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dmelikyandmelikyan
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remove deprecated functionality
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‎README.md‎

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@@ -9,7 +9,6 @@ StackImpact is a production-grade performance profiler built for both production
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#### Features
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* Continuous hot spot profiling of CPU usage, memory allocation and blocking calls.
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* TensorFlow profiling.
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* Error and exception monitoring.
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* Health monitoring including CPU, memory, garbage collection and other runtime metrics.
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* Alerts on profile anomalies.
@@ -143,16 +142,6 @@ agent.start_allocation_profiler();
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agent.stop_allocation_profiler();
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```
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```python
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# Start TensorFlow profiler.
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agent.start_tf_profiler();
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```
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```python
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# Stop TensorFlow profiler and report the recorded profile to the Dashboard.
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agent.stop_tf_profiler();
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```
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#### Analyzing performance data in the Dashboard
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Once your application is restarted, you can start observing continuous CPU, memory, I/O, and other hot spot profiles, execution bottlenecks as well as process metrics in the [Dashboard](https://dashboard.stackimpact.com/).

‎README.rst‎

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@@ -22,7 +22,6 @@ Features
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- Continuous hot spot profiling of CPU usage, memory allocation and
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blocking calls.
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- TensorFlow profiling.
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- Error and exception monitoring.
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- Health monitoring including CPU, memory, garbage collection and other
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runtime metrics.
@@ -39,9 +38,8 @@ The StackImpact profiler agent is imported into a program and used as a
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normal package. When the program runs, various sampling profilers are
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started and stopped automatically by the agent and/or programmatically
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using the agent methods. The agent periodically reports recorded
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profiles and metrics to the StackImpact Dashboard. If an application has
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multiple processes, also referred to as workers, instances or nodes,
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only one or two processes will have active agents at any point of time.
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profiles and metrics to the StackImpact Dashboard. The agent can also
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operate in manual mode, which should be used in development only.
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Documentation
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^^^^^^^^^^^^^
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# Stop heap allocation profiler and report the recorded profile to the Dashboard.
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agent.stop_allocation_profiler();
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.. code:: python
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# Start TensorFlow profiler.
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agent.start_tf_profiler();
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.. code:: python
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# Stop TensorFlow profiler and report the recorded profile to the Dashboard.
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agent.stop_tf_profiler();
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Analyzing performance data in the Dashboard
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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@@ -222,7 +210,4 @@ Overhead
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--------
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The agent overhead is measured to be less than 1% for applications under
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high load. For applications that are horizontally scaled to multiple
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processes, StackImpact agents are only active on a small subset of the
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processes at any point of time, therefore the total overhead is much
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lower.
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high load.

‎examples/tensorflow/app.py‎

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‎examples/tensorflow/manual.py‎

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‎examples/tensorflow/manual_keras.py‎

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‎setup.py‎

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@@ -6,7 +6,7 @@ def read(fname):
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setup(
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name = 'stackimpact',
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version = '1.2.4',
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version = '1.2.6',
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description = 'StackImpact Python Profiler',
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long_description = read('README.rst'),
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author = 'StackImpact',

‎stackimpact/agent.py‎

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@@ -26,14 +26,15 @@
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from .profilers.cpu_profiler import CPUProfiler
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from .profilers.allocation_profiler import AllocationProfiler
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from .profilers.block_profiler import BlockProfiler
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from .profilers.tf_profiler import TFProfiler
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class Span(object):
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def __init__(self, stop_func = None):
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if stop_func:
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self.stop_func = stop_func
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else:
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self.stop_func = None
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def stop(self):
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class Agent(object):
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AGENT_VERSION = "1.2.4"
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AGENT_VERSION = "1.2.6"
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SAAS_DASHBOARD_ADDRESS = "https://agent-api.stackimpact.com"
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def __init__(self, **kwargs):
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config.report_interval = 120
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self.block_reporter = ProfileReporter(self, BlockProfiler(self), config)
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config = ProfilerConfig()
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config.log_prefix = 'TensorFlow profiler'
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config.max_profile_duration = 20
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config.max_span_duration = 5
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config.max_span_count = 30
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config.span_interval = 20
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config.report_interval = 120
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self.tf_reporter = ProfileReporter(self, TFProfiler(self), config)
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self.options = None
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self.cpu_reporter.setup()
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self.allocation_reporter.setup()
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self.block_reporter.setup()
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self.tf_reporter.setup()
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self.span_reporter.setup()
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self.error_reporter.setup()
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self.process_reporter.setup()
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self.cpu_reporter.start()
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self.allocation_reporter.start()
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self.block_reporter.start()
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self.tf_reporter.start()
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self.span_reporter.start()
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self.error_reporter.start()
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self.process_reporter.start()
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self.cpu_reporter.stop()
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self.allocation_reporter.stop()
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self.block_reporter.stop()
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self.tf_reporter.stop()
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self.span_reporter.stop()
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self.error_reporter.stop()
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self.process_reporter.stop()
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active_reporters.append(self.allocation_reporter)
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if self.block_reporter.started:
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active_reporters.append(self.block_reporter)
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if self.tf_reporter.started:
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active_reporters.append(self.tf_reporter)
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if len(active_reporters) > 0:
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selected_reporter = active_reporters[int(math.floor(random.random() * len(active_reporters)))]
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self._stop_profiler(self.block_reporter)
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def start_tf_profiler(self):
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self._start_profiler(self.tf_reporter)
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def stop_tf_profiler(self):
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self._stop_profiler(self.tf_reporter)
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def destroy(self):
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if not self.agent_started:
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self.log('Agent has not been started')
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self.cpu_reporter.stop()
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self.allocation_reporter.stop()
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self.block_reporter.stop()
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self.tf_reporter.stop()
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self.error_reporter.stop()
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self.span_reporter.stop()
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self.process_reporter.stop()
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self.cpu_reporter.destroy()
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self.allocation_reporter.destroy()
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self.block_reporter.destroy()
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self.tf_reporter.destroy()
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self.error_reporter.destroy()
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self.span_reporter.destroy()
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self.process_reporter.destroy()

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