mwandJunie
a28a4a049b
- Update OpenClaw version references from 2026.3.13 to 2026.3.28 in README.md, docs/OPENCLAW_ARCHITECTURE.md, and gateway_plugins.txt. - Remove outdated MacOS maintenance instructions (log rotation, credential syncing) from remote luca project. - Redirect remote luca documentation to explicitly refer back to the mw-macbook-pro repository for MacOS-specific routines. - Updated CHANGELOG to reflect version bump and cross-repo document alignments. - Removed completed implementation plan. Co-authored-by: Junie <junie@jetbrains.com>
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
import ollama
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import time
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import psutil
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import os
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import json
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def get_vram_usage():
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try:
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# On macOS, we can check the 'ollama' process or use 'ollama ps'
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# For simplicity, we'll shell out to 'ollama ps' to get GPU residency
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result = os.popen('ollama ps').read()
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return result
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except:
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return "Unknown"
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def benchmark_vision(model_name="moondream", image_path=None):
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print(f"\n--- Benchmarking Vision: {model_name} ---")
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if not image_path or not os.path.exists(image_path):
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print("No image provided for vision test.")
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return
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start_time = time.time()
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response = ollama.generate(
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model=model_name,
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prompt="Describe this image in detail.",
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images=[image_path],
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stream=False
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)
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duration = time.time() - start_time
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# Ollama returns tokens/sec in the response metadata
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eval_count = response.get('eval_count', 0)
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eval_duration = response.get('eval_duration', 1) / 1e9 # ns to s
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prompt_eval_count = response.get('prompt_eval_count', 0)
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prompt_eval_duration = response.get('prompt_eval_duration', 1) / 1e9
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print(f"Vision Response: {response['response'][:100]}...")
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print(f"Prompt Eval Rate: {prompt_eval_count / prompt_eval_duration:.2f} tokens/s")
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print(f"Generation Rate: {eval_count / eval_duration:.2f} tokens/s")
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print(f"VRAM Residency:\n{get_vram_usage()}")
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def benchmark_context(model_name="qwen2-128k", context_size=131072):
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print(f"\n--- Benchmarking Context: {model_name} ({context_size} tokens) ---")
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# Generate a long dummy context
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# Each 'word' is approx 1.3 tokens
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dummy_text = "This is a context verification test. " * (context_size // 10)
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start_time = time.time()
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response = ollama.generate(
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model=model_name,
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prompt=f"{dummy_text}\n\nTask: What was the first sentence of this text?",
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stream=False
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)
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duration = time.time() - start_time
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eval_count = response.get('eval_count', 0)
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eval_duration = response.get('eval_duration', 1) / 1e9
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prompt_eval_count = response.get('prompt_eval_count', 0)
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prompt_eval_duration = response.get('prompt_eval_duration', 1) / 1e9
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print(f"Context Response: {response['response']}")
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print(f"Prompt Eval Rate (Context Parsing): {prompt_eval_count / prompt_eval_duration:.2f} tokens/s")
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print(f"Generation Rate: {eval_count / eval_duration:.2f} tokens/s")
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print(f"VRAM Residency:\n{get_vram_usage()}")
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if __name__ == "__main__":
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# Ensure we have a sample image
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# For CI/Automation, we'll try to find any image in the repo or use a placeholder
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sample_image = "/Users/MWsatwareAG/internal/mw-macbook-pro/ml-test/sample.png"
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# Create a small dummy image if it doesn't exist for the script to run
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if not os.path.exists(sample_image):
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import matplotlib.pyplot as plt
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import numpy as np
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plt.imshow(np.random.rand(100,100,3))
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plt.savefig(sample_image)
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print(f"Created dummy image at {sample_image}")
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benchmark_vision(model_name="moondream", image_path=sample_image)
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# 128k context test
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try:
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benchmark_context(model_name="qwen2-128k-gpu")
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except Exception as e:
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print(f"Context test failed (likely VRAM/OOM): {e}")
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