diff --git a/CLAUDE.md b/CLAUDE.md
index 59f7748..bbedca2 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -17,14 +17,7 @@ This is `worker-tetra`, a RunPod Serverless worker template that provides dynami
- **Function Executor** (`src/function_executor.py:12`): Handles individual function execution with full output capture (stdout, stderr, logs)
- **Class Executor** (`src/class_executor.py:14`): Manages class instantiation and method execution with instance persistence and metadata tracking
-### 2. HuggingFace Model Cache-Ahead (`src/huggingface_cache.py`)
-- **Model Pre-Caching**: Downloads HuggingFace models before user code execution
-- **Cache Validation**: Checks if models are already cached to avoid redundant downloads
-- **Authentication**: Supports HF_TOKEN for private/gated model access
-- **Transfer Acceleration**: Uses hf_transfer when HF_HUB_ENABLE_HF_TRANSFER=1 is set
-- **Transparent Caching**: User code references models without knowing they're pre-cached
-
-### 3. Dependency Management System (`src/dependency_installer.py:14`)
+### 2. Dependency Management System (`src/dependency_installer.py:14`)
- **Python Package Installation**: UV-based package management with environment-aware configuration (Docker vs local)
- **System Package Installation**: APT/Nala-based system dependency handling with acceleration support
- **Differential Installation**: Optimized package installation that skips already-installed packages
@@ -32,25 +25,25 @@ This is `worker-tetra`, a RunPod Serverless worker template that provides dynami
- **System Package Filtering**: Intelligent detection of system-available packages to avoid redundant installation
- **Universal Subprocess Integration**: All subprocess operations use centralized logging utility
-### 4. Universal Subprocess Utility (`src/subprocess_utils.py`)
+### 3. Universal Subprocess Utility (`src/subprocess_utils.py`)
- **Centralized Subprocess Operations**: All subprocess calls use `run_logged_subprocess` for consistency
- **Automatic Logging Integration**: All subprocess output flows through log streamer at DEBUG level
- **Environment-Aware Execution**: Handles Docker vs local environment differences automatically
- **Standardized Error Handling**: Consistent FunctionResponse pattern for all subprocess operations
- **Timeout Management**: Configurable timeouts with proper cleanup on timeout/cancellation
-### 5. Serialization & Protocol Management
+### 4. Serialization & Protocol Management
- **Protocol Definitions** (`src/remote_execution.py:13`): Pydantic models for request/response with validation
- **Serialization Utils** (`src/serialization_utils.py`): CloudPickle-based data serialization for function arguments and results
- **Base Executor** (`src/base_executor.py`): Common execution interface and environment setup
-### 6. Tetra SDK Integration (`tetra-rp/` submodule)
+### 5. Tetra SDK Integration (`tetra-rp/` submodule)
- **Client Interface**: `@remote` decorator for marking functions for remote execution
- **Resource Management**: GPU/CPU configuration and provisioning through LiveServerless objects
- **Live Serverless**: Dynamic infrastructure provisioning with auto-scaling
- **Protocol Buffers**: Communication protocol definitions for distributed execution
-### 7. Testing Infrastructure (`tests/`)
+### 6. Testing Infrastructure (`tests/`)
- **Unit Tests** (`tests/unit/`): Component-level testing for individual modules with mocking
- **Integration Tests** (`tests/integration/`): End-to-end workflow testing with real execution
- **Test Fixtures** (`tests/conftest.py:1`): Shared test data, mock objects, and utility functions
@@ -58,15 +51,15 @@ This is `worker-tetra`, a RunPod Serverless worker template that provides dynami
- **Full Coverage**: All handler tests pass with environment-aware dependency installation
- **Cross-Platform**: Works correctly in both Docker containers and local macOS/Linux environments
-### 8. Build & Deployment Pipeline
+### 7. Build & Deployment Pipeline
- **Docker Containerization**: GPU (`Dockerfile`) and CPU (`Dockerfile-cpu`) image builds
- **CI/CD Pipeline**: Automated testing, linting, and releases (`.github/workflows/`)
- **Quality Gates** (`Makefile:104`): Format checking, type checking, test coverage requirements
- **Release Management**: Automated semantic versioning and Docker Hub deployment
-### 9. Configuration & Constants
+### 8. Configuration & Constants
- **Constants** (`src/constants.py`): System-wide configuration values (NAMESPACE, LARGE_SYSTEM_PACKAGES)
-- **Environment Configuration**: RunPod API integration and HuggingFace cache settings
+- **Environment Configuration**: RunPod API integration
## Architecture
@@ -90,13 +83,12 @@ This is `worker-tetra`, a RunPod Serverless worker template that provides dynami
### Key Patterns
1. **Remote Function Execution**: Functions decorated with `@remote` are automatically executed on RunPod GPU workers
-2. **Composition Pattern**: RemoteExecutor uses specialized components (DependencyInstaller, HuggingFaceCacheAhead, Executors)
+2. **Composition Pattern**: RemoteExecutor uses specialized components (DependencyInstaller, Executors)
3. **Dynamic Dependency Management**: Dependencies specified in decorators are installed at runtime with differential updates
-4. **HuggingFace Cache-Ahead**: Models specified in `hf_models_to_cache` are pre-downloaded before execution
-5. **Universal Subprocess Operations**: All subprocess calls use centralized `run_logged_subprocess` for consistent logging and error handling
-6. **Environment-Aware Configuration**: Automatic Docker vs local environment detection for appropriate installation methods
-7. **Serialization**: Uses cloudpickle + base64 encoding for function arguments and results
-8. **Resource Configuration**: `LiveServerless` objects define GPU requirements, scaling, and worker configuration
+4. **Universal Subprocess Operations**: All subprocess calls use centralized `run_logged_subprocess` for consistent logging and error handling
+5. **Environment-Aware Configuration**: Automatic Docker vs local environment detection for appropriate installation methods
+6. **Serialization**: Uses cloudpickle + base64 encoding for function arguments and results
+7. **Resource Configuration**: `LiveServerless` objects define GPU requirements, scaling, and worker configuration
## Development Commands
@@ -148,8 +140,6 @@ git submodule update --remote --rebase # Update tetra-rp to latest
- `HF_TOKEN`: Optional authentication token for private/gated HuggingFace models
- `HF_HOME=/hf-cache`: HuggingFace cache location, set outside `/root/.cache` to exclude from volume sync
- `DEBIAN_FRONTEND=noninteractive`: Set during system package installation
-- `UV_CACHE_DIR`: Package cache configuration
-- `VIRTUAL_ENV`: Virtual environment path configuration
### Resource Configuration
Configure GPU resources using `LiveServerless` objects:
@@ -205,7 +195,6 @@ gpu_config = LiveServerless(
│ ├── function_executor.py # Function execution with output capture
│ ├── class_executor.py # Class execution with persistence
│ ├── dependency_installer.py # Python and system dependency management
-│ ├── huggingface_cache.py # HuggingFace model cache-ahead system
│ ├── serialization_utils.py # CloudPickle serialization utilities
│ ├── base_executor.py # Common execution interface
│ ├── constants.py # System-wide configuration constants
diff --git a/Makefile b/Makefile
index 2f84a0e..e6c24ed 100644
--- a/Makefile
+++ b/Makefile
@@ -24,6 +24,7 @@ update: # Upgrade all dependencies
uv sync --upgrade --all-groups
uv lock --upgrade
git submodule update --remote
+ make protocols
clean: # Remove build artifacts and cache files
rm -rf dist build *.egg-info
@@ -32,7 +33,12 @@ clean: # Remove build artifacts and cache files
find . -type f -name "*.pkl" -delete
setup: dev # Initialize project, sync deps, update submodules
- git submodule update --init --recursive
+ @if [ ! -f "tetra-rp/.git" ]; then \
+ git submodule update --init --recursive; \
+ fi
+ make protocols
+
+protocols: # Copy remote_execution protocol from submodule
cp tetra-rp/src/tetra_rp/protos/remote_execution.py src/
build: # Build both GPU and CPU Docker images
diff --git a/docs/Endpoint Persistence.md b/docs/Endpoint Persistence.md
index f492615..e29a5bd 100644
--- a/docs/Endpoint Persistence.md
+++ b/docs/Endpoint Persistence.md
@@ -14,7 +14,7 @@
- First container boots, and checks for volume presence and endpoint workspace. Create if not found.
- 1. Container will proceed to download any system, python or HF pre-cache instructed from the remote decorator.
+ 1. Container will proceed to download any system or python dependencies in parallel as instructed from the remote decorator.
2. Container runs its job.
@@ -45,7 +45,7 @@ graph TD
H -->|No| J[Launch CDR Daemon
Hydrate /app ← Workspace
Then Monitor /app →
Workspace]
- G --> K[Download Dependencies
System + Python + HF]
+ G --> K[Download Dependencies
System + Python]
I --> K
J --> L[Skip Downloads
Use Cached Data]
diff --git a/src/huggingface_cache.py b/src/huggingface_cache.py
deleted file mode 100644
index 9afc41f..0000000
--- a/src/huggingface_cache.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""
-HuggingFace model download caching.
-
-This module provides cache-ahead downloads for HuggingFace models and datasets.
-"""
-
-import os
-import asyncio
-import logging
-
-from huggingface_hub import snapshot_download, scan_cache_dir
-from huggingface_hub.errors import CacheNotFound
-from remote_execution import FunctionResponse
-
-
-class HuggingFaceCacheAhead:
- """Cache-ahead downloads for HuggingFace models and files."""
-
- def __init__(self):
- self.logger = logging.getLogger(__name__)
-
- async def cache_model_download_async(
- self, model_id: str, revision: str = "main"
- ) -> FunctionResponse:
- """
- Async wrapper for pre-downloading HuggingFace models.
-
- Args:
- model_id: HuggingFace model identifier
- revision: Model revision/branch
-
- Returns:
- FunctionResponse with download results
- """
- return await asyncio.to_thread(self.cache_model_download, model_id, revision)
-
- def cache_model_download(
- self, model_id: str, revision: str = "main"
- ) -> FunctionResponse:
- """
- Pre-download HuggingFace model using HF Hub's native caching.
-
- This method downloads the complete model snapshot to HF's standard cache
- location. HF Hub automatically uses hf_transfer/hf_xet acceleration when
- HF_HUB_ENABLE_HF_TRANSFER=1 is set in the environment.
-
- Args:
- model_id: HuggingFace model identifier
- revision: Model revision/branch
-
- Returns:
- FunctionResponse with download results
- """
- try:
- # Check if model is already cached
- cache_hit = self._is_model_cached(model_id, revision)
- if cache_hit:
- self.logger.info(f"Model {model_id} already cached, skipping download")
- return FunctionResponse(
- success=True,
- stdout=f"Model {model_id} already cached (cache hit)",
- )
-
- self.logger.info(f"Started downloading model {model_id}")
-
- # Get HF authentication token if available
- hf_token = os.environ.get("HF_TOKEN")
-
- # Use HF Hub's native snapshot download with acceleration
- snapshot_path = snapshot_download(
- repo_id=model_id,
- revision=revision,
- token=hf_token,
- # HF automatically uses HF_HOME/HF_HUB_CACHE from environment
- # and applies hf_transfer acceleration when available
- )
-
- success_message = f"Successfully cached model {model_id} to {snapshot_path}"
- self.logger.info(success_message)
-
- return FunctionResponse(
- success=True,
- stdout=success_message,
- )
-
- except Exception as e:
- return FunctionResponse(
- success=False,
- error=f"Failed to cache-ahead model {model_id}: {str(e)}",
- )
-
- def _is_model_cached(self, model_id: str, revision: str = "main") -> bool:
- """
- Check if a model is already cached locally.
-
- Args:
- model_id: HuggingFace model identifier
- revision: Model revision/branch/commit hash
-
- Returns:
- True if model is cached, False otherwise
- """
- try:
- cache_info = scan_cache_dir()
- for repo in cache_info.repos:
- if repo.repo_id == model_id:
- # If revision is "main", accept any cached version of the model
- if revision == "main":
- return len(repo.revisions) > 0
-
- # Check for specific revision by commit hash
- for rev in repo.revisions:
- if rev.commit_hash.startswith(revision) or revision.startswith(
- rev.commit_hash
- ):
- return True
- return False
- except CacheNotFound:
- # Cache directory doesn't exist yet - this is expected on first use
- self.logger.debug(
- f"Cache directory not found for {model_id}, will be created on download"
- )
- return False
- except Exception as e:
- self.logger.debug(f"Cache check failed for {model_id}: {e}")
- return False
diff --git a/src/remote_executor.py b/src/remote_executor.py
index 39e1d03..1577e46 100644
--- a/src/remote_executor.py
+++ b/src/remote_executor.py
@@ -1,7 +1,6 @@
import logging
import asyncio
from typing import List, Any
-from huggingface_cache import HuggingFaceCacheAhead
from remote_execution import FunctionRequest, FunctionResponse, RemoteExecutorStub
from dependency_installer import DependencyInstaller
from function_executor import FunctionExecutor
@@ -25,7 +24,6 @@ def __init__(self):
self.dependency_installer = DependencyInstaller()
self.function_executor = FunctionExecutor()
self.class_executor = ClassExecutor()
- self.hf_cache = HuggingFaceCacheAhead()
self.cache_sync = CacheSyncManager()
async def ExecuteFunction(self, request: FunctionRequest) -> FunctionResponse:
@@ -54,11 +52,7 @@ async def ExecuteFunction(self, request: FunctionRequest) -> FunctionResponse:
try:
# Hydrate cache from volume if needed (before any installations)
- has_installations = (
- request.dependencies
- or request.system_dependencies
- or request.hf_models_to_cache
- )
+ has_installations = request.dependencies or request.system_dependencies
if has_installations:
await self.cache_sync.hydrate_from_volume()
@@ -148,13 +142,6 @@ async def _install_dependencies_parallel(
tasks.append(task)
task_names.append("python_dependencies")
- # Add HF model cache-ahead tasks
- if request.hf_models_to_cache:
- for model_id in request.hf_models_to_cache:
- task = self.hf_cache.cache_model_download_async(model_id)
- tasks.append(task)
- task_names.append(f"hf_model_{model_id}")
-
if not tasks:
return FunctionResponse(success=True, stdout="No dependencies to install")
@@ -189,15 +176,6 @@ async def _install_dependencies_sequential(
return sys_installed
self.logger.info(sys_installed.stdout)
- # Cache-ahead HuggingFace models if requested (should not happen when acceleration disabled)
- if request.accelerate_downloads and request.hf_models_to_cache:
- for model_id in request.hf_models_to_cache:
- cache_result = self.hf_cache.cache_model_download(model_id)
- if cache_result.success:
- self.logger.info(cache_result.stdout)
- else:
- self.logger.warning(cache_result.error)
-
# Install Python dependencies next
if request.dependencies:
py_installed = self.dependency_installer.install_dependencies(
diff --git a/src/tests/test_hf_accelerated_input.json b/src/tests/test_hf_accelerated_input.json
deleted file mode 100644
index 31c764e..0000000
--- a/src/tests/test_hf_accelerated_input.json
+++ /dev/null
@@ -1,11 +0,0 @@
-{
- "input": {
- "function_name": "test_hf_acceleration_with_volume",
- "function_code": "def test_hf_acceleration_with_volume():\n import os\n import time\n from transformers import AutoTokenizer\n \n start_time = time.time()\n \n # Test HF model download with acceleration enabled\n model_name = 'gpt2'\n print(f'Testing accelerated HF model download: {model_name}')\n \n tokenizer = AutoTokenizer.from_pretrained(model_name)\n \n download_time = time.time() - start_time\n \n # Check cache paths\n cache_info = {\n 'hf_home': os.environ.get('HF_HOME'),\n 'transformers_cache': os.environ.get('TRANSFORMERS_CACHE'),\n 'virtual_env': os.environ.get('VIRTUAL_ENV'),\n 'download_time': round(download_time, 2)\n }\n \n print(f'Download completed in {download_time:.2f}s')\n print(f'Cache paths: {cache_info}')\n \n return {\n 'model_name': model_name,\n 'vocab_size': tokenizer.vocab_size,\n 'cache_info': cache_info,\n 'acceleration_enabled': True,\n 'test_completed': True\n }\n",
- "dependencies": ["transformers"],
- "accelerate_downloads": true,
- "hf_models_to_cache": ["gpt2"],
- "args": [],
- "kwargs": {}
- }
-}
\ No newline at end of file
diff --git a/tests/integration/test_handler_integration.py b/tests/integration/test_handler_integration.py
index 6b7fdc6..0eca974 100644
--- a/tests/integration/test_handler_integration.py
+++ b/tests/integration/test_handler_integration.py
@@ -1,10 +1,8 @@
-import os
import pytest
import json
import base64
import cloudpickle
from pathlib import Path
-from unittest.mock import patch
from handler import handler, RemoteExecutor
from remote_execution import FunctionRequest
@@ -464,268 +462,3 @@ def process_data(data):
assert decoded_result["sum"] == 15
assert decoded_result["name"] == "test"
assert decoded_result["processed"] is True
-
- @pytest.mark.integration
- @pytest.mark.asyncio
- async def test_hf_cache_ahead_basic(self):
- """Test basic HuggingFace model cache-ahead functionality."""
- event = {
- "input": {
- "function_name": "test_model_usage",
- "function_code": """
-def test_model_usage():
- from transformers import AutoTokenizer
-
- # Use the pre-cached model
- tokenizer = AutoTokenizer.from_pretrained("gpt2")
- result = tokenizer("Hello world")
-
- return {
- "tokens": len(result["input_ids"]),
- "model": "gpt2"
- }
-""",
- "dependencies": ["transformers"],
- "hf_models_to_cache": ["gpt2"],
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- result = await handler(event)
-
- assert result["success"] is True
- decoded_result = cloudpickle.loads(base64.b64decode(result["result"]))
- assert decoded_result["model"] == "gpt2"
- assert decoded_result["tokens"] > 0
-
- @pytest.mark.integration
- @pytest.mark.asyncio
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- async def test_hf_cache_hit_scenario(self, mock_is_cached):
- """Test cache hit detection prevents redundant downloads."""
- # First call: simulate cache miss
- mock_is_cached.return_value = False
-
- event = {
- "input": {
- "function_name": "first_call",
- "function_code": """
-def first_call():
- return "first"
-""",
- "hf_models_to_cache": ["gpt2"],
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- result1 = await handler(event)
- assert result1["success"] is True
-
- # Second call: simulate cache hit
- mock_is_cached.return_value = True
-
- result2 = await handler(event)
- assert result2["success"] is True
- # Verify cache hit message in stdout
- assert "already cached" in result2["stdout"] or "cache hit" in result2["stdout"]
-
- @pytest.mark.integration
- @pytest.mark.asyncio
- async def test_hf_multiple_models_parallel(self):
- """Test downloading multiple HF models in parallel."""
- event = {
- "input": {
- "function_name": "multi_model_test",
- "function_code": """
-def multi_model_test():
- return "models cached"
-""",
- "hf_models_to_cache": ["gpt2", "distilbert-base-uncased"],
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- result = await handler(event)
-
- assert result["success"] is True
- # Both models should be mentioned in stdout
- stdout_lower = result["stdout"].lower()
- assert "gpt2" in stdout_lower or "model" in stdout_lower
-
- @pytest.mark.integration
- @pytest.mark.asyncio
- @patch.dict(os.environ, {"HF_TOKEN": "test_token_value"})
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- async def test_hf_authentication_with_token(
- self, mock_is_cached, mock_snapshot_download
- ):
- """Test HF_TOKEN is used for authentication."""
- mock_is_cached.return_value = False
- mock_snapshot_download.return_value = "/cache/path/private-model"
-
- event = {
- "input": {
- "function_name": "private_model_test",
- "function_code": """
-def private_model_test():
- return "authenticated"
-""",
- "hf_models_to_cache": ["private/model"],
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- result = await handler(event)
-
- assert result["success"] is True
- # Verify token was passed to snapshot_download
- mock_snapshot_download.assert_called()
- call_kwargs = mock_snapshot_download.call_args.kwargs
- assert call_kwargs["token"] == "test_token_value"
-
- @pytest.mark.integration
- @pytest.mark.asyncio
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- async def test_hf_cache_failure_continues_execution(
- self, mock_is_cached, mock_snapshot_download
- ):
- """Test that cache failures don't block function execution."""
- mock_is_cached.return_value = False
- mock_snapshot_download.side_effect = Exception("Network error")
-
- event = {
- "input": {
- "function_name": "resilient_test",
- "function_code": """
-def resilient_test():
- return "execution continues despite cache failure"
-""",
- "hf_models_to_cache": ["invalid-model"],
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- result = await handler(event)
-
- # Execution should fail at dependency installation stage
- # since cache-ahead failed
- assert result["success"] is False
- assert (
- "Network error" in result["error"] or "Failed to cache" in result["error"]
- )
-
- @pytest.mark.integration
- @pytest.mark.asyncio
- async def test_hf_cache_with_custom_revision(self):
- """Test caching specific model revisions."""
- event = {
- "input": {
- "function_name": "revision_test",
- "function_code": """
-def revision_test():
- return "revision cached"
-""",
- "hf_models_to_cache": ["gpt2"], # Default revision (main)
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- result = await handler(event)
-
- assert result["success"] is True
-
- @pytest.mark.integration
- @pytest.mark.asyncio
- async def test_hf_cache_ahead_prevents_redownload(self):
- """
- Test that cache-ahead prevents re-downloads when user code references the model.
-
- This is the critical test: User's code doesn't know model was pre-cached,
- but should use cached version instead of downloading from network.
- """
- import time
-
- # Step 1: Cache-ahead the model WITHOUT using it
- cache_event = {
- "input": {
- "function_name": "just_cache",
- "function_code": """
-def just_cache():
- return "model should be cached now"
-""",
- "hf_models_to_cache": ["gpt2"],
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- cache_result = await handler(cache_event)
- assert cache_result["success"] is True
-
- # Step 2: User code loads the model (doesn't know it's cached)
- # If cache works, this should be FAST (< 1 second)
- # If cache fails, this downloads from network (> 5 seconds)
- use_event = {
- "input": {
- "function_name": "use_cached_model",
- "function_code": """
-def use_cached_model():
- import time
- from transformers import AutoTokenizer
-
- start = time.time()
-
- # User code doesn't know model was pre-cached
- tokenizer = AutoTokenizer.from_pretrained("gpt2")
-
- load_time = time.time() - start
-
- return {
- "load_time_seconds": round(load_time, 2),
- "vocab_size": tokenizer.vocab_size,
- "cache_was_used": load_time < 5.0 # Cache hit should be < 5s
- }
-""",
- "dependencies": ["transformers"],
- "accelerate_downloads": True,
- "args": [],
- "kwargs": {},
- }
- }
-
- start_time = time.time()
- use_result = await handler(use_event)
- total_time = time.time() - start_time
-
- assert use_result["success"] is True
- decoded_result = cloudpickle.loads(base64.b64decode(use_result["result"]))
-
- # Verify model loaded successfully
- assert decoded_result["vocab_size"] == 50257 # GPT2 vocab size
-
- # Critical assertion: model load was fast (cache hit)
- assert decoded_result["cache_was_used"] is True, (
- f"Model took {decoded_result['load_time_seconds']}s to load. "
- f"Expected < 5s (cache hit), suggesting re-download occurred."
- )
-
- # Total execution should also be fast
- assert total_time < 30, (
- f"Total execution took {total_time:.2f}s. "
- f"Should be < 30s with cached model."
- )
diff --git a/tests/unit/test_huggingface_cache.py b/tests/unit/test_huggingface_cache.py
deleted file mode 100644
index 1106daf..0000000
--- a/tests/unit/test_huggingface_cache.py
+++ /dev/null
@@ -1,172 +0,0 @@
-"""Tests for HuggingFaceCacheAhead component."""
-
-import os
-from unittest.mock import patch, Mock
-import pytest
-
-from huggingface_cache import HuggingFaceCacheAhead
-from remote_execution import FunctionResponse
-
-
-class TestHuggingFaceCacheAhead:
- """Test HuggingFace cache-ahead functionality."""
-
- def setup_method(self):
- """Setup for each test method."""
- self.hf_cache = HuggingFaceCacheAhead()
-
- @patch("huggingface_cache.scan_cache_dir")
- def test_is_model_cached_returns_true_when_cached(self, mock_scan):
- """Test cache detection returns True when model is cached."""
- # Mock cache info with our model present
- mock_repo = Mock()
- mock_repo.repo_id = "gpt2"
- mock_rev = Mock()
- mock_rev.commit_hash = "main"
- mock_repo.revisions = [mock_rev]
-
- mock_cache_info = Mock()
- mock_cache_info.repos = [mock_repo]
- mock_scan.return_value = mock_cache_info
-
- result = self.hf_cache._is_model_cached("gpt2", "main")
- assert result is True
-
- @patch("huggingface_cache.scan_cache_dir")
- def test_is_model_cached_returns_false_when_not_cached(self, mock_scan):
- """Test cache detection returns False when model is not cached."""
- # Mock empty cache
- mock_cache_info = Mock()
- mock_cache_info.repos = []
- mock_scan.return_value = mock_cache_info
-
- result = self.hf_cache._is_model_cached("gpt2", "main")
- assert result is False
-
- @patch("huggingface_cache.scan_cache_dir")
- def test_is_model_cached_returns_false_on_error(self, mock_scan):
- """Test cache detection returns False when cache check fails."""
- mock_scan.side_effect = Exception("Cache error")
-
- result = self.hf_cache._is_model_cached("gpt2", "main")
- assert result is False
-
- @patch.dict(os.environ, {"HF_TOKEN": "test_token_12345"})
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- def test_cache_model_download_uses_hf_token(
- self, mock_is_cached, mock_snapshot_download
- ):
- """Test that HF_TOKEN is passed to snapshot_download when present."""
- mock_is_cached.return_value = False
- mock_snapshot_download.return_value = "/cache/path/gpt2"
-
- result = self.hf_cache.cache_model_download("gpt2")
-
- assert result.success is True
- mock_snapshot_download.assert_called_once_with(
- repo_id="gpt2", revision="main", token="test_token_12345"
- )
-
- @patch.dict(os.environ, {}, clear=True)
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- def test_cache_model_download_without_token(
- self, mock_is_cached, mock_snapshot_download
- ):
- """Test that None is passed when HF_TOKEN is not present."""
- mock_is_cached.return_value = False
- mock_snapshot_download.return_value = "/cache/path/gpt2"
-
- result = self.hf_cache.cache_model_download("gpt2")
-
- assert result.success is True
- mock_snapshot_download.assert_called_once_with(
- repo_id="gpt2", revision="main", token=None
- )
-
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- def test_cache_model_download_skips_when_cached(self, mock_is_cached):
- """Test that download is skipped when model is already cached."""
- mock_is_cached.return_value = True
-
- result = self.hf_cache.cache_model_download("gpt2")
-
- assert result.success is True
- assert "already cached" in result.stdout
- assert "cache hit" in result.stdout
-
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- def test_cache_model_download_success(self, mock_is_cached, mock_snapshot_download):
- """Test successful model download."""
- mock_is_cached.return_value = False
- mock_snapshot_download.return_value = "/cache/path/gpt2"
-
- result = self.hf_cache.cache_model_download("gpt2")
-
- assert result.success is True
- assert "gpt2" in result.stdout
- assert "/cache/path/gpt2" in result.stdout
-
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- def test_cache_model_download_handles_network_error(
- self, mock_is_cached, mock_snapshot_download
- ):
- """Test error handling for network failures."""
- mock_is_cached.return_value = False
- mock_snapshot_download.side_effect = Exception("Network error")
-
- result = self.hf_cache.cache_model_download("gpt2")
-
- assert result.success is False
- assert "Failed to cache-ahead" in result.error
- assert "gpt2" in result.error
-
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- def test_cache_model_download_handles_invalid_model(
- self, mock_is_cached, mock_snapshot_download
- ):
- """Test error handling for invalid model IDs."""
- mock_is_cached.return_value = False
- mock_snapshot_download.side_effect = Exception("Model not found")
-
- result = self.hf_cache.cache_model_download("invalid-model-id")
-
- assert result.success is False
- assert "Failed to cache-ahead" in result.error
-
- @pytest.mark.asyncio
- @patch("huggingface_cache.asyncio.to_thread")
- async def test_cache_model_download_async_delegates_to_sync(self, mock_to_thread):
- """Test async wrapper properly delegates to sync method."""
- mock_response = FunctionResponse(success=True, stdout="Model cached")
- mock_to_thread.return_value = mock_response
-
- result = await self.hf_cache.cache_model_download_async("gpt2", "v1.0")
-
- mock_to_thread.assert_called_once()
- call_args = mock_to_thread.call_args
- assert call_args[0][0] == self.hf_cache.cache_model_download
- assert call_args[0][1] == "gpt2"
- assert call_args[0][2] == "v1.0"
- assert result == mock_response
-
- @patch.dict(os.environ, {}, clear=True)
- @patch("huggingface_cache.snapshot_download")
- @patch("huggingface_cache.HuggingFaceCacheAhead._is_model_cached")
- def test_cache_model_download_with_custom_revision(
- self, mock_is_cached, mock_snapshot_download
- ):
- """Test downloading specific model revision."""
- mock_is_cached.return_value = False
- mock_snapshot_download.return_value = "/cache/path/gpt2-v2"
-
- result = self.hf_cache.cache_model_download("gpt2", revision="v2.0")
-
- assert result.success is True
- mock_snapshot_download.assert_called_once_with(
- repo_id="gpt2", revision="v2.0", token=None
- )
diff --git a/tests/unit/test_remote_executor.py b/tests/unit/test_remote_executor.py
index f801805..379525e 100644
--- a/tests/unit/test_remote_executor.py
+++ b/tests/unit/test_remote_executor.py
@@ -263,45 +263,3 @@ async def test_no_hydration_without_dependencies(self):
# Verify hydration was NOT called (no dependencies)
mock_hydrate.assert_not_called()
-
- @pytest.mark.asyncio
- async def test_hydration_with_hf_models(self):
- """Test that hydrate_from_volume is called when hf_models_to_cache is present."""
- request = FunctionRequest(
- function_name="test_func",
- function_code="def test_func(): return 'test'",
- hf_models_to_cache=["bert-base-uncased"],
- args=[],
- kwargs={},
- )
-
- with (
- patch.object(
- self.executor.cache_sync,
- "hydrate_from_volume",
- new_callable=AsyncMock,
- ) as mock_hydrate,
- patch.object(
- self.executor.cache_sync,
- "mark_baseline",
- ) as mock_baseline,
- patch.object(
- self.executor.hf_cache,
- "cache_model_download_async",
- new_callable=AsyncMock,
- ) as mock_hf_cache,
- patch.object(self.executor.function_executor, "execute") as mock_execute,
- ):
- from remote_execution import FunctionResponse
-
- mock_hf_cache.return_value = FunctionResponse(
- success=True, stdout="Model cached"
- )
- mock_execute.return_value = Mock(success=True, result="encoded_result")
-
- await self.executor.ExecuteFunction(request)
-
- # Verify hydration was called (hf_models present)
- mock_hydrate.assert_called_once()
- # Verify baseline was marked
- mock_baseline.assert_called_once()
diff --git a/tetra-rp b/tetra-rp
index 80fe195..d64b7f2 160000
--- a/tetra-rp
+++ b/tetra-rp
@@ -1 +1 @@
-Subproject commit 80fe195d1e4f97a8f874bd365ed92714e3fbf1f9
+Subproject commit d64b7f22e1d320e775a7a4561b04e4bd26b0470f
diff --git a/uv.lock b/uv.lock
index 9dbdbab..ef96dd3 100644
--- a/uv.lock
+++ b/uv.lock
@@ -29,7 +29,7 @@ wheels = [
[[package]]
name = "aiohttp"
-version = "3.12.15"
+version = "3.13.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohappyeyeballs" },
@@ -41,76 +41,76 @@ dependencies = [
{ name = "propcache" },
{ name = "yarl" },
]
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[package.optional-dependencies]
@@ -118,6 +118,7 @@ speedups = [
{ name = "aiodns" },
{ name = "brotli", marker = "platform_python_implementation == 'CPython'" },
{ name = "brotlicffi", marker = "platform_python_implementation != 'CPython'" },
+ { name = "zstandard", marker = "platform_python_implementation == 'CPython'" },
]
[[package]]
@@ -156,7 +157,7 @@ wheels = [
[[package]]
name = "anyio"
-version = "4.10.0"
+version = "4.11.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "exceptiongroup", marker = "python_full_version < '3.11'" },
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