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submitted by /u/Field_Great [visit reddit] [comments] |
Category: Misc
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submitted by /u/Field_Great [visit reddit] [comments] |
NVIDIA released NVIDIA Clara Parabricks Pipelines version 3.5, adding a set of new features to the software suite that accelerates end-to-end genome sequencing analysis.
NVIDIA recently released NVIDIA Clara Parabricks Pipelines version 3.5, adding a set of new features to the software suite that accelerates end-to-end genome sequencing analysis.
With the release of v3.5, Clara Parabricks Pipelines now provides acceleration to Google’s DeepVariant 1.0, in addition to a suite of existing DNA and RNA tools. The addition of DeepVariant to Parabricks Pipelines brings highly-accurate variant calling for both short- and long-read sequencing data to the community.
This new release also enables graphical reports of QC metrics from binary alignment map (BAM) files to variant call files (VCF). Researchers can use these graphical reports to better assess the quality of their sequencing data and the subsequent variant calling before moving the results for additional downstream analysis.
Parabricks Pipelines is packaged with enterprise support for A100 and other NVIDIA GPUs, offering one of the industry’s fastest compute frameworks for whole genome and whole exome applications. For a whole genome at 30x coverage, a server with 32 virtual CPUs takes about 1,200 minutes to generate a variant call file (VCF), while a server with eight A100 Tensor Core GPUs running Clara Parabricks takes less than 25 minutes to go from FASTQ to VCF.
Start a free one month trial of NVIDIA Clara Parabricks Pipelines today and learn how to get set up in just 10 minutes with this step-by-step instructional video.
Learn how to train your own gesture recognition deep learning pipeline. We’ll start with a pre-trained detection model, repurpose it for hand detection, and use it together with the purpose-built gesture recognition model.
In this webinar, you will learn how to train your own gesture recognition deep learning pipeline. We’ll start with a pre-trained detection model, repurpose it for hand detection, and use it together with the purpose-built gesture recognition model.
NVIDIA pre-trained deep learning models and the Transfer Learning Toolkit (TLT) give you a rapid path to building your next AI project. Whether you’re a DIY enthusiast or building a next-gen product with AI, you can use these models out of the box or fine-tune with your own dataset. The purpose-built, pre-trained models are trained on the large datasets collected and curated by NVIDIA and can be applied to a wide range of use cases. TLT is a simple AI toolkit, shipped with Jupyter notebooks, that requires little to no coding for taking pre-trained models and customizing them with your own data.
Date: March 3, 2021
Time: 11:00am – 12:00pm PT
Duration: 1 hour
Join this webinar to explore:
- Highly optimized pre-trained models for various industry use cases
- How to fine-tune with your own data on new pre-trained models and use them to reduce your total development time
- Developing an end-to-end training pipeline and deploying the trained model on NVIDIA SDKs
Join us after the presentation for a live Q&A session.
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The following shows up in the command prompt
Im trying to create a chatbot using neuralnines tutorial but I ran into a problem
C:UserschakkDesktopchatbot>python main.py 2021-02-21 16:14:30.544425: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-02-21 16:14:30.544542: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 2021-02-21 16:14:31.738286: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set 2021-02-21 16:14:31.738724: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll 2021-02-21 16:14:31.757352: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce RTX 2080 SUPER computeCapability: 7.5 coreClock: 1.815GHz coreCount: 48 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 462.00GiB/s 2021-02-21 16:14:31.757788: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-02-21 16:14:31.758162: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found 2021-02-21 16:14:31.759016: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found 2021-02-21 16:14:31.759344: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found 2021-02-21 16:14:31.759848: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found 2021-02-21 16:14:31.760201: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found 2021-02-21 16:14:31.760504: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found 2021-02-21 16:14:31.760798: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found 2021-02-21 16:14:31.760831: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... 2021-02-21 16:14:31.761295: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-02-21 16:14:31.761890: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-02-21 16:14:31.761963: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 2021-02-21 16:14:31.762260: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set C:UserschakkDesktopchatbot>python main.py 2021-02-21 16:21:50.579668: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-02-21 16:21:50.579795: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 2021-02-21 16:21:51.786031: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set 2021-02-21 16:21:51.786480: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll 2021-02-21 16:21:51.797963: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
submitted by /u/AviationAddiction21
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I’m working on an experiment where users can interact with a 3D object using their gestures or hands. Posenet/Handpose is a great library, but the performance is not up to par just yet, without any 3d object the frame rate hovers around 10-12FPS which is not enough if you want to build an interactive installation.
Is there a way to optimize this, especially on macOS?
I’ve tried the following;
- Using web worker (didn’t help much)
- Using WebSocket and run TensorFlow on the server (Didn’t help much, because I can’t run the GPU backend)
What I haven’t tried.
- Run a TPU server, a bit excessive and perhaps costly? Or is there an alternative for this?
- Run it on an Nvidia platform (Might need to rent)
submitted by /u/buangakun3
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A package to sizeably boost your performance
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I am glad to present the TensorFlow implementation of “Gradient Centralization” a new optimization technique to sizeably boost your performance 🚀, available as a ready-to-use Python package! Project Repo: https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow Please consider giving it a ⭐ if you like it😎. Here is an example showing the impact of the package! submitted by /u/Rishit-dagli |
I have a dataset that contains a lot of audio files that I want Tensorflow to differentiate from. (For example, this audio is a women counting numbers, this audio is just plain static)
I have no idea where to start and googling just gives me some specific datasets that are used to differentiate songs.
What is a good place to start for audio processing?
submitted by /u/TuckleBuck88
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I am using Keras for boundary/contour detection using a Unet. When I use binary cross-entropy as the loss, the losses decrease over time as expected the predicted boundaries look reasonable
However, I have tried custom losses for Dice, Focal, IOU, with varying LRs, and none of them are working well. I either get NaNs or non-decreasing/barely-decreasing values for the losses. This is regardless of what I use for the LR, whether it be .01 to 1e-6, or whether I vary the ALPHA and GAMMA and other parameters. This doesn’t make sense since for my images, most of the pixels are the background, and the pixels corresponding to boundaries are the minority. For imbalanced datasets, IOU, Dice, and Focal should work better than binary Cross-Entropy
The code I used for the losses are from https://www.kaggle.com/bigironsphere/loss-function-library-keras-pytorch#Jaccard/Intersection-over-Union-(IoU)-Loss
def DiceLoss(targets, inputs, smooth=1e-6): #flatten label and prediction tensors inputs = K.flatten(inputs) targets = K.flatten(targets) intersection = K.sum(K.dot(targets, inputs)) dice = (2*intersection + smooth) / (K.sum(targets) + K.sum(inputs) + smooth) return 1 - dice ALPHA = 0.8 GAMMA = 2 def FocalLoss(targets, inputs, alpha=ALPHA, gamma=GAMMA): inputs = K.flatten(inputs) targets = K.flatten(targets) BCE = K.binary_crossentropy(targets, inputs) BCE_EXP = K.exp(-BCE) focal_loss = K.mean(alpha * K.pow((1-BCE_EXP), gamma) * BCE) return focal_loss def IoULoss(targets, inputs, smooth=1e-6): #flatten label and prediction tensors inputs = K.flatten(inputs) targets = K.flatten(targets) intersection = K.sum(K.dot(targets, inputs)) total = K.sum(targets) + K.sum(inputs) union = total - intersection IoU = (intersection + smooth) / (union + smooth) return 1 - IoU
Even if I try different code for the losses, such as the code below
smooth = 1. def dice_coef(y_true, y_pred): y_true_f = K.flatten(y_true) y_pred_f = K.flatten(y_pred) intersection = K.sum(y_true_f * y_pred_f) return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth) def dice_coef_loss(y_true, y_pred): return -dice_coef(y_true, y_pred)
the loss values still don’t improve. That is, it will show something like
loss: nan - dice_coef_loss: .9607 - val_loss: nan - val_dice_coef_loss: .9631
and the values won’t change much for each epoch
can anyone help?
submitted by /u/74throwaway
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Object detection api import error This was what i managed to do succesfully. Basicallly followed instructions from here Gilbert Tanner github. git clone https://github.com/tensorflow/models.git cd models/research # Compile protos. protoc object_detection/protos/*.proto –python_out=. # Install TensorFlow Object Detection API. i manually copied setup.py from packages/tf2 folder to object_detection/packages/tf2/setup.py python -m pip install . All these ran smoothly without warning messages. I tested it with
python -c “import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000,1000])))”
python generate_tfrecord.py –csv_input=images/train_labels.csv –image_dir=images/train –output_path=train.record However, when i tried to Training the model with python model_main_tf2.py –pipeline_config_path=training/ssd_efficientdet_d0_512x512_coco17_tpu-8.config –model_dir=training –alsologtostderr this came up. submitted by /u/slpypnda |
