Improve CI Performance.

.github/workflows/ci.yml

@@ -21,9 +21,9 @@ jobs:
       CXX: g++-${{ matrix.gcc }}
       DFTRACER_BUILD_TYPE: "Debug"
       DFTRACER_ENABLE: 1
-      DFTRACER_LOG_LEVEL: "DEBUG"
+      DFTRACER_LOG_LEVEL: "INFO"
       DLIO_EXEC: ${{ matrix.venv == 'via-setup' && 'dlio_benchmark' || 'python dlio_benchmark/main.py' }}
-      GOTCHA_DEBUG: 3
+      GOTCHA_DEBUG: 1
       OMPI_ALLOW_RUN_AS_ROOT: 1
       OMPI_ALLOW_RUN_AS_ROOT_CONFIRM: 1
       PYTHON_VER: ${{ matrix.python }}
@@ -227,9 +227,9 @@ jobs:
         run: |
           source ${VENV_PATH}/bin/activate
           rm -rf output data checkpoints
-          mpirun -np 2 ${DLIO_EXEC} workload=resnet50_a100 ++workload.workflow.generate_data=True ++workload.dataset.num_files_train=4
-          mpirun -np 2 ${DLIO_EXEC} workload=resnet50_h100 ++workload.workflow.generate_data=True ++workload.dataset.num_files_train=4
-          mpirun -np 2 ${DLIO_EXEC} workload=resnet50_h100 ++workload.workflow.generate_data=True ++workload.dataset.num_files_train=4 ++workload.dataset.format=synthetic
+          mpirun -np 2 ${DLIO_EXEC} workload=resnet50_a100 ++workload.workflow.generate_data=True ++workload.dataset.num_files_train=16 ++workload.reader.read_threads=1
+          mpirun -np 2 ${DLIO_EXEC} workload=resnet50_h100 ++workload.workflow.generate_data=True ++workload.dataset.num_files_train=16 ++workload.reader.read_threads=1
+          mpirun -np 2 ${DLIO_EXEC} workload=resnet50_h100 ++workload.workflow.generate_data=True ++workload.dataset.num_files_train=16 ++workload.reader.read_threads=1 ++workload.dataset.format=synthetic
           rm -rf data
       - name: test_cosmoflow
         run: |

dlio_benchmark/data_generator/hdf5_generator.py

@@ -45,38 +45,29 @@ def generate(self):
         """
         super().generate()
         np.random.seed(10)
-        samples_per_iter=max(1, int(self._args.generation_buffer_size/self._args.record_length))
         record_labels = [0] * self.num_samples
         dim = self.get_dimension(self.total_files_to_generate)
+        chunks = None
+        if self.enable_chunking:
+            chunk_dimension = int(math.ceil(math.sqrt(self.chunk_size)))
+            if chunk_dimension > self._dimension:
+                chunk_dimension = self._dimension
+            chunks = (1, chunk_dimension, chunk_dimension)
+        compression = None
+        compression_level = None
+        if self.compression != Compression.NONE:
+            compression = str(self.compression)
+            if self.compression == Compression.GZIP:
+                compression_level = self.compression_level
         for i in dlp.iter(range(self.my_rank, int(self.total_files_to_generate), self.comm_size)):
-            progress(i, self.total_files_to_generate, "Generating HDF5 Data")
             dim1 = dim[2*i]
             dim2 = dim[2*i+1]
-            records = np.random.randint(255, size=(samples_per_iter, dim1, dim2), dtype=np.uint8)
+            records = np.random.randint(255, size=(dim1, dim2, self.num_samples), dtype=np.uint8)
             out_path_spec = self.storage.get_uri(self._file_list[i])
+            progress(i+1, self.total_files_to_generate, "Generating NPZ Data")
             hf = h5py.File(out_path_spec, 'w')
-            chunks = None
-            if self.enable_chunking:
-                chunk_dimension = int(math.ceil(math.sqrt(self.chunk_size)))
-                if chunk_dimension > self._dimension:
-                    chunk_dimension = self._dimension
-                chunks = (1, chunk_dimension, chunk_dimension)
-            compression = None
-            compression_level = None
-            if self.compression != Compression.NONE:
-                compression = str(self.compression)
-                if self.compression == Compression.GZIP:
-                    compression_level = self.compression_level
-            dset = hf.create_dataset('records', (self.num_samples, dim1, dim2), chunks=chunks, compression=compression,
-                                     compression_opts=compression_level, dtype=np.uint8)
-            samples_written = 0
-            while samples_written < self.num_samples:
-                if samples_per_iter < self.num_samples-samples_written:
-                    samples_to_write = samples_per_iter
-                else:
-                    samples_to_write = self.num_samples-samples_written
-                dset[samples_written:samples_written+samples_to_write] = records[:samples_to_write]
-                samples_written += samples_to_write
+            hf.create_dataset('records', (self.num_samples, dim1, dim2), chunks=chunks, compression=compression,
+                                    compression_opts=compression_level, dtype=np.uint8, data=records)
             hf.create_dataset('labels', data=record_labels)
             hf.close()
         np.random.seed()

tests/dlio_benchmark_test.py

@@ -48,7 +48,8 @@ def init():
     DLIOMPI.get_instance().initialize()
 
 def finalize():
-    DLIOMPI.get_instance().finalize()
+    # DLIOMPI.get_instance().finalize()
+    pass
 
 def clean(storage_root="./") -> None:
     comm.Barrier()