Instructions to use callensxavier/v10g2-dqn-compiler-optimization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use callensxavier/v10g2-dqn-compiler-optimization with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="callensxavier/v10g2-dqn-compiler-optimization", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
File size: 3,533 Bytes
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"train": [
"polybench::kernels::atax::LARGE",
"polybench::kernels::atax::MEDIUM",
"open_mt::atomic_histogram",
"microbench::mandelbrot",
"microbench::quicksort",
"polybench::blas::gesummv::LARGE",
"polybench::blas::gesummv::MEDIUM",
"polybench::blas::gemm::LARGE",
"polybench::blas::gemm::MEDIUM",
"polybench::solvers::ludcmp::LARGE",
"polybench::solvers::ludcmp::MEDIUM",
"polybench::stencils::adi::LARGE",
"polybench::stencils::adi::MEDIUM",
"open_mt::omp_pi_reduction",
"open_mt::condvar_pipeline",
"polybench::solvers::durbin::LARGE",
"polybench::solvers::durbin::MEDIUM",
"polybench::stencils::fdtd-2d::LARGE",
"polybench::stencils::fdtd-2d::MEDIUM",
"polybench::datamining::correlation::LARGE",
"polybench::datamining::correlation::MEDIUM",
"microbench::lu_decomposition",
"cpp_bench::logsumexp",
"microbench::producer_consumer",
"microbench::nbody",
"cpp_bench::matmul_int",
"microbench::spmv_csr",
"polybench::stencils::heat-3d::LARGE",
"polybench::stencils::heat-3d::MEDIUM",
"polybench::solvers::cholesky::LARGE",
"polybench::solvers::cholesky::MEDIUM",
"cpp_bench::lu_decomp",
"cpp_bench::horner_poly",
"microbench::strassen",
"polybench::kernels::doitgen::LARGE",
"polybench::kernels::doitgen::MEDIUM",
"cpp_bench::int_dotprod",
"polybench::blas::symm::LARGE",
"polybench::blas::symm::MEDIUM",
"polybench::medley::deriche::LARGE",
"polybench::medley::deriche::MEDIUM",
"microbench::matrix_multiply_tiled",
"polybench::solvers::gramschmidt::LARGE",
"polybench::solvers::gramschmidt::MEDIUM",
"open_mt::omp_montecarlo_integral",
"polybench::blas::syrk::LARGE",
"polybench::blas::syrk::MEDIUM",
"microbench::parallel_sort",
"polybench::stencils::jacobi-1d::LARGE",
"polybench::stencils::jacobi-1d::MEDIUM",
"microbench::cache_oblivious_transpose",
"cpp_bench::popcount_bits",
"open_mt::omp_matmul_blocked",
"polybench::blas::trmm::LARGE",
"polybench::blas::trmm::MEDIUM",
"microbench::lockfree_stack",
"polybench::kernels::mvt::LARGE",
"polybench::kernels::mvt::MEDIUM",
"cpp_bench::kahan_sum",
"open_mt::sieve_primes",
"open_mt::crc32_stream",
"cpp_bench::radix_sort",
"polybench::solvers::lu::LARGE",
"polybench::solvers::lu::MEDIUM",
"cpp_bench::mandelbrot"
],
"val": [
"polybench::blas::gemver::LARGE",
"polybench::blas::gemver::MEDIUM",
"polybench::solvers::trisolv::LARGE",
"polybench::solvers::trisolv::MEDIUM",
"cpp_bench::saxpy_fixed",
"polybench::medley::nussinov::LARGE",
"polybench::medley::nussinov::MEDIUM",
"polybench::blas::syr2k::LARGE",
"polybench::blas::syr2k::MEDIUM",
"polybench::kernels::2mm::LARGE",
"polybench::kernels::2mm::MEDIUM",
"microbench::parallel_reduction",
"microbench::matmul_openmp",
"open_mt::threaded_mergesort",
"cpp_bench::binary_trees",
"polybench::kernels::3mm::LARGE",
"polybench::kernels::3mm::MEDIUM"
],
"test": [
"polybench::kernels::bicg::LARGE",
"polybench::kernels::bicg::MEDIUM",
"polybench::datamining::covariance::LARGE",
"polybench::datamining::covariance::MEDIUM",
"cpp_bench::spectral_norm",
"polybench::medley::floyd-warshall::LARGE",
"polybench::medley::floyd-warshall::MEDIUM",
"microbench::aes_simple",
"cpp_bench::welford_variance",
"polybench::stencils::seidel-2d::LARGE",
"polybench::stencils::seidel-2d::MEDIUM",
"open_mt::threadpool_dotproduct",
"polybench::stencils::jacobi-2d::LARGE",
"polybench::stencils::jacobi-2d::MEDIUM",
"cpp_bench::newton_sqrt"
]
} |