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When moving to extremely large models reading, writing and manipulating checkpoints becomes a bottleneck. For that reason Cerebras has moved to using an HDF5 based file format in order to store checkpoints. The content of the checkpoints remains the same so they are convertible to native formats for use outside of Cerebras. Specific details about utilities and interfaces are provided for each framework supported.
Tensorflow Checkpoint Format¶
Our use of the tensorflow estimator interface leads to the use of the tensorflow saver for checkpoint interactions. Unfortunately, the saver doesn’t provide the ability to do iterative updates when writing to a single file.
Starting with existing checkpoint¶
Our appliance estimator will use an existing checkpoint if provided in the
model_dir or as a warm start path. However, we also provide a utility to convert from tensorflow to our H5 format. That is provided either as a command line utility when the wheel is installed or a package.
$ tensorflow-to-h5 --help usage: tensorflow-to-h5 [-h] saver_path h5_path Convert Tensorflow Saver Checkpoint to Cerebras H5 Format positional arguments: saver_path Path to existing saver checkpoint h5_path Path to store converted h5 checkpoint $ python >>> from cerebras_tensorflow.saver import tf_h5_saver
Converting Cerebras checkpoint¶
After training on the appliance converting from the Cerebras format back to tensorflow saver can also be performed vis a command line utility or a package.
$ h5-to-tensorflow --help usage: h5-to-tensorflow [-h] h5_path saver_path Convert Cerebras H5 Checkpoint to Tensorflow Saver Format positional arguments: h5_path Path to existing h5 checkpoint saver_path Path to store converted saver checkpoint $ python >>> from cerebras_tensorflow.saver import tf_h5_saver
For models larger than 20B, writing a tensorflow saver checkpoint can use a prohibitive amount of ram or swap. In this case, using the package provides more flexibility for sharding a checkpoint by weight names in the desired layout.