cog-triton

A cog implementation of Nvidia's Triton server

Create a Replicate Model with cog-triton

Currently, we use yolo, a CLI tool we've built to help with non-standard Replicate workflows. To get started, install yolo:

sudo curl -o /usr/local/bin/yolo -L "https://github.com/replicate/yolo/releases/latest/download/yolo_$(uname -s)_$(uname -m)"
sudo chmod +x /usr/local/bin/yolo

Once you have yolo installed, follow these steps:

1. Compile a TensorRT engine with cog-triton

2. If it doesn't exist already, you'll need to create the Replicate Model to which you'll push your cog-triton model

You can create a new Replicate Model via web or our API. To keep things simple, we'll use the latter method.

First, set a Replicate API token.

export REPLICATE_API_TOKEN=<your-api-token>

curl -s -X POST -H "Authorization: Token $REPLICATE_API_TOKEN" \
    -d '{"owner": "my-username", "name": "my-new-model", "visibility": "private", "hardware": "gpu-a40-large"}' \
    https://api.replicate.com/v1/models 

We'll call our model staging-gpt2-triton-trt-llm

2. Instantiate a cog-triton model with your TRT-LLM engine

staging-gpt2-triton-trt-llm

yolo push \
--base r8.im/replicate-internal/cog-triton@sha256:5d784bf5f449a0578ceb903265bb756dae146a267fc075b4c77021babedc6637 \
--dest r8.im/replicate-internal/staging-gpt2-triton-trt-llm \
-e COG_WEIGHTS=https://replicate.delivery/pbxt/CUDp32x5hO6GMBWprN8o24vWOLZbnYm7AAoRTxLfe0CUfglkA/engine.tar

Run cog-triton locally

To run cog-triton locally, you must either pull the cog-triton Replicate image or build your own image.

Preparation to run cog-triton locally with Replicate image

Pull and tag the cog-triton image

Go here and pick the version you want to run locally. For our purposes, we'll set the version ID as an environment variable so that the code chunks below won't get stale.

export COG_TRITON_VERSION=<version-id-here>

Then, click the version hash. We need to set our Replicate API Token and you can do that manually, or navigate to the HTTP tab in your browser and copy the export command.

export REPLICATE_API_TOKEN=<token-here>

Next, navigate to the Docker tab under Input. This will display a code chunk like with a Docker run command like:

docker run -d -p 5000:5000 --gpus=all r8.im/replicate-internal/cog-triton@sha256:2db2b5c2e199975fef07ed9045608ed7adc7796744041fa54d3ae9d13db6c3cf

We'll use the image reference to write a pull command:

docker pull r8.im/replicate-internal/cog-triton@sha256:${COG_TRITON_VERSION}

After the image has been pulled, you should tag it so that all the docker commands in this README will work. First, find the IMAGE ID for the image you just pulled, e.g. via docker images. Then run the command below after replacing <image id> with your image id.

docker tag <image id> cog-triton:latest

Run an engine built with cog-trt-llm

Copy all model artifacts from cog-trt-llm/engine_outputs/ to triton_model_repo/tensorrt_llm/1/:

cp -r ../cog-trt-llm/engine_outputs/* triton_model_repo/tensorrt_llm/1/

Run the cog-triton image:

docker run --rm -it -p 5000:5000 --gpus=all --workdir /src  --net=host --volume $(pwd)/.:/src/. --ulimit memlock=-1 --shm-size=20g cog-triton /bin/bash
python -m cog.server.http

Make a request:

curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
        "prompt": "What is machine learning?"
    }
  }' \
  http://localhost:5000/predictions

Performance tests with test_perf.py

time python3 scripts/test_perf.py --target cog-triton --rate 8 --unit rps --duration 30 --n_input_tokens 100 --n_output_tokens 100

Development

This repository builds 4 different images:

• cog-triton-builder, which builds TRT-LLM engines.
• cog-triton-runner-80, suitable to run engines built on, and for, nvidia A100's
• cog-triton-runner-86, suitable for A40
• cog-triton-runner-90, suitable for H100 and H200.

Here's a full GPU compatibility list.

End-to-end build process

Cog-triton is pre-release and not stable. This build process currently requires nix to be installed (with the config setting experimental-features = nix-command flakes). We recommend the DeterminateSystems Nix installer, which will set this setting for you.

1. Install nix:

$ curl --proto '=https' --tlsv1.2 -sSf -L https://install.determinate.systems/nix | sh -s -- install --extra-conf "trusted-users = $USER"

2. Clone the cog-triton repo:

$ git clone https://github.com/replicate/cog-triton
$ cd cog-triton

3. Build cog-triton-builder (builder)

$ nix build .#packages.x86_64-linux.cog-triton-builder && ./result load
[...]
Loaded image: cog-triton-builder:1hz2v478b382h6qqwdgxivqqb2bm1kad

This command will eventually output the image id loaded into the local docker daemon.

4. Build cog-triton-runner-86 (runner)

$ nix build .#packages.x86_64-linux.cog-triton-runner-86 && ./result load
[..]
Loaded image: cog-triton-runner-86:zknc2pj8kx9kmmicmjmswd2yj343lpd1

Build a TRT-LLM Model

The cog-triton-builder image takes in a cog-trt-llm build configs and outputs an engine.tar suitable to run on the same hardware it's running on. In this example, we grab one from official-language-models. Open mistral-7b-instruct-v0.2/build_config.yaml raw in your browser and copy-paste the URL with the token.

$ docker run -d -p 5000:5000 --gpus=all cog-triton-builder:1hz2v478b382h6qqwdgxivqqb2bm1kad
$ curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": { 
        "config":"https://raw.githubusercontent.com/replicate/official-language-models/main/models/mistral-7b-instruct-v0.2/build_config.yaml?token=<example-token-see-instructions>"
    }
  }' http://localhost:5000/predictions
$ docker container cp <container name>:/src/engine.tar ./engine.tar

Run a TRT-LLM Model

When you have this engine, you can use it with the cog-triton-runner images.

1. Extract the engine.tar

$ rm -rf triton_model_repo/tensorrt_llm/1/
$ mkdir -p triton_model_repo/tensorrt_llm/1/
$ tar xvf ./engine.tar -C triton_model_repo/tensorrt_llm/1/

2. Run the image This runs the cog-triton-runner-86 image that's been built in the previous steps. Adjust accordingly for your GPU and image tag.

$ docker run --rm -it -p 5000:5000 -p 8000:8000 --gpus=all --ulimit memlock=-1 --shm-size=20g --volume $(pwd)/triton_model_repo/tensorrt_llm/1/:/src/triton_model_repo/tensorrt_llm/1/ cog-triton-runner-86:zknc2pj8kx9kmmicmjmswd2yj343lpd1
$ 

7. Curl a request

You can curl directly to the Triton server:

curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
        "prompt": "What is machine learning?"
    }
  }' \
  http://localhost:5000/predictions

curl -X POST localhost:8000/v2/models/ensemble/generate -d '{"text_input": "What is machine learning?", "max_tokens": 20, "bad_words": "", "stop_words": ""}'

curl -X POST localhost:8000/v2/models/ensemble/generate -d '{"text_input": "Water + Fire = Steam\nEarth + Water = Plant\nHuman + Robe = Judge\nCow + Fire = Steak\nKing + Ocean = Poseidon\nComputer + Spy =", "max_tokens": 20, "bad_words": "", "stop_words": ""}'

curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
        "prompt": "Water + Fire = Steam\nEarth + Water = Plant\nHuman + Robe = Judge\nCow + Fire = Steak\nKing + Ocean = Poseidon\nComputer + Spy ="
    }
  }' \
  http://localhost:5000/predictions

  curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
        "prompt": "Water + Fire = Steam\nEarth + Water = Plant\nHuman + Robe = Judge\nCow + Fire = Steak\nKing + Ocean = Poseidon\nComputer + Spy ="
    }
  }' \
  http://localhost:5000/predictions

Local Testing

Test cog performance

The mock_cog_triton directory provides a mocked cog-triton server that emits tokens at a fixed rate. It also includes a performance test script that reports client side and server-side performance metrics.

This should eventually be integrated into a test suite, but to maintain some visibility into performance continuity, we can run it manually and eyeball.

To do this, start the mock_cog_triton cog server:

docker run --rm -it -p 5000:5000  --workdir /src  --net=host --volume $(pwd)/.:/src/. cog-triton bash -c "cd mock_cog_triton && python -m cog.server.http"

and execute the performance script:

python3 mock_cog_triton/test_perf.py  --unit batch --duration 10 --tps 100 --n_output_tokens 128 --output_method yield --rate 24

Expected output with these input parameters is shown below. Note that Single-stream TPS metrics are quite close to the server-side metrics.

------------------------------
Test Configuration:
------------------------------
Output Method: yield
Mode: batch
Rate: 24.0 batch
Duration: 60 seconds
Output tokens: 128
------------------------------
Concurrency levels:
Mode concurrency: 24
Mean concurrency: 13.8055
Median concurrency: 24.0
Max concurrency: 24
Min concurrency: 0
------------------------------
Statistics for completed predictions:
------------------------------
Single-stream TPS:
SSTPS - Std: 1.498
SSTPS - Median: 93.177
SSTPS - Mean: 93.082
SSTPS - Max: 96.346
SSTPS - Min: 87.296
------------------------------
Latency - Std: 0.023 seconds
Median response latency: 1.374 seconds
Mean response latency: 1.375 seconds
Max response latency: 1.466 seconds
Min response latency: 1.329 seconds
------------------------------
Server-side metrics:
------------------------------
Server-side TPS
--Expected mean: 100.000, Actual mean: 95.557
--Expected std: 0.000, Actual std: 1.198
--Expected median: 100.000, Actual median: 95.535
--Expected min: 100.000, Actual min: 90.650
--Expected max: 100.000, Actual max: 97.710
Response Latency
--Expected mean: 1.280, Actual mean: 1.340
--Expected std: 0.000, Actual std: 0.018
--Expected median: 1.280, Actual median: 1.340
--Expected min: 1.280, Actual min: 1.310
--Expected max: 1.280, Actual max: 1.410
Time to First Token
--Expected mean: 0.010, Actual mean: 0.010
--Expected std: 0.000, Actual std: 0.000
--Expected median: 0.010, Actual median: 0.010
--Expected min: 0.010, Actual min: 0.010
--Expected max: 0.010, Actual max: 0.010
------------------------------
Total requests made: 600
Total requests started: 600
Total requests completed: 600
Failure rate: 0.000, Total failures: 0
Cog already running prediction: 0
E2E throughput: 9.988 rps