I am working with Ubuntu in WSL and tried to install the required packages for a repo with:
$ conda install --file requirements.txt
I got a PackageNotFoundError for a bunch of different packages. I search on anaconda.org for the required channels and added them. But it doesn't matter which channels I add I always get a PackageNotFoundError for the last two remaining packages:
$ conda install --file requirements.txt
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
PackagesNotFoundError: The following packages are not available from current channels:
- openssl==1.1.1=h7b6447c_0
- intel-openmp==2019.5=281
Current channels:
- https://conda.anaconda.org/fastchan/linux-64
- https://conda.anaconda.org/fastchan/noarch
- https://conda.anaconda.org/cctbx202208/linux-64
- https://conda.anaconda.org/cctbx202208/noarch
- https://conda.anaconda.org/pytorch/linux-64
- https://conda.anaconda.org/pytorch/noarch
- https://conda.anaconda.org/conda-forge/linux-64
- https://conda.anaconda.org/conda-forge/noarch
- https://repo.anaconda.com/pkgs/main/linux-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/r/linux-64
- https://repo.anaconda.com/pkgs/r/noarch
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.
Anaconda.org says conda-forge, fastchan, cctbx202208 for openssl but even though I added all of them it's still not found.
The next thing I tried was to install it with pip:
$ pip install openssl==1.1.1
ERROR: Could not find a version that satisfies the requirement openssl==1.1.1 (from versions: none)
ERROR: No matching distribution found for openssl==1.1.1
But pip detects none versions of this package. Same with intel-openmp, but pip does find packages but not the one I want 2019.5:
$ pip install intel-openmp==2019.5
ERROR: Could not find a version that satisfies the requirement intel-openmp==2019.5 (from versions: 2018.0.0, 2018.0.3, 2019.0, 2020.0.133, 2021.1.1, 2021.1.2, 2021.2.0, 2021.3.0, 2021.4.0, 2022.0.1, 2022.0.2, 2022.1.0, 2022.2.0, 2022.2.1)
ERROR: No matching distribution found for intel-openmp==2019.5
So my question is, is there another way to install the two packages or do they not exists anymore? Because the repo from which I got the code has its last commit from 3 years ago...
Edit:
I tried this command:
conda install -c anaconda openssl
and it installs openssl, but the latest version and than the code still says openssl is missing.
I also tried:
conda install -c anaconda openssl=1.1.1
but I get the same error as in the beginning (PackageNotFoundError in the channels).
Edit2:
TrackNPred is the repo I cloned and want to get working.
As for the required channel, I just searched for the package name on anaconda.org and add the channels i see to my anaconda config with:
conda config --add channels new_channel
I'm not sure if I need the exact version of a package as it's listed in the requirements.txt or if the code also works with another version of the two missing packages.
Edit3:
I changed in the requirements.txt:
openssl=1.1.1*
intel-openmp=2019.5
and that worked.
If you don't require exact package build replication, then edit the file to loosen up the specifications.
I see the intel channel has intel-openmp=2019.5=intel_218 and any openssl=1.1.1 should do. So, find the respective entries in the requirements.txt file and edit them to have:
openssl=1.1.1*
intel-openmp=2019.5
then recreate the environment, with intel channel added.
conda config --set channel_priority flexible
conda create -n foo -c defaults -c intel --file requirements.txt
I am facing 4 problems when I tried to install TensorFlow on Apple M1:
Conda has supported M1 since 2022.05.06 but most of articles I googled talk about using Miniforge, e.g. So I feel they are all kind of outdated.
How To Install TensorFlow on M1 Mac (The Easy Way)
AI - Apple Silicon Mac M1 natively supports TensorFlow 2.8 GPU acceleration
How to Setup TensorFlow on Apple M1 Pro and M1 Max (works for M1 too)
How To Install TensorFlow 2.7 on MacBook Pro M1 Pro With Ease
I used the latest conda 4.13 to setup my python environment(3.8, 3.9 and 3.10) successfully but when I tried to install tensorflow I got the error "No matching distribution found for tensorflow" (all failed).
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
The answers in Could not find a version that satisfies the requirement tensorflow didn't help. I can't find useful information on https://www.tensorflow.org/ too, actually https://www.tensorflow.org/install just said pip install tensorflow.
I tried to run pip install tensorflow-macos and it succeeded.
I read from the above "works for M1 too" article mentioned "Apple's fork of TensorFlow is called tensorflow-macos" although I can't find much information about that. For example, https://www.tensorflow.org/ does not mention that. I also found from https://developer.apple.com/forums/thread/686926 that someone hit that "ERROR: No matching distribution found for tensorflow-macos" (but I didn't).
All the articles I googled, including above 4 articles and this Tensorflow on macOS Apple M1, all say I also need to run the following 2 commands
conda install -c apple tensorflow-deps
pip install tensorflow-metal
But do I really need to that? I can't find this information from https://www.tensorflow.org/.
What are these 2 packages tensorflow-deps and tensorflow-metal ?
Distilling the official directions from Apple (as of 13 July 2022), one would create an environment using the following YAML:
tf-metal-arm64.yaml
name: tf-metal
channels:
- apple
- conda-forge
dependencies:
- python=3.9 ## specify desired version
- pip
- tensorflow-deps
## uncomment for use with Jupyter
## - ipykernel
## PyPI packages
- pip:
- tensorflow-macos
- tensorflow-metal ## optional, but recommended
Edit to include additional packages.
Creating environment
Before creating the environment we need to know what the base architecture is. Check this with conda config --show subdir.
Native (osx-arm64) base
If you have installed a native osx-arm64 Miniforge variant (I recommend Mambaforge), then you can create with:
mamba env create -n my_tf_env -f tf-metal-arm64.yaml
Note: If you don't have Mamba, then substitute conda for mamba; or install it for much faster solving: conda install -n base mamba.
Emulated (osx-64) base
If you do not have a native base, then you will need to override the subdir setting:
## create env
CONDA_SUBDIR=osx-arm64 mamba env create -n my_tf_env -f tf-metal-arm64.yaml
## activate
mamba activate my_tf_env
## permanently set the subdir
conda config --env --set subdir osx-arm64
Be sure to always activate the environment before installing or updating packages.
I battled with this for hours. The current instructions at https://developer.apple.com/metal/tensorflow-plugin/ specify using Miniconda and can be summarized as:
conda create -y --name cv python
conda activate cv
conda install -y -c apple tensorflow-deps
python -m pip install tensorflow-macos tensorflow-metal
As of Jan 2023, these instructions are riddled with issues:
Symptom: you ran conda install -c apple tensorflow-deps expecting to get the current version (2.10.0) , but conda list tensorflow-deps shows tensorflow-deps 2.9.0
Reason: Apple's tensorflow-deps package v2.10.0 depends on numpy >=1.23.2,<1.23.3. There is no such version of numpy in Anaconda (only conda-forge). Anaconda's dependency resolution silently falls back to an older version of tensorflow-deps. This will cause more problems as you continue with the instructions.
Symptom: you ran conda install -c apple tensorflow-deps==2.10.0 and got UnsatisfiableError: The following specifications were found to be incompatible with each other
Reason: Same as above, but at least Anaconda has told you about it. It doesn't mention what the incompatibility is, because that would be helpful.
Symptom: "RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf"
Reason: If you install tensorflow-deps 2.9.0 (or Anaconda installs it for you due to the above) you will get numpy 1.22.3. When you pip install tensorflow-macos tensorflow-metal, you will get tensorflow-macos 2.11.0 and tensorflow-metal 0.7.0, one or both of which is binary-incompatible with numpy 1.22.3.
Symptom: 2023-01-22 15:16:23.209301: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id
Problem: TensorFlow 2.11 has introduced an incompatibility between optimizers and pluggable architectures. You can have TF 2.11, but you'll need to use a legacy optimizer.
Solutions
I have found 3 options for a working GPU-accelerated TF install on Apple Silicon using Anaconda.
It will help your debugging to get a minimal test script like the one from https://developer.apple.com/metal/tensorflow-plugin/ which is:
import tensorflow as tf
cifar = tf.keras.datasets.cifar100
(x_train, y_train), (x_test, y_test) = cifar.load_data()
model = tf.keras.applications.ResNet50(
include_top=True,
weights=None,
input_shape=(32, 32, 3),
classes=100,)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
model.fit(x_train, y_train, epochs=5, batch_size=64)
This way you can check GPU usage by running TF_MLC_LOGGING=1 python tf_arch_test.py and watching Activity Monitor. While feeling which side of the laptop is burning your legs can be a helpful indicator of GPU usage, it's not always reliable.
Fix #1: Add conda-forge as a channel, use the legacy optimizer in your code.
conda config --add channels conda-forge
conda config --set channel_priority strict
conda create -y --name cv
conda activate cv
conda install -y -c apple tensorflow-deps
python -m pip install tensorflow-macos
python -m pip install tensorflow-metal
python --version
conda list|grep -E '(tensorflow|numpy)'
TF_MLC_LOGGING=1 python tf_arch_test.py
You will get:
Python 3.10.8
numpy 1.23.2 py310h127c7cf_0 conda-forge
tensorflow-deps 2.10.0 0 apple
tensorflow-estimator 2.11.0 pypi_0 pypi
tensorflow-macos 2.11.0 pypi_0 pypi
tensorflow-metal 0.7.0 pypi_0 pypi
You will need to modify your code to use one of the legacy optimizers. e.g.:
model.compile(
optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=1e-3),
loss=loss_fn,
metrics=["accuracy"],
)
Fix #2: Add conda-forge as a channel, pin the version numbers to avoid the pluggable architecture issue.
conda config --add channels conda-forge
conda config --set channel_priority strict
conda create -y --name cv
conda activate cv
conda install -y -c apple tensorflow-deps==2.10.0
python -m pip install tensorflow-macos==2.10.0
python -m pip install tensorflow-metal==0.6.0
python --version
conda list|grep -E '(tensorflow|numpy)'
You will get:
Python 3.10.8
numpy 1.23.2 py310h127c7cf_0 conda-forge
tensorflow-deps 2.10.0 0 apple
tensorflow-estimator 2.10.0 pypi_0 pypi
tensorflow-macos 2.10.0 pypi_0 pypi
tensorflow-metal 0.6.0 pypi_0 pypi
Fix #3: Don't add conda-forge, pin the version numbers way back to the last ones that actually worked:
conda create -y --name cv python
conda activate cv
conda install -y -c apple tensorflow-deps==2.9.0
python -m pip install tensorflow-macos==2.9.2
python -m pip install tensorflow-metal==0.5.1
python --version
conda list|grep -E '(tensorflow|numpy)'
You will get:
Python 3.10.9
numpy 1.22.3 py310hdb36b11_0
numpy-base 1.22.3 py310h5e3e9f0_0
tensorflow-deps 2.9.0 0 apple
tensorflow-estimator 2.9.0 pypi_0 pypi
tensorflow-macos 2.9.2 pypi_0 pypi
tensorflow-metal 0.5.1 pypi_0 pypi
Download and install Conda env:
https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate
Install the TensorFlow dependencies:
conda install -c apple tensorflow-deps
Install base TensorFlow:
python -m pip install tensorflow-macos
Install base TensorFlow-metal:
python -m pip install tensorflow-metal
Create Conda Environment:
conda create -n tensorflow-env tensorflow
conda activate tensorflow-env
First of all, TensorFlow does not support officially the Mac M1. They don't distribute packages precompiled for the Mac M1 (and its specific arm64 arch), hence the tensorflow-macos package, which is maintained by Apple. TensorFlow distributes, as far as I know, official wheels only for x86 (Linux, Windows, Mac), and the Raspberry PI (arm64).
Apple is using a specific plugin in Tensorflow to make the framework compatible with Metal, the graphic stack of MacOS. To put it in a other way, they are leveraging the PluggableDevice API of Tensorflow to write code that translates the TensorFlow operations to code that the GPU of the M1 understands.
Those two packages contain respectively:
tensorflow-deps the dependencies to run Tensorflow on arm64, i.e python, numpy, grpcio and h5py. This is more of a convenience package, I believe.
tensorflow-metal: a plugin to make tensorflow able to run on metal, the shader API of MacOS (comparable to the low level APIs of Vulkan or DirectX12 on other platforms). You can think of it as a replacement of CUDA, if you are used to run TensorFlow on Nvidia GPUs.
Without the tensorflow-metal package, TensorFlow won't be able to leverage the GPU of the M1, but will still be able to run code on the CPU.
The two answers I got have helped better understand how to install TensorFlow on m1. But I would like share my experience too.
About tensorflow-deps. I do need it, without it pip failed to installed grpcio, and thus actually failed to install tensorflow-macos. When I first asked the question I didn't pay enough attention to output of pip install tensorflow-macos.
About tensorflow-macos package, actually https://blog.tensorflow.org/2020/11/accelerating-tensorflow-performance-on-mac.html has the full information. BTW that article, published 2020-11-18, said "In the near future, we’ll be making updates like this even easier for users to get these performance numbers by integrating the forked version into the TensorFlow master branch." But according to Lescurel's answer it seems they have not.
I didn't know the concept of PluggableDevice (as in Lescurel's), so even when I visited https://github.com/apple/tensorflow_macos I was still confused. Take a look at that article if you do not know that either, basically it will let TensorFlow support new devices.
For 4 articles I listed, "works for M1 too" is the most helpful. It actually explained why I need tensorflow-deps & tensorflow-metal. But part of reasons I did not pay enough attention beforehand were: a) I want to use conda, not miniforge, all these package manager tools scare me a little bit (come from nodejs background, npm, yarn, yarn2, pnmp). The answer from merv also suggested another one mamba, but I think I will pass. b) I don't use homebrew, basically all the articles talking about installing ts on m1 mentioned installing homebrew first. But I use macport, for the reason I mentioned here (again I am bit scared of these these package manager tools)
Using environment.yaml like the one in merv's answer is a reliable way to install tensorflow!
BTW, once I have figured out the whole process of installing tensorflow, install pytorch is a lot easier as pytorch also supports M1 now, check here https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Official instructions from Apple are available here.
At the time of writing:
conda create python=3.10.6 --name <NAME>
conda activate <NAME>
conda install -c apple tensorflow-deps
python -m pip install tensorflow-macos
python -m pip install tensorflow-metal
I installed a working version of tensorflow 2.9.2 on my M1 Mac with pip. First, install pyenv and python 3.10.6. Next, pip install tensorflow-metal and finally pip install tensorflow-macos. That's it, no need for tensorflow-deps.
If your model complains about the unavailability of cuDNN and runs slowly, try adjusting your script to enable cuDNN as per tensorflow docs
Worked for me using Python 3.10.8 via Homebrew and following the instructions from Apple but using the instructions for "x86: AMD" instead.
Check Python version:
% which python3.10
/opt/homebrew/bin/python3.10
Create venv, activate it (prompt will change), and update pip:
% python3.10 -m venv ~/py310-tf-metal
% source ~/py310-tf-metal/bin/activate
(py310-tf-metal) % which python
~/py310-tf-metal/bin/python
(py310-tf-metal) % python -m pip install -U pip
...
Successfully installed pip-22.3.1
Install tensorflow-macos:
(py310-tf-metal) % python -m pip install tensorflow-macos
...
Successfully installed MarkupSafe-2.1.1 absl-py-1.3.0 astunparse-1.6.3 cachetools-5.2.0 certifi-2022.9.24 charset-normalizer-2.1.1 flatbuffers-22.11.23 gast-0.4.0 google-auth-2.14.1 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.50.0 h5py-3.7.0 idna-3.4 keras-2.10.0 keras-preprocessing-1.1.2 libclang-14.0.6 markdown-3.4.1 numpy-1.23.5 oauthlib-3.2.2 opt-einsum-3.3.0 packaging-21.3 protobuf-3.19.6 pyasn1-0.4.8 pyasn1-modules-0.2.8 pyparsing-3.0.9 requests-2.28.1 requests-oauthlib-1.3.1 rsa-4.9 six-1.16.0 tensorboard-2.10.1 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-estimator-2.10.0 tensorflow-macos-2.10.0 termcolor-2.1.1 typing-extensions-4.4.0 urllib3-1.26.13 werkzeug-2.2.2 wheel-0.38.4 wrapt-1.14.1
Install tensorflow-metal:
(py310-tf-metal) % python -m pip install tensorflow-metal
Collecting tensorflow-metal
Downloading tensorflow_metal-0.6.0-cp310-cp310-macosx_12_0_arm64.whl (1.4 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.4/1.4 MB 25.5 MB/s eta 0:00:00
Requirement already satisfied: six>=1.15.0 in ./Venvs/py310-tf-metal/lib/python3.10/site-packages (from tensorflow-metal) (1.16.0)
Requirement already satisfied: wheel~=0.35 in ./Venvs/py310-tf-metal/lib/python3.10/site-packages (from tensorflow-metal) (0.38.4)
Installing collected packages: tensorflow-metal
Successfully installed tensorflow-metal-0.6.0
Test using the CIFAR training script at the Apple page:
import tensorflow as tf
cifar = tf.keras.datasets.cifar100
(x_train, y_train), (x_test, y_test) = cifar.load_data()
model = tf.keras.applications.ResNet50(
include_top=True,
weights=None,
input_shape=(32, 32, 3),
classes=100,)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
model.fit(x_train, y_train, epochs=5, batch_size=64)
Save above as testcifar.py and run it:
(py310-tf-metal) % python testcifar.py
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
169001437/169001437 [==============================] - 3s 0us/step
Metal device set to: Apple M1
systemMemory: 16.00 GB
maxCacheSize: 5.33 GB
2022-11-28 07:58:10.715660: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-11-28 07:58:10.715837: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
2022-11-28 07:58:14.736843: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
Epoch 1/5
...
2022-11-28 07:58:21.975675: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
...
Epoch 5/5
782/782 [==============================] - 206s 264ms/step - loss: 4.0877 - accuracy: 0.1292
I had to downgrade tensorflow to get it to work on Macbook Pro M2:
pip install tensorflow-macos==2.9
pip install tensorflow-metal==0.5.0
In a simple test, I tried to create a basic Conda environment, export that environment to a YAML file, and re-create the environment from the YAML file on the exact same computing instance.
No matter what I try, the re-create step hangs up (fails) with no error message (timeout)
This is on an AWS EC2 Linux instance.
Command sequence as follows:
conda create -n myenv python=3.10.4
conda activate myenv
conda list ## output is shown below
conda env export > newenv.yml ## yml contents are shown below...so far so good
conda deactivate
conda env create -n newenv --f newenv.yml ## this is where it hangs up/freezes
<output> Collecting package metadata (repodata.json): -
<output> Collecting package metadata (repodata.json): - Killed ## after about 5mins
I have also tried multiple variations where I remove myenv before trying to re-create it using the same name (myenv). Folder permissions to the anaconda3/envs folder are 775 and there is no problem creating the environment. I have already updated, cleaned, and re-initialized Conda and have re-booted my instance multiple times.
Any help/ideas would be greatly appreciated. This is my first Python project.
Conda list output
(myenv) [ec2-user#ip-172-31-93-141 ~]$ conda list
# packages in environment at /home/ec2-user/anaconda3/envs/myenv:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
ca-certificates 2022.9.24 ha878542_0 conda-forge
ld_impl_linux-64 2.39 hcc3a1bd_1 conda-forge
libffi 3.3 h58526e2_2 conda-forge
libgcc-ng 12.2.0 h65d4601_19 conda-forge
libgomp 12.2.0 h65d4601_19 conda-forge
libsqlite 3.40.0 h753d276_0 conda-forge
libstdcxx-ng 12.2.0 h46fd767_19 conda-forge
libuuid 1.41.5 h5eee18b_0
libzlib 1.2.13 h166bdaf_4 conda-forge
ncurses 6.3 h27087fc_1 conda-forge
openssl 1.1.1s h0b41bf4_1 conda-forge
pip 22.3.1 pyhd8ed1ab_0 conda-forge
python 3.10.4 h12debd9_0
readline 8.1.2 h0f457ee_0 conda-forge
setuptools 65.5.1 pyhd8ed1ab_0 conda-forge
sqlite 3.40.0 h4ff8645_0 conda-forge
tk 8.6.12 h27826a3_0 conda-forge
tzdata 2022g h191b570_0 conda-forge
wheel 0.38.4 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
zlib 1.2.13 h166bdaf_4 conda-forge
YAML file output
name: myenv
channels:
- conda-forge
- defaults
dependencies:
- _libgcc_mutex=0.1=conda_forge
- _openmp_mutex=4.5=2_gnu
- bzip2=1.0.8=h7f98852_4
- ca-certificates=2022.9.24=ha878542_0
- ld_impl_linux-64=2.39=hcc3a1bd_1
- libffi=3.3=h58526e2_2
- libgcc-ng=12.2.0=h65d4601_19
- libgomp=12.2.0=h65d4601_19
- libsqlite=3.40.0=h753d276_0
- libstdcxx-ng=12.2.0=h46fd767_19
- libuuid=1.41.5=h5eee18b_0
- libzlib=1.2.13=h166bdaf_4
- ncurses=6.3=h27087fc_1
- openssl=1.1.1s=h0b41bf4_1
- pip=22.3.1=pyhd8ed1ab_0
- python=3.10.4=h12debd9_0
- readline=8.1.2=h0f457ee_0
- setuptools=65.5.1=pyhd8ed1ab_0
- sqlite=3.40.0=h4ff8645_0
- tk=8.6.12=h27826a3_0
- tzdata=2022g=h191b570_0
- wheel=0.38.4=pyhd8ed1ab_0
- xz=5.2.6=h166bdaf_0
- zlib=1.2.13=h166bdaf_4
prefix: /home/ec2-user/anaconda3/envs/myenv
Possibly a channel mixing issue. I see python and libuuid come from defaults (i.e., main, anaconda) channel, whereas everything else is Conda Forge. If you have channel_priority: strict, then any packages in conda-forge channel (which is given priority by the YAML because it is first), will mask any packages of the same name in the lower priority channel (defaults).
Concretely, Conda is told to find python with a specific build (h12debd9_0) that is only available on defaults, but because conda-forge has python packages available, only those will be considered. The hanging is probably just Conda trying to "explain" why it can't find a solution.
Immediate Workaround
If this is the issue, then changing the channel priority to flexible should get it working.
conda config --set channel_priority flexible
Better: Avoid mixing channels
If you instead started from conda-forge to begin with, then everything should be fine. Conda Forge is entirely self-sufficient these days. Whereas Anaconda users often need to use Conda Forge packages to fill in the gaps. This practice (which is encouraged by Anaconda documentation with little warning about how problematic it is) generates a substantial portion of user issues.
Personally, I recommend a base install of Mambaforge. It puts conda-forge as the default, installs a minimum of packages in the base environment, and comes with Mamba for fast environment solving.
The problem turned out to be a memory issue (as in, lack thereof). I was trying to do this on an AWS EC2 instance that was on the t2.micro free tier. This instance only had 1GB of memory (RAM) and buried in the feedback I got from the dmesg command (run from the terminal after the command failed) I noticed an "Out of Memory" line. I increased the instance size and the problem went away. Thanks for the help!
I am trying to download a list of packages from a requirements.txt file on python using VSC and the M1 mac. I made a conda environment and tried to download the packages from the file but I the error
PackagesNotFoundError: The following packages are not available from current channels:
I added many channels to my environment already: here is the ones I have so far:
https://conda.anaconda.org/conda-forge/osx-arm64
https://conda.anaconda.org/conda-forge/noarch
https://conda.anaconda.org/matsci/osx-arm64
https://conda.anaconda.org/matsci/noarch
https://conda.anaconda.org/abinit/osx-arm64
https://conda.anaconda.org/abinit/noarch
https://repo.anaconda.com/pkgs/main/osx-arm64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/osx-arm64
https://repo.anaconda.com/pkgs/r/noarch
I was able to download tensorflow-probability but the version is 0.7, when I need 0.11.1, in which I get the error when I try to specify the version when installing via conda.
Here is a list of the other requirements/packages that haven't been able to download:
jupyter-client
jupyter-console
jupyter-core
sklearn
ignnition
importlib
pickle5
prometheus-client
ipython-genutils
Note: I just took the version number out of the requirements.txt file because before that, basically none of them would work, and there is around 150 packages.
I'm wondering if this is a problem with M1 chip? Should I try running via Virtual Machine? Any help is greatly appreciated!
(I'm aware of this question Cannot install ggplot with anaconda but that is aimed at Windows, and I'm running a Linux OS)
I'm attempting to install the ggplot package in a python3 (v3.6.0) Anaconda environment:
$ conda install ggplot
Fetching package metadata .............
PackageNotFoundError: Package missing in current linux-64 channels:
- ggplot
Close matches found; did you mean one of these?
ggplot: r-ggplot2, r-gplots
If I use conda search I get:
$ conda search ggplot
Fetching package metadata .............
r-ggplot2 1.0.0 0 defaults
1.0.0 0a defaults
1.0.1 r3.2.2_0 defaults
1.0.1 r3.2.0_0 defaults
1.0.1 r3.2.1_0 defaults
1.0.1 r3.2.1_0a defaults
1.0.1 r3.2.2_0a defaults
1.0.1 r3.2.0_0a defaults
2.1.0 r3.3.1_0 defaults
2.2.0 r3.3.1_0 defaults
2.2.0 r3.3.2_0 defaults
but if I search https://anaconda.org/search for ggplot I get lots of results.
The questions: why am I not seeing those results when using conda search? What is the difference between ggplot and r-ggplot2 (the package it offers to install when I search for ggplot)?
Why am I not seeing those results when using conda search?
The difference for search is that conda search only searches in your channels, anaconda search or the search on anaconda.org includes all (public) channels. The name in front of the package name is the channel, for example xyz/ggplot the xyz is the channel.
What is the difference between ggplot and r-ggplot2
It's probably a naming convention. anaconda has several R based packages and it's likely they seperate them using the r- prefix from more regular python packages. So if you don't plan to use it with "R" you should probably look for a suitable candidate without the r-.