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Deep Graph Library (DGL) — graph neural network framework. GCN, GAT, GraphSAGE, RGCN, and custom message-passing. Heterogeneous graphs, temporal graphs, and large-scale training with mini-batch sampling.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill dgl命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
基于 SOC 职业分类
正在显示 SKILL.md
| name | dgl |
| description | Deep Graph Library (DGL) — graph neural network framework. GCN, GAT, GraphSAGE, RGCN, and custom message-passing. Heterogeneous graphs, temporal graphs, and large-scale training with mini-batch sampling. |
| tags | ["dgl","graph-neural-network","gnn","message-passing","deep-learning","python","zorai"] |
Deep Graph Library (DGL) provides graph neural network implementations: GCN, GAT, GraphSAGE, GIN, RGCN, and custom message-passing. Supports heterogeneous graphs, temporal graphs, mini-batch training, and distributed sampling for large-scale graph learning.
uv pip install dgl
import torch
import torch.nn.functional as F
from dgl.nn import GraphConv
class GCN(torch.nn.Module):
def __init__(self, in_feats, hidden, out_feats):
super().__init__()
self.conv1 = GraphConv(in_feats, hidden)
self.conv2 = GraphConv(hidden, out_feats)
def forward(self, g, features):
x = F.relu(self.conv1(g, features))
x = self.conv2(g, x)
return F.log_softmax(x, dim=1)
sampler = dgl.dataloading.NeighborSampler([10, 10])
train_dataloader = dgl.dataloading.DataLoader(
g, train_nids, sampler,
batch_size=1024, shuffle=True, num_workers=4)