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import requests | |
from tabulate import tabulate | |
from datetime import datetime, timezone | |
# Retrieve open pull requests | |
open_url = 'https://api.github.com/repos/vllm-project/vllm/pulls?state=open&per_page=100' | |
# merged_url = 'https://api.github.com/repos/vllm-project/vllm/pulls?state=closed&per_page=100' | |
open_response = requests.get(open_url) | |
# merged_response = requests.get(merged_url) |
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diff --git a/examples/run.py b/examples/run.py | |
index 9a05434..d7946db 100644 | |
--- a/examples/run.py | |
+++ b/examples/run.py | |
@@ -135,6 +135,12 @@ def parse_arguments(args=None): | |
choices=["hf", "nemo"], | |
help="The source of lora checkpoint.") | |
+ parser.add_argument( | |
+ '--run_profiling', |
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# An unique identifier for the head node and workers of this cluster. | |
cluster_name: gpu-docker | |
# The maximum number of workers nodes to launch in addition to the head. | |
max_workers: 2 | |
docker: | |
image: "rayproject/ray-ml:latest-gpu" | |
container_name: "ray_nvidia_docker" # e.g. ray_docker |
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# An unique identifier for the head node and workers of this cluster. | |
cluster_name: gpu-docker | |
# The maximum number of workers nodes to launch in addition to the head | |
# node. | |
max_workers: 2 | |
docker: | |
image: "rayproject/ray-ml:latest-gpu" | |
container_name: "ray_nvidia_docker" # e.g. ray_docker |
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# An unique identifier for the head node and workers of this cluster. | |
cluster_name: gpu-docker | |
# The maximum number of workers nodes to launch in addition to the head | |
# node. | |
max_workers: 2 | |
# This executes all commands on all nodes in the docker container, | |
# and opens all the necessary ports to support the Ray cluster. | |
# Empty string means disabled. |
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# An unique identifier for the head node and workers of this cluster. | |
cluster_name: gpu-docker | |
# The maximum number of workers nodes to launch in addition to the head | |
# node. | |
max_workers: 2 | |
# This executes all commands on all nodes in the docker container, | |
# and opens all the necessary ports to support the Ray cluster. | |
# Empty string means disabled. |
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# An unique identifier for the head node and workers of this cluster. | |
cluster_name: gpu-docker | |
# The maximum number of workers nodes to launch in addition to the head | |
# node. | |
max_workers: 2 | |
# The autoscaler will scale up the cluster faster with higher upscaling speed. | |
# E.g., if the task requires adding more nodes then autoscaler will gradually | |
# scale up the cluster in chunks of upscaling_speed*currently_running_nodes. |
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# An unique identifier for the head node and workers of this cluster. | |
cluster_name: gpu-docker | |
# The maximum number of workers nodes to launch in addition to the head | |
# node. | |
max_workers: 2 | |
# The autoscaler will scale up the cluster faster with higher upscaling speed. | |
# E.g., if the task requires adding more nodes then autoscaler will gradually | |
# scale up the cluster in chunks of upscaling_speed*currently_running_nodes. |
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from transformers import DetrImageProcessor, DetrForObjectDetection | |
import torch | |
from PIL import Image | |
import requests | |
import boto3 | |
import io | |
import time | |
import pandas as pd | |
import numpy as np | |
from ray.train.huggingface import HuggingFacePredictor |
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import torch | |
import torch.nn.functional as F | |
from torchmetrics import Accuracy | |
from torch.utils.data import DataLoader, Subset | |
from torchvision.datasets import MNIST | |
from torchvision import transforms | |
import pytorch_lightning as pl | |
from ray.air.config import ScalingConfig | |
from ray.train.lightning import LightningTrainer, LightningConfigBuilder |
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