Examples with NotebookOutput com.simiacryptus.util.io.NotebookOutput used on opensource projects

Example 1 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class CudnnTest method testTensorListMT.

private void testTensorListMT(@Nonnull NotebookOutput log, @Nonnull int[] dimensions, int length, double tolerance, int accumulations) {
    @Nonnull Supplier<TensorList> factory = () -> TensorArray.wrap(IntStream.range(0, length).mapToObj(j -> {
        @Nonnull Tensor tensor = new Tensor(dimensions);
        Arrays.parallelSetAll(tensor.getData(), this::random);
        return tensor;
    }).toArray(j -> new Tensor[j]));
    log.code(() -> {
        @Nonnull ListeningExecutorService pool = MoreExecutors.listeningDecorator(Executors.newFixedThreadPool(5));
        PrintStream out = SysOutInterceptor.INSTANCE.currentHandler();
        try {
            List<ListenableFuture<Object>> collect = IntStream.range(0, 16).mapToObj(workerNumber -> {
                @Nonnull TimedResult<TensorList> originalTiming = TimedResult.time(() -> factory.get());
                TensorList original = originalTiming.result;
                logger.info(String.format("[%s] Calculated test data in %.4fsec", workerNumber, originalTiming.seconds()));
                @Nonnull ListenableFuture<TensorList> mutableDataFuture = pool.submit(() -> CudaSystem.run(gpu -> {
                    PrintStream oldHandler = SysOutInterceptor.INSTANCE.setCurrentHandler(out);
                    @Nonnull TimedResult<CudaTensor> timedResult = TimedResult.time(() -> {
                        return gpu.getTensor(original, Precision.Double, MemoryType.Managed, false);
                    });
                    logger.info(String.format("[%s] Wrote %s in %.4f seconds, Device %d: %s", workerNumber, Arrays.toString(dimensions), timedResult.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
                    SysOutInterceptor.INSTANCE.setCurrentHandler(oldHandler);
                    return CudaTensorList.wrap(timedResult.result, length, dimensions, Precision.Double);
                }, original));
                @Nonnull TimedResult<List<TensorList>> accumulantTiming = TimedResult.time(() -> IntStream.range(0, accumulations).mapToObj(x -> factory.get()).collect(Collectors.toList()));
                List<TensorList> accumulants = accumulantTiming.result;
                @Nonnull TimedResult<TensorList> finalResultTiming = TimedResult.time(() -> {
                    return accumulants.stream().map(x -> {
                        x.addRef();
                        return x;
                    }).reduce((a, b) -> {
                        TensorList sum = a.addAndFree(b);
                        b.freeRef();
                        return sum;
                    }).map(x -> {
                        TensorList sum = x.add(original);
                        x.freeRef();
                        return sum;
                    }).orElseGet(() -> {
                        original.addRef();
                        return original;
                    });
                });
                logger.info(String.format("[%s] Calculated accumulant in %.4fsec", workerNumber, accumulantTiming.seconds()));
                @Nonnull ListenableFuture<TensorList> accumulated = Futures.transform(mutableDataFuture, (x) -> {
                    PrintStream oldHandler = SysOutInterceptor.INSTANCE.setCurrentHandler(out);
                    @Nonnull AtomicReference<TensorList> mutableGpuData = new AtomicReference<>(x);
                    accumulants.stream().parallel().forEach(delta -> {
                        CudaSystem.run(gpu -> {
                            @Nonnull TimedResult<CudaTensorList> timedWrite = TimedResult.time(() -> {
                                @Nullable CudaTensor cudaMemory = gpu.getTensor(delta, Precision.Double, MemoryType.Managed, false);
                                delta.freeRef();
                                return CudaTensorList.wrap(cudaMemory, length, dimensions, Precision.Double);
                            });
                            @Nonnull TimedResult<Void> timedAccumulation = TimedResult.time(() -> {
                                synchronized (mutableGpuData) {
                                    mutableGpuData.getAndUpdate(y -> {
                                        TensorList add = y.add(timedWrite.result);
                                        y.freeRef();
                                        return add;
                                    });
                                }
                                timedWrite.result.freeRef();
                            });
                            logger.info(String.format("[%s] Wrote in %.4f seconds and accumulated %s in %.4f seconds, Device %d: %s", workerNumber, timedAccumulation.seconds(), Arrays.toString(dimensions), timedWrite.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
                        }, delta);
                    });
                    SysOutInterceptor.INSTANCE.setCurrentHandler(oldHandler);
                    return mutableGpuData.get();
                }, pool);
                TensorList finalResult = finalResultTiming.result;
                logger.info(String.format("[%s] Calculated final data in %.4fsec", workerNumber, finalResultTiming.seconds()));
                return Futures.transform(accumulated, (write) -> {
                    original.freeRef();
                    PrintStream oldHandler = SysOutInterceptor.INSTANCE.setCurrentHandler(out);
                    CudaSystem.run(gpu -> {
                        @Nonnull TimedResult<Boolean> timedVerify = TimedResult.time(() -> {
                            @Nonnull TensorList minus = finalResult.minus(write);
                            double diffVal = minus.stream().mapToDouble(x -> {
                                double v = Arrays.stream(x.getData()).map(Math::abs).max().getAsDouble();
                                x.freeRef();
                                return v;
                            }).max().getAsDouble();
                            minus.freeRef();
                            return diffVal < tolerance;
                        });
                        logger.info(String.format("[%s] Read %s and verified in %.4fs using device %d: %s", workerNumber, Arrays.toString(dimensions), timedVerify.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
                        if (!timedVerify.result)
                            Assert.assertTrue(finalResult.prettyPrint() + " != " + write.prettyPrint(), timedVerify.result);
                        write.freeRef();
                    });
                    SysOutInterceptor.INSTANCE.setCurrentHandler(oldHandler);
                    finalResult.freeRef();
                    return null;
                }, pool);
            }).collect(Collectors.toList());
            List<Object> objects = Futures.allAsList(collect).get();
        } catch (@Nonnull InterruptedException | ExecutionException e) {
            throw new RuntimeException(e);
        } finally {
            pool.shutdown();
        }
    });
}

Example 2 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class CudnnTest method memoryTransfer.

private void memoryTransfer(@Nonnull NotebookOutput log, int... size) {
    @Nonnull Supplier<TensorList> factory = () -> TensorArray.wrap(IntStream.range(0, 1).mapToObj(j -> {
        @Nonnull Tensor tensor = new Tensor(size);
        Arrays.parallelSetAll(tensor.getData(), this::random);
        return tensor;
    }).toArray(j -> new Tensor[j]));
    TensorList original = factory.get();
    log.code(() -> {
        CudaTensor write = CudaSystem.run(gpu -> {
            @Nonnull TimedResult<CudaTensor> timedResult = TimedResult.time(() -> {
                return gpu.getTensor(original, Precision.Double, MemoryType.Managed, false);
            });
            int deviceNumber = gpu.getDeviceId();
            logger.info(String.format("Wrote %s bytes in %.4f seconds, Device %d: %s", Arrays.toString(size), timedResult.seconds(), deviceNumber, CudaDevice.getDeviceName(deviceNumber)));
            return timedResult.result;
        }, original);
        CudnnHandle.forEach(gpu -> {
            @Nonnull Tensor readCopy = new Tensor(size);
            @Nonnull TimedResult<CudaMemory> timedResult = TimedResult.time(() -> {
                CudaMemory cudaMemory = write.getMemory(gpu);
                CudaMemory read = cudaMemory.read(Precision.Double, readCopy.getData());
                cudaMemory.freeRef();
                return read;
            });
            @Nonnull TimedResult<Boolean> timedVerify = TimedResult.time(() -> {
                @Nullable Tensor tensor = original.get(0);
                boolean equals = tensor.equals(readCopy);
                tensor.freeRef();
                return equals;
            });
            int deviceNumber = gpu.getDeviceId();
            logger.info(String.format("Read %s bytes in %.4f seconds and verified in %.4fs using device %d: %s", Arrays.toString(size), timedResult.seconds(), timedVerify.seconds(), deviceNumber, CudaDevice.getDeviceName(deviceNumber)));
            if (!timedVerify.result)
                Assert.assertTrue(original.prettyPrint() + " != " + readCopy.prettyPrint(), timedVerify.result);
            readCopy.freeRef();
        });
        write.freeRef();
    });
    original.freeRef();
}

Example 3 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class CudnnTest method testTensorList.

private void testTensorList(@Nonnull NotebookOutput log, @Nonnull int[] dimensions, int length, double tolerance, int accumulations) {
    @Nonnull Supplier<TensorList> factory = () -> TensorArray.wrap(IntStream.range(0, length).mapToObj(j -> {
        @Nonnull Tensor tensor = new Tensor(dimensions);
        Arrays.parallelSetAll(tensor.getData(), this::random);
        return tensor;
    }).toArray(j -> new Tensor[j]));
    log.code(() -> {
        @Nonnull TimedResult<TensorList> originalTiming = TimedResult.time(() -> factory.get());
        logger.info(String.format("Calculated test data in %.4fsec", originalTiming.seconds()));
        TensorList original = originalTiming.result;
        @Nonnull AtomicReference<TensorList> mutableGpuData = new AtomicReference<>(CudaSystem.run(gpu -> {
            @Nonnull TimedResult<CudaTensor> timedResult = TimedResult.time(() -> {
                return gpu.getTensor(original, Precision.Double, MemoryType.Managed, false);
            });
            logger.info(String.format("Wrote %s in %.4f seconds, Device %d: %s", Arrays.toString(dimensions), timedResult.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
            return CudaTensorList.wrap(timedResult.result, length, dimensions, Precision.Double);
        }, original));
        CudnnHandle.forEach(ctx -> {
            @Nonnull TimedResult<TensorList> timedResult = TimedResult.time(() -> (mutableGpuData.get() instanceof CudaTensorList) ? ((CudaTensorList) mutableGpuData.get()).getHeapCopy() : mutableGpuData.get());
            @Nonnull TimedResult<Boolean> timedVerify = TimedResult.time(() -> {
                @Nonnull TensorList minus = original.minus(timedResult.result);
                double variance = minus.stream().mapToDouble(x -> Arrays.stream(x.getData()).map(Math::abs).max().getAsDouble()).max().getAsDouble();
                minus.freeRef();
                return variance < tolerance;
            });
            logger.info(String.format("Read %s in %.4f seconds and verified in %.4fs using device %d: %s", Arrays.toString(dimensions), timedResult.seconds(), timedVerify.seconds(), ctx.getDeviceId(), CudaDevice.getDeviceName(ctx.getDeviceId())));
            if (!timedVerify.result)
                Assert.assertTrue(original.prettyPrint() + " != " + timedResult.result.prettyPrint(), timedVerify.result);
            timedResult.result.freeRef();
        });
        @Nonnull TimedResult<List<TensorList>> accumulantTiming = TimedResult.time(() -> IntStream.range(0, accumulations).mapToObj(x -> factory.get()).collect(Collectors.toList()));
        logger.info(String.format("Calculated accumulant in %.4fsec", accumulantTiming.seconds()));
        List<TensorList> accumulants = accumulantTiming.result;
        accumulants.stream().forEach(accumulant -> {
            CudaSystem.run(gpu -> {
                @Nonnull TimedResult<TensorList> timedWrite = TimedResult.time(() -> {
                    return CudaTensorList.wrap(gpu.getTensor(accumulant, Precision.Double, MemoryType.Managed, false), length, dimensions, Precision.Double);
                });
                @Nonnull TimedResult<Void> timedAccumulation = TimedResult.time(() -> {
                    mutableGpuData.getAndUpdate(x -> x.add(timedWrite.result)).freeRef();
                    timedWrite.result.freeRef();
                });
                logger.info(String.format("Wrote in %.4f seconds and accumulated %s in %.4f seconds, Device %d: %s", timedAccumulation.seconds(), Arrays.toString(dimensions), timedWrite.seconds(), gpu.getDeviceId(), CudaDevice.getDeviceName(gpu.getDeviceId())));
            }, accumulant);
        });
        @Nonnull TimedResult<TensorList> finalResultTiming = TimedResult.time(() -> {
            return accumulants.stream().reduce((a, b) -> {
                TensorList sum = a.addAndFree(b);
                b.freeRef();
                return sum;
            }).map(x -> {
                TensorList sum = x.add(original);
                x.freeRef();
                return sum;
            }).orElseGet(() -> {
                original.addRef();
                return original;
            });
        });
        original.freeRef();
        logger.info(String.format("Calculated final data in %.4fsec", finalResultTiming.seconds()));
        TensorList finalResult = finalResultTiming.result;
        CudnnHandle.forEach(ctx -> {
            @Nonnull TimedResult<Boolean> timedVerify = TimedResult.time(() -> {
                @Nonnull TensorList minus = finalResult.minus(mutableGpuData.get());
                double diffVal = minus.stream().mapToDouble(x -> {
                    double v = Arrays.stream(x.getData()).map(Math::abs).max().getAsDouble();
                    x.freeRef();
                    return v;
                }).max().getAsDouble();
                minus.freeRef();
                return diffVal < tolerance;
            });
            logger.info(String.format("Read %s and verified in %.4fs using device %d: %s", Arrays.toString(dimensions), timedVerify.seconds(), ctx.getDeviceId(), CudaDevice.getDeviceName(ctx.getDeviceId())));
            if (!timedVerify.result)
                Assert.assertTrue(finalResult.prettyPrint() + " != " + mutableGpuData.get().prettyPrint(), timedVerify.result);
        });
        mutableGpuData.get().freeRef();
        finalResult.freeRef();
    });
}

Example 4 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class MnistTestBase method validate.

/**
 * Validate.
 *
 * @param log     the log
 * @param network the network
 */
public void validate(@Nonnull final NotebookOutput log, @Nonnull final Layer network) {
    log.h1("Validation");
    log.p("If we apply our model against the entire validation dataset, we get this accuracy:");
    log.code(() -> {
        return MNIST.validationDataStream().mapToDouble(labeledObject -> predict(network, labeledObject)[0] == parse(labeledObject.label) ? 1 : 0).average().getAsDouble() * 100;
    });
    log.p("Let's examine some incorrectly predicted results in more detail:");
    log.code(() -> {
        @Nonnull final TableOutput table = new TableOutput();
        MNIST.validationDataStream().map(labeledObject -> {
            final int actualCategory = parse(labeledObject.label);
            @Nullable final double[] predictionSignal = network.eval(labeledObject.data).getData().get(0).getData();
            final int[] predictionList = IntStream.range(0, 10).mapToObj(x -> x).sorted(Comparator.comparing(i -> -predictionSignal[i])).mapToInt(x -> x).toArray();
            // We will only examine mispredicted rows
            if (predictionList[0] == actualCategory)
                return null;
            @Nonnull final LinkedHashMap<CharSequence, Object> row = new LinkedHashMap<>();
            row.put("Image", log.image(labeledObject.data.toGrayImage(), labeledObject.label));
            row.put("Prediction", Arrays.stream(predictionList).limit(3).mapToObj(i -> String.format("%d (%.1f%%)", i, 100.0 * predictionSignal[i])).reduce((a, b) -> a + ", " + b).get());
            return row;
        }).filter(x -> null != x).limit(10).forEach(table::putRow);
        return table;
    });
}

Example 5 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class MnistTestBase method report.

/**
 * Report.
 *
 * @param log            the log
 * @param monitoringRoot the monitoring root
 * @param history        the history
 * @param network        the network
 */
public void report(@Nonnull final NotebookOutput log, @Nonnull final MonitoredObject monitoringRoot, @Nonnull final List<Step> history, @Nonnull final Layer network) {
    if (!history.isEmpty()) {
        log.code(() -> {
            @Nonnull final PlotCanvas plot = ScatterPlot.plot(history.stream().map(step -> new double[] { step.iteration, Math.log10(step.point.getMean()) }).toArray(i -> new double[i][]));
            plot.setTitle("Convergence Plot");
            plot.setAxisLabels("Iteration", "log10(Fitness)");
            plot.setSize(600, 400);
            return plot;
        });
    }
    @Nonnull final String modelName = "model" + modelNo++ + ".json";
    log.p("Saved model as " + log.file(network.getJson().toString(), modelName, modelName));
    log.h1("Metrics");
    log.code(() -> {
        try {
            @Nonnull final ByteArrayOutputStream out = new ByteArrayOutputStream();
            JsonUtil.writeJson(out, monitoringRoot.getMetrics());
            return out.toString();
        } catch (@Nonnull final IOException e) {
            throw new RuntimeException(e);
        }
    });
}