Determined is an open-source deep learning training platform that makes buildingmodels fast and easy. Determined enables you to:
Determined integrates these features into an easy-to-use, high-performance deeplearning environment — which means you can spend your time building modelsinstead of managing infrastructure.
To use Determined, you can continue using popular DL frameworks such asTensorFlow and PyTorch; you just need to update your model code to integratewith the Determined API.
Follow these instructions to install and set up docker.
# Start a Determined cluster locally.
python3.7 -m venv ~/.virtualenvs/test
. ~/.virtualenvs/test/bin/activate
pip install determined
# To start a cluster with GPUs, remove `no-gpu` flag.
det deploy local cluster-up --no-gpu
# Access web UI at localhost:8080. By default, "determined" user accepts a blank password.
# Navigate to a Determined example.
git clone https://github.com/determined-ai/determined
cd determined/examples/computer_vision/cifar10_pytorch
# Submit job to train a single model on a single node.
det experiment create const.yaml .
See our installation guide for details on how to install Determined, including on AWS and GCP.
For a brief introduction to using Determined, check out ourQuick Start Guide.
To use an existing deep learning model with Determined, follow thetutorial for your preferred deep learning framework:
The documentation for the latest version of Determined can always be foundhere.
If you need help, want to file a bug report, or just want to keep up-to-datewith the latest news about Determined, please join the Determined community!
security@determined.ai
.在写Flink程序的时候(以最简单的WordCount案例为例),有时会使用Lambda表达式来简化,如下边程序中的flatMap算子和Map算子处,都是用了Lambda表达式来简写: public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamE
大家在利用pandas读取excel文件的时候,一定会碰到类似的问题;这里主要写一下我踩过的坑: #官网pandas对engine的解释 enginestr, default None If io is not a buffer or path, this must be set to identify io. Supported engines: “xlrd”, “openpyxl”, “odf
在定义Flink数据源的时候出现了下面的错误 Exception in thread "main" org.apache.flink.api.common.functions.InvalidTypesException: The return type of function 'Custom Source' could not be determined automatically, due to
Warning: build attribute vendor section TI missing in " : compatibility cannot be determined。是什么属性丢失了? 出现这个问题,说明使用的库文件是由一个老版本的编译/链接工具所创建的,在使用新版本的工具进行编译/链接时,这个老的库文件缺失了某些属性,导致了这个警告的产生。在C2000 DSP的开发中,有几个
场景:当pandas的DF转换成spark的DF的时候报错 ValueError: Some of types cannot be determined after inferring 报错原因是 存在字段spark无法推断它的类型 解决方案,直接全部转换成str b['request_market'] = b['request_market'].astype(str) b['reques