Update Django, Ray, Ubuntu

.pre-commit-config.yaml

@@ -1,5 +1,5 @@
 default_language_version:
-  python: python3.8
+  python: python3.10
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.6.0

README.md

@@ -414,4 +414,5 @@ See [LICENSE](LICENSE.md).
 
 ## Contact
 
-For technical questions about SCOS Actions, contact Justin Haze, [[email protected]](mailto:[email protected])
+For technical questions about SCOS Actions, contact the
+[ITS Spectrum Monitoring Team](mailto:[email protected]).

pyproject.toml

@@ -43,14 +43,14 @@ classifiers = [
 
 dependencies = [
     "environs>=9.5.0",
-    "django>=3.2.18,<4.0",
+    "django>=4.2,<5.0",
     "its_preselector @ git+https://github.com/NTIA/[email protected]",
     "msgspec>=0.16.0,<1.0.0",
     "numexpr>=2.8.3",
     "numpy>=1.22.0",
     "psutil>=5.9.4",
     "python-dateutil>=2.0",
-    "ray>=2.6.3,<2.8.0",
+    "ray>=2.10.0",
     "ruamel.yaml>=0.15",
     "scipy>=1.8.0",
     "sigmf @ git+https://github.com/NTIA/SigMF@multi-recording-archive",
@@ -65,7 +65,7 @@ test = [
 dev = [
     "hatchling>=1.14.1,<2.0",
     "pre-commit>=3.3.1,<4.0",
-    "ray[default]>=2.4.0",
+    "ray[default]>=2.10.0",
     "scos-actions[test]",
 ]
 

scos_actions/__init__.py

@@ -1 +1 @@
-__version__ = "10.0.2"
+__version__ = "11.0.0"

scos_actions/actions/acquire_sea_data_product.py

@@ -113,7 +113,7 @@
 FFT_WINDOW = get_fft_window(FFT_WINDOW_TYPE, FFT_SIZE)
 FFT_WINDOW_ECF = get_fft_window_correction(FFT_WINDOW, "energy")
 IMPEDANCE_OHMS = 50.0
-NUM_ACTORS = 3  # Number of ray actors to initialize
+NUM_ACTORS = env.int("RAY_WORKERS", default=3)  # Number of ray actors to initialize
 
 # Create power detectors
 TD_DETECTOR = create_statistical_detector("TdMeanMaxDetector", ["max", "mean"])
@@ -417,14 +417,10 @@ def __init__(self, params: dict, iir_sos: np.ndarray):
         self.apd_worker = AmplitudeProbabilityDistribution.remote(
             params[APD_BIN_SIZE_DB], params[APD_MIN_BIN_DBM], params[APD_MAX_BIN_DBM]
         )
-        self.workers = [
-            self.fft_worker,
-            self.pvt_worker,
-            self.pfp_worker,
-            self.apd_worker,
-        ]
+
         del params
 
+    @ray.method(num_returns=4)
     def run(self, iqdata: np.ndarray) -> list:
         """
         Filter the input IQ data and concurrently compute FFT, PVT, PFP, and APD results.
@@ -436,9 +432,11 @@ def run(self, iqdata: np.ndarray) -> list:
         # Filter IQ and place it in the object store
         iqdata = ray.put(sosfilt(sos=self.iir_sos, x=iqdata))
         # Compute PSD, PVT, PFP, and APD concurrently.
-        # Do not wait until they finish. Yield references to their results.
-        yield [worker.run.remote(iqdata) for worker in self.workers]
-        del iqdata
+        fft_reference = self.fft_worker.run.remote(iqdata)
+        pvt_reference = self.pvt_worker.run.remote(iqdata)
+        pfp_reference = self.pfp_worker.run.remote(iqdata)
+        apd_reference = self.apd_worker.run.remote(iqdata)
+        return fft_reference, pvt_reference, pfp_reference, apd_reference
 
 
 class NasctnSeaDataProduct(Action):
@@ -541,7 +539,7 @@ def __call__(self, sensor: Sensor, schedule_entry: dict, task_id: int):
         logger.debug(f"Spawned {NUM_ACTORS} supervisor actors in {toc-tic:.2f} s")
 
         # Collect all IQ data and spawn data product computation processes
-        dp_procs, cpu_speed, reference_points = [], [], []
+        data_products_refs, cpu_speed, reference_points = [], [], []
         capture_tic = perf_counter()
 
         for i, parameters in enumerate(self.iteration_params):
@@ -552,10 +550,10 @@ def __call__(self, sensor: Sensor, schedule_entry: dict, task_id: int):
                 )
             # Start data product processing but do not block next IQ capture
             tic = perf_counter()
-
-            dp_procs.append(
+            data_products_refs.append(
                 iq_processors[i % NUM_ACTORS].run.remote(measurement_result["data"])
             )
+
             del measurement_result["data"]
             toc = perf_counter()
             logger.debug(f"IQ data delivered for processing in {toc-tic:.2f} s")
@@ -585,35 +583,43 @@ def __call__(self, sensor: Sensor, schedule_entry: dict, task_id: int):
             [],
         )
         result_tic = perf_counter()
-        for channel_data_process in dp_procs:
-            # Retrieve object references for channel data
-            channel_data_refs = ray.get(channel_data_process)
+        channel_count = len(data_products_refs)
+        logger.debug(f"Have {channel_count} channel results")
+        for index in range(len(data_products_refs)):
+            logger.debug(f"Working on channel {index}")
             channel_data = []
-            for i, data_ref in enumerate(channel_data_refs):
-                # Now block until the data is ready
-                data = ray.get(data_ref)
-                if i == 1:
-                    # Power-vs-Time results, a tuple of arrays
-                    data, summaries = data  # Split the tuple
-                    max_max_ch_pwrs.append(DATA_TYPE(summaries[0]))
-                    med_mean_ch_pwrs.append(DATA_TYPE(summaries[1]))
-                    mean_ch_pwrs.append(DATA_TYPE(summaries[2]))
-                    median_ch_pwrs.append(DATA_TYPE(summaries[3]))
-                    del summaries
-                if i == 3:  # Separate condition is intentional
-                    # APD result: append instead of extend,
-                    # since the result is a single 1D array
-                    channel_data.append(data)
-                else:
-                    # For 2D arrays (PSD, PVT, PFP)
-                    channel_data.extend(data)
+            # Now block until the data is ready
+            dp_refs_tuple = ray.get(data_products_refs[index])
+            psd_ref, pvt_ref, pfp_ref, apd_ref = dp_refs_tuple
+            psd_data = ray.get(psd_ref)
+            channel_data.extend(psd_data)
+
+            pvt_data = ray.get(pvt_ref)
+            # Power-vs-Time results, a tuple of arrays
+            data, summaries = pvt_data  # Split the tuple
+            max_max_ch_pwrs.append(DATA_TYPE(summaries[0]))
+            med_mean_ch_pwrs.append(DATA_TYPE(summaries[1]))
+            mean_ch_pwrs.append(DATA_TYPE(summaries[2]))
+            median_ch_pwrs.append(DATA_TYPE(summaries[3]))
+            channel_data.extend(data)
+            del summaries
+
+            pfp_data = ray.get(pfp_ref)
+            channel_data.extend(pfp_data)
+
+            # APD result: append instead of extend,
+            # since the result is a single 1D array
+            apd_data = ray.get(apd_ref)
+            channel_data.append(apd_data)
+
             toc = perf_counter()
             logger.debug(f"Waited {toc-tic} s for channel data")
             all_data.extend(NasctnSeaDataProduct.transform_data(channel_data))
+
         for ray_actor in iq_processors:
             ray.kill(ray_actor)
         result_toc = perf_counter()
-        del dp_procs, iq_processors, channel_data, channel_data_refs
+        del iq_processors, channel_data
         logger.debug(f"Got all processed data in {result_toc-result_tic:.2f} s")
 
         # Build metadata and convert data to compressed bytes

scos_actions/hardware/utils.py

@@ -40,9 +40,8 @@ def get_current_cpu_clock_speed() -> float:
     :return:
     """
     try:
-        out = subprocess.run("lscpu | grep 'MHz'", shell=True, capture_output=True)
-        spd = str(out.stdout).split("\\n")[0].split()[2]
-        return float(spd)
+        cpu_freq = psutil.cpu_freq()
+        return cpu_freq.current
     except Exception as e:
         logger.error("Unable to retrieve current CPU speed")
         raise e