12:33:24.301 - model_store.store - INFO - Starting model reconstruction from shards\n", + "2025-01-21 12:33:24.312 - model_store.store - INFO - Loaded model from 1 shards (0.0108s)\n", + "2025-01-21 12:33:24.312 - model_store.store - INFO - Total load operation completed (0.0150s)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction for input 4.0 with loaded model: 7.460770606994629\n" + ] + } + ], + "source": [ + "# Load the model from Redis\n", + "loaded_model = model_store.load_model(model_name)\n", + "\n", + "prediction = loaded_model(x_test).numpy()[0, 0]\n", + "print(f\"Prediction for input 4.0 with loaded model: {prediction}\")" + ] + }, + { + "cell_type": "markdown", + "id": "db4db02e", + "metadata": {}, + "source": [ + "# Clear the model store" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/model_store/__init__.py b/model_store/__init__.py new file mode 100644 index 0000000..a6bfa6c --- /dev/null +++ b/model_store/__init__.py @@ -0,0 +1 @@ +from .store import ModelStore diff --git a/model_store/registry.py b/model_store/registry.py new file mode 100644 index 0000000..c5edfe6 --- /dev/null +++ b/model_store/registry.py @@ -0,0 +1,294 @@ +""" +model_registry.py + +Implements a ModelRegistry class that tracks model versioning and metadata +using RedisVL. Also includes a Pydantic model for model version metadata. +""" + +import json +from typing import List, Optional + +from pydantic import BaseModel, Field +from redis import Redis +from redisvl.index import SearchIndex +from redisvl.query import FilterQuery +from redisvl.query.filter import FilterExpression, Tag + +from model_store.utils import current_timestamp, new_model_version + + +class ModelVersion(BaseModel): + """ + Metadata for a specific model version stored in Redis. + """ + + name: str = Field(..., description="Unique identifier for the model") + description: str = Field( + default="", description="Optional description of this model version" + ) + version: str = Field( + default_factory=new_model_version, + description="Version identifier (e.g. semantic version or UUID)", + ) + created_at: float = Field( + default_factory=current_timestamp, + description="Unix timestamp when this version was created", + ) + shard_keys: List[str] = Field( + default=[], description="Redis keys containing the serialized model data" + ) + + +class ModelRegistry: + """ + Manages versioning and metadata for machine learning models stored in Redis. + + Uses RedisVL for efficient querying and indexing of model metadata. Each model + version is stored as a JSON document with searchable fields for name, version, + description and creation time. + + The registry maintains an index of model versions and their metadata, allowing + for efficient querying and retrieval of model versions by name, version, or + creation time. + """ + + def __init__(self, redis_client: Redis) -> None: + """ + Initialize the model registry. + + Args: + redis_client (Redis): Initialized Redis client for + storage and querying. + """ + self.registry_idx = SearchIndex.from_dict( + { + "index": { + "name": "model_registry", + "prefix": "model", + "storage_type": "json", + "key_separator": ":", + }, + "fields": [ + {"name": "name", "type": "tag"}, + {"name": "description", "type": "text"}, + {"name": "version", "type": "tag"}, + {"name": "created_at", "type": "numeric"}, + ], + } + ) + self.registry_idx.set_client(redis_client) + # Create index only if it doesn't exist + self.registry_idx.create(overwrite=False, drop=False) + + @staticmethod + def model_version_key(name: str, version: str) -> str: + """ + Generate Redis key for storing a specific model version. + + Args: + name (str): Model name + version (str): Model version identifier + + Returns: + str: Formatted Redis key in the format "model:{name}:{version}" + """ + return f"model:{name}:{version}" + + def add_version(self, name: str, **kwargs) -> ModelVersion: + """ + Add a new model version to the registry. + + Args: + name (str): Model name. + **kwargs: Additional model metadata fields (version, + description, shard_keys). + + Returns: + ModelVersion: Model version metadata object. + + Raises: + ValidationError: If required fields are missing or invalid. + """ + model_version = ModelVersion(name=name, **kwargs) + key = self.model_version_key(model_version.name, model_version.version) + if not self.registry_idx.client.exists(key): + self.registry_idx.load(data=[model_version.model_dump()], keys=[key]) + return model_version + raise ValueError(f"Model {name} version {model_version.version} already exists") + + def set_model_shards(self, name: str, version: str, shard_keys: List[str]): + key = self.model_version_key(name, version) + if self.registry_idx.client.exists(key): + self.registry_idx.client.json().set(key, "$.shard_keys", shard_keys) + else: + raise ValueError( + f"Failed to set shard keys. Model {name} and version {version} does not exist" + ) + + def _query_version( + self, model_filter: FilterExpression, latest: bool = False + ) -> ModelVersion: + """ + Query model versions using a filter expression. + + Args: + model_filter (FilterExpression): RediSearch filter expression to match models. + latest (bool): If True, sort by creation time to get most recent version. + Defaults to False. + + Returns: + ModelVersion: Matching ModelVersion instance. + + Raises: + ValueError: If no matching model version is found. + """ + query = FilterQuery( + filter_expression=model_filter, + num_results=1, + return_fields=[ + "name", + "description", + "version", + "created_at", + "$.shard_keys", + ], + ) + + if latest: + query.sort_by("created_at", asc=False) + + results = self.registry_idx.query(query) + + if not results: + raise ValueError(f"No model version found matching filter: {model_filter}") + + return ModelVersion( + name=results[0]["name"], + description=results[0]["description"], + version=results[0]["version"], + created_at=results[0]["created_at"], + shard_keys=json.loads(results[0]["$.shard_keys"]), + ) + + def get_version(self, name: str, version: str) -> ModelVersion: + """ + Retrieve metadata for a specific model version. + + Args: + name (str): Model identifier. + version (str): Version identifier. + + Returns: + ModelVersion: ModelVersion instance containing metadata. + + Raises: + ValueError: If the requested model version is not found + """ + model_filter = (Tag("name") == name) & (Tag("version") == version) + return self._query_version(model_filter) + + def get_latest_version(self, name: str) -> ModelVersion: + """ + Get the most recently created version of a model. + + Args: + name (str): Model identifier. + + Returns: + ModelVersion: ModelVersion instance for the latest version. + + Raises: + ValueError: If no versions exist for the model. + """ + model_filter = Tag("name") == name + return self._query_version(model_filter, latest=True) + + def get_all_versions(self, name: str) -> List[ModelVersion]: + """ + Get all versions of a model sorted by creation time (newest first). + + Args: + name (str): Model identifier. + + Returns: + List[ModelVersion]: List of ModelVersion instances for + all versions of the model, sorted by creation time with + newest first. + + Raises: + ValueError: If no versions exist for the model. + """ + query = FilterQuery( + filter_expression=Tag("name") == name, + return_fields=[ + "name", + "description", + "version", + "created_at", + "$.shard_keys", + ], + ) + query.sort_by("created_at", asc=False) + + model_versions: List[ModelVersion] = [] + + for results in self.registry_idx.paginate(query, page_size=50): + if results: + model_versions.extend( + [ + ModelVersion( + name=result["name"], + description=result["description"], + version=result["version"], + created_at=result["created_at"], + shard_keys=json.loads(result["$.shard_keys"]), + ) + for result in results + ] + ) + + if not model_versions: + raise ValueError(f"No versions found for model: {name}") + + return model_versions + + def delete_version(self, name: str, version: str) -> int: + """ + Delete a specific model version from the registry. + + Args: + name (str): Model identifier. + version (str): Version identifier to delete. + + Returns: + int: Number of model versions deleted (0 or 1). + + Raises: + ValueError: If the specified version does not exist. + """ + version_key = self.model_version_key(name, version) + deleted_count = self.registry_idx.drop_keys([version_key]) + return deleted_count + + def clear(self, name: Optional[str] = None) -> int: + """ + Clear model versions from the registry. + + Args: + name (Optional[str]): If provided, only clear versions of this model. + If None, clear all model versions. + + Returns: + int: Number of model versions that were deleted. + """ + if name: + model_versions = self.get_all_versions(name) + model_version_keys = [ + self.model_version_key(model_version.name, model_version.version) + for model_version in model_versions + ] + self.registry_idx.drop_keys(model_version_keys) + deleted_count = len(model_version_keys) + else: + deleted_count = self.registry_idx.clear() + return deleted_count diff --git a/model_store/serialize.py b/model_store/serialize.py new file mode 100644 index 0000000..9184110 --- /dev/null +++ b/model_store/serialize.py @@ -0,0 +1,30 @@ +import pickle +from typing import Any, Protocol + + +class SerializationError(Exception): + """Raised when model serialization or deserialization fails.""" + + pass + + +class Serializer(Protocol): + """Protocol defining the interface for model serialization.""" + + def dumps(self, obj: Any) -> bytes: + """Serialize object to bytes.""" + pass + + def loads(self, data: bytes) -> Any: + """Deserialize bytes to object.""" + pass + + +class PickleSerializer(Serializer): + """Default serializer implementation using pickle.""" + + def dumps(self, obj: Any) -> bytes: + return pickle.dumps(obj, protocol=pickle.HIGHEST_PROTOCOL) + + def loads(self, data: bytes) -> Any: + return pickle.loads(data) diff --git a/model_store/shard_manager.py b/model_store/shard_manager.py new file mode 100644 index 0000000..e383e3a --- /dev/null +++ b/model_store/shard_manager.py @@ -0,0 +1,105 @@ +from typing import Any, Iterator, List + +from model_store.serialize import PickleSerializer, SerializationError, Serializer + + +class ModelShardManager: + """ + Manages sharding and serialization of model data. + + This class handles breaking large model objects into manageable shards and serializing + them for storage. It provides an abstraction layer between raw model objects and their + serialized/sharded representation ready for storage. + + Key features: + - Configurable shard size to optimize for different storage backends + - Pluggable serialization via the Serializer protocol + - Maintains data integrity across sharding/reassembly + + Example: + >>> manager = ModelShardManager() + >>> shards = manager.to_shards(large_model) # Split model into shards + >>> reconstructed = manager.from_shards(shards) # Reassemble model + """ + + def __init__(self, shard_size: int, serializer: Serializer = PickleSerializer()): + """ + Initialize the shard manager. + + Args: + shard_size (int): Maximum size in bytes for each shard. + serializer (Serializer): Serializer implementation to use. Defaults to pickle. + """ + if shard_size <= 0: + raise ValueError("Shard size must be positive") + + self.shard_size = shard_size + self.serializer = serializer + + def _shardify(self, data: bytes) -> Iterator[bytes]: + """ + Split serialized data into fixed-size shards. + + Args: + data (bytes): The full serialized model data. + + Yields: + bytes: Successive shards of the data, each up to shard_size in length. + """ + total_size = len(data) + for start in range(0, total_size, self.shard_size): + yield data[start : start + self.shard_size] + + @staticmethod + def shard_key(model_name: str, model_version: str, idx: int) -> str: + """ + Generate a storage key for a model shard. + + Args: + model_name (str): Name of the model. + model_version (str): Version identifier of the model. + idx (int): Shard index. + + Returns: + str: Formatted storage key for the model shard. + """ + return f"shard:{model_name}:{model_version}:{idx}" + + def to_shards(self, model: Any) -> List[bytes]: + """ + Convert model into smaller chunks (shards) ready for storage. + + Args: + model (Any): The model object to shard. + + Returns: + List[bytes]: List of binary shards derived from the model. + TODO -- returns a generator here + + Raises: + SerializationError: If model serialization fails. + """ + try: + serialized_data = self.serializer.dumps(model) + return self._shardify(serialized_data) + except Exception as e: + raise SerializationError(f"Failed to serialize model: {str(e)}") from e + + def from_shards(self, shards: List[bytes]) -> Any: + """ + Reconstruct a model from its shards. + + Args: + shards (List[bytes]): List of binary shards to reassemble. + + Returns: + Any: The reconstructed model object. + + Raises: + SerializationError: If model deserialization fails. + """ + try: + serialized_data = b"".join(shards) + return self.serializer.loads(serialized_data) + except Exception as e: + raise SerializationError(f"Failed to deserialize model: {str(e)}") from e diff --git a/model_store/store.py b/model_store/store.py new file mode 100644 index 0000000..acaa01c --- /dev/null +++ b/model_store/store.py @@ -0,0 +1,227 @@ +from typing import Any, List, Optional + +from redis import Redis + +from model_store.registry import ModelRegistry, ModelVersion +from model_store.shard_manager import ModelShardManager, SerializationError +from model_store.utils import PIPELINE_BATCH_SIZE, current_timestamp, setup_logger + +logger = setup_logger(__name__) + + +class ModelStoreError(Exception): + """Raised when model store I/O operations fail.""" + pass + + +class ModelStore: + """ + High-level interface for storing and retrieving AI/ML models from Redis. + + The ModelStore provides a simple API for saving and loading models, + handling versioning, metadata, and efficient storage internally. It uses the + ModelRegistry for version tracking and metadata management, and the ModelShardManager + for serialization and sharding. + + Example: + >>> store = ModelStore(redis_client) + >>> store.save_model(model, name="my_model", description="My trained model") + >>> loaded_model = store.load_model("my_model") + """ + + def __init__(self, redis_client: Redis, shard_size: int = 1024 * 1024): + """ + Initialize the ModelStore. + + Args: + redis_client (Redis): An initialized Redis client instance. + shard_size (int, optional): Maximum size in bytes for each model shard. + Defaults to 1MB. + """ + if not isinstance(redis_client, Redis): + raise TypeError("Must provide a valid Redis client instance.") + + self.redis_client = redis_client + self.model_registry = ModelRegistry(redis_client) + self.shard_manager = ModelShardManager(shard_size=shard_size) + + def _to_redis(self, model: Any, name: str, version: str) -> List[str]: + """ + Serialize, shard and store a model in Redis. + + Args: + model (Any): The model object to store. + name (str): Name for the model. + version (str): Version identifier for the model. + + Returns: + List[str]: List of Redis keys where the model shards were stored. + + Raises: + ModelStoreError: If serialization or storage fails. + """ + start_time = current_timestamp() + logger.info("Starting model serialization and storage") + + try: + shard_keys: List[str] = [] + + # Store shards in Redis + with self.redis_client.pipeline(transaction=False) as pipe: + # Serialize and shard the model + logger.debug("Serializing and sharding model") + for i, shard in enumerate(self.shard_manager.to_shards(model)): + skey = self.shard_manager.shard_key(name, version, i) + shard_keys.append(skey) + # Store under shard keys in Redis + pipe.set(skey, shard) + if i % PIPELINE_BATCH_SIZE == 0: + logger.debug("Executing pipeline batch") + pipe.execute() + pipe.execute() + + except SerializationError as e: + raise ModelStoreError(f"Failed to serialize model: {str(e)}") from e + except Exception as e: + # Clean up any shards that were stored before the error + logger.error("Error during storage, cleaning up shards") + self.redis_client.delete(*shard_keys) + raise ModelStoreError(f"Failed to store model shards: {str(e)}") from e + + duration = current_timestamp() - start_time + logger.info(f"Stored model in {len(shard_keys)} shards ({duration:.4f}s)") + return shard_keys + + def save_model(self, model: Any, name: str, **kwargs) -> str: + """ + Store a model in Redis with versioning and metadata. + + Saves the model by: + 1. Creating a model version record in the registry. + 2. Serializing and storing the model in chunks. + 3. Updating the registry with chunk locations. + + Args: + model (Any): The model object to store. + name (str): Name for the model. + **kwargs: Additional model metadata fields (version, + description, shard_keys). + + Returns: + str: The created model version. + + Raises: + ModelStoreError: If model storage or registration fails. + """ + total_start = current_timestamp() + model_version: Optional[ModelVersion] = None + logger.info(f"Starting save operation for model '{name}'") + + try: + # Create model version record + st = current_timestamp() + logger.debug("Creating model version record") + model_version = self.model_registry.add_version(name=name, **kwargs) + logger.info(f"Added version record ({current_timestamp()-st:.4f}s)") + + # Store model chunks and get their keys + logger.debug("Starting model storage") + shard_keys = self._to_redis(model, name, model_version.version) + + # Update model version with shard locations + st = current_timestamp() + logger.debug("Updating model version with shard locations") + self.model_registry.set_model_shards( + name, model_version.version, shard_keys + ) + logger.info(f"Set shards ({current_timestamp()-st:.4f}s)") + + total_duration = current_timestamp() - total_start + logger.info(f"Total save operation completed in {total_duration:.4f}s") + return model_version.version + + except Exception as e: + # Clean up the version record if storage fails + if model_version: + logger.error("Error during save, cleaning up version record") + self.model_registry.delete_version(name, model_version.version) + if not isinstance(e, ModelStoreError): + raise ModelStoreError(f"Failed to save model: {str(e)}") from e + raise + + def _from_redis(self, shard_keys: List[str]) -> Any: + """ + Load and reconstruct a model from its shards in Redis. + + Args: + shard_keys (List[str]): List of Redis keys containing the model shards. + + Returns: + Any: The reconstructed model object. + + Raises: + ModelStoreError: If shard retrieval or deserialization fails + """ + start_time = current_timestamp() + shards: List[bytes] = [] + logger.info("Starting model reconstruction from shards") + + try: + # Retrieve shards from Redis + logger.debug("Retrieving shards from Redis") + with self.redis_client.pipeline(transaction=False) as pipe: + for i, skey in enumerate(shard_keys): + pipe.get(skey) + if i % PIPELINE_BATCH_SIZE == 0: + shards.extend(pipe.execute()) + shards.extend(pipe.execute()) + + # Deserialize the model + logger.debug("Deserializing model from shards") + model = self.shard_manager.from_shards(shards) + + duration = current_timestamp() - start_time + logger.info(f"Loaded model from {len(shard_keys)} shards ({duration:.4f}s)") + return model + + except Exception as e: + raise ModelStoreError(f"Failed to load model: {str(e)}") from e + + def load_model(self, name: str, version: Optional[str] = None) -> Any: + """ + Load a model from Redis by name and optional version. + + Args: + name (str): Unique identifier for the model. + version (Optional[str]): Specific version to load. If None, + loads the latest version. + + Returns: + Any: The reconstructed model object. + + Raises: + ModelStoreError: If model loading fails + """ + total_start = current_timestamp() + logger.info(f"Starting load operation for model '{name}'") + + try: + st = current_timestamp() + if not version: + logger.debug("Retrieving latest version") + model_version = self.model_registry.get_latest_version(name) + else: + logger.debug(f"Retrieving specific version: {version}") + model_version = self.model_registry.get_version(name, version) + + logger.info(f"Retrieved version metadata ({current_timestamp()-st:.4f}s)") + model = self._from_redis(model_version.shard_keys) + + total_duration = current_timestamp() - total_start + logger.info(f"Total load operation completed ({total_duration:.4f}s)") + return model + + except Exception as e: + if not isinstance(e, ModelStoreError): + raise ModelStoreError(f"Failed to load model {name}: {str(e)}") from e + raise diff --git a/model_store/utils.py b/model_store/utils.py new file mode 100644 index 0000000..8ebdad9 --- /dev/null +++ b/model_store/utils.py @@ -0,0 +1,53 @@ +import logging +from datetime import datetime, timezone +from uuid import uuid4 + +#: How many commands to queue in a Redis pipeline before executing. +PIPELINE_BATCH_SIZE = 256 + + +def setup_logger(name): + """Configure the model store logger with appropriate formatting.""" + logger = logging.getLogger(name) + + # Only add handler if logger doesn't already have handlers + if not logger.handlers: + # Create console handler + handler = logging.StreamHandler() + + # Create formatter + formatter = logging.Formatter( + "%(asctime)s.%(msecs)03d - %(name)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + # Add formatter to handler + handler.setFormatter(formatter) + + # Add handler to logger + logger.addHandler(handler) + + # Set default level to INFO + logger.setLevel(logging.INFO) + + return logger + + +def current_timestamp() -> float: + """ 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newline at end of file diff --git a/scripts.py b/scripts.py new file mode 100644 index 0000000..1c54de9 --- /dev/null +++ b/scripts.py @@ -0,0 +1,42 @@ +import subprocess + + +def format(): + subprocess.run(["isort", "./model_store", "--profile", "black"], check=True) + subprocess.run(["black", "./model_store"], check=True) + + +def check_format(): + subprocess.run(["black", "--check", "./model_store"], check=True) + + +def sort_imports(): + subprocess.run(["isort", "./model_store", "./tests/", "--profile", "black"], check=True) + + +def check_sort_imports(): + subprocess.run( + ["isort", "./model_store", "--check-only", "--profile", "black"], check=True + ) + + +def check_lint(): + subprocess.run(["pylint", "--rcfile=.pylintrc", "./model_store"], check=True) + + +def check_mypy(): + subprocess.run(["python", "-m", "mypy", "./model_store"], check=True) + + +def test(): + subprocess.run(["python", "-m", "pytest", "--log-level=CRITICAL"], check=True) + + +def test_verbose(): + subprocess.run( + ["python", "-m", "pytest", "-vv", "-s", "--log-level=CRITICAL"], check=True + ) + + +def test_notebooks(): + subprocess.run(["cd", "docs/", "&&", "poetry run treon", "-v"], check=True)