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tao_iva_classification_ops.py
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tao_iva_classification_ops.py
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import kfp.dsl as dsl
from kubernetes import client as k8s_client
__TAO_CONTAINER_VERSION__='tao-toolkit-tf-kf:3.21.08'
#
# General Structures and Operators
#
class ObjectDict(dict):
def __getattr__(self, name):
if name in self:
return self[name]
else:
raise AttributeError("No such attribute: " + name)
class TAORunCommandOp(dsl.ContainerOp):
def __init__(self, name, command, args):
super(TAORunCommandOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=[command],
arguments=[args],
file_outputs={}
)
#
# Pre-Trained Model Operators
#
class TAOPullOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, model_dir, model_name):
super(TAOPullOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['/opt/ngccli/ngc'],
arguments=['registry',
'model',
'download-version',
model_name,
'--dest', '%s/%s' % (tao_mount_dir, model_dir)
],
file_outputs={}
)
#
# Classification Operators
#
class TAODatasetConvertOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, export_spec_path, tfrecords_path):
super(TAODatasetConvertOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['tao-dataset-convert'],
arguments=[
'-d', '%s/%s' % (tao_mount_dir, export_spec_path),
'-o', '%s/%s' % (tao_mount_dir, tfrecords_path)
],
file_outputs={}
)
name=name
class TAOTrainClassificationOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, results_dir, spec_file, num_gpus):
super(TAOTrainClassificationOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['classification'],
arguments=[
'train',
'--gpus', num_gpus,
'-k', api_key,
'-e', '%s/%s' % (tao_mount_dir, spec_file),
'-r', '%s/%s' % (tao_mount_dir, results_dir)
],
file_outputs={}
)
name=name
class TAOExportClassificationOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, input_file, output_file):
super(TAOExportClassificationOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['classification'],
arguments=[
'export',
'-m',
'%s/%s' % (tao_mount_dir , input_file),
'-k', api_key,
'-o', '%s/%s' % (tao_mount_dir, output_file)
],
file_outputs={}
)
name=name
# Added a version of Export to support INT8 creation for sample classification workflow
class TAOExportClassificationAdvancedOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, input_file, output_file, calibration_file, data_type, num_batches, calibration_cache_file):
super(TAOExportClassificationAdvancedOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['classification'],
arguments=[
'export',
'-m', '%s/%s' % (tao_mount_dir , input_file),
'-k', api_key,
'-o', '%s/%s' % (tao_mount_dir, output_file),
'--cal_data_file', '%s/%s' % (tao_mount_dir, calibration_file),
'--data_type', data_type,
'--batches', num_batches,
'--cal_cache_file', '%s/%s' % (tao_mount_dir, calibration_cache_file),
'-v'
],
file_outputs={}
)
name=name
class TAOPruneClassificationOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, pretrained_model, output_dir,
pruning_threshold, equalization_criterion):
super(TAOPruneClassificationOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['classification'],
arguments=[
'prune',
'-k', api_key,
'-m', '%s/%s' % (tao_mount_dir, pretrained_model),
'-o', '%s/%s' % (tao_mount_dir, output_dir),
'-pth', pruning_threshold,
'-eq', equalization_criterion
],
file_outputs={}
)
name=name
class TAOEvaluateClassificationOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, spec_file):
super(TAOEvaluateClassificationOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['classification'],
arguments=[
"evaluate",
'-e', '%s/%s' % (tao_mount_dir, spec_file),
'-k', api_key
],
file_outputs={}
)
name=name
class TAOInferenceClassificationOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, model, image_dir,
batch_size, classmap, spec_file):
super(TAOInferenceClassificationOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['classification'],
arguments=[
"inference",
'-k', api_key,
'-e', '%s/%s' % (tao_mount_dir, spec_file),
'-m', '%s/%s' % (tao_mount_dir, model),
'-d', '%s/%s' % (tao_mount_dir, image_dir),
'-b', batch_size,
'-cm', '%s/%s' % (tao_mount_dir, classmap)
],
file_outputs={}
)
name=name
class TAOCalibrateClassificationOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, spec_file, max_batches, calibration_file):
super(TAOCalibrateClassificationOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['classification'],
arguments=[
'calibration_tensorfile',
'-e', '%s/%s' % (tao_mount_dir, spec_file),
'-m', max_batches,
'-o', '%s/%s' % (tao_mount_dir, calibration_file),
],
file_outputs={}
)
name=name
#
# TRT Operators
#
class TAOConvertTRTOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, model, output_trt_file, output_layer, dims, input_type, max_trt_batch_size, precision, batch_size):
super(TAOConvertTRTOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['converter'],
arguments=[
'%s/%s' % (tao_mount_dir, model),
'-k', api_key,
'-o', output_layer,
'-d', dims,
'-i', input_type,
'-m', max_trt_batch_size,
'-t', precision,
'-e', '%s/%s' % (tao_mount_dir, output_trt_file),
'-b', batch_size
],
file_outputs={}
)
name=name
class TAOConvertTRTCalibrateOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, api_key, model, calib_cache_file, output_trt_file, output_layer, dims, input_type, max_trt_batch_size, precision, batch_size):
super(TAOConvertTRTCalibrateOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['converter'],
arguments=[
'%s/%s' % (tao_mount_dir, model),
'-k', api_key,
'-o', output_layer,
'-c', '%s/%s' % (tao_mount_dir, calib_cache_file),
'-d', dims,
'-i', input_type,
'-m', max_trt_batch_size,
'-t', precision,
'-e', '%s/%s' % (tao_mount_dir, output_trt_file),
'-b', batch_size
],
file_outputs={}
)
name=name
parse_key_values={"mean":"1", "median":"2", "max":"3", "min":"4", "90percent":"5"}
class TAOParseResultsOp(dsl.ContainerOp):
def __init__(self, name, value_id, path_prefix, filename):
super(TAOParseResultsOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['/bin/bash'],
arguments=[
'-c', "awk '{for(i=1;i<=NF;i++)if($i~/^-?[0-9]+\.[0-9]+$/){print $i}}' %s/%s | sed -n %sp > %s/%s.txt" % (path_prefix, filename, parse_key_values[value_id], path_prefix, value_id)
],
file_outputs={'output': '%s/%s.txt' % (path_prefix, value_id)})
name=name
#
# Deployment Operators
#
# Copy final models to Triton model directory
#
class TAODeployOp(dsl.ContainerOp):
def __init__(self, name, tao_mount_dir, model_file, destination):
super(TAODeployOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['cp'],
arguments=[
'-r', "%s/%s %s" % (tao_mount_dir, model_file, destination)
],
file_outputs={})
name=name
#
# Misc Operators
#
class KubeflowFetchOp(dsl.ContainerOp):
def __init__(self, path_prefix, filename):
super(KubeflowFetchOp, self).__init__(
name='FetchOp',
image=__TAO_CONTAINER_VERSION__,
command=['ls'],
arguments=[
'-lah', path_prefix
],
file_outputs={'output': '%s/%s' % (path_prefix, filename)})
class KubeflowLSOp(dsl.ContainerOp):
def __init__(self, name, path):
super(KubeflowLSOp, self).__init__(
name=name,
image=__TAO_CONTAINER_VERSION__,
command=['/bin/bash'],
arguments=[
'-c', 'ls', '-lah', path
],
file_outputs={}
)
name=name
class PrintOp(dsl.ContainerOp):
def __init__(self, name, command):
super(PrintOp, self).__init__(
name=name,
image='alpine:3.6',
command=command)