This project has adopted the code of conduct defined by theContributor Covenantto clarify expected behavior in our community. For more information, see the.NET Foundation Code of Conduct. Code examples Here is a code snippet for training a model to predict sentiment from text samples. You can ...
AZUREML_ARM_PROJECT_NAME:Azure 机器学习试验的名称。 AZUREML_SERVICE_ENDPOINT:AML 服务的终结点 URL。 AZUREML_WORKSPACE_ID:Azure 机器学习工作区的 ID。 AZUREML_EXPERIMENT_ID:Azure 机器学习试验的 ID。 AZUREML_SCRIPT_DIRECTORY_NAME:source_directory 复制到的 DBFS 中的目录路径。 复制 (This p...
Use Azure Machine Learning to create your production-ready ML project in a cloud-based Python Jupyter Notebook using Azure Machine Learning Python SDK v2.
Your support helps promote the project and lets others know that it's worth checking out. Thank you for your support! 🌟 🤸 Quickstart Install ZenML via PyPI. Python 3.9 - 3.12 is required: pip install "zenml[server]" notebook Take a tour with the guided quickstart by running: zenml...
開啟Visual Studio,然後選擇create a new project。 在搜尋列中,輸入Python並選取Python Application作為專案範本。 在組態視窗中: 命名專案。 在這裡,我們稱之為PyTorchTraining。 選擇專案的位置。 如果您使用 VS2019,請確定Create directory for solution已核取 。
vllm-project / vllm Sponsor Star 45.7k Code Issues Pull requests Discussions A high-throughput and memory-efficient inference and serving engine for LLMs amd cuda inference pytorch transformer llama gpt rocm model-serving tpu hpu mlops xpu llm inferentia llmops llm-serving qwen deepseek ...
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning. - Unity-Technologies/ml-
整个Project包含MLOps整个生命周期需要的所有文件,核心算法包在telco_churn文件夹中。其中红色框是ML Engineer需要使用的文件,绿色框是Data Scientist需要使用的文件。 telco_churn算法文件夹中包含了feature-table-creation、model-train、model-deployment、model-inference-batch部分。
GitHub - hidet-org/hidet: An open-source efficient deep learning framework/compiler, written in python. auto-schedule 优化, 比Ansor和auto-schedule更优。 已有深度学习编译器的主要问题:1. loop-oriented的scheudling原语不能涵盖某些更细粒度的优化策略,例如double buffering即ping-pong buffer;2.kernel优化...
import os import random import paddle from tqdm import tqdm def load_feat(path): feat = paddle.load(path) return feat def shift(x, n): if n < 0: # left = x[0].repeat(-n, 1) left = x[0].tile([-n, 1]) right = x[:n] elif n > 0: # right = x[-1].repeat(n, 1...