tensorboardX | tensorboard for pytorch | Machine Learning library
kandi X-RAY | tensorboardX Summary
kandi X-RAY | tensorboardX Summary
Write TensorBoard events with simple function call. The current release (v2.3) is tested on anaconda3, with PyTorch 1.8.1 / torchvision 0.9.1 / tensorboard 2.5.0.
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Top functions reviewed by kandi - BETA
- Add an embedding
- Compute log embedding
- Append a pre - trained embeddings
- Upload a file to S3
- Add an image
- Logs an image
- Clean the tag name
- Create an image summary
- Add a histogram summary
- Add a scalar
- Add an image summary
- Add a PR curve
- Add hparams
- Add a mesh
- Adds a PR curve
- Add a video
- Draws fuslli faces
- Add audio data
- Adds training operators to the model
- Get loss function
- Add a leNet model
- Runs the event loop
- Add video to Tensorboard
- Add PR curve
- Add scalars
- Deprecated
tensorboardX Key Features
tensorboardX Examples and Code Snippets
Lines of Code : 55License : Permissive (MIT)
Copy# demo.py
import torch
import torchvision.utils as vutils
import numpy as np
import torchvision.models as models
from torchvision import datasets
from tensorboardX import SummaryWriter
resnet18 = models.resnet18(False)
writer = SummaryWriter()
samp from tensorboardX import SummaryWriter
# Creates writer1 object.
# The log will be saved in 'runs/exp'
writer1 = SummaryWriter('runs/exp')
# Creates writer2 object with auto generated file name
# The log directory will be something like 'runs/Aug20 import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
from tensorboardX import SummaryWriter
dummy_input = (torch.zeros(1, 3),)
# torch.rand(1, 3, 32, 32) 在DaiNet7中使用的
class Linear import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
from tensorboardX import SummaryWriter
dummy_input = (torch.zeros(1, 3),)
class LinearInLinear(nn.Module):
def __init__(s try:
import caffe2.python.predictor.predictor_exporter as pe
except ImportError:
print('Please check that Caffe2 is installed correctly to run this demo.')
import numpy as np
import os
import shutil
from caffe2.python import core, model_help #!/usr/bin/env python
"""Chainer example: train a VAE on MNIST
"""
from __future__ import print_function
import argparse
import matplotlib
# Disable interactive backend
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import sudo apt install libpython3.9-dev
conda env create --file requirements.txt
pip install --upgrade tensorboardX
try:from tensorboardX import SummaryWriter
except:from tensorboardX import SummaryWriter
# replace RUN pip3 install -r /tmp/requirements.txt
RUN python3 -m pip install --upgrade pip setuptools wheel Community Discussions
Trending Discussions on tensorboardX
QUESTION
What would be the proper way of passing the PyTorch dictionary dataset to the TensorBoard add_graph(model, data).
May seems similar to the Question1, Qeustion2 and Question3, however, couldn't find the right way of handling with dictionary dataset.
Error Message
Dictionary inputs to traced functions must have consistent type. Found Tensor and List[str]
Error occurs, No graph saved
Below are my anonymized scripts of the project.
train.py
ANSWER
Answered 2022-Jan-14 at 09:06The error message tells you that the entries of the dictionary must all be the same type, but in your case you seem to have a Tensor in one entry but a list of strings in another entry. You'd have to make sure that all entries have the same type.
QUESTION
I have encountered these strange errors upon trying to install these 2 libraries (Cython_bbox and lap), which are part of other libraries that I need when running pip install -r requirements.txt,
which contains the following
ANSWER
Answered 2021-Dec-29 at 11:32Try this :
QUESTION
I downloaded a requirements.txt file from a GitHub repository, but it appears to be little different than the normal format of requirements.txt file.
- Can you tell me how the author generated this kind of
requirements.txtfile? Which tools did they use? - How can I use this particular file format to instantiate the Python environment? I have tried executing the commands
conda install --file requirements.txtandpip install -r requirements.txton a Windows ‘ machine, but to no avail.
https://github.com/wvangansbeke/Unsupervised-Classification/blob/master/requirements.txt
...ANSWER
Answered 2021-Oct-17 at 01:46This looks like a conda environment.yml file. It can be used to create a conda environment, like so
QUESTION
In ipython if I do:
...ANSWER
Answered 2021-Oct-03 at 19:39See if upgrading the module would help..
QUESTION
I am following the instruction (https://github.com/huggingface/transfer-learning-conv-ai) to install conv-ai from huggingface, but I got stuck on the docker build step: docker build -t convai .
I am using Mac 10.15, python 3.8, increased Docker memory to 4G.
I have tried the following ways to solve the issue:
- add
numpyinrequirements.txt - add
RUN pip3 install --upgrade setuptoolsin Dockerfile - add
--upgradetoRUN pip3 install -r /tmp/requirements.txtin Dockerfile - add
RUN pip3 install numpybeforeRUN pip3 install -r /tmp/requirements.txtin Dockerfile - add
RUN apt-get install python3-numpybeforeRUN pip3 install -r /tmp/requirements.txtin Dockerfile - using python 3.6.13 because of this post, but it has exact same error.
- I am currently working on debugging inside the container by entering right before the
RUN pip3 install requirements.txt
Can anyone help me on this? Thank you!!
The error:
...ANSWER
Answered 2021-Mar-12 at 15:47Did you try adding numpy into the requirements.txt? It looks to me that it is missing.
QUESTION
I have a problem with updating packages in conda. The list of my installed packages is:
...ANSWER
Answered 2021-Apr-14 at 20:26Channel pypi means that the package was installed with pip. You may need to upgrade it with pip as well
QUESTION
This is a specific instance of a general problem that I run into when updating packages using conda. I have an environment that is working great on machine A. I want to transfer it to machine B. But, machine A has GTX1080 gpus, and due to configuration I cannot control, requires cudatoolkit 10.2. Machine B has A100 gpus, and due to configuration I cannot control, requires cudatoolkit 11.1
I can easily export Machine A's environment to yml, and create a new environment on Machine B using that yml. However, I cannot seem to update cudatoolkit to 11.1 on that environment on Machine B. I try
...ANSWER
Answered 2021-Mar-22 at 03:02I'd venture the issue is that recreating from a YAML that includes versions and builds will establish those versions and builds as explicit specifications for that environment moving forward. That is, Conda will regard explicit specifications as hard requirements that it cannot mutate and so if even a single one of the dependencies of cudatoolkit also needs to be updated in order to use version 11, Conda will not know how to satisfy it without violating those previously specified constraints.
Specifically, this is what I see when searching (assuming linux-64 platform):
QUESTION
Having trouble with CUDA + Pytorch this is the error. I reinstalled CUDA and cudnn multiple times.
Conda env is detecting GPU but its giving errors with pytorch and certain cuda libraries. I tried with Cuda 10.1 and 10.0, and cudnn version 8 and 7.6.5, Added cuda to path and everything.
However anaconda is showing cuda tool kit 9.0 is installed, whilst I clearly installed 10.0, so I am not entirely sure what's the deal with that.
...ANSWER
Answered 2021-Mar-20 at 10:44From the list of libraries, it looks like you've installed CPU only version of the Pytorch.
QUESTION
I am building a Docker container based on python:3.7-slim-stretch (same problem also happens on python:3.7-slim-stretch), and it is getting Killed on
ANSWER
Answered 2021-Feb-22 at 06:09I experience something similar on Windows when my docker containers run out of memory in WSL. I think the settings are different for Mac, but it looks like there is info here on setting the VM RAM/disk size/swap file settings for Docker for Desktop on Mac:
QUESTION
I want to run a deep learning training that uses matplotlib internally for creating graphs and then use the created figure and dump it to disk as image.
The dumping part is done for me using tensorboardX and that part works.
The problem:
plt.subplots(4, 1) opens a window, which slows down my program, especially when not all figures get closed upon request.
I want the same functionality without GUI:
- Create the subplots.
- Plot into them.
- Have a figure object with the plots drawn (and not shown on screen) and do whatever I want with it.
Sample code (in pytorch-lightning) for context only, I don't expect anyone to have to reproduce this, as the question is clear.
...ANSWER
Answered 2021-Jan-19 at 14:35Inspired by @Mr.T, I investigated some more and got a similar solution with different syntax.
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