From de73da1b0fc1ae46e6cab06d2972960df1c7a325 Mon Sep 17 00:00:00 2001 From: sgkit benchmark bot Date: Mon, 2 Sep 2024 10:59:39 +0000 Subject: [PATCH] Update benchmarks --- ...chmarks_plink.PlinkSpeedSuite.track_write_plink_speed.json | 2 +- .../benchmarks_stats.TimeSuite.time_count_call_alleles.json | 2 +- .../benchmarks_stats.TimeSuite.time_count_cohort_alleles.json | 2 +- ...s_vcf.VcfCompressionSuite.track_zarr_compression_size.json | 2 +- .../benchmarks_vcf.VcfSpeedSuite.track_vcf_to_zarr_speed.json | 2 +- .../benchmarks_vcf.VcfSpeedSuite.track_zarr_to_vcf_speed.json | 2 +- .../machine-github-actions/python-3.9/summary.json | 2 +- ...chmarks_plink.PlinkSpeedSuite.track_write_plink_speed.json | 2 +- .../benchmarks_stats.TimeSuite.time_count_call_alleles.json | 2 +- .../benchmarks_stats.TimeSuite.time_count_cohort_alleles.json | 2 +- ...s_vcf.VcfCompressionSuite.track_zarr_compression_size.json | 2 +- .../benchmarks_vcf.VcfSpeedSuite.track_vcf_to_zarr_speed.json | 2 +- .../benchmarks_vcf.VcfSpeedSuite.track_zarr_to_vcf_speed.json | 2 +- index.json | 2 +- info.json | 4 ++-- results/github-actions/ee90b6e4-conda-py3.9.json | 1 + 16 files changed, 17 insertions(+), 16 deletions(-) create mode 100644 results/github-actions/ee90b6e4-conda-py3.9.json diff --git a/graphs/branch-HEAD/machine-github-actions/python-3.9/benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed.json b/graphs/branch-HEAD/machine-github-actions/python-3.9/benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed.json index 4d4950c..67f4f24 100644 --- a/graphs/branch-HEAD/machine-github-actions/python-3.9/benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed.json +++ b/graphs/branch-HEAD/machine-github-actions/python-3.9/benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed.json @@ -1 +1 @@ -[[1266, 42.58273466695535], [1274, 43.374283317223274], [1277, 40.56045872781966], [1279, 42.79450290850147], [1281, 42.784843077714704], [1282, 42.02110735384595], [1286, 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{"benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed": {"code": "class PlinkSpeedSuite:\n def track_write_plink_speed(self) -> None:\n # throw away first run due to numba jit compilation\n for _ in range(2):\n duration = _time_func(write_plink, self.ds, path=self.output_plink)\n return _to_mb_per_s(get_dir_size(self.dir), duration)\n\n def setup(self) -> None:\n self.ds = simulate_genotype_call_dataset(\n n_variant=1000000, n_sample=1000, seed=0\n )\n \n self.dir = Path(tempfile.mkdtemp())\n self.output_plink = self.dir / \"plink_out\"", "name": "benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "bd0a5d65a483fcc3805fe0397ded0bd7a60b220a9121a1e2fa2fccd2b4dcf304"}, "benchmarks_stats.TimeSuite.time_count_call_alleles": {"code": "class TimeSuite:\n def time_count_call_alleles(self) -> None:\n count_call_alleles(self.count_call_alleles_ds)\n\n def setup(self) -> None:\n self.count_call_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n self.count_cohort_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n sample_cohort = np.repeat(\n [0, 1], self.count_cohort_alleles_ds.dims[\"samples\"] // 2\n )\n self.count_cohort_alleles_ds[\"sample_cohort\"] = xr.DataArray(\n sample_cohort, dims=\"samples\"\n )", "min_run_count": 2, "name": "benchmarks_stats.TimeSuite.time_count_call_alleles", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "99097810ae6b766dfae46c4f296762848ccf073c2fd2f01204786c3957ae8c42", "warmup_time": -1}, "benchmarks_stats.TimeSuite.time_count_cohort_alleles": {"code": "class TimeSuite:\n def time_count_cohort_alleles(self) -> None:\n count_cohort_alleles(self.count_cohort_alleles_ds)\n\n def setup(self) -> None:\n self.count_call_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n self.count_cohort_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n sample_cohort = np.repeat(\n [0, 1], self.count_cohort_alleles_ds.dims[\"samples\"] // 2\n )\n self.count_cohort_alleles_ds[\"sample_cohort\"] = xr.DataArray(\n sample_cohort, dims=\"samples\"\n )", "min_run_count": 2, "name": "benchmarks_stats.TimeSuite.time_count_cohort_alleles", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "fb11db6a5b627835f0a682aa2ce1c793f9156522529ddfde2938933cc2278ff3", "warmup_time": -1}, "benchmarks_vcf.VcfCompressionSuite.track_zarr_compression_size": {"code": "class VcfCompressionSuite:\n def track_zarr_compression_size(self) -> None:\n encoding = {\n \"variant_AF\": {\n \"filters\": [\n FixedScaleOffset(offset=0, scale=10000, dtype=\"f4\", astype=\"u2\")\n ],\n },\n \"call_DS\": {\n \"filters\": [\n FixedScaleOffset(offset=0, scale=100, dtype=\"f4\", astype=\"u1\")\n ],\n },\n \"variant_DR2\": {\n \"filters\": [\n FixedScaleOffset(offset=0, scale=100, dtype=\"f4\", astype=\"u1\")\n ],\n },\n }\n \n kwargs = zarr_array_sizes(self.input_vcf)\n \n vcf_to_zarr(\n self.input_vcf,\n self.output_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n chunk_length=500_000,\n encoding=encoding,\n **kwargs,\n )\n \n original_size = du(self.input_vcf)\n zarr_size = du(self.output_zarr)\n \n return float(zarr_size) / original_size\n\n def setup(self) -> None:\n asv_env_dir = os.environ[\"ASV_ENV_DIR\"]\n self.input_vcf = Path(\n asv_env_dir,\n \"project/sgkit/tests/io/vcf/data/1kg_target_chr20_38_imputed_chr20_500000.vcf.bgz\",\n )\n \n tmp_path = Path(tempfile.mkdtemp())\n self.output_zarr = tmp_path.joinpath(\"1000G.out.zarr\")", "name": "benchmarks_vcf.VcfCompressionSuite.track_zarr_compression_size", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "dc81912750ea5a54712af421cbe8af7a6b531ef662ebf983c7e142966344b137"}, "benchmarks_vcf.VcfSpeedSuite.track_vcf_to_zarr_speed": {"code": "class VcfSpeedSuite:\n def track_vcf_to_zarr_speed(self) -> None:\n duration = _time_func(\n vcf_to_zarr,\n self.input_vcf,\n self.output_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n field_defs=self.field_defs,\n chunk_length=1_000,\n target_part_size=None,\n )\n return _to_mb_per_s(os.path.getsize(self.input_vcf), duration)\n\n def setup(self) -> None:\n asv_env_dir = os.environ[\"ASV_ENV_DIR\"]\n path = Path(\n asv_env_dir,\n \"project/sgkit/tests/io/vcf/data/1000G.phase3.broad.withGenotypes.chr20.10100000.vcf.gz\",\n )\n tmp_path = Path(tempfile.mkdtemp())\n self.input_vcf = tmp_path.joinpath(\"1000G.in.vcf\").as_posix()\n self.input_zarr = tmp_path.joinpath(\"1000G.in.zarr\").as_posix()\n self.output_zarr = tmp_path.joinpath(\"1000G.out.zarr\").as_posix()\n self.output_vcf = tmp_path.joinpath(\"1000G.out.vcf\").as_posix()\n \n # decompress file into temp dir so we can measure speed of vcf_to_zarr for uncompressed text\n _gunzip(path, self.input_vcf)\n \n # create a zarr input file so we can measure zarr_to_vcf speed\n self.field_defs = {\n \"FORMAT/AD\": {\"Number\": \"R\"},\n }\n vcf_to_zarr(\n self.input_vcf,\n self.input_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n field_defs=self.field_defs,\n chunk_length=1_000,\n target_part_size=None,\n )", "name": "benchmarks_vcf.VcfSpeedSuite.track_vcf_to_zarr_speed", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "ddb5fdcd4aebc966f439fca8b63f35169eb26ec25b7916bac38bb34462de8d8c"}, "benchmarks_vcf.VcfSpeedSuite.track_zarr_to_vcf_speed": {"code": "class VcfSpeedSuite:\n def track_zarr_to_vcf_speed(self) -> None:\n # throw away first run due to numba jit compilation\n for _ in range(2):\n duration = _time_func(zarr_to_vcf, self.input_zarr, self.output_vcf)\n return _to_mb_per_s(os.path.getsize(self.output_vcf), duration)\n\n def setup(self) -> None:\n asv_env_dir = os.environ[\"ASV_ENV_DIR\"]\n path = Path(\n asv_env_dir,\n \"project/sgkit/tests/io/vcf/data/1000G.phase3.broad.withGenotypes.chr20.10100000.vcf.gz\",\n )\n tmp_path = Path(tempfile.mkdtemp())\n self.input_vcf = tmp_path.joinpath(\"1000G.in.vcf\").as_posix()\n self.input_zarr = tmp_path.joinpath(\"1000G.in.zarr\").as_posix()\n self.output_zarr = tmp_path.joinpath(\"1000G.out.zarr\").as_posix()\n self.output_vcf = tmp_path.joinpath(\"1000G.out.vcf\").as_posix()\n \n # decompress file into temp dir so we can measure speed of vcf_to_zarr for uncompressed text\n _gunzip(path, self.input_vcf)\n \n # create a zarr input file so we can measure zarr_to_vcf speed\n self.field_defs = {\n \"FORMAT/AD\": {\"Number\": \"R\"},\n }\n vcf_to_zarr(\n self.input_vcf,\n self.input_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n field_defs=self.field_defs,\n chunk_length=1_000,\n target_part_size=None,\n )", "name": "benchmarks_vcf.VcfSpeedSuite.track_zarr_to_vcf_speed", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "b3e02860bad64aa5888fc20c1e3b54934ee5d2285b65e07dcf4bd09d2ab69be1"}}, "machines": {"github-actions": {"machine": "github-actions", "version": 1}}, "tags": {"0.1.0a1": 402, "0.2.0a1": 540, "0.3.0a1": 665, "0.3.0a2": 667, "0.3.0": 715, "0.4.0": 831, "0.5.0": 888, "0.6.0": 1098, "0.7.0": 1199}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file +{"project": "sgkit", "project_url": "https://sgkit-dev.github.io/sgkit/", "show_commit_url": "http://github.com/sgkit-dev/sgkit/commit/", "hash_length": 8, "revision_to_hash": {"402": "712f2cf1e5bba0812bf5990eafc61a3d570e940f", "540": "da81a168f1feafb3504a7616669d49645d002b71", "665": "119c0b93c4ec4fa6ad3c7d7a3b4c943761fca6f9", "667": "6790d9526178fa22494d1a0cb78d5d709ff5849b", "715": "5d58d09f22f023c866e6b252662f84f83302eb61", "831": "4169d9a37eb7974ee2ff38e08f60002ee6f95f52", "877": 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"benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "bd0a5d65a483fcc3805fe0397ded0bd7a60b220a9121a1e2fa2fccd2b4dcf304"}, "benchmarks_stats.TimeSuite.time_count_call_alleles": {"code": "class TimeSuite:\n def time_count_call_alleles(self) -> None:\n count_call_alleles(self.count_call_alleles_ds)\n\n def setup(self) -> None:\n self.count_call_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n self.count_cohort_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n sample_cohort = np.repeat(\n [0, 1], self.count_cohort_alleles_ds.dims[\"samples\"] // 2\n )\n self.count_cohort_alleles_ds[\"sample_cohort\"] = xr.DataArray(\n sample_cohort, dims=\"samples\"\n )", "min_run_count": 2, "name": "benchmarks_stats.TimeSuite.time_count_call_alleles", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "99097810ae6b766dfae46c4f296762848ccf073c2fd2f01204786c3957ae8c42", "warmup_time": -1}, "benchmarks_stats.TimeSuite.time_count_cohort_alleles": {"code": "class TimeSuite:\n def time_count_cohort_alleles(self) -> None:\n count_cohort_alleles(self.count_cohort_alleles_ds)\n\n def setup(self) -> None:\n self.count_call_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n self.count_cohort_alleles_ds = simulate_genotype_call_dataset(\n n_variant=100_000, n_sample=1000\n )\n sample_cohort = np.repeat(\n [0, 1], self.count_cohort_alleles_ds.dims[\"samples\"] // 2\n )\n self.count_cohort_alleles_ds[\"sample_cohort\"] = xr.DataArray(\n sample_cohort, dims=\"samples\"\n )", "min_run_count": 2, "name": "benchmarks_stats.TimeSuite.time_count_cohort_alleles", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "fb11db6a5b627835f0a682aa2ce1c793f9156522529ddfde2938933cc2278ff3", "warmup_time": -1}, "benchmarks_vcf.VcfCompressionSuite.track_zarr_compression_size": {"code": "class VcfCompressionSuite:\n def track_zarr_compression_size(self) -> None:\n encoding = {\n \"variant_AF\": {\n \"filters\": [\n FixedScaleOffset(offset=0, scale=10000, dtype=\"f4\", astype=\"u2\")\n ],\n },\n \"call_DS\": {\n \"filters\": [\n FixedScaleOffset(offset=0, scale=100, dtype=\"f4\", astype=\"u1\")\n ],\n },\n \"variant_DR2\": {\n \"filters\": [\n FixedScaleOffset(offset=0, scale=100, dtype=\"f4\", astype=\"u1\")\n ],\n },\n }\n \n kwargs = zarr_array_sizes(self.input_vcf)\n \n vcf_to_zarr(\n self.input_vcf,\n self.output_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n chunk_length=500_000,\n encoding=encoding,\n **kwargs,\n )\n \n original_size = du(self.input_vcf)\n zarr_size = du(self.output_zarr)\n \n return float(zarr_size) / original_size\n\n def setup(self) -> None:\n asv_env_dir = os.environ[\"ASV_ENV_DIR\"]\n self.input_vcf = Path(\n asv_env_dir,\n \"project/sgkit/tests/io/vcf/data/1kg_target_chr20_38_imputed_chr20_500000.vcf.bgz\",\n )\n \n tmp_path = Path(tempfile.mkdtemp())\n self.output_zarr = tmp_path.joinpath(\"1000G.out.zarr\")", "name": "benchmarks_vcf.VcfCompressionSuite.track_zarr_compression_size", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "dc81912750ea5a54712af421cbe8af7a6b531ef662ebf983c7e142966344b137"}, "benchmarks_vcf.VcfSpeedSuite.track_vcf_to_zarr_speed": {"code": "class VcfSpeedSuite:\n def track_vcf_to_zarr_speed(self) -> None:\n duration = _time_func(\n vcf_to_zarr,\n self.input_vcf,\n self.output_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n field_defs=self.field_defs,\n chunk_length=1_000,\n target_part_size=None,\n )\n return _to_mb_per_s(os.path.getsize(self.input_vcf), duration)\n\n def setup(self) -> None:\n asv_env_dir = os.environ[\"ASV_ENV_DIR\"]\n path = Path(\n asv_env_dir,\n \"project/sgkit/tests/io/vcf/data/1000G.phase3.broad.withGenotypes.chr20.10100000.vcf.gz\",\n )\n tmp_path = Path(tempfile.mkdtemp())\n self.input_vcf = tmp_path.joinpath(\"1000G.in.vcf\").as_posix()\n self.input_zarr = tmp_path.joinpath(\"1000G.in.zarr\").as_posix()\n self.output_zarr = tmp_path.joinpath(\"1000G.out.zarr\").as_posix()\n self.output_vcf = tmp_path.joinpath(\"1000G.out.vcf\").as_posix()\n \n # decompress file into temp dir so we can measure speed of vcf_to_zarr for uncompressed text\n _gunzip(path, self.input_vcf)\n \n # create a zarr input file so we can measure zarr_to_vcf speed\n self.field_defs = {\n \"FORMAT/AD\": {\"Number\": \"R\"},\n }\n vcf_to_zarr(\n self.input_vcf,\n self.input_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n field_defs=self.field_defs,\n chunk_length=1_000,\n target_part_size=None,\n )", "name": "benchmarks_vcf.VcfSpeedSuite.track_vcf_to_zarr_speed", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "ddb5fdcd4aebc966f439fca8b63f35169eb26ec25b7916bac38bb34462de8d8c"}, "benchmarks_vcf.VcfSpeedSuite.track_zarr_to_vcf_speed": {"code": "class VcfSpeedSuite:\n def track_zarr_to_vcf_speed(self) -> None:\n # throw away first run due to numba jit compilation\n for _ in range(2):\n duration = _time_func(zarr_to_vcf, self.input_zarr, self.output_vcf)\n return _to_mb_per_s(os.path.getsize(self.output_vcf), duration)\n\n def setup(self) -> None:\n asv_env_dir = os.environ[\"ASV_ENV_DIR\"]\n path = Path(\n asv_env_dir,\n \"project/sgkit/tests/io/vcf/data/1000G.phase3.broad.withGenotypes.chr20.10100000.vcf.gz\",\n )\n tmp_path = Path(tempfile.mkdtemp())\n self.input_vcf = tmp_path.joinpath(\"1000G.in.vcf\").as_posix()\n self.input_zarr = tmp_path.joinpath(\"1000G.in.zarr\").as_posix()\n self.output_zarr = tmp_path.joinpath(\"1000G.out.zarr\").as_posix()\n self.output_vcf = tmp_path.joinpath(\"1000G.out.vcf\").as_posix()\n \n # decompress file into temp dir so we can measure speed of vcf_to_zarr for uncompressed text\n _gunzip(path, self.input_vcf)\n \n # create a zarr input file so we can measure zarr_to_vcf speed\n self.field_defs = {\n \"FORMAT/AD\": {\"Number\": \"R\"},\n }\n vcf_to_zarr(\n self.input_vcf,\n self.input_zarr,\n fields=[\"INFO/*\", \"FORMAT/*\"],\n field_defs=self.field_defs,\n chunk_length=1_000,\n target_part_size=None,\n )", "name": "benchmarks_vcf.VcfSpeedSuite.track_zarr_to_vcf_speed", "param_names": [], "params": [], "type": "track", "unit": "unit", "version": "b3e02860bad64aa5888fc20c1e3b54934ee5d2285b65e07dcf4bd09d2ab69be1"}}, "machines": {"github-actions": {"machine": "github-actions", "version": 1}}, "tags": {"0.1.0a1": 402, "0.2.0a1": 540, "0.3.0a1": 665, "0.3.0a2": 667, "0.3.0": 715, "0.4.0": 831, "0.5.0": 888, "0.6.0": 1098, "0.7.0": 1199, "0.8.0": 1288, "0.9.0": 1306}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file diff --git a/info.json b/info.json index 3a6fc16..6355a1b 100644 --- a/info.json +++ b/info.json @@ -1,4 +1,4 @@ { - "asv-version": "0.6.3", - "timestamp": 1711736322468 + "asv-version": "0.6.4", + "timestamp": 1725274779167 } \ No newline at end of file diff --git a/results/github-actions/ee90b6e4-conda-py3.9.json b/results/github-actions/ee90b6e4-conda-py3.9.json new file mode 100644 index 0000000..d32d3d8 --- /dev/null +++ b/results/github-actions/ee90b6e4-conda-py3.9.json @@ -0,0 +1 @@ +{"commit_hash": "ee90b6e4fd5921df20c221ef1deb9c2341f3c5a4", "env_name": "conda-py3.9", "date": 1725267052000, "params": {"machine": "github-actions", "python": "3.9"}, "python": "3.9", "requirements": {}, "env_vars": {}, "result_columns": ["result", "params", "version", "started_at", "duration", "stats_ci_99_a", "stats_ci_99_b", "stats_q_25", "stats_q_75", "stats_number", "stats_repeat", "samples", "profile"], "results": {"benchmarks_stats.TimeSuite.time_count_call_alleles": [[0.11959582350002051], [], "99097810ae6b766dfae46c4f296762848ccf073c2fd2f01204786c3957ae8c42", 1725274710268, 14.82, [0.11924], [0.11983], [0.1193], [0.11964], [1], [10]], "benchmarks_stats.TimeSuite.time_count_cohort_alleles": [[0.12123717699995495], [], "fb11db6a5b627835f0a682aa2ce1c793f9156522529ddfde2938933cc2278ff3", 1725274717666, 22.322, [0.12108], [0.12168], [0.12122], [0.12142], [1], [7]], "benchmarks_plink.PlinkSpeedSuite.track_write_plink_speed": [[42.21441034409479], [], "bd0a5d65a483fcc3805fe0397ded0bd7a60b220a9121a1e2fa2fccd2b4dcf304", 1725274693828, 16.44], "benchmarks_vcf.VcfCompressionSuite.track_zarr_compression_size": [[0.7251310678573966], [], "dc81912750ea5a54712af421cbe8af7a6b531ef662ebf983c7e142966344b137", 1725274728553, 13.624], "benchmarks_vcf.VcfSpeedSuite.track_vcf_to_zarr_speed": [[9.412680649521766], [], "ddb5fdcd4aebc966f439fca8b63f35169eb26ec25b7916bac38bb34462de8d8c", 1725274742177, 17.034], "benchmarks_vcf.VcfSpeedSuite.track_zarr_to_vcf_speed": [[66.03019473834071], [], "b3e02860bad64aa5888fc20c1e3b54934ee5d2285b65e07dcf4bd09d2ab69be1", 1725274759211, 18.793]}, "durations": {}, "version": 2} \ No newline at end of file