triton-operator-env-config

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Verify and build the required environment for Triton operator development on the Ascend platform, including configurations of dependencies such as CANN, Python/torch/torch_npu/triton-ascend and PATH environment variables. This is used when users need to configure the Triton operator development environment, check the installation of CANN/torch/triton-ascend, or verify whether the environment is available.

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Sourceascend/agent-skills

Added on2026-04-15

NPX Install

npx skill4agent add ascend/agent-skills triton-operator-env-config

SKILL.md Content (Chinese)

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Triton Operator Development Environment Configuration

Core Principles

Environment checks must be performed in order, as each step depends on the success of the previous one.

Prerequisite: Obtain the Latest Matching Requirements (MANDATORY)

Matching versions may be updated, so you must obtain the latest official documentation first.

MANDATORY - READ ENTIRE PAGE: Visit and fully read the official online documentation at https://triton-ascend.readthedocs.io/zh-cn/latest/quick_start.html and https://triton-ascend.readthedocs.io/zh-cn/latest/installation_guide.html to get the latest:

Python version requirements
torch / torch_npu version requirements
triton-ascend version requirements
CANN version requirements
Version correspondence of each component

Follow the version requirements in the documentation to update the version numbers in subsequent steps.

Currently known version correspondence (as of 2026-03-26):

Triton-Ascend Version	CANN Version	Release Date
3.2.0	8.5.0 (Recommended)	2026/01/16
3.2.0rc4	8.3.RC2	2025/11/20

torch_npu Version: 2.7.1

Environment Check and Configuration Process

1. CANN Environment Configuration (Step 1)

CANN environment check must be done first

Execute
```
npu-smi info
```
to check if the driver is loaded successfully
Execute
```
which bisheng
```
to check if the CANN environment is loaded and the npuir compiler is obtained, a path should be output

If there is no output, try loading the CANN environment:

Priority:

source /usr/local/Ascend/cann/set_env.sh

Alternative:

source /usr/local/Ascend/ascend-toolkit/set_env.sh

Perform step 1 and step 2 checks again. If unsuccessful, remind the user to check and resolve the CANN environment configuration, and suggest downloading and installing CANN from https://www.hiascend.com/cann/download

2. Python Version Check

If encountering Python issues, use miniconda to create an environment as the first solution

Check the current Python path:
```
which python3
```
If failed, try executing:
```
export PATH="/usr/bin:$PATH"
```
Perform step 1 check again. If unsuccessful, remind the user to install Python, preferring the miniconda method below
If Python3 exists, check the Python version:
```
python3 --version
```
, which must comply with the requirements in the official quick_start.md documentation

3. Python Environment Installation (On-Demand)

If the user needs to install a Python environment, first execute
```
conda init bash
```
to check if a conda environment exists. If yes, jump to step 5; if no, execute step 2 to install

Execute

uname -m

to confirm the current system architecture If the system architecture is aarch64, execute:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh

If the system architecture is x86_64, execute:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

Execute the installation script:
```
bash Miniconda3-latest-Linux-x86_64.sh
```
or
```
bash Miniconda3-latest-Linux-aarch64.sh
```
, activate the miniconda environment after installation as prompted
Check if miniconda is installed successfully:
```
conda init bash
```

Create a Python environment:

conda create -n triton python=<official required version>

Activate the Python environment:
```
conda activate triton
```

4. torch Configuration

Ensure all previous steps are successful

Execute:
```
pip list | grep "torch"
```
Check the torch / torch_npu versions, which must comply with the requirements in the official documentation

If versions do not meet requirements, install according to the documentation:

First install torch:

pip install torch==<official required version>

If encountering the error

ERROR: No matching distribution found for torch==2.7.1+cpu

, try:

bash

pip install torch==2.7.1+cpu --index-url https://download.pytorch.org/whl/cpu

Then install torch_npu:

pip install torch_npu==<official required version>

Check if the torch environment is configured successfully:
```
python3 -c "import torch; print(torch.__version__)"
```

Run a simple torch code to check if the NPU device is loaded successfully:

python3 -c "import torch; a = torch.randn(2, 3); print(a)"

, which should output results similar to:

tensor([[2.86, 1.0406, 1.5811], [0.8329, 1.0024, 1.3639]])

5. triton-ascend Configuration

Execute:
```
pip list | grep "triton"
```
Community Triton and Triton-Ascend cannot coexist:
- If native triton is installed, uninstall it first:
```
pip uninstall triton
```
- If triton-ascend is also installed, uninstall it first, then reinstall
Install the latest triton-ascend package:
```
pip install triton-ascend
```
For installing the nightly version, refer to: https://triton-ascend.readthedocs.io/zh-cn/latest/installation_guide.html#nightly-build

Environment Validation (MANDATORY)

Used to confirm that the current terminal environment can execute Triton operators normally.

MANDATORY - READ ENTIRE FILE: Before performing the validation, you must fully read

scripts/01-vector-add.py

.

Perform validation: