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Asksage Python API Setup

AskSage is a secure and extensible generative AI platform designed for government and commercial organizations, with a particular focus on the public sector and regulated industries. It provides a way for teams to leverage various large language models (LLMs) and other AI capabilities in a secure and compliant environment. Key features of AskSage include: Multi-model access: Support for various LLMs including GPT, Claude, Gemini, and specialized government-approved models Enterprise security: SOC 2 compliance, data encryption, and air-gapped deployment options Audit trails: Complete logging and monitoring for regulatory compliance Custom integrations: API access for embedding AI capabilities into existing workflows Content filtering: Built-in safety measures and content moderation A comprehensive example is provided by AskSage here.

Saturday, September 6, 2025 | 5 minutes Read

Pytorch Distributed Data Parallel With Model Parallel in an HPC Environment

Distributed Data Parallel with Model Parallel in an HPC environment Objective This tutorial is on : how to separate a model and put it on multiple GPUs. how to train such model in a distributed data parallel fashion. how to use torch.distributed.launch and create a slurm job script for HPC environment. Model Parallel (Pipelining) When a model is too large to fit in one GPU device, we can cut it in half and put each part on different GPU device. To do this, we need to partition the model into “head” and “tail” and specify which device to put them on. In the following toy example, we simply put the first part in to current GPU device and the second part to the next device.

• Documentation

Thursday, December 12, 2019 | 5 minutes Read

Guides On Choosing Deep Learning Server

Introduction Choosing the right GPU server for deep learning is the first problem presented to the research teams among industry and academia. This article is to introduce a few tips in picking the right hardware for your team. If the purpose of the server is mainly for development, a RTX server would be the most cost effective. It is for production, namely to go though TB to PB of data, it is better to use high-end scalable servers. So one can train a single model in parallel efficiently.

Monday, June 10, 2019 | 3 minutes Read

Learning PyTorch Part I

Introduction Currently, I am participating the deep learning part1v2 course as an “international fellow”. This course is taught by Jeremy Howard from fast.ai. The course is not available to the public yet, but it will be in future. During the course, Jeremy introduced PyTorch and the fastai package built on top of PyTorch. Before this, I only used Tensorflow and Keras. PyTorch is quite different (in a good way). I am very impressed by the elegant and flexible design of PyTorch. I would like to introduce some features I think interesting.

Monday, November 13, 2017 | 3 minutes Read