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README.md

GroqFlow Proof Points

Proof points demonstrate how GroqFlow is able to successfully build and execute a model on Groq hardware, while maintaining model accuracy. The models are organized by category.

  • Computer Vision (CV)
  • Natural Language Processing (NLP)
  • Speech Processing

Table of Contents

Prerequisites

The following tasks are required to enable running proof point scripts:

  • Download and install the GroqWare Suite packages from from the Groq Customer Portal:
    • groq-devtools package, for model development and builds
    • groq-runtime package, for running computations on hardware (Groq Hardware must be present to install)
    • If building and executing a proof point on the same host machine, download and install both of the above packages.
  • Clone the GroqFlow Repository
  • Set up and activate a groqflow environment
  • Pip install the helper files for the proof points
    • pip install -e {path_to}/groqflow/demo_helpers

Support Matrix

The following relates the proof point models with the version of the GroqWare Suite (SDK) in which they are supported.

Computer Vision

Proof Point Model Supported SDK Version
DeiT-tiny >=0.9.2.1
GoogleNet >=0.9.2.1
MobileNetV2 >=0.9.2.1
ResNet50 >=0.9.2.1
SqueezeNet >=0.9.2.1
Yolo v6 >=0.11.0

Natural Language Processing

Proof Point Model Supported SDK Version(s)
Bert Tiny >=0.9.2.1
Bert Base >=0.9.2.1
Bert Quantized 0.9.2.1
DistilBERT >=0.9.2.1
ELECTRA >=0.9.2.1
MiniLM v2 >=0.9.2.1
RoBERTa >=0.9.2.1

Speech Processing

Proof Point Model Supported SDK Version
M5 >=0.9.2.1

Running A Script

Each proof point will first build a GroqModel, and then evaluate the model on both a CPU and Groq hardware. If access to Groq hardware is available, the build and model evaluation steps can be run in a single step. However, a two step process has also been provided in case resource management requires that the build and evaluation steps be carried out on separate machines. Provided here are the general steps to run a script, but each proof point has a README with that provides any requirements and features that are specific to the model.

Note: Builds for large models can take several minutes. To avoid a time commitment surprise, the build time is included in the README for each proof point.

Build and Evaluate on a Single Machine

Navigate to the folder containing the proof point and read the model's details in the README.

  • Install the requirement.txt file.

    pip install -r requirements.txt

  • Build and evaluate the proof point:

    python {proof_point_name}.py

Build and Evaluate on Separate Machines

Navigate to the folder containing the proof point and read the model's details in the README.

  • Install the requirement.txt file.

    pip install -r requirements.txt

  • Build the model by running the command with the --build flag as shown below:

    python {proof_point_name}.py --build
    • If the model already exists in cache, it will not be rebuilt unless the model code or build changes.

  • Transfer the proof point script and the .iop files to the machine connected to Groq Hardware.

    • The resulting build artifacts will be located in the GroqFlow Cache directory for the proof point, ~/.cache/groqflow/{proof_point_name}. These artifacts include log files, ONNX files, inputs, the yaml state file, and the compile folder.

    • The .iop files can be found within the compile folder in the cache directory. There will be a file for each card used to execute the model on Groq hardware. Copy these files to the same location on the second machine:

      ~/.cache/groqflow/{proof_point_name}/compile/*.iop

  • Once the proof point is copied to the same cache directory and the initial prerequisites are met, the script can be run a second time with the --execute flag to evaluate the model.

    python {proof_point_name}.py --execute