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Make Machine Learning Faster

Analyze training and inference with Graphsignal Profiler to make it
faster and more efficient.

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  * 
    Sign up for a free account to get an API key.

  * 
    Automatically profile training and inference using Graphsignal
    Profiler.

  * 
    Analyze profiles for performance bottlenecks and issues.

import graphsignal
graphsignal.configure(
    api_key='my_api_key', workload_name='train_model')
...
# profile TensorFlow or use profile_span_pt() for PyTorch
with graphsignal.profile_span_tf() as span:
    # training step, prediction call, etc.

See Quick Start Guide.

Machine Learning Profiler

Improve training and inference efficiency and reduce costs.

Training and inference profiling
    Graphsignal provides operation and kernel level statistics about
    machine learning runs, e.g. training steps or prediction calls,
    to enable optimization and tuning.
Integration and support
    Add a few lines of code to automatically profile TensorFlow and
    PyTorch jobs and model serving.
Any environment
    The profiler only needs outbound connection to Graphsignal. No
    inbound ports or additional software is required, allowing it run
    everywhere.
Data privacy
    No code or data is sent to Graphsignal cloud, only run statistics
    and metadata.

Technology and platform agnostic machine learning profiling

TensorFlow Keras PyTorch Hugging Face Cuda

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