#Codelink v2 manuals
#Codelink v2 code

#Codelink v2 code

This makes it more cost effective for medium to large organizations with casual users and multiple shifts.ĬodeLink ® Online provides precise code and claim information including the entire listing of CPT ®, HCPCS, ICD-10-CM codes, notes, include and exclude notes and specialty libraries. And unlike other coding solutions, CodeLink ® Online is licensed per concurrent user, not named user. It is not cluttered with unnecessary content that adds little value.

#Codelink v2 manuals

It is purposely organized to look and feel like familiar coding manuals yet provide advanced software features to speed up your job.

CodeLink ® Online is the #1 preferred coding solution for medium to large coding shops due to its intuitive user interface and licensing model. The reference implementations for the benchmarks are here.CodeLink ® Online advanced medical coding software generates CPT ® and ICD code data to expedite claim submission and minimize denials.

Model used: The model used to produce the results, which may or may not match the Closed Division requirement.

Details: link to metadata for submission.Įach Open division row may add the following information:.

Benchmark Results: Results for each benchmark as described above.

Software: The ML framework and primary ML hardware library used.

Accelerator and count: The type and number of accelerators used, if accelerators perform the majority of ML compute.

Processor and count: The type and number of CPUs used, if CPUs perform the majority of ML compute.

Submitter: The organization that submitted the results.

Each Closed division row contains the following information: Using the same software stack and hardware platform.

Research, Development, or Internal (RDI) contain experimental, in development, or internal-use hardware or software.Įach row in the results table is a set of results produced by a single submitter.Preview systems must be submittable as Available in the next submission round.Available systems contain only components that are available for purchase or for rent in the cloud.MLPerf divides benchmark results into Categories based on availability. The Open division is intended to foster faster models and optimizers and allows any ML approach that can reach the target quality. The Closed division is intended to compare hardware platforms or software frameworks “apples-to-apples” and requires using the same model and optimizer as the reference implementation. MLPerf has two Divisions that allow different levels of flexibility during reimplementation. MLPerf aims to encourage innovation in software as well as hardware by allowing submitters to reimplement the reference implementations. Imaging benchmark results are very roughly +/- 2.5% and other benchmarks are very roughly +/- 5%.įor non-HPC training, results that converged in fewer epochs than the reference implementation run with the same hyperparameters were normalized to the expected number of epochs. Even the multiple result average is not sufficient to eliminate all variance. To account for the substantial variance in ML training times, final results are obtained by measuring the benchmark a benchmark-specific number of times, discarding the lowest and highest results, and averaging the remaining results. Each benchmark measures the wallclock time required to train a model on the specified dataset to achieve the specified quality target.