Saxml is an experimental system that serves Paxml, JAX, and PyTorch models for inference.
A Sax cell (aka Sax cluster) consists of an admin server and a group of model servers. The admin server keeps track of model servers, assigns published models to model servers to serve, and helps clients locate model servers serving specific published models.
The example below walks through setting up a Sax cell and starting a TPU or GPU model server in the cell.
Install the
gcloud
CLI and set the default account and project:
gcloud config set account <your-email-account>
gcloud config set project <your-project>
Create a Cloud Storage bucket:
GSBUCKET=sax-data
gcloud storage buckets create gs://${GSBUCKET}
Create a Compute Engine VM instance:
gcloud compute instances create sax-admin \
--zone=us-central1-b \
--machine-type=e2-standard-8 \
--boot-disk-size=200GB \
--scopes=https://www.googleapis.com/auth/cloud-platform
Use this guide to enable the Cloud TPU API in a Google Cloud project.
Create a Cloud TPU VM instance:
gcloud compute tpus tpu-vm create sax-tpu \
--zone=us-central2-b \
--accelerator-type=v4-8 \
--version=tpu-vm-v4-base \
--scopes=https://www.googleapis.com/auth/cloud-platform
Alternatively or in addition to the Cloud TPU VM instance, create a Compute Engine VM instance with GPUs:
gcloud compute instances create sax-gpu \
--zone=us-central1-b \
--machine-type=n1-standard-32 \
--accelerator=count=4,type=nvidia-tesla-v100 \
--maintenance-policy=TERMINATE \
--boot-disk-size=200GB \
--scopes=https://www.googleapis.com/auth/cloud-platform
Consider creating a VM instance using the "GPU-optimized Debian 10 with CUDA 11.0" image instead, so the Nvidia CUDA stack doesn't need to be manually installed as described below.
SSH to the Compute Engine VM instance:
gcloud compute ssh --zone=us-central1-b sax-admin
Inside the VM instance, clone the Sax repo and initialize the environment:
git clone https://github.com/google/saxml.git
cd saxml
saxml/tools/init_cloud_vm.sh
Configure the Sax admin server. This only needs to be done once:
bazel run saxml/bin:admin_config -- \
--sax_cell=/sax/test \
--sax_root=gs://${GSBUCKET}/sax-root \
--fs_root=gs://${GSBUCKET}/sax-fs-root \
--alsologtostderr
Start the Sax admin server:
bazel run saxml/bin:admin_server -- \
--sax_cell=/sax/test \
--sax_root=gs://${GSBUCKET}/sax-root \
--port=10000 \
--alsologtostderr
SSH to the Cloud TPU VM instance:
gcloud compute tpus tpu-vm ssh --zone=us-central2-b sax-tpu
Inside the VM instance, clone the Sax repo and initialize the environment:
git clone https://github.com/google/saxml.git
cd saxml
saxml/tools/init_cloud_vm.sh
Start the Sax model server:
SAX_ROOT=gs://${GSBUCKET}/sax-root \
bazel run saxml/server:server -- \
--sax_cell=/sax/test \
--port=10001 \
--platform_chip=tpuv4 \
--platform_topology=2x2x1 \
--alsologtostderr
You should see a log message "Joined [admin server IP:port]" from the model server to indicate it has successfully joined the admin server.
SSH to the Compute Engine VM instance:
gcloud compute ssh --zone=us-central1-b sax-gpu
Install the Nvidia GPU driver,
CUDA, and
cuDNN.
Note that Sax by default requires CUDA 11. To switch to CUDA 12,
edit requirements-cuda.txt
and replace jaxlib==0.4.7+cuda11.cudnn86
with
jaxlib==0.4.7+cuda12.cudnn88
.
Inside the VM instance, clone the Sax repo and initialize the environment:
git clone https://github.com/google/saxml.git
cd saxml
saxml/tools/init_cloud_vm.sh
Enable the GPU-specific requirements.txt
file:
cp requirements-cuda.txt requirements.txt
Start the Sax model server:
SAX_ROOT=gs://${GSBUCKET}/sax-root \
bazel run saxml/server:server -- \
--sax_cell=/sax/test \
--port=10001 \
--platform_chip=v100 \
--platform_topology=4 \
--jax_platforms=cuda \
--alsologtostderr
You should see a log message "Joined [admin server IP:port]" from the model server to indicate it has successfully joined the admin server.
Sax comes with a command-line tool called saxutil
for easy usage:
# From the `saxml` repo root directory:
alias saxutil='bazel run saxml/bin:saxutil -- --sax_root=gs://${GSBUCKET}/sax-root'
saxutil
supports the following commands:
saxutil help
: Show general help or help about a particular command.saxutil ls
: List all cells, all models in a cell, or a particular model.saxutil publish
: Publish a model.saxutil unpublish
: Unpublish a model.saxutil update
: Update a model.saxutil lm.generate
: Use a language model generate suffixes from a prefix.saxutil lm.score
: Use a language model to score a prefix and suffix.saxutil lm.embed
: Use a language model to embed text into a vector.saxutil vm.generate
: Use a vision model to generate images from text.saxutil vm.classify
: Use a vision model to classify an image.saxutil vm.embed
: Use a vision model to embed an image into a vector.
As an example, Sax comes with a Pax language model servable on a Cloud TPU VM v4-8 instance. You can use it to verify Sax is correctly set up by publishing and using the model with a dummy checkpoint.
saxutil publish \
/sax/test/lm2b \
saxml.server.pax.lm.params.lm_cloud.LmCloudSpmd2BTest \
None \
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Check if the model is loaded by looking at the "selected replica address" column of this command's output:
saxutil ls /sax/test/lm2b
When the model is loaded, issue a query:
saxutil lm.generate /sax/test/lm2b "Q: Who is Harry Porter's mother? A: "
The result will be printed in the terminal.
To use a real checkpoint with the model, follow the Paxml tutorial to generate a checkpoint. The model can then be published in Sax like this:
saxutil publish \
/sax/test/lm2b \
saxml.server.pax.lm.params.lm_cloud.LmCloudSpmd2B \
gs://${GSBUCKET}/checkpoints/checkpoint_00000000 \
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Use the same saxutil lm.generate
command as above to query the model.
First get LLaMA pytorch_vars from Meta, then run the following script to convert the LLaMA PyTorch checkpoint to SAX format
python3 -m saxml/tools/convert_llama_ckpt --base llama_7b --pax pax_7b
Then start the GPU server
SAX_ROOT=gs://${GSBUCKET}/sax-root \
bazel run saxml/server:server -- \
--sax_cell=/sax/test \
--port=10001 \
--platform_chip=a100 \
--platform_topology=1 \
--jax_platforms=cuda \
--alsologtostderr
Finally move the converted ckpt to your google cloud data bucket and publish the model
saxutil publish \
/sax/test/llama-7b \
saxml.server.pax.lm.params.lm_cloud.LLaMA7BFP16 \
gs://sax-data/pax-llama/7B \
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