def define_benchmark(benchmark_log_dir=True, bigquery_uploader=True): """Register benchmarking flags. Args: benchmark_log_dir: Create a flag to specify location for benchmark logging. bigquery_uploader: Create flags for uploading results to BigQuery. Returns: A list of flags for core.py to marks as key flags. """ key_flags = [] if benchmark_log_dir: flags.DEFINE_string( name="benchmark_log_dir", short_name="bld", default=None, help=help_wrap("The location of the benchmark logging.")) if bigquery_uploader: flags.DEFINE_string( name="gcp_project", short_name="gp", default=None, help=help_wrap( "The GCP project name where the benchmark will be uploaded.")) flags.DEFINE_string( name="bigquery_data_set", short_name="bds", default="test_benchmark", help=help_wrap( "The Bigquery dataset name where the benchmark will be uploaded." )) flags.DEFINE_string( name="bigquery_run_table", short_name="brt", default="benchmark_run", help=help_wrap("The Bigquery table name where the benchmark run " "information will be uploaded.")) flags.DEFINE_string( name="bigquery_metric_table", short_name="bmt", default="benchmark_metric", help=help_wrap( "The Bigquery table name where the benchmark metric " "information will be uploaded.")) return key_flags
def define_image(data_format=True): """Register image specific flags. Args: data_format: Create a flag to specify image axis convention. Returns: A list of flags for core.py to marks as key flags. """ key_flags = [] if data_format: flags.DEFINE_enum( name="data_format", short_name="df", default=None, enum_values=["channels_first", "channels_last"], help=help_wrap( "A flag to override the data format used in the model. " "channels_first provides a performance boost on GPU but is not " "always compatible with CPU. If left unspecified, the data format " "will be chosen automatically based on whether TensorFlow was " "built for CPU or GPU.")) key_flags.append("data_format") return key_flags
def define_base(data_dir=True, model_dir=True, train_epochs=True, epochs_between_evals=True, stop_threshold=True, batch_size=True, multi_gpu=False, num_gpu=True, hooks=True, export_dir=True): """Register base flags. Args: data_dir: Create a flag for specifying the input data directory. model_dir: Create a flag for specifying the model file directory. train_epochs: Create a flag to specify the number of training epochs. epochs_between_evals: Create a flag to specify the frequency of testing. stop_threshold: Create a flag to specify a threshold accuracy or other eval metric which should trigger the end of training. batch_size: Create a flag to specify the batch size. multi_gpu: Create a flag to allow the use of all available GPUs. num_gpu: Create a flag to specify the number of GPUs used. hooks: Create a flag to specify hooks for logging. export_dir: Create a flag to specify where a SavedModel should be exported. Returns: A list of flags for core.py to marks as key flags. """ key_flags = [] if data_dir: flags.DEFINE_string(name="data_dir", short_name="dd", default="/tmp", help=help_wrap("The location of the input data.")) key_flags.append("data_dir") if model_dir: flags.DEFINE_string( name="model_dir", short_name="md", default="/tmp", help=help_wrap("The location of the model checkpoint files.")) key_flags.append("model_dir") if train_epochs: flags.DEFINE_integer( name="train_epochs", short_name="te", default=1, help=help_wrap("The number of epochs used to train.")) key_flags.append("train_epochs") if epochs_between_evals: flags.DEFINE_integer( name="epochs_between_evals", short_name="ebe", default=1, help=help_wrap("The number of training epochs to run between " "evaluations.")) key_flags.append("epochs_between_evals") if stop_threshold: flags.DEFINE_float( name="stop_threshold", short_name="st", default=None, help=help_wrap("If passed, training will stop at the earlier of " "train_epochs and when the evaluation metric is " "greater than or equal to stop_threshold.")) if batch_size: flags.DEFINE_integer( name="batch_size", short_name="bs", default=32, help=help_wrap("Batch size for training and evaluation.")) key_flags.append("batch_size") assert not (multi_gpu and num_gpu) if multi_gpu: flags.DEFINE_bool( name="multi_gpu", default=False, help=help_wrap("If set, run across all available GPUs.")) key_flags.append("multi_gpu") if num_gpu: flags.DEFINE_integer( name="num_gpus", short_name="ng", default=1 if tf.test.is_gpu_available() else 0, help=help_wrap( "How many GPUs to use with the DistributionStrategies API. The " "default is 1 if TensorFlow can detect a GPU, and 0 otherwise." )) if hooks: # Construct a pretty summary of hooks. hook_list_str = (u"\ufeff Hook:\n" + u"\n".join( [u"\ufeff {}".format(key) for key in hooks_helper.HOOKS])) flags.DEFINE_list( name="hooks", short_name="hk", default="LoggingTensorHook", help=help_wrap( u"A list of (case insensitive) strings to specify the names of " u"training hooks.\n{}\n\ufeff Example: `--hooks ProfilerHook," u"ExamplesPerSecondHook`\n See official.utils.logs.hooks_helper " u"for details.".format(hook_list_str))) key_flags.append("hooks") if export_dir: flags.DEFINE_string( name="export_dir", short_name="ed", default=None, help=help_wrap( "If set, a SavedModel serialization of the model will " "be exported to this directory at the end of training. " "See the README for more details and relevant links.")) key_flags.append("export_dir") return key_flags
def define_benchmark(benchmark_log_dir=True, bigquery_uploader=True): """Register benchmarking flags. Args: benchmark_log_dir: Create a flag to specify location for benchmark logging. bigquery_uploader: Create flags for uploading results to BigQuery. Returns: A list of flags for core.py to marks as key flags. """ key_flags = [] flags.DEFINE_enum( name="benchmark_logger_type", default="BaseBenchmarkLogger", enum_values=["BaseBenchmarkLogger", "BenchmarkFileLogger", "BenchmarkBigQueryLogger"], help=help_wrap("The type of benchmark logger to use. Defaults to using " "BaseBenchmarkLogger which logs to STDOUT. Different " "loggers will require other flags to be able to work.")) flags.DEFINE_string( name="benchmark_test_id", short_name="bti", default=None, help=help_wrap("The unique test ID of the benchmark run. It could be the " "combination of key parameters. It is hardware " "independent and could be used compare the performance " "between different test runs. This flag is designed for " "human consumption, and does not have any impact within " "the system.")) if benchmark_log_dir: flags.DEFINE_string( name="benchmark_log_dir", short_name="bld", default=None, help=help_wrap("The location of the benchmark logging.") ) if bigquery_uploader: flags.DEFINE_string( name="gcp_project", short_name="gp", default=None, help=help_wrap( "The GCP project name where the benchmark will be uploaded.")) flags.DEFINE_string( name="bigquery_data_set", short_name="bds", default="test_benchmark", help=help_wrap( "The Bigquery dataset name where the benchmark will be uploaded.")) flags.DEFINE_string( name="bigquery_run_table", short_name="brt", default="benchmark_run", help=help_wrap("The Bigquery table name where the benchmark run " "information will be uploaded.")) flags.DEFINE_string( name="bigquery_run_status_table", short_name="brst", default="benchmark_run_status", help=help_wrap("The Bigquery table name where the benchmark run " "status information will be uploaded.")) flags.DEFINE_string( name="bigquery_metric_table", short_name="bmt", default="benchmark_metric", help=help_wrap("The Bigquery table name where the benchmark metric " "information will be uploaded.")) @flags.multi_flags_validator( ["benchmark_logger_type", "benchmark_log_dir"], message="--benchmark_logger_type=BenchmarkFileLogger will require " "--benchmark_log_dir being set") def _check_benchmark_log_dir(flags_dict): benchmark_logger_type = flags_dict["benchmark_logger_type"] if benchmark_logger_type == "BenchmarkFileLogger": return flags_dict["benchmark_log_dir"] return True return key_flags
def define_base(data_dir=True, model_dir=True, clean=True, train_epochs=True, epochs_between_evals=True, stop_threshold=True, batch_size=True, num_gpu=True, hooks=True, export_dir=True, distribution_strategy=True): """Register base flags. Args: data_dir: Create a flag for specifying the input data directory. model_dir: Create a flag for specifying the model file directory. train_epochs: Create a flag to specify the number of training epochs. epochs_between_evals: Create a flag to specify the frequency of testing. stop_threshold: Create a flag to specify a threshold accuracy or other eval metric which should trigger the end of training. batch_size: Create a flag to specify the batch size. num_gpu: Create a flag to specify the number of GPUs used. hooks: Create a flag to specify hooks for logging. export_dir: Create a flag to specify where a SavedModel should be exported. distribution_strategy: Create a flag to specify which Distribution Strategy to use. Returns: A list of flags for core.py to marks as key flags. """ key_flags = [] if data_dir: flags.DEFINE_string(name="data_dir", short_name="dd", default="/tmp", help=help_wrap("The location of the input data.")) key_flags.append("data_dir") if model_dir: flags.DEFINE_string( name="model_dir", short_name="md", default="/tmp", help=help_wrap("The location of the model checkpoint files.")) key_flags.append("model_dir") if clean: flags.DEFINE_boolean( name="clean", default=False, help=help_wrap("If set, model_dir will be removed if it exists.")) key_flags.append("clean") if train_epochs: flags.DEFINE_integer( name="train_epochs", short_name="te", default=1, help=help_wrap("The number of epochs used to train.")) key_flags.append("train_epochs") if epochs_between_evals: flags.DEFINE_integer( name="epochs_between_evals", short_name="ebe", default=1, help=help_wrap("The number of training epochs to run between " "evaluations.")) key_flags.append("epochs_between_evals") if stop_threshold: flags.DEFINE_float( name="stop_threshold", short_name="st", default=None, help=help_wrap("If passed, training will stop at the earlier of " "train_epochs and when the evaluation metric is " "greater than or equal to stop_threshold.")) if batch_size: flags.DEFINE_integer( name="batch_size", short_name="bs", default=32, help=help_wrap( "Batch size for training and evaluation. When using " "multiple gpus, this is the global batch size for " "all devices. For example, if the batch size is 32 " "and there are 4 GPUs, each GPU will get 8 examples on " "each step.")) key_flags.append("batch_size") if export_dir: flags.DEFINE_string( name="export_dir", short_name="ed", default=None, help=help_wrap( "If set, a SavedModel serialization of the model will " "be exported to this directory at the end of training. " "See the README for more details and relevant links.")) key_flags.append("export_dir") return key_flags
def define_performance(num_parallel_calls=True, inter_op=True, intra_op=True, synthetic_data=True, max_train_steps=True, dtype=True): """Register flags for specifying performance tuning arguments. Args: num_parallel_calls: Create a flag to specify parallelism of data loading. inter_op: Create a flag to allow specification of inter op threads. intra_op: Create a flag to allow specification of intra op threads. synthetic_data: Create a flag to allow the use of synthetic data. max_train_steps: Create a flags to allow specification of maximum number of training steps dtype: Create flags for specifying dtype. Returns: A list of flags for core.py to marks as key flags. """ key_flags = [] if num_parallel_calls: flags.DEFINE_integer( name="num_parallel_calls", short_name="npc", default=multiprocessing.cpu_count(), help=help_wrap("The number of records that are processed in parallel " "during input processing. This can be optimized per " "data set but for generally homogeneous data sets, " "should be approximately the number of available CPU " "cores. (default behavior)")) if inter_op: flags.DEFINE_integer( name="inter_op_parallelism_threads", short_name="inter", default=0, help=help_wrap("Number of inter_op_parallelism_threads to use for CPU. " "See TensorFlow config.proto for details.") ) if intra_op: flags.DEFINE_integer( name="intra_op_parallelism_threads", short_name="intra", default=0, help=help_wrap("Number of intra_op_parallelism_threads to use for CPU. " "See TensorFlow config.proto for details.")) if synthetic_data: flags.DEFINE_bool( name="use_synthetic_data", short_name="synth", default=False, help=help_wrap( "If set, use fake data (zeroes) instead of a real dataset. " "This mode is useful for performance debugging, as it removes " "input processing steps, but will not learn anything.")) if max_train_steps: flags.DEFINE_integer( name="max_train_steps", short_name="mts", default=None, help=help_wrap( "The model will stop training if the global_step reaches this " "value. If not set, training will run until the specified number " "of epochs have run as usual. It is generally recommended to set " "--train_epochs=1 when using this flag." )) if dtype: flags.DEFINE_enum( name="dtype", short_name="dt", default="fp32", enum_values=DTYPE_MAP.keys(), help=help_wrap("The TensorFlow datatype used for calculations. " "Variables may be cast to a higher precision on a " "case-by-case basis for numerical stability.")) flags.DEFINE_integer( name="loss_scale", short_name="ls", default=None, help=help_wrap( "The amount to scale the loss by when the model is run. Before " "gradients are computed, the loss is multiplied by the loss scale, " "making all gradients loss_scale times larger. To adjust for this, " "gradients are divided by the loss scale before being applied to " "variables. This is mathematically equivalent to training without " "a loss scale, but the loss scale helps avoid some intermediate " "gradients from underflowing to zero. If not provided the default " "for fp16 is 128 and 1 for all other dtypes.")) loss_scale_val_msg = "loss_scale should be a positive integer." @flags.validator(flag_name="loss_scale", message=loss_scale_val_msg) def _check_loss_scale(loss_scale): # pylint: disable=unused-variable if loss_scale is None: return True # null case is handled in get_loss_scale() return loss_scale > 0 return key_flags