Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.stdout.txt filter=lfs diff=lfs merge=lfs -text
+fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.stdout_v1.txt filter=lfs diff=lfs merge=lfs -text
+fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.stdout.txt filter=lfs diff=lfs merge=lfs -text
+fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.stdout_v1.txt filter=lfs diff=lfs merge=lfs -text
+fold_2/logs.models.fold_2.ENCSR662RIZ/logfile.modelling.fold_2.ENCSR662RIZ.stdout.txt filter=lfs diff=lfs merge=lfs -text
+fold_2/logs.models.fold_2.ENCSR662RIZ/logfile.modelling.fold_2.ENCSR662RIZ.stdout_v1.txt filter=lfs diff=lfs merge=lfs -text
+fold_3/logs.models.fold_3.ENCSR662RIZ/logfile.modelling.fold_3.ENCSR662RIZ.stdout.txt filter=lfs diff=lfs merge=lfs -text
+fold_3/logs.models.fold_3.ENCSR662RIZ/logfile.modelling.fold_3.ENCSR662RIZ.stdout_v1.txt filter=lfs diff=lfs merge=lfs -text
+fold_4/logs.models.fold_4.ENCSR662RIZ/logfile.modelling.fold_4.ENCSR662RIZ.stdout.txt filter=lfs diff=lfs merge=lfs -text
+fold_4/logs.models.fold_4.ENCSR662RIZ/logfile.modelling.fold_4.ENCSR662RIZ.stdout_v1.txt filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,120 @@
+---
+license: mit
+library_name: chrombpnet
+tags:
+  - encode
+  - chrombpnet
+  - chromatin-accessibility
+  - DNASE
+  - H9
+  - hg38
+---
+# ENCODE ChromBPNet Atlas
+As part of the ENCODE 4 Project, we trained ChromBPNet models on 1,512 ENCODE DNAse-seq and ATAC-seq across 408 biosamples. Here, we provide all models for open-source use.
+
+For more information about the models, see:
+- Main ENCODE 4 Paper
+- [A unified lexicon of predictive DNA sequence motifs from ENCODE transcription factor binding and chromatin accessibility assays](https://doi.org/10.5281/zenodo.17123347) (Deshpande et al., Zenodo 2025)
+- [ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants](https://doi.org/10.1101/2024.12.25.630221) (Pampari et al., bioRxiv 2024)
+
+## ChromBPNet model: DNASE in H9 (ENCSR662RIZ)
+- Model: ChromBPNet
+- Assay: DNASE-seq
+- Experiment: [ENCSR662RIZ](https://www.encodeproject.org/experiments/ENCSR662RIZ/)
+- Model annotation: [ENCSR493ORC](https://www.encodeproject.org/annotations/ENCSR493ORC/)
+- Biosample: H9 (Full name: Homo sapiens H9)
+- Cell slim(s): embryonic-cell,stem-cell
+- Organ slim(s): embryo
+- Developmental slim(s): None
+- System slim(s): None
+- Assembly: hg38
+
+## Directory structure
+- `fold_0`: Model of 5-fold cross-validation: Fold 0
+    - `model.chrombpnet.fold_0.encid.h5`: full chrombpnet model that combines both bias and corrected model in .h5 format
+    - `model.chrombpnet_nobias.fold_0.encid.h5`: bias-corrected accessibility model in .h5 format (Use for all biological discovery)
+    - `model.bias_scaled.fold_0.encid.h5`: bias model in .h5 format
+    - `model.chrombpnet.fold_0.encid.tar`: full chrombpnet model that combines both bias and  corrected model in SavedModel format. After being untarred, it results in a directory named "chrombpnet".
+    - `model.chrombpnet_nobias.fold_0.encid.tar`: bias-corrected accessibility model in SavedModel format (Use for all biological discovery). After being untarred, it results in a directory named "chrombpnet_wo_bias".
+    - `model.bias_scaled.fold_0.encid.tar`: bias model in SavedModel format. After being untarred, it results in a directory named "bias_model_scaled".
+    - `logs.models.fold_0.encid`: folder containing log files for training models
+- `fold_1`: Model of 5-fold coss-validation: Fold 1
+- `fold_2`: Model of 5-fold cross-validation: Fold 2
+- `fold_3`: Model of 5-fold cross-validation: Fold 3
+- `fold_4`: Model of 5-fold cross-validation: Fold 4
+
+# Instructions
+## 1. Pseudocode for loading models in .h5 format 
+
+(1) Use the code in python after appropriately defining `model_in_h5_format` and `inputs`. \
+(2) `inputs` is a one hot encoded sequence of shape (N,2114,4). Here N corresponds to the 
+number of tested sequences, 2114 is the input sequence length and 4 corresponds to [A,C,G,T].
+
+```python
+import tensorflow as tf
+from tensorflow.keras.utils import get_custom_objects
+from tensorflow.keras.models import load_model
+
+custom_objects={"tf": tf}
+get_custom_objects().update(custom_objects)
+
+model=load_model(model_in_h5_format,compile=False)
+outputs = model(inputs)
+```
+
+The list `outputs` consists of two elements. The first element has a shape of (N, 1000) and
+contains logit predictions for a 1000-base-pair output. The second element, with a shape of
+(N, 1), contains logcount predictions. To transform these predictions into per-base signals, 
+follow the provided pseudo code lines below.
+
+```python
+import numpy as np
+
+def softmax(x, temp=1):
+    norm_x = x - np.mean(x,axis=1, keepdims=True)
+    return np.exp(temp*norm_x)/np.sum(np.exp(temp*norm_x), axis=1, keepdims=True)
+    
+predictions = softmax(outputs[0]) * (np.exp(outputs[1])-1)
+```
+
+## 2. Pseudocode for loading models in .tar format
+
+(1) First untar the directory as follows `tar -xvf model.tar`. \
+(2) Use the code below in python after appropriately defining `model_dir_untared` and `inputs`. \
+(3) `inputs` is a one hot encoded sequence of shape (N,2114,4). Here N corresponds to the number
+of tested sequences, 2114 is the input sequence length and 4 corresponds to ACGT.
+
+Reference: https://www.tensorflow.org/api_docs/python/tf/saved_model/load
+
+```python
+import tensorflow as tf
+
+model = tf.saved_model.load('model_dir_untared')
+outputs = model.signatures['serving_default'](**{'sequence':inputs.astype('float32')})
+```
+
+The variable `outputs` represents a dictionary containing two key-value pairs. The first key
+is `logits_profile_predictions`, holding a value with a shape of (N, 1000). This value corresponds
+to logit predictions for a 1000-base-pair output. The second key, named `logcount_predictions``, 
+is associated with a value of shape (N, 1), representing logcount predictions. To transform these
+predictions into per-base signals, utilize the provided pseudo code lines mentioned below.
+
+```python
+import numpy as np
+def softmax(x, temp=1):
+    norm_x = x - np.mean(x,axis=1, keepdims=True)
+    return np.exp(temp*norm_x)/np.sum(np.exp(temp*norm_x), axis=1, keepdims=True)
+    
+predictions = softmax(outputs["logits_profile_predictions"]) * (np.exp(outputs["logcount_predictions"])-1)
+```
+
+## Docker image to load and use the models
+- https://hub.docker.com/r/kundajelab/chrombpnet-atlas/ (tag:v1)
+
+## Code for ChromBPNet
+- https://github.com/kundajelab/chrombpnet/
+
+# License & citation
+External data users may freely download, analyze and publish results based on any ENCODE data without restrictions.
+
+Released under the [ENCODE data-use policy](https://www.encodeproject.org/about/data-use-policy/). Please cite the ENCODE Project Consortium and the model software: [ChromBPNet](https://github.com/kundajelab/chrombpnet) (Pampari et al., bioRxiv 2024).

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.args.json

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+{
+    "genome": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/reference/hg38.genome.fa",
+    "bigwig": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//preprocessing/bigWigs/ENCSR662RIZ.bigWig",
+    "peaks": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0//filtered.peaks.bed",
+    "nonpeaks": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0//filtered.nonpeaks.bed",
+    "output_prefix": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0//chrombpnet",
+    "chr_fold_path": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/splits/fold_0.json",
+    "trackables": [
+        "logcount_predictions_loss",
+        "loss",
+        "logits_profile_predictions_loss",
+        "val_logcount_predictions_loss",
+        "val_loss",
+        "val_logits_profile_predictions_loss"
+    ],
+    "epochs": 50,
+    "early_stop": 5,
+    "batch_size": 64,
+    "learning_rate": 0.001,
+    "params": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0//chrombpnet_model_params.tsv",
+    "seed": 1234,
+    "architecture_from_file": "/home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/models/chrombpnet_with_bias_model.py"
+}

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.bias_formatting.stderr.txt

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+INFO:    underlay of /etc/localtime required more than 50 (88) bind mounts
+INFO:    underlay of /usr/bin/nvidia-smi required more than 50 (355) bind mounts
+2023-08-18 16:48:36.170676: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:41.021789: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 16:48:41.025787: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
+2023-08-18 16:48:41.050397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:45:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
+coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s
+2023-08-18 16:48:41.050482: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:41.139726: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 16:48:41.139895: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 16:48:41.159259: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 16:48:41.245706: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 16:48:41.314687: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 16:48:41.337587: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 16:48:41.340881: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 16:48:41.342834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 16:48:41.343193: 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 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2023-08-18 16:48:41.343292: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 16:48:41.343600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:45:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
+coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s
+2023-08-18 16:48:41.343625: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:41.343646: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 16:48:41.343662: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 16:48:41.343677: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 16:48:41.343693: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 16:48:41.343707: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 16:48:41.343722: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 16:48:41.343736: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 16:48:41.344222: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 16:48:41.346366: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:45.021998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
+2023-08-18 16:48:45.022062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
+2023-08-18 16:48:45.022076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
+2023-08-18 16:48:45.025646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22421 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:45:00.0, compute capability: 8.6)
+2023-08-18 16:48:45.796589: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.bias_formatting.stdout.txt

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+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/bias_model_scaled.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/new_model_formats/bias_model_scaled

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.chrombpnet.params.json

@@ -0,0 +1,11 @@
+{
+    "counts_loss_weight": "20.3",
+    "filters": "512",
+    "n_dil_layers": "8",
+    "bias_model_path": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/bias_model_scaled.h5",
+    "inputlen": "2114",
+    "outputlen": "1000",
+    "max_jitter": "500",
+    "chr_fold_path": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/splits/fold_0.json",
+    "negative_sampling_ratio": "0.1"
+}

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.chrombpnet_data_params.tsv

@@ -0,0 +1,3 @@
+counts_sum_min_thresh	5.0
+counts_sum_max_thresh	2943.0
+trainings_pts_post_thresh	170674

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.chrombpnet_formatting.stderr.txt

@@ -0,0 +1,40 @@
+INFO:    underlay of /etc/localtime required more than 50 (88) bind mounts
+INFO:    underlay of /usr/bin/nvidia-smi required more than 50 (355) bind mounts
+2023-08-18 15:42:06.530675: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:08.850506: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 15:42:08.853952: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
+2023-08-18 15:42:09.387772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:84:00.0 name: NVIDIA A100-SXM4-80GB computeCapability: 8.0
+coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 79.15GiB deviceMemoryBandwidth: 1.85TiB/s
+2023-08-18 15:42:09.387858: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:09.410207: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 15:42:09.410298: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 15:42:09.419832: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 15:42:09.424443: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 15:42:09.440246: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 15:42:09.444435: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 15:42:09.445371: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 15:42:09.480114: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 15:42:09.480415: 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 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2023-08-18 15:42:09.481267: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 15:42:09.503377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:84:00.0 name: NVIDIA A100-SXM4-80GB computeCapability: 8.0
+coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 79.15GiB deviceMemoryBandwidth: 1.85TiB/s
+2023-08-18 15:42:09.503408: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:09.503429: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 15:42:09.503444: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 15:42:09.503458: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 15:42:09.503471: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 15:42:09.503484: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 15:42:09.503497: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 15:42:09.503509: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 15:42:09.536525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 15:42:09.537837: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:12.604435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
+2023-08-18 15:42:12.604532: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
+2023-08-18 15:42:12.604559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
+2023-08-18 15:42:12.769502: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 75650 MB memory) -> physical GPU (device: 0, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:84:00.0, compute capability: 8.0)
+2023-08-18 15:42:14.887681: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
+/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:1059: UserWarning:  is not loaded, but a Lambda layer uses it. It may cause errors.
+  , UserWarning)

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.chrombpnet_formatting.stdout.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/chrombpnet.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/new_model_formats/chrombpnet

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.chrombpnet_model_params.tsv

@@ -0,0 +1,9 @@
+counts_loss_weight	20.3
+filters	512
+n_dil_layers	8
+bias_model_path	/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/bias_model_scaled.h5
+inputlen	2114
+outputlen	1000
+max_jitter	500
+chr_fold_path	/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/splits/fold_0.json
+negative_sampling_ratio	0.1

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.chrombpnet_no_bias_formatting.stderr.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/chrombpnet_wo_bias.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/new_model_formats/chrombpnet_wo_bias

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.chrombpnet_no_bias_formatting.stdout.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/chrombpnet_wo_bias.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_0/new_model_formats/chrombpnet_wo_bias

fold_0/logs.models.fold_0.ENCSR662RIZ/logfile.modelling.fold_0.ENCSR662RIZ.epoch_loss.csv

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fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.args.json

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+{
+    "genome": "/scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa",
+    "bigwig": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//preprocessing/bigWigs/ENCSR662RIZ.bigWig",
+    "peaks": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_1//filtered.peaks.bed",
+    "nonpeaks": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_1//filtered.nonpeaks.bed",
+    "output_prefix": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_1//chrombpnet",
+    "chr_fold_path": "/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_1.json",
+    "trackables": [
+        "logcount_predictions_loss",
+        "loss",
+        "logits_profile_predictions_loss",
+        "val_logcount_predictions_loss",
+        "val_loss",
+        "val_logits_profile_predictions_loss"
+    ],
+    "epochs": 50,
+    "early_stop": 5,
+    "batch_size": 64,
+    "learning_rate": 0.001,
+    "params": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_1//chrombpnet_model_params.tsv",
+    "seed": 1234,
+    "architecture_from_file": "/home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/models/chrombpnet_with_bias_model.py"
+}

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.bias_formatting.stderr.txt

@@ -0,0 +1,38 @@
+INFO:    underlay of /etc/localtime required more than 50 (88) bind mounts
+INFO:    underlay of /usr/bin/nvidia-smi required more than 50 (355) bind mounts
+2023-08-18 16:48:36.124326: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:40.743490: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 16:48:40.747695: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
+2023-08-18 16:48:40.791405: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:06:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
+coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s
+2023-08-18 16:48:40.791509: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:40.847060: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 16:48:40.847251: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 16:48:40.863698: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 16:48:40.948464: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 16:48:41.090886: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 16:48:41.336169: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 16:48:41.338899: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 16:48:41.341803: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 16:48:41.342219: 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 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2023-08-18 16:48:41.343140: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 16:48:41.343839: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:06:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
+coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s
+2023-08-18 16:48:41.343874: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:41.343908: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 16:48:41.343927: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 16:48:41.343944: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 16:48:41.343960: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 16:48:41.343977: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 16:48:41.343993: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 16:48:41.344009: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 16:48:41.345264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 16:48:41.346685: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:44.263830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
+2023-08-18 16:48:44.263936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
+2023-08-18 16:48:44.263949: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
+2023-08-18 16:48:44.267568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22421 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:06:00.0, compute capability: 8.6)
+2023-08-18 16:48:45.103202: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.bias_formatting.stdout.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/bias_model_scaled.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/new_model_formats/bias_model_scaled

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.chrombpnet.params.json

@@ -0,0 +1,11 @@
+{
+    "counts_loss_weight": "20.4",
+    "filters": "512",
+    "n_dil_layers": "8",
+    "bias_model_path": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_1/bias_model_scaled.h5",
+    "inputlen": "2114",
+    "outputlen": "1000",
+    "max_jitter": "500",
+    "chr_fold_path": "/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_1.json",
+    "negative_sampling_ratio": "0.1"
+}

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.chrombpnet_data_params.tsv

@@ -0,0 +1,3 @@
+counts_sum_min_thresh	5.0
+counts_sum_max_thresh	2944.23
+trainings_pts_post_thresh	172028

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.chrombpnet_formatting.stderr.txt

@@ -0,0 +1,40 @@
+INFO:    underlay of /etc/localtime required more than 50 (88) bind mounts
+INFO:    underlay of /usr/bin/nvidia-smi required more than 50 (355) bind mounts
+2023-08-18 15:42:06.485136: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:08.972006: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 15:42:08.976304: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
+2023-08-18 15:42:10.481744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:45:00.0 name: NVIDIA A100-SXM4-80GB computeCapability: 8.0
+coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 79.15GiB deviceMemoryBandwidth: 1.85TiB/s
+2023-08-18 15:42:10.481923: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:10.500606: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 15:42:10.500760: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 15:42:10.510176: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 15:42:10.514783: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 15:42:10.530452: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 15:42:10.534903: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 15:42:10.535822: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 15:42:10.677853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 15:42:10.678273: 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 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2023-08-18 15:42:10.679164: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 15:42:10.763600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:45:00.0 name: NVIDIA A100-SXM4-80GB computeCapability: 8.0
+coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 79.15GiB deviceMemoryBandwidth: 1.85TiB/s
+2023-08-18 15:42:10.763736: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:10.763781: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 15:42:10.763797: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 15:42:10.763825: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 15:42:10.763842: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 15:42:10.763855: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 15:42:10.763869: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 15:42:10.763882: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 15:42:10.869497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 15:42:10.871022: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 15:42:12.829389: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
+2023-08-18 15:42:12.829539: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
+2023-08-18 15:42:12.829556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
+2023-08-18 15:42:12.872966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 75650 MB memory) -> physical GPU (device: 0, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:45:00.0, compute capability: 8.0)
+2023-08-18 15:42:15.029542: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
+/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:1059: UserWarning:  is not loaded, but a Lambda layer uses it. It may cause errors.
+  , UserWarning)

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.chrombpnet_formatting.stdout.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/chrombpnet.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/new_model_formats/chrombpnet

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.chrombpnet_model_params.tsv

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+counts_loss_weight	20.4
+filters	512
+n_dil_layers	8
+bias_model_path	/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_1/bias_model_scaled.h5
+inputlen	2114
+outputlen	1000
+max_jitter	500
+chr_fold_path	/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_1.json
+negative_sampling_ratio	0.1

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.chrombpnet_no_bias_formatting.stderr.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/chrombpnet_wo_bias.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/new_model_formats/chrombpnet_wo_bias

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.chrombpnet_no_bias_formatting.stdout.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/chrombpnet_wo_bias.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_1/new_model_formats/chrombpnet_wo_bias

fold_1/logs.models.fold_1.ENCSR662RIZ/logfile.modelling.fold_1.ENCSR662RIZ.epoch_loss.csv

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fold_2/logs.models.fold_2.ENCSR662RIZ/logfile.modelling.fold_2.ENCSR662RIZ.args.json

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+{
+    "genome": "/scratch/groups/akundaje/anusri/chromatin_atlas/reference/hg38.genome.fa",
+    "bigwig": "/oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//preprocessing/bigWigs/ENCSR662RIZ.bigWig",
+    "peaks": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_2//filtered.peaks.bed",
+    "nonpeaks": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_2//filtered.nonpeaks.bed",
+    "output_prefix": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_2//chrombpnet",
+    "chr_fold_path": "/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_2.json",
+    "trackables": [
+        "logcount_predictions_loss",
+        "loss",
+        "logits_profile_predictions_loss",
+        "val_logcount_predictions_loss",
+        "val_loss",
+        "val_logits_profile_predictions_loss"
+    ],
+    "epochs": 50,
+    "early_stop": 5,
+    "batch_size": 64,
+    "learning_rate": 0.001,
+    "params": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_2//chrombpnet_model_params.tsv",
+    "seed": 1234,
+    "architecture_from_file": "/home/users/anusri/chromatin-atlas-anvil/sherlock/chrombpnet/src/training/models/chrombpnet_with_bias_model.py"
+}

fold_2/logs.models.fold_2.ENCSR662RIZ/logfile.modelling.fold_2.ENCSR662RIZ.bias_formatting.stderr.txt

@@ -0,0 +1,38 @@
+INFO:    underlay of /etc/localtime required more than 50 (88) bind mounts
+INFO:    underlay of /usr/bin/nvidia-smi required more than 50 (355) bind mounts
+2023-08-18 16:48:36.095602: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:40.739642: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 16:48:40.743913: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
+2023-08-18 16:48:40.770256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:45:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
+coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s
+2023-08-18 16:48:40.770347: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:40.847009: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 16:48:40.847209: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 16:48:40.864105: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 16:48:40.948459: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 16:48:41.090845: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 16:48:41.336146: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 16:48:41.338887: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 16:48:41.341691: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 16:48:41.342086: 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 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2023-08-18 16:48:41.343001: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
+2023-08-18 16:48:41.343735: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
+pciBusID: 0000:45:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
+coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.69GiB deviceMemoryBandwidth: 871.81GiB/s
+2023-08-18 16:48:41.343769: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:41.343804: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
+2023-08-18 16:48:41.343822: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
+2023-08-18 16:48:41.343838: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
+2023-08-18 16:48:41.343855: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
+2023-08-18 16:48:41.343871: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
+2023-08-18 16:48:41.343888: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
+2023-08-18 16:48:41.343905: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
+2023-08-18 16:48:41.344957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
+2023-08-18 16:48:41.346331: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
+2023-08-18 16:48:44.057121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
+2023-08-18 16:48:44.057219: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
+2023-08-18 16:48:44.057234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
+2023-08-18 16:48:44.060796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22421 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:45:00.0, compute capability: 8.6)
+2023-08-18 16:48:44.936198: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.

fold_2/logs.models.fold_2.ENCSR662RIZ/logfile.modelling.fold_2.ENCSR662RIZ.bias_formatting.stdout.txt

@@ -0,0 +1 @@
+singularity exec --nv /home/groups/akundaje/anusri/simg/tf-atlas_gcp-modeling.sif python get_new_tf_model_format.py -i /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_2/bias_model_scaled.h5 -o /oak/stanford/groups/akundaje/projects/chromatin-atlas-2022/DNASE//ENCSR662RIZ//chrombpnet_model_feb15_fold_2/new_model_formats/bias_model_scaled

fold_2/logs.models.fold_2.ENCSR662RIZ/logfile.modelling.fold_2.ENCSR662RIZ.chrombpnet.params.json

@@ -0,0 +1,11 @@
+{
+    "counts_loss_weight": "20.2",
+    "filters": "512",
+    "n_dil_layers": "8",
+    "bias_model_path": "/scratch/groups/akundaje/anusri/chromatin_atlas/DNASE/ENCSR662RIZ//chrombpnet_model_feb15_fold_2/bias_model_scaled.h5",
+    "inputlen": "2114",
+    "outputlen": "1000",
+    "max_jitter": "500",
+    "chr_fold_path": "/scratch/groups/akundaje/anusri/chromatin_atlas/splits/fold_2.json",
+    "negative_sampling_ratio": "0.1"
+}

fold_2/logs.models.fold_2.ENCSR662RIZ/logfile.modelling.fold_2.ENCSR662RIZ.chrombpnet_data_params.tsv

@@ -0,0 +1,3 @@
+counts_sum_min_thresh	6.0
+counts_sum_max_thresh	2933.21
+trainings_pts_post_thresh	177027