check and confirm docker compose on gpu

Dockerfile

@@ -1,16 +1,31 @@
-# Stage 1: Base image with Poetry and dependencies
-FROM pytorch/pytorch:2.4.0-cuda12.4-cudnn9-runtime as base
+# Stage 1: Base image with CUDA 12.2, cuDNN 9, and minimal runtime for PyTorch
+FROM nvidia/cuda:12.2.0-runtime-ubuntu20.04 as base
 
 LABEL maintainer="Soutrik soutrik1991@gmail.com" \
       description="Base Docker image for running a Python app with Poetry and GPU support."
 
-# Install Poetry and necessary system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends curl && \
-    curl -sSL https://install.python-poetry.org | python3 - && \
+# Install necessary system dependencies, including Python 3.10
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update && apt-get install -y --no-install-recommends \
+    python3.10 \
+    python3.10-venv \
+    python3.10-dev \
+    python3-pip \
+    curl \
+    git \
+    build-essential && \
     apt-get clean && rm -rf /var/lib/apt/lists/*
 
-# Add Poetry to the PATH explicitly
-ENV PATH="/root/.local/bin:$PATH"
+# Set Python 3.10 as the default
+RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1 && \
+    update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1 && \
+    python --version
+
+# Install Poetry
+RUN curl -sSL https://install.python-poetry.org | python3 - && \
+    ln -s /root/.local/bin/poetry /usr/local/bin/poetry
 
 # Configure Poetry environment
 ENV POETRY_NO_INTERACTION=1 \
@@ -23,7 +38,7 @@ WORKDIR /app
 # Copy pyproject.toml and poetry.lock to install dependencies
 COPY pyproject.toml poetry.lock /app/
 
-# Install dependencies without installing the package itself
+# Install Python dependencies without building the app itself
 RUN --mount=type=cache,target=/tmp/poetry_cache poetry install --only main --no-root
 
 # Stage 2: Build stage for the application
@@ -38,13 +53,11 @@ COPY main.py /app/main.py
 # Stage 3: Final runtime stage
 FROM base as runner
 
-# Copy application source code and necessary files from builder stage
+# Copy application source code and dependencies from the builder stage
 COPY --from=builder /app/src /app/src
 COPY --from=builder /app/configs /app/configs
 COPY --from=builder /app/.project-root /app/.project-root
 COPY --from=builder /app/main.py /app/main.py
-
-# Copy virtual environment from the builder stage
 COPY --from=builder /app/.venv /app/.venv
 
 # Copy client files
@@ -53,8 +66,11 @@ COPY run_client.sh /app/run_client.sh
 # Set the working directory to /app
 WORKDIR /app
 
-# Set the environment path to use the virtual environment
+# Add virtual environment to PATH
 ENV PATH="/app/.venv/bin:$PATH"
 
-# Default command
+# Install PyTorch with CUDA 12.2 support (adjusted for compatibility)
+RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu122
+
+# Default command to run the application
 CMD ["python", "-m", "main"]

docker-compose.yaml

@@ -19,6 +19,13 @@ services:
       - default
     env_file:
       - .env
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
 
   eval:
     build:
@@ -38,6 +45,13 @@ services:
       - default
     env_file:
       - .env
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
   
 
   server:
@@ -61,6 +75,13 @@ services:
       - .env
     ports:
       - "8080:8080"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
       
   client:
     build:
@@ -82,6 +103,14 @@ services:
     env_file:
       - .env
 
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+
   
 volumes:
   data:

ec2_runner_setup.md

@@ -335,4 +335,23 @@ If successful, it should show details of the T4 GPU.
 - **PyTorch**: Installed with CUDA 12.2 support
 
 This setup ensures your system is ready for deep learning workloads with the T4 GPU.
-```
+
+Install conda and create a new environment for the project
+Install pytorch and torchvision in the new environment
+Install other dependencies like numpy, pandas, matplotlib, etc.
+Run the project code in the new environment
+>>> import torch
+>>> print(torch.cuda.is_available())
+>>> print(torch.cuda.get_device_name(0))
+>>> print(torch.version.cuda)
+```
+__CUDA Docker Setup__:
+```bash
+# If you are using docker and want to run a container with CUDA support
+sudo apt install -y nvidia-container-toolkit
+nvidia-ctk --version
+sudo systemctl restart docker
+sudo systemctl status docker
+docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu20.04 nvidia-smi
+docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu20.04 nvcc --version
+```

main.py

@@ -1,3 +1,5 @@
-from loguru import logger
+import torch
 
-logger.info("Hello, World!")
+print(torch.cuda.is_available())  # Should return True if our GPU is enabled
+print(torch.cuda.get_device_name(0))  # Should return "Tesla T4" if our GPU is enabled
+print(torch.version.cuda)  # Should return "12.4" if our GPU is enabled

poetry.lock

@@ -246,92 +246,93 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.5"
+version = "3.11.6"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
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+    {file = "aiohttp-3.11.6.tar.gz", hash = "sha256:fd9f55c1b51ae1c20a1afe7216a64a88d38afee063baa23c7fce03757023c999"},
 ]
 
 [package.dependencies]
 aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
+async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
@@ -544,8 +545,10 @@ files = [
 ]
 
 [package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
@@ -646,6 +649,9 @@ files = [
     {file = "asyncpg-0.30.0.tar.gz", hash = "sha256:c551e9928ab6707602f44811817f82ba3c446e018bfe1d3abecc8ba5f3eac851"},
 ]
 
+[package.dependencies]
+async-timeout = {version = ">=4.0.3", markers = "python_version < \"3.11.0\""}
+
 [package.extras]
 docs = ["Sphinx (>=8.1.3,<8.2.0)", "sphinx-rtd-theme (>=1.2.2)"]
 gssauth = ["gssapi", "sspilib"]
@@ -883,6 +889,8 @@ mypy-extensions = ">=0.4.3"
 packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
@@ -1614,6 +1622,9 @@ files = [
     {file = "coverage-7.6.7.tar.gz", hash = "sha256:d79d4826e41441c9a118ff045e4bccb9fdbdcb1d02413e7ea6eb5c87b5439d24"},
 ]
 
+[package.dependencies]
+tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
+
 [package.extras]
 toml = ["tomli"]
 
@@ -2133,6 +2144,20 @@ files = [
     {file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"},
 ]
 
+[[package]]
+name = "exceptiongroup"
+version = "1.2.2"
+description = "Backport of PEP 654 (exception groups)"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
+    {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
+]
+
+[package.extras]
+test = ["pytest (>=6)"]
+
 [[package]]
 name = "fastapi"
 version = "0.115.5"
@@ -4248,6 +4273,9 @@ files = [
     {file = "multidict-6.1.0.tar.gz", hash = "sha256:22ae2ebf9b0c69d206c003e2f6a914ea33f0a932d4aa16f236afc049d9958f4a"},
 ]
 
+[package.dependencies]
+typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -4768,7 +4796,7 @@ files = [
 ]
 
 [package.dependencies]
-numpy = {version = ">=1.23.2", markers = "python_version == \"3.11\""}
+numpy = {version = ">=1.22.4", markers = "python_version < \"3.11\""}
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
 tzdata = ">=2022.7"
@@ -5791,9 +5819,11 @@ files = [
 
 [package.dependencies]
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
+exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
 pluggy = ">=1.5,<2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
@@ -6048,6 +6078,9 @@ files = [
     {file = "redis-5.0.8.tar.gz", hash = "sha256:0c5b10d387568dfe0698c6fad6615750c24170e548ca2deac10c649d463e9870"},
 ]
 
+[package.dependencies]
+async-timeout = {version = ">=4.0.3", markers = "python_full_version < \"3.11.3\""}
+
 [package.extras]
 hiredis = ["hiredis (>1.0.0)"]
 ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
@@ -7245,6 +7278,17 @@ dev = ["tokenizers[testing]"]
 docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
 testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
 
+[[package]]
+name = "tomli"
+version = "2.1.0"
+description = "A lil' TOML parser"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "tomli-2.1.0-py3-none-any.whl", hash = "sha256:a5c57c3d1c56f5ccdf89f6523458f60ef716e210fc47c4cfb188c5ba473e0391"},
+    {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"},
+]
+
 [[package]]
 name = "tomlkit"
 version = "0.13.2"
@@ -7620,6 +7664,7 @@ h11 = ">=0.8"
 httptools = {version = ">=0.5.0", optional = true, markers = "extra == \"standard\""}
 python-dotenv = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
 pyyaml = {version = ">=5.1", optional = true, markers = "extra == \"standard\""}
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
 uvloop = {version = ">=0.14.0,<0.15.0 || >0.15.0,<0.15.1 || >0.15.1", optional = true, markers = "(sys_platform != \"win32\" and sys_platform != \"cygwin\") and platform_python_implementation != \"PyPy\" and extra == \"standard\""}
 watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
 websockets = {version = ">=10.4", optional = true, markers = "extra == \"standard\""}
@@ -8177,5 +8222,5 @@ type = ["pytest-mypy"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "3.11.9"
-content-hash = "10964bfc333a340ec3ed898145e7078c04f58357f2b1554b741eb596f6e1e0a6"
+python-versions = "3.10.15"
+content-hash = "f716178afcbcd2f78554e71f39d279ce266c633a668a8246716f601c76801647"

pyproject.toml

@@ -7,7 +7,7 @@ license = "Apache-2.0"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = "3.11.9"
+python = "3.10.15"
 black = "24.8.0"
 coverage = ">=7.6.1"
 hydra-colorlog = "1.2.0"

src/litserve_benchmark.py

@@ -6,6 +6,11 @@ import requests
 import psutil
 from urllib.request import urlopen
 import matplotlib.pyplot as plt
+from loguru import logger
+import os
+from dotenv import load_dotenv, find_dotenv
+
+load_dotenv(find_dotenv())
 
 # Try importing `gpustat` for GPU monitoring
 try:
@@ -16,7 +21,7 @@ except ImportError:
     GPU_AVAILABLE = False
 
 # Constants
-SERVER_URL = "http://localhost:8080"  # Base server URL
+SERVER_URL = os.getenv("SERVER_URL", "http://localhost:8080")
 TEST_IMAGE_URL = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"
 
 
@@ -28,7 +33,7 @@ def fetch_and_prepare_payload():
         img_data = urlopen(TEST_IMAGE_URL).read()
         return base64.b64encode(img_data).decode("utf-8")
     except Exception as e:
-        print(f"Error fetching the image: {e}")
+        logger.info(f"Error fetching the image: {e}")
         return None
 
 
@@ -47,7 +52,7 @@ def send_request(payload, batch=False):
         predictions = response.json() if response.status_code == 200 else None
         return response_time, response.status_code, predictions
     except Exception as e:
-        print(f"Error sending request: {e}")
+        logger.info(f"Error sending request: {e}")
         return None, None, None
 
 
@@ -73,7 +78,7 @@ def benchmark_api(num_requests=100, concurrency_level=10, batch=False):
     """
     payload = fetch_and_prepare_payload()
     if not payload:
-        print("Error preparing payload. Benchmark aborted.")
+        logger.info("Error preparing payload. Benchmark aborted.")
         return
 
     payloads = [payload] * num_requests if batch else [payload]
@@ -114,11 +119,11 @@ def benchmark_api(num_requests=100, concurrency_level=10, batch=False):
     avg_response_time = np.mean(response_times) * 1000 if response_times else 0  # ms
     requests_per_second = num_requests / total_benchmark_time
 
-    print("\n--- Sample Predictions ---")
+    logger.info("\n--- Sample Predictions ---")
     for i, prediction in enumerate(
         predictions[:5]
     ):  # Show predictions for the first 5 requests
-        print(f"Request {i + 1}: {prediction}")
+        logger.info(f"Request {i + 1}: {prediction}")
 
     return {
         "total_requests": num_requests,
@@ -134,66 +139,56 @@ def benchmark_api(num_requests=100, concurrency_level=10, batch=False):
 
 def run_benchmarks():
     """
-    Run comprehensive benchmarks and create plots.
+    Run comprehensive benchmarks and create separate plots for CPU and GPU usage.
     """
     concurrency_levels = [1, 8, 16, 32]
     metrics = []
 
-    print("Running API benchmarks...")
+    logger.info("Running API benchmarks...")
     for concurrency in concurrency_levels:
-        print(f"\nTesting concurrency level: {concurrency}")
+        logger.info(f"\nTesting concurrency level: {concurrency}")
         result = benchmark_api(
             num_requests=50, concurrency_level=concurrency, batch=False
         )
         if result:
             metrics.append(result)
-            print(
+            logger.info(
                 f"Concurrency {concurrency}: "
                 f"{result['requests_per_second']:.2f} reqs/sec, "
                 f"CPU: {result['avg_cpu_usage']:.1f}%, "
                 f"GPU: {result['avg_gpu_usage']:.1f}%"
             )
 
-    # Generate plots
-    plt.figure(figsize=(12, 6))
-
-    # Throughput
-    plt.subplot(1, 2, 1)
-    plt.plot(
-        concurrency_levels,
-        [m["requests_per_second"] for m in metrics],
-        "r-o",
-        label="Throughput",
-    )
-    plt.xlabel("Concurrency Level")
-    plt.ylabel("Requests per Second")
-    plt.title("API Throughput")
-    plt.grid(True)
-
-    # Resource Usage
-    plt.subplot(1, 2, 2)
+    # Generate CPU Usage Plot
+    plt.figure(figsize=(10, 5))
     plt.plot(
         concurrency_levels,
         [m["avg_cpu_usage"] for m in metrics],
         "b-o",
         label="CPU Usage",
     )
+    plt.xlabel("Concurrency Level")
+    plt.ylabel("CPU Usage (%)")
+    plt.title("CPU Usage vs. Concurrency Level")
+    plt.grid(True)
+    plt.savefig("artifacts/cpu_usage.png")
+    logger.info("CPU usage plot saved as 'cpu_usage.png'.")
+
+    # Generate GPU Usage Plot
     if GPU_AVAILABLE:
+        plt.figure(figsize=(10, 5))
         plt.plot(
             concurrency_levels,
             [m["avg_gpu_usage"] for m in metrics],
             "g-o",
             label="GPU Usage",
         )
-    plt.xlabel("Concurrency Level")
-    plt.ylabel("Resource Usage (%)")
-    plt.title("Resource Usage")
-    plt.legend()
-    plt.grid(True)
-
-    plt.tight_layout()
-    plt.savefig("benchmark_results.png")
-    print("Benchmark results saved as 'benchmark_results.png'.")
+        plt.xlabel("Concurrency Level")
+        plt.ylabel("GPU Usage (%)")
+        plt.title("GPU Usage vs. Concurrency Level")
+        plt.grid(True)
+        plt.savefig("artifacts/gpu_usage.png")
+        logger.info("GPU usage plot saved as 'gpu_usage.png'.")
 
 
 if __name__ == "__main__":