benjaminshafii

const Anthropic = require('@anthropic-ai/sdk');
const path = require('path');
const YAML = require('yaml');
const fs = require('fs');

// Initialize Anthropic SDK
const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

rain-1

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

• Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

mkolod

ImageNet validation set fix:

0. The training set is organized in directories, with each directory matching a class, e.g. "n01751748" matching "sea snake." However, the valset is a flat dir of JPEGs. The ImageNet labels provided in the devkit for the validation set (ILSVRC2012_validation_ground_truth.txt) are not consistent with the ordering used by PyTorch/TF/Keras/MXNet/Caffe, etc. for pre-trained models. For example, in the the above ground truth label file, "sea snake" is 490, but in PyTorch/TF, it's 65.
   Proof:
   • ImageNet validation labels: https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
   • DL framework labels: https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
1. Untar the valset file, you will get a flat dir of JPEGs.
2. Pull in the unflattening script into the directory where the val images were unpa

lucidrains

# link to package https://github.com/lucidrains/slot-attention

import torch
from torch import nn

class Residual(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
    def forward(self, x):

mikhailov-work

# Copyright 2019 Google LLC.
# SPDX-License-Identifier: Apache-2.0

# Author: Anton Mikhailov

turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913

fielding

Repo

Haven't decided if I am going to submit code to it yet

Questions

👍 - Unlocked & Done 😊 - Locked & Done 🔒 - Locked & Not Done [] - Not yet Done

mcarilli

This example is based on main_amp.py from the Apex imagenet amp examples and can be used with the same example commands. It demonstrates batch replay (instead of batch skipping) with the dynamic gradient scaling used by Amp.

Batch replay requires a bit of user-side control flow, but is fairly straightforward.

Ctrl+f "added for batch replay" in main_amp_replay.py below to see what was changed. There should only be 5 instances, found entirely in this section.

Vimdiffing main_amp_replay.py and main_amp.py from the Apex example directory is also instructive. Again, there should be few differences.

See the "Batch replay" example in the Automatic Mixed Precision RFC for a preview of how I plan this will wor

dojoteef

'''
Memory profiling utilities
'''
import gc
import inspect
import linecache
import os.path
import sys
import time
import threading

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

SSS135

import time

import tensor_comprehensions as tc
import tensor_comprehensions.tc_unit as tcu
import torch
import torch.cuda
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable, Function